The synchronization process leads to set *α*0=0, therefore, the correlation matrix becomes:

$$\mathbf{R}\_{\mathfrak{a}\_{0}} = \sigma\_{b}^{2} \left\{ \beta \mathbf{v}\_{\mathfrak{a}\_{0}} \mathbf{v}\_{\mathfrak{a}\_{0}}^{\*} + \mathbf{I} \right\}. \tag{28}$$

Equation 28 highlights a maximum eigenvalue which associated eigenvector contains the corresponding spreading sequence (excluding the effects of the global transmission filter), and *M*−1 eigenvalues (equal on average to the noise power at the receiver side). The largest eigenvalue corresponds to the eigenvector **v***α*0 , i.e., the signal spreading sequence (up to the channel effects).

The eigenvectors are calculated up to a complex multiplicative factor due to diagonalization. The complex factor effects are cancelled by normalizing the estimated eigenvector phase. It is made by maximizing its real part and imposing the positivity of its first component real part. In order to determine if the sequence whether initially complex or real, the variances of its real and imaginary parts (here **v***α*0 ) after normalization are compared. The sequence is real when the standard deviation of its real part-to-the standard deviation of its imaginary part ration exceeds 2.5. This threshold seems to be sufficient because of eigenvectors normalization.

This stage allows us to eliminate the effects of the global transmission filter, as shown on Fig. 8 (a) and (b). The last stage is the *binarization* detailed in (Nsiala Nzéza, 2006). It consists in determining the chip period *Tc* (hence the length of the sequences) before seeking the binary sequence the nearest to the spreading sequence estimated in the least squares sense for each of the sequences to be estimated, as highlighted on Fig. 8 (c) and (d).

#### **5. Extension to the multiuser/multirate case**

Starting with Equation 11, we will progressively extend to the multiuser/multirate case the blind methods developped in Section 4. The assumptions of independent, centered and noise-uncorrelated signals leads to the following equations:

$$\hat{R}\_{\rm ss}(\theta) = \sum\_{i=0}^{S-1} \sum\_{n=0}^{N\_u^i - 1} \hat{R}\_{s\_{\pi}s\_{\pi i}}(\theta)\_\prime \text{ and } \hat{R}\_{yy}(\theta) = \hat{R}\_{\rm ss}(\theta) + \hat{R}\_{bb}(\theta), \tag{29}$$

where *Rsn*,*isn*,*i* (*θ*) and *Rbb*(*θ*) are the estimates of the (*n*, *i*)*th* noise-unaffected signal and that of the noise autocorrelation fluctuations, respectively. Indeed, since the fluctuations are computed from many randomly-selected windows, they do not depend on the signals relative delays *Tdn*,*i* nor on channel paths delays. The Multiple Access Interference (MAI) noise impact is similar to that of additive noise. So, as in single-user case, both MAI and Gaussian Additive noise fluctuations are uniformly distributed over the desired frequency band. Moreover, channel gains act as a multiplicative factor on fluctuations average amplitude.

Let us consider the channel-corrupted signal and the assumptions made in Section 2. Therefore, it is fairly easy to demonstrate that on average, high amplitudes of the fluctuations of the autocorrelation estimator, denoted Φ*s*(*θ*), are obtained for each *θ* multiple of each symbol period *Tsi* , *i* = 0, ··· , *S* − 1. Since the symbol periods are different, let us denote Φ*i*(*θ*) the fluctuations of the autocorrelation estimator of the *Ni u* signals *sn*,*i*(*t*) transmitting at the same data rate *Tsi* . Let us also term *m*Φ*i* the mean value of the fluctuations Φ*i*(*θ*) for each value *θ* multiple of *Tsi* ; it leads to:

12 Will-be-set-by-IN-TECH

Equation 28 highlights a maximum eigenvalue which associated eigenvector contains the corresponding spreading sequence (excluding the effects of the global transmission filter), and *M*−1 eigenvalues (equal on average to the noise power at the receiver side). The largest eigenvalue corresponds to the eigenvector **v***α*0 , i.e., the signal spreading sequence (up to the

The eigenvectors are calculated up to a complex multiplicative factor due to diagonalization. The complex factor effects are cancelled by normalizing the estimated eigenvector phase. It is made by maximizing its real part and imposing the positivity of its first component real part. In order to determine if the sequence whether initially complex or real, the variances of its real and imaginary parts (here **v***α*0 ) after normalization are compared. The sequence is real when the standard deviation of its real part-to-the standard deviation of its imaginary part ration exceeds 2.5. This threshold seems to be sufficient because of eigenvectors normalization.

This stage allows us to eliminate the effects of the global transmission filter, as shown on Fig. 8 (a) and (b). The last stage is the *binarization* detailed in (Nsiala Nzéza, 2006). It consists in determining the chip period *Tc* (hence the length of the sequences) before seeking the binary sequence the nearest to the spreading sequence estimated in the least squares sense for each

Starting with Equation 11, we will progressively extend to the multiuser/multirate case the blind methods developped in Section 4. The assumptions of independent, centered and

that of the noise autocorrelation fluctuations, respectively. Indeed, since the fluctuations are computed from many randomly-selected windows, they do not depend on the signals relative delays *Tdn*,*i* nor on channel paths delays. The Multiple Access Interference (MAI) noise impact is similar to that of additive noise. So, as in single-user case, both MAI and Gaussian Additive noise fluctuations are uniformly distributed over the desired frequency band. Moreover,

Let us consider the channel-corrupted signal and the assumptions made in Section 2. Therefore, it is fairly easy to demonstrate that on average, high amplitudes of the fluctuations of the autocorrelation estimator, denoted Φ*s*(*θ*), are obtained for each *θ* multiple of each

(*θ*) and *Rbb*(*θ*) are the estimates of the (*n*, *i*)*th* noise-unaffected signal and

, *i* = 0, ··· , *S* − 1. Since the symbol periods are different, let us denote

. Let us also term *m*Φ*i* the mean value of the fluctuations Φ*i*(*θ*) for each

(*θ*), and *Ryy*(*θ*) = *Rss*(*θ*) + *Rbb*(*θ*), (29)

*u* signals *sn*,*i*(*t*) transmitting at

of the sequences to be estimated, as highlighted on Fig. 8 (c) and (d).

**5. Extension to the multiuser/multirate case**

*Rss*(*θ*) =

noise-uncorrelated signals leads to the following equations:

*Ni u*−1 ∑ *n*=0

Φ*i*(*θ*) the fluctuations of the autocorrelation estimator of the *Ni*

*Rsn*,*isn*,*i*

channel gains act as a multiplicative factor on fluctuations average amplitude.

*S*−1 ∑ *i*=0

*α*0 + **I**

. (28)

The synchronization process leads to set *α*0=0, therefore, the correlation matrix becomes:

**R***α*0 = *σ*2 *b β***v***α*0**v**∗

**4.3 Spreading code and symbols estimation**

channel effects).

where *Rsn*,*isn*,*i*

symbol period *Tsi*

the same data rate *Tsi*

$$\Phi\_i(\theta) = m\_{\Phi\_i} \cdot p g n\_{T\_{s\_i}}(\theta)\_{\prime} \tag{30}$$

where *pgnTsi* (*θ*) = ∑+∞ *k*=−∞ *δ*(*θ* − *kTsi* ), and the function *δ*(*θ*) is the KRONECKER function. Consequently, the signals being independent and centered by assumption, the fluctuations Φ*s*(*θ*) of the global noise-unaffected signal, for each value *θ* multiple of *Tsi* , can be expressed as:

$$\Phi\_{\mathbf{s}}(\theta) = \sum\_{i=0}^{S-1} \Phi\_i(\theta) = \sum\_{i=0}^{S-1} m\_{\Phi\_i} \cdot pgn\_{T\_{i\_l}}(\theta). \tag{31}$$

Thus, theoretical calculations developed in Subsection 4.1.2 remain valid in this case, considering each group *i* separately. Hence, the average amplitude of the fluctuations in each group *i* becomes:

$$m\_{\Phi\_i} = \sum\_{n=0}^{N\_u^l - 1} \frac{T\_{s\_l}}{T\_F} \sigma\_{s\_{n\ell}}^4 = N\_u^l \frac{T\_{s\_l}}{T\_F} \sigma\_{s\_l}^4 = N\_u^l \frac{L\_i T\_c}{T\_F} \sigma\_{s\_{l'}}^4 \tag{32}$$

where *σ*4 *si* = 1 *Ni u* ∑*Ni u*−1 *n*=0 *σ*4 *sn*,*i* can be considered as the average received power of signals within the group *i*. Equation 32 evidences that, the higher the number of users in a group *i* is, the greater the average amplitude of the fluctuations due to signals within this group is. Then, let us set to *m*Φ*snir* both MAI and Gaussian Additive noise contribution to fluctuations. Therefore, the longer the sequence is, the higher the peaks of correlation fluctuations are, and the biggest amplitude is usually exhibited by the fluctuations of the users transmitting at the lowest data rate. This approach is a powerful tool to estimate symbol periods, and it allows to differentiate the various transmitted data rate and to distinguish between the different standards. As proved in (Nsiala Nzéza, 2006), their mean and standard deviation are given by:

$$\begin{aligned} \, \_{\mathfrak{M}\_{\rm snir}} &= \frac{\sigma\_{\rm snir}^{4}}{\, W T\_{\rm F}} \quad \text{(a)}\\ \, \_{\mathfrak{O}\_{\rm snir}} &= \sqrt{\frac{2}{M}} \frac{\sigma\_{\rm snir}^{4}}{\, W T\_{\rm F}} \quad \text{(b)} \end{aligned} \tag{33}$$

where *σ*4 *snir* represents both MAI and Gaussian Additive noise power. Completing, using Equations 30, 31, 32 and 33, the global fluctuations can be expressed as follows:

$$
\Phi(\theta) = \Phi\_{\mathbf{s}}(\theta) + \Phi\_{\text{srir}}(\theta). \tag{34}
$$

Equation 34 shows that the overall fluctuations are composed of fluctuations due to the signal and noise (Gaussaian additive and MAI). It also proves that only the contribution of the signal exhibits peaks at multiple of *Tsi* in each group *i*. The global fluctuactions curve highlights a superposition of regularly spaced peaks within each group *i*, while the noise is uniformly distributed in the band. As a result, within each group of *i*, the blind synchronization process can be performed, also allowing to determine the number of interfering users *Ni u* as detailed in Subsection . In addition, note that the impact of MAI noise becomes negligible if the interest first goes to peaks with the highest amplitude fluctuations (Nzéza et al., 2006), i.e., to signals transmitted at max *i*=0,···,*S*−1 {*Tsi* }.

where *Ni*

*u* = 4, *τ*˜0 = 0, *τ*˜1 = 0.1509, *τ*˜2 = 0.3784 and *τ*˜3 = 0.6979. However, from Equation

*n* + 1 *Ni u*

< *τ*˜*n*+1. (37)

119

*b* = *β* = 1 in Equation 24 leads to Equation 38, whose

*n*+1 = 1 + �*d f* − *τ*˜*n*+1� (b), (38)

(b). (39)

*u* − 1. (40)

*u* − 1 in all

*n* decreases while *λ*−1 *n* increases,

*n*+1 = 2 − �*d f* − *τ*˜*n*+1�

, *n* = 0, ··· , *Ni*

*∂df* = −1 < 0

36, we demonstrated that according to *τ*˜*n*, a local minimum of Equation 36 may not belong to the interval [*τ*˜*n τ*˜*n*+1[; in this case, there could not be a synchronization peak at *d f* = *τ*˜*n*: it is the phenomenon of synchronization peaks masking, as illustrated in Fig. 9 (b), with *τ*˜0 = 0, *τ*˜1 = 0.0448, *τ*˜2 = 0.068 and *τ*˜3 = 0.3853. By studying the behaviour of criterion *F* in the

< *τ*˜*n* <

Equation 37 gives the necessary and sufficient condition of the synchronization peaks existence, as proved in (Nsiala Nzéza, 2006). Although the masking of peaks phenomenon occurs if the timing offsets of multiple users are close to each other in an asynchronous system, the synchronization can however be achieved combining the process described above with the modified-Pastd algorithm (Nsiala Nzéza, 2006). Thus, this consideration led us to analyze the

*λ*0

⎧ ⎨ ⎩

and conversely. The same result is obtained from Equation 39 (b), and the values for which they are equal are local minima. Therefore, the MEVB criterion is defined as the maximum value between two consecutive largest eigenvalues, which is equivalent to the following

Since eigenvalues are linear functions of *d f* , Equation 40 always presents maxima, and synchronization peaks at *d f* = *τ*˜*n*, contrary to the FSNB criterion, as illustrated in Fig. 10, with the same parameters as those used in Fig. 9 (b). Thus, this discussion highlights that the performances obtained by using the MEVB criterion will be better than those obtained by using the FSNB criterion, as it will be shown in Section 5.3. Let us note that both criteria

equations above. At last, the number of synchronization peaks gives the number of interfering users. A particular emphasis has to be placed on the FSNB criterion due to some masking peaks. Moreover, once all peak positions are known, the differences between their positions and the corresponding user for which the normalized shift is the closest to 0 or 1, give an

(a),

� *λ*0 *n*, *λ*−1 *n*+1 �

can easily be derived for downlink transmissions by setting *τn* = 0, *n* = 0, ··· , *Ni*

*λ*−1

*∂λ*0 *n*+1

*∂λ*−1 *n*+1 *∂df* = 1 > 0

vicinity of points such that *d f* = *τ*˜*n*, leads to the finding of the following condition:

*n Ni u*

Despreading of DS-CDMA Signals in Multirate Multiuser Cognitive Radio Systems

*τ*˜*n*−1 <

MEV-based (MEVB) criterion described hereinafter.

derivative with respect to the shift *d f* is given by Equation 39:

*λ*−1 *n* = 1 + �*d f* − *τ*˜*n*� (a), �

*∂df* = 1 > 0

Equation 39 (a) shows that for any shift *d f* ∈ [*τn*, *τn*+1[, *λ*0

M(*d f*) = max *τn*≤*df* <*τn*+1

*n* = 2 − �*d f* − *τ*˜*n*�

**5.1.2 MEVB criterion theoretical development**

⎧ ⎨ ⎩ *∂λ*0 *n ∂df* = −1 < 0

*∂λ*−1 *n*

Without loss of generality, by setting *σ*2

Blind Detection, Parameters Estimation and

� *λ*0

estimation of the transmission delays.

function M:

Fig. 9. Theoretical FSNB criterion *F* in uplink for two cases.

#### **5.1 Blind multiuser synchronization process**

We first extend FSNB criterion to show that this criterion may exhibit the phenomenon of synchronisation peaks masking. Therefore, the modifed Pastd algorithm (Nsiala Nzéza, 2006)[pp. 115-116] may be jointly performed. Alternatively, we present another criterion based on the Maximum Eigenvalue Behaviour (MEVB) according to analysis window shifts. MEVB-based criterion provides a significant improvement of performances which is mainly due to suppression of synchronisation peaks masking that occurred with the FSNB criterion.

#### **5.1.1 FSNB criterion theoretical analysis**

For more clearness, let us set *Tsi* =*Ts*,(∗)*n*,*i*=(∗)*n* within a group *i*. Since normalized shifts *αn* restrict the study into the interval [0, 1[, there is a periodicity on the relative positions of an analysis window and that of signals. By setting �*x*� ≡ *x modulo* 1, *x* ∈ **R**, *τn* = *Tdn Ts* and *d f* = *Df Ts* , leads to *α*� *n* = �*d f* − *τn*�, *n* = 0, ··· , *Ni u* − 1. Consequently, since delays *τn* are assumed constant, Equation 27 which only depends on shifts *d f* becomes:

$$F(d\_f) = 1 + \sum\_{n=0}^{N\_u^i - 1} \{ \langle d\_f - \mathfrak{r}\_n \rangle^2 - \langle d\_f - \mathfrak{r}\_n \rangle \}. \tag{35}$$

Equation 35 shows that each signal is synchronized when *d f* is equal to its corresponding transmission delay. Let us recall that a peak is a point of a curve from which, while moving by lower or higher values, the curve is always decreasing. As well as synchronization peaks are expected at points *d f* = *τn*. This implies to examine criterion *F* behaviour within ]*τn*−1, *τn*] and [*τn*, *τn*+1[. However for *n* = 0, or for *n* = *Ni u* − 1, intervals ]*τ*−1, *τ*0] and [*τNi u*−1, *τNi u* [ do not exist. Thus, by setting *τ*˜*n* = *τ*�*n*�*Ni u* , �*n*�*Ni u* ≡ *n modulo Ni u* leads to take these intervals into account, and Equation 35 can be rewritten as:

$$F(d\_f) = 1 + \sum\_{n=0}^{N\_u^l - 1} \{ \langle d\_f - \mathfrak{r}\_n \rangle^2 - \langle d\_f - \mathfrak{r}\_n \rangle \}. \tag{36}$$

Equation 36 is a convex quadratic function of *d f* over any interval [*τ*˜*n*, *τ*˜*n*+1[ whose peaks should be located at points such that *d f* = *τ*˜*n*, *n* = 0, ··· , *Ni u* − 1, as illustrated in Fig. 9 (a), where *Ni u* = 4, *τ*˜0 = 0, *τ*˜1 = 0.1509, *τ*˜2 = 0.3784 and *τ*˜3 = 0.6979. However, from Equation 36, we demonstrated that according to *τ*˜*n*, a local minimum of Equation 36 may not belong to the interval [*τ*˜*n τ*˜*n*+1[; in this case, there could not be a synchronization peak at *d f* = *τ*˜*n*: it is the phenomenon of synchronization peaks masking, as illustrated in Fig. 9 (b), with *τ*˜0 = 0, *τ*˜1 = 0.0448, *τ*˜2 = 0.068 and *τ*˜3 = 0.3853. By studying the behaviour of criterion *F* in the vicinity of points such that *d f* = *τ*˜*n*, leads to the finding of the following condition:

$$\mathfrak{r}\_{\mathfrak{n}-1} < \frac{n}{N\_{\mathfrak{u}}^{i}} < \mathfrak{r}\_{\mathfrak{n}} < \frac{n+1}{N\_{\mathfrak{u}}^{i}} < \mathfrak{r}\_{\mathfrak{n}+1}.\tag{37}$$

Equation 37 gives the necessary and sufficient condition of the synchronization peaks existence, as proved in (Nsiala Nzéza, 2006). Although the masking of peaks phenomenon occurs if the timing offsets of multiple users are close to each other in an asynchronous system, the synchronization can however be achieved combining the process described above with the modified-Pastd algorithm (Nsiala Nzéza, 2006). Thus, this consideration led us to analyze the MEV-based (MEVB) criterion described hereinafter.

#### **5.1.2 MEVB criterion theoretical development**

14 Will-be-set-by-IN-TECH

0.2

0.4

FSNB Criterion

We first extend FSNB criterion to show that this criterion may exhibit the phenomenon of synchronisation peaks masking. Therefore, the modifed Pastd algorithm (Nsiala Nzéza, 2006)[pp. 115-116] may be jointly performed. Alternatively, we present another criterion based on the Maximum Eigenvalue Behaviour (MEVB) according to analysis window shifts. MEVB-based criterion provides a significant improvement of performances which is mainly due to suppression of synchronisation peaks masking that occurred with the FSNB criterion.

*αn* restrict the study into the interval [0, 1[, there is a periodicity on the relative positions of an analysis window and that of signals. By setting �*x*� ≡ *x modulo* 1, *x* ∈ **R**, *τn* = *Tdn*

Equation 35 shows that each signal is synchronized when *d f* is equal to its corresponding transmission delay. Let us recall that a peak is a point of a curve from which, while moving by lower or higher values, the curve is always decreasing. As well as synchronization peaks are expected at points *d f* = *τn*. This implies to examine criterion *F* behaviour within ]*τn*−1, *τn*]

, �*n*�*Ni*

Equation 36 is a convex quadratic function of *d f* over any interval [*τ*˜*n*, *τ*˜*n*+1[ whose peaks

*n* = �*d f* − *τn*�, *n* = 0, ··· , *Ni*

*Ni u*−1 ∑ *n*=0

*u*

*Ni u*−1 ∑ *n*=0

assumed constant, Equation 27 which only depends on shifts *d f* becomes:

*F*(*d f*) = 1 +

*F*(*d f*) = 1 +

should be located at points such that *d f* = *τ*˜*n*, *n* = 0, ··· , *Ni*

and [*τn*, *τn*+1[. However for *n* = 0, or for *n* = *Ni*

into account, and Equation 35 can be rewritten as:

do not exist. Thus, by setting *τ*˜*n* = *τ*�*n*�*Ni*

0.6

0.8

0 0.2 0.4 0.6 0.8 1 0

criterion minima peaks

maxima x−coordinates

Normalized analysis window shifts

(b) synchronization peaks masking

=*Ts*,(∗)*n*,*i*=(∗)*n* within a group *i*. Since normalized shifts

*u* − 1. Consequently, since delays *τn* are

{�*d f* − *τn*�2 − �*d f* − *τn*�}. (35)

*u* − 1, intervals ]*τ*−1, *τ*0] and [*τNi*

{�*d f* − *τ*˜*n*�2 − �*d f* − *τ*˜*n*�}. (36)

*u* ≡ *n modulo Ni*

*Ts*

*u*−1, *τNi u* [

*u* leads to take these intervals

*u* − 1, as illustrated in Fig. 9 (a),

0 0.2 0.4 0.6 0.8 1 0.2

(a) ideal case

**5.1 Blind multiuser synchronization process**

**5.1.1 FSNB criterion theoretical analysis**

For more clearness, let us set *Tsi*

*Ts* , leads to *α*�

and *d f* = *Df*

Normalized analysis window shifts

Fig. 9. Theoretical FSNB criterion *F* in uplink for two cases.

criterion minima synchronization peaks maxima x−coordinates

0.25 0.3 0.35 0.4 0.45 0.5

FSNB Criterion

Without loss of generality, by setting *σ*2 *b* = *β* = 1 in Equation 24 leads to Equation 38, whose derivative with respect to the shift *d f* is given by Equation 39:

$$\begin{cases} \lambda\_n^0 = 2 - \langle d\_f - \mathfrak{T}\_n \rangle \\ \lambda\_n^{-1} = 1 + \langle d\_f - \mathfrak{T}\_n \rangle \end{cases} \quad \text{(a)}, \quad \begin{cases} \lambda\_{n+1}^0 = 2 - \langle d\_f - \mathfrak{T}\_{n+1} \rangle \\ \lambda\_{n+1}^{-1} = 1 + \langle d\_f - \mathfrak{T}\_{n+1} \rangle \end{cases} \quad \text{(b)}, \tag{38}$$

$$\begin{cases} \frac{\partial \lambda\_u^0}{\partial d\_f} = -1 < 0\\ \frac{\partial \lambda\_u^{-1}}{\partial d\_f} = 1 > 0 \end{cases} \quad \text{(a)},\\ \begin{cases} \frac{\partial \lambda\_{u+1}^0}{\partial d\_f} = -1 < 0\\ \frac{\partial \lambda\_{u+1}^{-1}}{\partial d\_f} = 1 > 0 \end{cases} \quad \text{(b)}. \tag{39}$$

Equation 39 (a) shows that for any shift *d f* ∈ [*τn*, *τn*+1[, *λ*0 *n* decreases while *λ*−1 *n* increases, and conversely. The same result is obtained from Equation 39 (b), and the values for which they are equal are local minima. Therefore, the MEVB criterion is defined as the maximum value between two consecutive largest eigenvalues, which is equivalent to the following function M:

$$\mathcal{M}(d\_f) = \max\_{\tau\_n \le d\_f < \tau\_{n+1}} \left( \lambda\_{n'}^0 \lambda\_{n+1}^{-1} \right), \ n = 0, \cdots, \ N\_u^i - 1. \tag{40}$$

Since eigenvalues are linear functions of *d f* , Equation 40 always presents maxima, and synchronization peaks at *d f* = *τ*˜*n*, contrary to the FSNB criterion, as illustrated in Fig. 10, with the same parameters as those used in Fig. 9 (b). Thus, this discussion highlights that the performances obtained by using the MEVB criterion will be better than those obtained by using the FSNB criterion, as it will be shown in Section 5.3. Let us note that both criteria can easily be derived for downlink transmissions by setting *τn* = 0, *n* = 0, ··· , *Ni u* − 1 in all equations above. At last, the number of synchronization peaks gives the number of interfering users. A particular emphasis has to be placed on the FSNB criterion due to some masking peaks. Moreover, once all peak positions are known, the differences between their positions and the corresponding user for which the normalized shift is the closest to 0 or 1, give an estimation of the transmission delays.

0 1 2 3 4 5 6 0

126.5 127 127.5 128 128.5 129 129.5 0

fluctuations: Φ(θ) 150th peak

 Time (in μs)

(b) Estimation of the symbol period *Ts*1

0 100 200 300 400 1.65

(b) Experimental FSNB criterion

Imaginary part of an estimated sequence

0 0.2 0.4 0.6 0.8 1 1.2 1.4 −2

Imaginary part of the sequence of reference

0 0.2 0.4 0.6 0.8 1 1.2 1.4 −2

Time (in μs)

Analysis window shifts in number of samples

τ 2

> criterion minima maxima real shifts

*u* = 4, *L*1 = 127.

τ 3

*u* = 4, SNR= −7dB.

τ 1

121 Blind Detection, Parameters Estimation and

Despreading of DS-CDMA Signals in Multirate Multiuser Cognitive Radio Systems

*u* = 2, *N*1

1.7 1.75 1.8 1.85 1.9 1.95 x 108

0 2

0 2

(c) Real and Imaginary parts of an estimated binary sequence

FSNB criterion

(a) Correlation fluctuations Φ(*τ*)

Number of samples 100 200 300 400 500 600 700

(a) Matrix R of double size before the

Real part of an estimated sequence

0 0.2 0.4 0.6 0.8 1 1.2 1.4 −2

Real part of the sequence of reference

0 0.2 0.4 0.6 0.8 1 1.2 1.4 −2

Time (in μs)

Fig. 12. Synchronization and sequences recovering, SNR= −7dB, *Ni*

fluctuations: Φ(θ) noise theoretical mean noise theoretical maximum

> Time (in μs)

Fig. 11. Estimation of the symbol period *N*0

500

sycnhronization

0 2

0 2

1000

1500

2000

Fig. 10. Function M equivalent to the theoretical MEVB criterion in uplink, *Ni u* = 4.

#### **5.2 Sequences identification and symbols recovering**

Once one of the interfering users within *i* is synchronised in uplink, e.g., *d f* = *τ*0, which corresponds to set *α*0 = 0, the correlation matrix becomes:

$$\mathbf{R} = \sigma\_b^2 \left\{ \beta \left( \mathbf{v}\_{a\_0} \mathbf{v}\_{a\_0}^\* + \sum\_{n=1}^{N\_\mu^i - 1} \left\{ (1 - a\_n) \mathbf{v}\_n^0 (\mathbf{v}\_n^0)^\* a\_n \mathbf{v}\_n^{-1} (\mathbf{v}\_n^{-1})^\* \right\} \right) + \mathbf{I} \right\}. \tag{41}$$

Equation 41 highlights a maximum eigenvalue which associated eigenvector contains the corresponding spreading sequence (excluding the effects of the global transmission filter), and 2(*Ni u* − 1) eigenvalues. Then, this process is performed in an iterative way so as to get the *Ni u* largest eigenvalues, which associated eigenvectors correspond to spreading sequences. In downlink, i.e., *τn* = 0, *αn* = *α*, *n* = 0, 1, ··· , *Ni u* − 1, the correlation matrix can be rewritten as:

$$\mathbf{R} = \sigma\_b^2 \left\{ \beta \left( \sum\_{n=0}^{N\_u^l - 1} \left\{ (1 - \alpha) \mathbf{v}\_n^0 (\mathbf{v}\_n^0)^\* + \alpha \mathbf{v}\_n^{-1} (\mathbf{v}\_n^{-1})^\* \right\} \right) + \mathbf{I} \right\}. \tag{42}$$

When users are synchronized, by using either the FSNB or the MEVB criterion, i.e., *α* = 0, the induced corrlation matrix may be expressed as:

$$\mathbf{R} = \sigma\_b^2 \left( \beta \sum\_{n=0}^{N\_u^i - 1} \mathbf{v}\_{lt} \mathbf{v}\_n^\* + \mathbf{I} \right). \tag{43}$$

Equation 43 exhibits the *Ni u* largest eigenvalues whose associated eigenvectors correspond to the spreading sequences, and the *M* − *Ni u* others are on average equal to the noise power. Finally, linear algebra techniques previously described in Section 4, applied to the estimated eigenvectors, allow to identify sequences used at the transmitter and to recover transmitted symbols.

#### **5.3 Simulation results in the multiuser multirate case**

Simulations were carried out with complex GOLD sequences of processing gains *L*0=31 and *L*1=127. The other parameters were set as follows. The common chip frequency *Fc*=100 MHz, the sampling frequency *Fe*=300 MHz, *TF*=2*μs*, *K*=300, *N*0 *u*=2, *N*1 *u*=4, and thus *Nu*=6.

16 Will-be-set-by-IN-TECH

1.7 1.75 1.8 1.85 1.9 1.95 2

MEVB criterion

Once one of the interfering users within *i* is synchronised in uplink, e.g., *d f* = *τ*0, which

(1 − *αn*)**v**0

Equation 41 highlights a maximum eigenvalue which associated eigenvector contains the corresponding spreading sequence (excluding the effects of the global transmission filter),

*u* largest eigenvalues, which associated eigenvectors correspond to spreading sequences. In

*n*(**v**0

When users are synchronized, by using either the FSNB or the MEVB criterion, i.e., *α* = 0, the

Finally, linear algebra techniques previously described in Section 4, applied to the estimated eigenvectors, allow to identify sequences used at the transmitter and to recover transmitted

Simulations were carried out with complex GOLD sequences of processing gains *L*0=31 and *L*1=127. The other parameters were set as follows. The common chip frequency *Fc*=100 MHz,

*n*(**v**0

*u* − 1) eigenvalues. Then, this process is performed in an iterative way so as to get the

*n*)∗ + *α***v**−1

**v***n***v**∗ *n* + **I**

*n*)∗*αn***v**−1

*n* (**v**−1 *n* )∗ � ⎞ ⎠ + **I**

⎞

*u* largest eigenvalues whose associated eigenvectors correspond to

*u*=2, *N*1

*u* others are on average equal to the noise power.

Fig. 10. Function M equivalent to the theoretical MEVB criterion in uplink, *Ni*

�

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.65

(b) other cases

*n* (**v**−1 *n* )∗ � ⎞ ⎠ +**I**

*u* − 1, the correlation matrix can be rewritten

⎫ ⎬

⎠ . (43)

*u*=4, and thus *Nu*=6.

*u* = 4.

⎫ ⎬ ⎭

. (41)

⎭ . (42)

criterion synchronization peaks minima

Normalized analysis windows shifts

0 0.2 0.4 0.6 0.8 1

synchronization peaks

criterion

minima

Normalized analysis windows shifts

(a) ideal case

**5.2 Sequences identification and symbols recovering**

corresponds to set *α*0 = 0, the correlation matrix becomes:

*α*0 + *Ni u*−1 ∑ *n*=1

�

**R** = *σ*2 *b*

(1 − *α*)**v**0

⎛ ⎝*β Ni u*−1 ∑ *n*=0

0.8

**R** = *σ*2 *b*

⎧ ⎨ ⎩ *β* ⎛ ⎝**v***α*0**v**∗

downlink, i.e., *τn* = 0, *αn* = *α*, *n* = 0, 1, ··· , *Ni*

induced corrlation matrix may be expressed as:

the spreading sequences, and the *M* − *Ni*

**5.3 Simulation results in the multiuser multirate case**

the sampling frequency *Fe*=300 MHz, *TF*=2*μs*, *K*=300, *N*0

⎧ ⎨ ⎩ *β* ⎛ ⎝ *Ni u*−1 ∑ *n*=0

**R** = *σ*2 *b*

Equation 43 exhibits the *Ni*

0.85

MEVB criterion

and 2(*Ni*

symbols.

*Ni*

as:

0.9

0.95

1

Fig. 11. Estimation of the symbol period *N*0 *u* = 2, *N*1 *u* = 4, SNR= −7dB.

(a) Matrix R of double size before the sycnhronization

(c) Real and Imaginary parts of an estimated binary sequence

Fig. 12. Synchronization and sequences recovering, SNR= −7dB, *Ni u* = 4, *L*1 = 127.

Sequence Recovering *N*

*i u* = *N i*

**6. Performances improvement of the proposed method**

amplitude peaks, set to *NFpeaks*.

**6.1 Implementation in the multiuser single rate case**

Compute fluctuations Φ(*τ*)

= 0? no

*NFpeaks*

Compute the double-size matrix R

Synchronization

*α*0 = arg *max αn*, *n*=0,··· ,*Nu*−1

> Modified Pastd

*u*−1 *n*=0 ∑+∞

123 Blind Detection, Parameters Estimation and

*k*=−∞ *an*,*i*(*k*)*hn*,*i*(*t* − *kTs*,*i*) + *b*(*t*)

yes End

*yi* = *yi* − *y*0

= 1? yes

no

Signals in the band of interest *yi*(*t*) = ∑*Ni*

Despreading of DS-CDMA Signals in Multirate Multiuser Cognitive Radio Systems

*u* + 1 Nb remaining

Fig. 14. Iterative implementation of the proposed scheme in the multiuser single rate case.

10−8 which is very close to that obtained at −5 dB after using the MEVB criterion.

performances after synchronizing using both criteria and estimating transmitted symbols. In agreement with the results shown in Fig. 13 (a), lower MBERs are obtained ( with a 2 dB gain) after using the MEVB criterion. Typically, in some cooperative systems, the MBER is about

Note first that the overall chain is implemented in a way to make it interactive with an operator. As claimed in Section 3, although the proposed method has been described sequentially, the most efficient implementation of the three steps is to link them back iteratively. This allows the global chain performances improvement by deflating the estimated signal. This will be especially important when the number of interfering users within a given group of users transmitting at the same data rate increases, or when the differences between data rate from one group to another one is very high. In these cases, some synchronization peaks can be masked as proven in Section 5. Hence, it is advisable to adopt the iterative approach summarized on Fig. 14 and Fig. 15. Therefore, firstly identifying in the multiuser detection step the group of users corresponding to the largest sequence or fluctuations

This case is shown on Fig. 14. Within the identified group of users among *NFpeaks*, the user corresponding to the synchronization criterion maxima is first synchronized. Besides, the

{�R�2 *F*}

users

Fig. 13. Both synchronization performances comparison for different SNRs, *N*1 *u* = 4, *L*1 = 127.

A *QPSK* modulation was considered in uplink. For the *N*1 *u* users, it was set *α*0 = 0.3228, *α*1 = 0.4226, *α*2 = 0.7533 and *α*3 = 0.9423. Fig. 11 (a) illustrates fluctuations of the correlation estimator Φ(*θ*). The curve clearly highlights two sets of equispaced peaks of different amplitudes. This means that two sets of spread spectrum signals transmitting at two different rates are hidden in the noise. Estimated symbol periods, (in *μs*) are *Ts*0 = 0.31, *Ts*1 = 1.27. To re-estimate these values, for each set of autocorrelation fluctuations, let us look for a maximum near the farthest peak on the right side, e.g., the 100*th* as shown in Fig. 11 (b), then we get 100×*Ts*1≈127*μs*.

Since symbol periods are available at this stage, e.g., *Ts*1 = 1.27*μs*, the analysis window length is set as *TF* = *Ts*1 . Fig. 12 (a) corresponding to the double size matrix R shows 4 overlapping matrices, suggesting that there are 4 interfering users. Matrix **R** before the synchronization corresponds to the (*M* × *M*)-matrix extracted from R. Fig. 12 (b) shows the experimental FSNB criterion calculated by moving along the diagonal of matrix R and calculating the squared norm of the shift-induced submatrix for each value of the shift. This squared norm will be maximum for the bright areas in Fig. 12 (a).

Fig. 12 (b) evidences 4 peaks which X-coordinates give the desynchronization times in number of samples. The number of peaks gives the number of active users over the analysis window, hence there are 4 interfering active users. By taking the oversampling factor into account, we obtain *α*0 ≈ 0.3281, *α*1 = 0.4226, *α*2 = 0.7533 and *α*3 = 0.9449.

Since transmission delays and shifts are supposed to be sorted in the ascending order, the first peak, i.e., for *α*0, corresponds to the reference user. Thus, *τ*1 = 0.0999, *τ*2 = 0.4252, and *τ*3 = 0.6221. These values are very close to the real ones, and allow the synchronization of interfering users. Fig. 12 (c) shows the real and imaginary parts of one of the estimated sequences, which are equal to that used at the transmitter side, respectively.

Fig. 13 (a) highlights the performances in terms of mean chip error rate (MCER), i.e., the average ratio of the number of wrong sequence chips to the total number of sequence chips. It shows very low MCERs after synchronizing using either the MEVB or FSNB criterion. Moreover, MECRs obtained after using the MEVB criterion are lower than those obtained after using the FSNB one (with a 3 dB gain), in agreement with the theoretical analysis. It also shows that in average, one chip at most is wrong with sequences of length 127. Fig. 13 (b) shows the performances in terms of mean bit error rate (MBER). It clearly evidences very good 18 Will-be-set-by-IN-TECH

10−6 10−5 10−4 10−3 10−2 10−1 100

Mean bit error rate

*α*1 = 0.4226, *α*2 = 0.7533 and *α*3 = 0.9423. Fig. 11 (a) illustrates fluctuations of the correlation estimator Φ(*θ*). The curve clearly highlights two sets of equispaced peaks of different amplitudes. This means that two sets of spread spectrum signals transmitting at two different rates are hidden in the noise. Estimated symbol periods, (in *μs*) are *Ts*0 = 0.31, *Ts*1 = 1.27. To re-estimate these values, for each set of autocorrelation fluctuations, let us look for a maximum near the farthest peak on the right side, e.g., the 100*th* as shown in Fig. 11 (b),

Since symbol periods are available at this stage, e.g., *Ts*1 = 1.27*μs*, the analysis window length is set as *TF* = *Ts*1 . Fig. 12 (a) corresponding to the double size matrix R shows 4 overlapping matrices, suggesting that there are 4 interfering users. Matrix **R** before the synchronization corresponds to the (*M* × *M*)-matrix extracted from R. Fig. 12 (b) shows the experimental FSNB criterion calculated by moving along the diagonal of matrix R and calculating the squared norm of the shift-induced submatrix for each value of the shift. This squared norm

Fig. 12 (b) evidences 4 peaks which X-coordinates give the desynchronization times in number of samples. The number of peaks gives the number of active users over the analysis window, hence there are 4 interfering active users. By taking the oversampling factor into account, we

Since transmission delays and shifts are supposed to be sorted in the ascending order, the first peak, i.e., for *α*0, corresponds to the reference user. Thus, *τ*1 = 0.0999, *τ*2 = 0.4252, and *τ*3 = 0.6221. These values are very close to the real ones, and allow the synchronization of interfering users. Fig. 12 (c) shows the real and imaginary parts of one of the estimated

Fig. 13 (a) highlights the performances in terms of mean chip error rate (MCER), i.e., the average ratio of the number of wrong sequence chips to the total number of sequence chips. It shows very low MCERs after synchronizing using either the MEVB or FSNB criterion. Moreover, MECRs obtained after using the MEVB criterion are lower than those obtained after using the FSNB one (with a 3 dB gain), in agreement with the theoretical analysis. It also shows that in average, one chip at most is wrong with sequences of length 127. Fig. 13 (b) shows the performances in terms of mean bit error rate (MBER). It clearly evidences very good

−14 −12 −10 −9 −8 −5 10−7

(b) Mean bit error rate

*u* users, it was set *α*0 = 0.3228,

after synchronizing using the FSNB criterion after synchronizing using the MEVB criterion

SNR (in dB)

−14 −12 −10 −9 −8 −5 10−6

(a) Mean chip error rate

will be maximum for the bright areas in Fig. 12 (a).

obtain *α*0 ≈ 0.3281, *α*1 = 0.4226, *α*2 = 0.7533 and *α*3 = 0.9449.

sequences, which are equal to that used at the transmitter side, respectively.

after synchronizing using the FSNB criterion after synchronizing using the MEVB criterion

SNR (in dB)

A *QPSK* modulation was considered in uplink. For the *N*1

Fig. 13. Both synchronization performances comparison for different

10−5

*u* = 4, *L*1 = 127.

then we get 100×*Ts*1≈127*μs*.

10−4

Mean chip error rate

SNRs, *N*1

10−3

10−2

Fig. 14. Iterative implementation of the proposed scheme in the multiuser single rate case.

performances after synchronizing using both criteria and estimating transmitted symbols. In agreement with the results shown in Fig. 13 (a), lower MBERs are obtained ( with a 2 dB gain) after using the MEVB criterion. Typically, in some cooperative systems, the MBER is about 10−8 which is very close to that obtained at −5 dB after using the MEVB criterion.

#### **6. Performances improvement of the proposed method**

Note first that the overall chain is implemented in a way to make it interactive with an operator. As claimed in Section 3, although the proposed method has been described sequentially, the most efficient implementation of the three steps is to link them back iteratively. This allows the global chain performances improvement by deflating the estimated signal. This will be especially important when the number of interfering users within a given group of users transmitting at the same data rate increases, or when the differences between data rate from one group to another one is very high. In these cases, some synchronization peaks can be masked as proven in Section 5. Hence, it is advisable to adopt the iterative approach summarized on Fig. 14 and Fig. 15. Therefore, firstly identifying in the multiuser detection step the group of users corresponding to the largest sequence or fluctuations amplitude peaks, set to *NFpeaks*.

#### **6.1 Implementation in the multiuser single rate case**

This case is shown on Fig. 14. Within the identified group of users among *NFpeaks*, the user corresponding to the synchronization criterion maxima is first synchronized. Besides, the

0 1 2 3 4 5 6 0

fluctuations: Φ(θ) noise theoretical mean noise theoretical maximum

Despreading of DS-CDMA Signals in Multirate Multiuser Cognitive Radio Systems

100

125 Blind Detection, Parameters Estimation and

200

300

400

500

1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 x 108

synchronization

FSNB criterion

Time (in μs)

Number of samples 100 200 300 400 500

t 0

(b) Double-size matrix R

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 1.2

t 1 Td

Time (in μs)

(b) Synchronization criterion

0 5 10 15 20 25 30 0

Number of samples (b) Eigenvalues after the

(a) Fluctuations Φ(*τ*)

0 5 10 15 20 25 30 0

Number of samples (a) Eigenvalues before the

Fig. 17. Illustration of the synchronization process.

Number of samples 100 200 300 400 500

(a) Extracted matrix **R***α*0

Fig. 18. Illustration of the third step of the proposed scheme.

Fig. 16. Fluctuations and double-size correlation matrix analysis.

2000

synchronization

100

200

300

400

500

4000

6000

8000

10000

Fig. 15. Iterative implementation of the proposed scheme in multiuser multirate case

number of synchronization peaks set to *NSpeaks* must be stored for further comparison in the algorithm. In the next step, the corresponding spreading sequences estimated will be the most reliable, since it will correspond to the eigenvector, thus the signal space associated with the largest eigenvalue.Ãl'At this stage, using the modified-Pastd algorithm aiming to estimate the number of users *Ni u* is necessary.Ãl'The identified signal is substracted from the global signal corresponding to signals transmitted at the same data rate. Then, if the number of remaining users *Nb* is not equal to one, the synchronization criterion may be computed again after the deflation, and the process is repeated as described on Fig. 14.

#### **6.2 Implementation in the multiuser multirate case**

The iterative implementation in the multiuser multirate context is illustrated on Fig. 15. In this case, first identify the group of users corresponding to the largest fluctuations amplitude peaks, and store the number *NFpeaks* of group of fluctuations peaks. Then, the number of iteration compared to *NFpeaks* is increased. Therefore, if *Nb* �= 1, restart the process by selecting another group of fluctuations. Note that, improving the performances is achueved by recomputing the function Φ(*θ*) after deflating the estimates of the signals whithin the identified group of interest.

#### **6.3 Discussion and illustration through simulation results**

In asynchronous multiuser multirate CDMA systems, the near-far effect is definitely present. Consequently, the multipath channels and/or under near-far effects may be mitigated through the iterative implementation of the proposed algorithms highlighted above. Indeed 20 Will-be-set-by-IN-TECH

*i*=0 *yi*(*t*)= ∑*S*−1 *i*=0 ∑*Ni u*−1 *n*=0 ∑+∞

Compute the double-size matrix R

yes *S* = *S*+ 1

= 1?

no

Compute fluctuations Φ(*τ*)

*NFpeaks*

Signals in the band of interest *y*(*t*)= ∑*S*−1

Synchronization

*α*0 = arg *max αn*, *n*=0,···,*Nu*−1

> Modified Pastd

*yi* = *yi* − *y*0

*k*=−∞ *an*,*i*(*k*)*hn*,*i*(*t* − *kTs*,*i*) + *b*(*t*)

= 1? yes

no

*u* is necessary.Ãl'The identified signal is substracted from the global signal

users

End

*u* + 1 Nb remaining

number of synchronization peaks set to *NSpeaks* must be stored for further comparison in the algorithm. In the next step, the corresponding spreading sequences estimated will be the most reliable, since it will correspond to the eigenvector, thus the signal space associated with the largest eigenvalue.Ãl'At this stage, using the modified-Pastd algorithm aiming to estimate the

corresponding to signals transmitted at the same data rate. Then, if the number of remaining users *Nb* is not equal to one, the synchronization criterion may be computed again after the

The iterative implementation in the multiuser multirate context is illustrated on Fig. 15. In this case, first identify the group of users corresponding to the largest fluctuations amplitude peaks, and store the number *NFpeaks* of group of fluctuations peaks. Then, the number of iteration compared to *NFpeaks* is increased. Therefore, if *Nb* �= 1, restart the process by selecting another group of fluctuations. Note that, improving the performances is achueved by recomputing the function Φ(*θ*) after deflating the estimates of the signals whithin the

In asynchronous multiuser multirate CDMA systems, the near-far effect is definitely present. Consequently, the multipath channels and/or under near-far effects may be mitigated through the iterative implementation of the proposed algorithms highlighted above. Indeed

Fig. 15. Iterative implementation of the proposed scheme in multiuser multirate case

{�R�2 *F*}

Sequence Recovering

number of users *Ni*

identified group of interest.

*N i u* = *N i*

deflation, and the process is repeated as described on Fig. 14.

**6.3 Discussion and illustration through simulation results**

**6.2 Implementation in the multiuser multirate case**

Fig. 16. Fluctuations and double-size correlation matrix analysis.

Fig. 17. Illustration of the synchronization process.

Fig. 18. Illustration of the third step of the proposed scheme.

Number of samples 100 200 300 400 500

0 5 10 15 20 25 30 0

Number of samples (b) Eigenvalues before the

0 5 10 15 20 25 30 0

Number of samples (b) Eigenvalues after the

*u*=2). As evidenced on Fig.

*u*−1)∗2∗*Qs*=10 overlapping subspaces are

127 Blind Detection, Parameters Estimation and

Despreading of DS-CDMA Signals in Multirate Multiuser Cognitive Radio Systems

Fig. 19. Subspace analysis before the synchronization process in the second loop after

synchronization

0.5

Besides, the criterion synchronization on Fig. 21 (b) proves that there are *Qs*=3 subspaces corresponding to the *Qs*=3 part of the spreading sequence. Therefore, synchronizing starting from the criteria maximum allows the extraction of the corresponding sub-matrix as indicated on Fig. 21 (c), on which linear algebra techniques application described in Section 4 lead to the estimation of a part the spreading sequence. The process is done *Qs*−1 times in order to

To complete, in the multiuser context, theoretical developments in Section 5 remain valid for the long case. Indeed, let us have a look on Fig. 22 obtained with the same parameters

not very remarkable as they correspond to the user received with a very low power. This is confirmed by the synchronization criterion plotted on Fig. 22 (b) which only emphasizes the shifts of the signal received with the highest power, i.e., the equispaced maxima of all the synchronization maxima. Fig. 22 (b) highlights the synchronization peaks masking. Hence,

*u*∗2∗*Qs*=20 overlapping subspaces, which suggests that there are two interfering

synchronization

1

1.5

2

2.5 x 104

(a) Double-size matrix R

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 2

t 1

Time (in μs)

(a) Synchronization criterion

Fig. 20. Synchronization process in the second loop after deflation.

estimate the whole spreading code as depicted on Fig. 21 (d).

users with long spreading codes. However, (*N*0

than on Figures 16 and 17, but with *Qs*=5 for the two users (*N*0

criterion minima maxima real shifts

100

200

300

400

500

2.5

3

3.5

FSNB criterion

4

4.5 x 108

deflation.

22 (a) *N*0

this iterative approach jointly uses the modifed-Pastd algorithm and a feedback on either synchronization criteria or fluctuations of correlation estimators computation. Besides, note that the deflation technique is widely used in CDMA signals detection in both blind and cooperative methods. The originality of our approach lies in the analysis of the fluctuations of correlation estimators, and in the synchronization criteria implemented.

As a result, the proposed algorithms may be applied to the long sequence code case, as investigated in Section 7 through simulation results.

Let us now illustrate the iterative implementation using the FSNB criterion with following simulations parameters were set: *N*0 *u* = 2 complex GOLD sequences of processing gain *L*0,0 = *L*1,0 = 255, with inequal powers: *σ*2 *s*0 = 1 and *σ*2 *s*1 = 0.5022, *σ*2 *b* = 5.3307, *Fc* = 300 MHz, *Fe* = 600 MHz, *TF* = 2*μs*, *K* = 894, a *QPSK* modulation was considered in uplink. For the *N*0 *u* users, it was set *t*0 = 0.2 *μs*, *t*1 = 0.6 *μs*, SNR0 = −6.3 dB, SNR1 = −9.78 dB, what leads to a global SNR� −11 dB at the receiver side. From Fig. 16 (a), we get *TS* = 0.85 *μs*, which allows to compute the double-size matrix R, as shown on Fig. 16 (b). Even though on Fig. 16 (a), an estimation was not possible for *N*0 *u*, the analysis of Fig. 16 (b) suggests that there could be *N*0 *u* = 2 two users.

This is also reinforced by the eigenvalue decomposition that reveals 4 largest eigenvalues, as illustrated on Fig. 17 (a). However, the synchronization criterion on Fig. 17 (b) exhibits only one peak, from which we get *t*0 = 0.208, and the estimates *Td* = 0.342 *μs*. The EVD on Fig. 17 (b) of the corresponding extrated matrix **R***α*0 on Fig. 18 (a) exhibits 3 largest eigenvalues as expected. Since the use of the modified-Pastd algorithm leads to *N*0 *u* = *N*0 *u*, the contibution of the estimated signal is substracted from the global received signal and we continue by recomputing the criterion in order to estimate the second user.

Computing again another double-size matrix as shown on Fig. 19 (a). Its EVD on Fig. 19 (b) leads to only two largest eigenvalues as expected. Besides, the obtained criterion in this second iteration on Fig. 20 (a) exhibits only one synchronization peak, which allows to synchronize the corresponding user. Furthermore, the sequence recovering process may be performed, starting with the eigenvector corresponding to the remaining largest eigenvalue as shown on Fig. 20 (b).

#### **7. Application to long spreading sequences transmission case**

In order to clear up the reader, the application of the proposed method will be done through simulations in uplink. The multiuser case can be easily deduced as it will be further shown. Following parameters were set: Complex GOLD sequence of Length *L*0=63, *Qs*=3 which

implies *QsTs*=*LTc* (see Subsection 2.1.2 for details), *BPSK* modulation *α*0=0.2, *Fe*=300 *MHz*, *Fc*=150 *MHz*, *TF*=2 *μs*,*K*=666, SNR = −3 dB. Fig. 21(a) illustrates double-size matrix R computed with the estimates *Qs*∗*TS*=1.20 *μs* in this case. The fluctuations curve is not represented here since it the same as in the short code case. The great difference is in estimating *Qs*∗*TS*=1.2588 *μs* rather than *TS*=*L*/*Fc*=0.42 *μs*, and we get *α*0=0.1667 which is very close to the real value. Seeing 2∗*Qs*=6 overlapping subspaces as expected. Compared to the short code case,i.e., *Qs*=1, obtaining 2∗*Qs*=2 overlapping subspaces, as detailed in Section 4.

22 Will-be-set-by-IN-TECH

this iterative approach jointly uses the modifed-Pastd algorithm and a feedback on either synchronization criteria or fluctuations of correlation estimators computation. Besides, note that the deflation technique is widely used in CDMA signals detection in both blind and cooperative methods. The originality of our approach lies in the analysis of the fluctuations

As a result, the proposed algorithms may be applied to the long sequence code case, as

Let us now illustrate the iterative implementation using the FSNB criterion with following

*Fe* = 600 MHz, *TF* = 2*μs*, *K* = 894, a *QPSK* modulation was considered in uplink. For the

This is also reinforced by the eigenvalue decomposition that reveals 4 largest eigenvalues, as illustrated on Fig. 17 (a). However, the synchronization criterion on Fig. 17 (b) exhibits only one peak, from which we get *t*0 = 0.208, and the estimates *Td* = 0.342 *μs*. The EVD on Fig. 17 (b) of the corresponding extrated matrix **R***α*0 on Fig. 18 (a) exhibits 3 largest eigenvalues as

of the estimated signal is substracted from the global received signal and we continue by

Computing again another double-size matrix as shown on Fig. 19 (a). Its EVD on Fig. 19 (b) leads to only two largest eigenvalues as expected. Besides, the obtained criterion in this second iteration on Fig. 20 (a) exhibits only one synchronization peak, which allows to synchronize the corresponding user. Furthermore, the sequence recovering process may be performed, starting with the eigenvector corresponding to the remaining largest eigenvalue

In order to clear up the reader, the application of the proposed method will be done through simulations in uplink. The multiuser case can be easily deduced as it will be further shown. Following parameters were set: Complex GOLD sequence of Length *L*0=63, *Qs*=3 which

implies *QsTs*=*LTc* (see Subsection 2.1.2 for details), *BPSK* modulation *α*0=0.2, *Fe*=300 *MHz*, *Fc*=150 *MHz*, *TF*=2 *μs*,*K*=666, SNR = −3 dB. Fig. 21(a) illustrates double-size matrix R computed with the estimates *Qs*∗*TS*=1.20 *μs* in this case. The fluctuations curve is not represented here since it the same as in the short code case. The great difference is in estimating *Qs*∗*TS*=1.2588 *μs* rather than *TS*=*L*/*Fc*=0.42 *μs*, and we get *α*0=0.1667 which is very close to the real value. Seeing 2∗*Qs*=6 overlapping subspaces as expected. Compared to the short code case,i.e., *Qs*=1, obtaining 2∗*Qs*=2 overlapping subspaces, as detailed in Section 4.

*u* users, it was set *t*0 = 0.2 *μs*, *t*1 = 0.6 *μs*, SNR0 = −6.3 dB, SNR1 = −9.78 dB, what leads to a global SNR� −11 dB at the receiver side. From Fig. 16 (a), we get *TS* = 0.85 *μs*, which allows to compute the double-size matrix R, as shown on Fig. 16 (b). Even though on Fig. 16

*s*0 = 1 and *σ*2

*u* = 2 complex GOLD sequences of processing gain

*u*, the analysis of Fig. 16 (b) suggests that there could

*u* = *N*0

*b* = 5.3307, *Fc* = 300 MHz,

*u*, the contibution

*s*1 = 0.5022, *σ*2

of correlation estimators, and in the synchronization criteria implemented.

expected. Since the use of the modified-Pastd algorithm leads to *N*0

**7. Application to long spreading sequences transmission case**

recomputing the criterion in order to estimate the second user.

investigated in Section 7 through simulation results.

simulations parameters were set: *N*0

*N*0

be *N*0

*L*0,0 = *L*1,0 = 255, with inequal powers: *σ*2

(a), an estimation was not possible for *N*0

*u* = 2 two users.

as shown on Fig. 20 (b).

Fig. 19. Subspace analysis before the synchronization process in the second loop after deflation.

Fig. 20. Synchronization process in the second loop after deflation.

Besides, the criterion synchronization on Fig. 21 (b) proves that there are *Qs*=3 subspaces corresponding to the *Qs*=3 part of the spreading sequence. Therefore, synchronizing starting from the criteria maximum allows the extraction of the corresponding sub-matrix as indicated on Fig. 21 (c), on which linear algebra techniques application described in Section 4 lead to the estimation of a part the spreading sequence. The process is done *Qs*−1 times in order to estimate the whole spreading code as depicted on Fig. 21 (d).

To complete, in the multiuser context, theoretical developments in Section 5 remain valid for the long case. Indeed, let us have a look on Fig. 22 obtained with the same parameters than on Figures 16 and 17, but with *Qs*=5 for the two users (*N*0 *u*=2). As evidenced on Fig. 22 (a) *N*0 *u*∗2∗*Qs*=20 overlapping subspaces, which suggests that there are two interfering users with long spreading codes. However, (*N*0 *u*−1)∗2∗*Qs*=10 overlapping subspaces are not very remarkable as they correspond to the user received with a very low power. This is confirmed by the synchronization criterion plotted on Fig. 22 (b) which only emphasizes the shifts of the signal received with the highest power, i.e., the equispaced maxima of all the synchronization maxima. Fig. 22 (b) highlights the synchronization peaks masking. Hence,

as we just estimated, and recomputing the synchronization criterion in order to estimate the

129 Blind Detection, Parameters Estimation and

Despreading of DS-CDMA Signals in Multirate Multiuser Cognitive Radio Systems

In this chapter, we described a fast and efficient blind spread spectrum signal analysis approach consisting on signal detection, synchronization and identification of all concomitant data rates and user's sharing the same bandwidth in DS-CDMA transmissions. All this signal processing bricks are dedicated to the self-reconfiguration of Cognitive Radio terminals. We have evidenced that this approach permits to differentiate various CDMA standards through the assessment of interfering signals data rate. The methods and algorithms we have developed for these three steps allow to determine CDMA signals parameters: number of standards or rates, number of users for each rate, type of long or short sequences, the resynchronization time, spreading sequences and to despread signals of each user. We have shown that even in a sequential implementation,the approach developed and implemented here is very efficient both in terms of detection, synchronization and identification. Moreover, its performances improvement has been addressed thourgh two synchronization criteria. The first one has a very low computational cost and the second one permit to greatly reduce the phenomenon of peak masking, related to resynchronization times too close or excessive power difference between two users. Alternatively, a proposed iterative implementation that can suppress almost all the problems of peak masking by successive subtractions of interfering users was detailed. From which, short or long codes DS-CDMA signals blind detection and identification may be performed. Finally, the major part of the signal processing block of this approach were implemented on an operational system for the monitoring of radio frequency

Buzzi, S., Venturino, L., Zappone, A. & Maio, A. D. (2010). Blind user detection in

Cheraghi, P., Ma, Y. & Tafazolli, R. (2010). A novel blind spectrum sensing approach for

Committee, C. . M. (1989). *Digital land mobile radio communications : final report*, Euratom

Ghavami, S. & Abolhassani, B. (2008). Blind chip rate estimation in multirate cdma

Ghavami, S. & Abolhassani, B. (2009). On the performance of blind chip rate estimation

Hosseini, S., H, A. & J.A., R. (2010). A new cyclostationary spectrum sensing approach in

Khodadad, F. S., Ganji, F. & Mohammad, A. (2010). A practical approach for coherent

cognitive radios, *PGNET 2010 Conference*, Marrakech, Morroco.

cognitive radio, *IEEE SPAWC*, Marrakech, Morroco, pp. 1–4.

doubly dispersive ds/cdma fading channels, *IEEE Transaction on Signal Processing*

publications - EUR 12160 EN, Information technologies and sciences, *Commission of*

transmissions using multirate sampling at slow flat fading channels, *IEEE ICCS 2008*,

in multi-rate cdma transmissions using multi-rate sampling in slow flat fading

signal surveillance and blind parameter assessment in asynchoronous ds-cdma systems in multipath channel, *Iranian Conference on Electrical Engineering*, Isfahan,

second interfering user as depicted in Subsection 6.3.

**8. Chapter summary and conclusion**

spectrum.

**9. References**

58(3): 1446–1451.

*the European Communities*.

Singapore, pp. 1344– 1348.

Iran, pp. 305–310.

channels, *Wireless Sensor Network* 2: 67–75.

Fig. 21. Application of the proposed algorithm to the single user long code case in uplink.

Fig. 22. Illustration of the synchronization process with *N*0 *u* = 2 users in uplink.

the process described above for the case on Fig. 21 may be performed in order to recover the corresponding spreading sequences for this user. Furthermore, after the deflation of the user as we just estimated, and recomputing the synchronization criterion in order to estimate the second interfering user as depicted in Subsection 6.3.

#### **8. Chapter summary and conclusion**

In this chapter, we described a fast and efficient blind spread spectrum signal analysis approach consisting on signal detection, synchronization and identification of all concomitant data rates and user's sharing the same bandwidth in DS-CDMA transmissions. All this signal processing bricks are dedicated to the self-reconfiguration of Cognitive Radio terminals. We have evidenced that this approach permits to differentiate various CDMA standards through the assessment of interfering signals data rate. The methods and algorithms we have developed for these three steps allow to determine CDMA signals parameters: number of standards or rates, number of users for each rate, type of long or short sequences, the resynchronization time, spreading sequences and to despread signals of each user. We have shown that even in a sequential implementation,the approach developed and implemented here is very efficient both in terms of detection, synchronization and identification. Moreover, its performances improvement has been addressed thourgh two synchronization criteria. The first one has a very low computational cost and the second one permit to greatly reduce the phenomenon of peak masking, related to resynchronization times too close or excessive power difference between two users. Alternatively, a proposed iterative implementation that can suppress almost all the problems of peak masking by successive subtractions of interfering users was detailed. From which, short or long codes DS-CDMA signals blind detection and identification may be performed. Finally, the major part of the signal processing block of this approach were implemented on an operational system for the monitoring of radio frequency spectrum.

#### **9. References**

24 Will-be-set-by-IN-TECH

4.8 4.9 5 5.1 5.2 5.3

−1

−1

4.1 4.15 4.2 4.25 4.3 4.35 4.4 4.45 x 107

the process described above for the case on Fig. 21 may be performed in order to recover the corresponding spreading sequences for this user. Furthermore, after the deflation of the user

Fig. 21. Application of the proposed algorithm to the single user long code case in uplink.

0

0

1

FSNB criterion

5.4 x 106

0 20 40 60 80 100 120 4.7

(b) Synchronization criterion

0 10 20 30 40 50 60

1 Estimated sequence

0 10 20 30 40 50 60

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 4.05

t 1

Time (in μs)

(b) Synchronization criterion

*u* = 2 users in uplink.

Number of chips (d) Sequence estimation after *binarization*

> t 0

criterrion minima maximum real shift

Analysis window shifts in number of samples

Reference sequence

Number of samples 50 100 150 200

(a) Double-size matrix R

Number of samples 20 40 60 80 100

Number of samples 100 200 300 400 500

Fig. 22. Illustration of the synchronization process with *N*0

(a) Double-size matrixR

(c) Extracted matrix **R**

100

200

300

400

500

**1. Introduction**

occupancy needs to be investigated thoroughly.

theoretical analysis Carr (1999).

the main investigation in our project.

Based on the conception of spectrum sharing, Cognitive Radio as a promising technology for optimizing utilization of the radio spectrum has emerged to revolutionize the next generation wireless communications industry Staple & Werbach (2004) Ashley (2006). In order to facilitate this technology, the present spectrum allocation strategies have to be re-examined and the actual spectrum occupancy information has to be studied systemically. To assess the feasibility of Cognitive Radio technology, the statistical information of the current spectral

**Measurement and Statistics**

**of Spectrum Occupancy**

**7**

**7**

Zhe Wang

*UK*

*Durham University*

While preliminary occupancy information can be retrieved from spectrum licences, essential details are often unknown generally, which include the location of transmitters,transmitter output power, and antenna type, etc. Additionally, licences do not specify how often the spectrum is being occupied. Furthermore, the local environment affects the propagation of radio waves a lot. While these affects can be simulated, the results are hardly precision. Hence, in order to categorize spectrum usage, practical spectrum monitoring are vastly preferable to

Two important characteristics of the spectrum are the propagation features and the amount of information which signals can carry. In general, signals sent using the higher frequencies have smaller propagation distances but a higher data carrying capacity. These propagation characteristics of the spectrum constrain the identified range of applications for which any particular band is suitable Saakian (2011). A portion of spectrum range from 30-3000 MHz is known to be suitable for a wide variety of services and is thus in great demand, which became

We studied the 100-2500 MHz spectrum with the radio monitoring systems which technical details have been fully recorded in this article. In this chapter,we will present the detail statistics of spectrum occupancies with graphics and tables, which give the overall profile of current spectrum usage in this spectrum. The conclusion of the statistical information from the spectrum monitoring experiments shows that the spectrum occupancy range from 100-2500 MHz are low indeed in the measuring locations and period. The average occupancies for most bands are less than 20%. Especially, the average occupancies in the 100-2500 MHz

## **Measurement and Statistics of Spectrum Occupancy**

Zhe Wang *Durham University UK*

#### **1. Introduction**

26 Will-be-set-by-IN-TECH

130 Advances in Cognitive Radio Systems

Khodadad, F. S., Ganji, F. & Safaei, A. (2010). A robust pn length estimation in down

Kibangou, A. Y. & de Almeida, A. (2010). Distribured parafac based ds-cdma blind receiver for wireless sensor networks, *IEEE SPAWC*, Marrakech, Morroco, pp. 1–5. Koivisto, T. & Koivunen, V. (2007). Blind despreading of short-code ds-cdma signals in asynchronous multi-user systems, *Signal Processing (87)* 87(11): 2560–2568. Meng, Y., You, M.-L., Luo, H.-W., Liu, G. & Yang, T. (2010). The linearly constrained lscma for

Mitola, J. (2000). *Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio*, PhD thesis, Royal Institute of Technology (KTH), Stockholm, Sweden. Nsiala Nzéza, C. (2006). *Récepteur Adaptatif Multi-Standards pour les Signaux à Étalement de*

Nsiala Nzéza, C., Gautier, R. & Burel, G. (2004). Blind synchronization and sequences

Nzéza, C. N., Gautier, R. & Burel, G. (2006). Blind Multiuser Detection in Multirate CDMA

Nzéza, C. N., Gautier, R. & Burel, G. (2008). Theoretical Performances Analysis of the Blind

Nzéza, C. N., Moniak, G., Berbineau, M., Gautier, R. & Burel, G. (2009). Blind MC-DS-CDMA

Shen, L., Wang, H. & andZhijin Zhao, W. Z. (2011). Blind spectrum sensing for cognitive

Williams, C., Beach, M., Neirynck, D., A. Nix, K. C., Morris, K., Kitchener, D., Presser, M., Li,

Yu, M., Chen, J., Shen, L. & Li, S. (2011). Blind separation of ds-cdma signals with ica method,

Zhang, T., Dai, S., Zhang, W., Ma, G. & Gao, X. (2011). Blind estimation of the pn sequence in lower snr ds-ss signals with residual carrier, *Digital Signal Processing In Press* .

CDMA Transmissions, *Military Technical Academy Journal*.

*Broadband Wireless Access Workshop*, Honolulu, Hawaii, USA.

*Communication Technology* , Angwon-Do, South Korea, pp. 951–955.

53(2): 199–209.

Brest, France.

10(6): 1721–1724.

Francisco, California, USA, pp. 1–5.

*IEEE Communication Magazine* 42(12): 15–32.

*Journal of Networks* 6(2): 198–205.

USA.

link low- snr ds-cdma multipath channels, *IEEE International Conference on Advanced*

blind multi-user detection, *Wireless Personal Communications: An International Journal*

*Spectre en Contexte Non Coopératif*, PhD thesis, Université de Bretagne Occidentale,

identification in cdma transmissions, *IEEE-AFCEA-MilCom*, Monterey, California,

Transmissions Using Fluctuations of Correlation Estimators, *IEEE Globecom 2006*, San

Multiuser Detection based on Fluctuations of Correlation Estimators in Multirate

parameters estimation in frequency selective channels, *IEEE-GlobeCom-5th IEEE*

radio channels with noise uncertainty, *IEEE Transaction on Wireless Communications*

Y. & McLaughlin, S. (2004). Personal area technologies for internetworked services,

Based on the conception of spectrum sharing, Cognitive Radio as a promising technology for optimizing utilization of the radio spectrum has emerged to revolutionize the next generation wireless communications industry Staple & Werbach (2004) Ashley (2006). In order to facilitate this technology, the present spectrum allocation strategies have to be re-examined and the actual spectrum occupancy information has to be studied systemically. To assess the feasibility of Cognitive Radio technology, the statistical information of the current spectral occupancy needs to be investigated thoroughly.

While preliminary occupancy information can be retrieved from spectrum licences, essential details are often unknown generally, which include the location of transmitters,transmitter output power, and antenna type, etc. Additionally, licences do not specify how often the spectrum is being occupied. Furthermore, the local environment affects the propagation of radio waves a lot. While these affects can be simulated, the results are hardly precision. Hence, in order to categorize spectrum usage, practical spectrum monitoring are vastly preferable to theoretical analysis Carr (1999).

Two important characteristics of the spectrum are the propagation features and the amount of information which signals can carry. In general, signals sent using the higher frequencies have smaller propagation distances but a higher data carrying capacity. These propagation characteristics of the spectrum constrain the identified range of applications for which any particular band is suitable Saakian (2011). A portion of spectrum range from 30-3000 MHz is known to be suitable for a wide variety of services and is thus in great demand, which became the main investigation in our project.

We studied the 100-2500 MHz spectrum with the radio monitoring systems which technical details have been fully recorded in this article. In this chapter,we will present the detail statistics of spectrum occupancies with graphics and tables, which give the overall profile of current spectrum usage in this spectrum. The conclusion of the statistical information from the spectrum monitoring experiments shows that the spectrum occupancy range from 100-2500 MHz are low indeed in the measuring locations and period. The average occupancies for most bands are less than 20%. Especially, the average occupancies in the 100-2500 MHz

of Spectrum Occupancy 3 • "limited numbers of nearby transmitters to prevent intermodulation or saturation problems that can arise even though pre-selection and/or filtering is used for survey

Measurement and Statistics of Spectrum Occupancy 133

• limited man-made noise such as impulsive noise from automobile ignition systems and

Figure 1 shows that the measurement locations for the spectrum occupancy project was the

Durham (54 46', 1 34')

Because of the complexity and sophistication of wireless communication technologies, it is an ever-increasing challenge to monitor the spectrum, particularly considering the rapid growth of wireless, satellite, and point-to-point communication devices. Key considerations in the design of spectrum monitoring systems include types of equipment, data rate and complexity of data capture and processing, degree of integration with software tools for analysis. While considered the limited project budget and the existing equipment, we integrated independently 3 different spectrum monitoring systems for different frequency

The monitoring system for 100-1500 MHz spectrum in this project was configured as Figure 2. The system consisted of an omnidirectional Dressler ARA-1500 active antenna range from 50 to 1500 MHz connected by a 6 m RF cable to a diplexer RSM-2000 which allows the DC current for the preamplifier to be applied to the centre conductor of the RF coax, eliminating

electrical machinery that can add to the received signals of interest."

roof and inside of Engineering Building, Durham University.

measurements

Fig. 1. Measurement location

bands to satisfy the technique requirements.

spectrum are less than 5%. This suggests that Cognitive Radio technology can physically play an important role in future communications, if the current radio spectrum allocation strategies would be modified Wang & Salous (2009).

#### **2. Radio spectrum occupancy monitoring**

#### **2.1 Considerations for spectrum monitoring**

Cognitive radio technologies require reconsideration the current regulations and policies of spectrum management. Spectrum monitoring is a fundamental function to support spectrum management. Spectrum measurements are critical to policy regulators and researchers in the development of new spectrum access technologies. Specifically, spectrum occupancy studies inspect what spectrum bands have low or no active utilization and thus may be appropriate for spectrum sharing. They also provide information on the signal characteristics within these bands, which is needed to design spectrum sharing algorithms. Referred to ITU (n.d.) the considerations of spectrum monitoring activities in our project include the following:

The overall goal of spectrum monitoring activities of our project is to depict the current levels of spectrum usage in the range 100 to 2500 MHz and its implications for cognitive radio. Central objectives include the following:

The measuring system should be chosen carefully to ensure capabilities exist with the spectrum management agency to effectively monitor and analyze the frequency bands.

#### **2.2 Monitoring system and site**

A successful spectrum survey requires careful selection of a measurement site. The location chosen for measuring will affect measured spectrum occupancy. For example, measurements made in Durham are probably representative of many towns that have similar scale and do not have heavy military activity or maritime radio navigation etc. Generally, a site for spectrum monitoring requires Sanders et al. (1996):

2 Will-be-set-by-IN-TECH

spectrum are less than 5%. This suggests that Cognitive Radio technology can physically play an important role in future communications, if the current radio spectrum allocation strategies

Cognitive radio technologies require reconsideration the current regulations and policies of spectrum management. Spectrum monitoring is a fundamental function to support spectrum management. Spectrum measurements are critical to policy regulators and researchers in the development of new spectrum access technologies. Specifically, spectrum occupancy studies inspect what spectrum bands have low or no active utilization and thus may be appropriate for spectrum sharing. They also provide information on the signal characteristics within these bands, which is needed to design spectrum sharing algorithms. Referred to ITU (n.d.) the

considerations of spectrum monitoring activities in our project include the following:

does not give any actual use information of that particular frequency.

• that licensed user information from the frequency management databases only indicates that the use of the frequency is authorized. The number of assignments on a frequency

• that efficient Spectrum Management can only satisfactorily proceed if the monitoring information provides the radio spectrum regulators with adequate reliable information

• that results of spectrum occupancy measurements will give information about the current use of frequencies to establish that the spectrum is being used efficiently and to assess the

The overall goal of spectrum monitoring activities of our project is to depict the current levels of spectrum usage in the range 100 to 2500 MHz and its implications for cognitive radio.

• to provide information of spectrum efficiency for determining planned and actual frequency usage and occupancy, and for assessing the feasibility of spectrum sharing

The measuring system should be chosen carefully to ensure capabilities exist with the spectrum management agency to effectively monitor and analyze the frequency bands.

A successful spectrum survey requires careful selection of a measurement site. The location chosen for measuring will affect measured spectrum occupancy. For example, measurements made in Durham are probably representative of many towns that have similar scale and do not have heavy military activity or maritime radio navigation etc. Generally, a site for spectrum

would be modified Wang & Salous (2009).

**2. Radio spectrum occupancy monitoring**

**2.1 Considerations for spectrum monitoring**

about the actual usage of the spectrum.

feasibility of the new technologies.

Central objectives include the following:

• to provide data for statistical modeling.

monitoring requires Sanders et al. (1996):

**2.2 Monitoring system and site**

technique.

• "limited numbers of nearby transmitters to prevent intermodulation or saturation problems that can arise even though pre-selection and/or filtering is used for survey measurements

of Spectrum Occupancy 3

• limited man-made noise such as impulsive noise from automobile ignition systems and electrical machinery that can add to the received signals of interest."

Figure 1 shows that the measurement locations for the spectrum occupancy project was the roof and inside of Engineering Building, Durham University.

Fig. 1. Measurement location

Because of the complexity and sophistication of wireless communication technologies, it is an ever-increasing challenge to monitor the spectrum, particularly considering the rapid growth of wireless, satellite, and point-to-point communication devices. Key considerations in the design of spectrum monitoring systems include types of equipment, data rate and complexity of data capture and processing, degree of integration with software tools for analysis. While considered the limited project budget and the existing equipment, we integrated independently 3 different spectrum monitoring systems for different frequency bands to satisfy the technique requirements.

The monitoring system for 100-1500 MHz spectrum in this project was configured as Figure 2. The system consisted of an omnidirectional Dressler ARA-1500 active antenna range from 50 to 1500 MHz connected by a 6 m RF cable to a diplexer RSM-2000 which allows the DC current for the preamplifier to be applied to the centre conductor of the RF coax, eliminating

of Spectrum Occupancy 5

Measurement and Statistics of Spectrum Occupancy 135

It is often helpful to make some simple characterization of the data in terms of summary statistics and graphics. The spectrum measurements contained in this chapter can only be used to assess the feasibility of using alternate services or systems under restricted conditions. Extrapolation of data in this paper to general spectrum occupancy for spectrum sharing requires consideration of additional factors. These include spectrum management regulations, types of missions performed in the bands and new spectrum requirements in the development and procurement stages. Also, measurement area, measurement site, and measurement

Highly dynamic bands where occupancy changes rapidly include those used by mobile radios (land, marine, and airborne) and airborne radars. These bands should be assigned a high priority and be measured often during a spectrum survey in order to maximize opportunities for signal detection. Bands that are not very dynamic in their occupancy such as those occupied by commercial radio and television signals or fixed emitters such as air traffic control radars need not be observed as often, because the same basic occupancy profile will be generated every time. Such bands should be given a low priority and less measurement time. An extreme case is that of the common carrier bands, which are essentially non-dynamic.

Boxplot in Figure 4 also known as a box-and-whisker diagram Freedman et al. (2007) is a convenient way of graphically depicting groups of numerical data. A boxplot shows a measure of central location (the median), two measures of dispersion (*Q*1 and *Q*3

inter-quantile range IQR), the skewness (from the orientation of the median relative to the quartiles) and potential outliers (marked individually). Boxplots are especially useful when comparing two or more sets of data. Figure 5 shows overall occupancy statistics of each band in the frequency range 100 MHz to 2500 MHz, where the threshold was set to -100 dBm. Figures 6 to 16 describe spectrum occupancy measurements and statistics of each band in the frequency range 100 MHz to 2500 MHz in Durham area during the period of 27/06/07 – 03/07/07. The spectrum occupancy in the *frequency domain* is shown in the top panel. This panel shows *Average* with time, in which the power values of each 10 kHz channel are linearly averaged during the measurement period, and *Maximum*, in which the result for any given

1 The quantile function is the inverse of the cumulative distribution function. The *p*-quantile is the value with the property that there is probability *p* of getting a value less than or equal to it. *Q*1 is 25-quantile,

1 and

Frequency span (MHz)/ sweep 12 Resolution bandwidth (kHz) 10 Sweep time (s) 6 Detection mode Sample RF attenuator (dB) 10 Reference level (dBm) -20

Table 2. Configuration parameters for scanning 1500-2500 MHz

system parameters should be considered Freedman et al. (2007).

**3. Statistics of spectrum occupancy**

*Q*3 is 75-quantile.

Model HP 8560A

the need for an additional DC power feed conductor. The RSM-2000 also contains a 20 dB RF adjustable attenuator allowing received signals to be attenuated over the entire frequency range. In order to increase the dynamic range of the system, a highpass filter Mini-Circuits SHP-100 was connected to the output of the RSM-2000. A Jim M-75 low noise amplifier was inserted in front of HP 8560A spectrum analyzer to decrease its noise figure. The GPIB bus was used for logging the trace data onto the hard disk of a PC and for transferring control command sequences to the spectrum analyzer. Table 1 lists the configuration parameters of spectrum analyzer for scanning 100-1500 MHz. Justifications will be given in the next section.

Fig. 2. Monitoring system for 100-1500 MHz spectrum

Table 1. Configuration parameters for scanning 100-1500 MHz

The monitoring system for 1500-2500 MHz spectrum was configured as in Figure 3. Instead of an omnidirectional antenna, an antenna array consisted of 4 directional Log-periodic antennas which enable the detection of signal incident directions. An RF switch controlled by the PC parallel port was used to choose a given antenna. Table 2 lists the configuration parameters of spectrum analyzer for scanning 1500-2500 MHz. Justifications will be given in the next section.

4 Will-be-set-by-IN-TECH

the need for an additional DC power feed conductor. The RSM-2000 also contains a 20 dB RF adjustable attenuator allowing received signals to be attenuated over the entire frequency range. In order to increase the dynamic range of the system, a highpass filter Mini-Circuits SHP-100 was connected to the output of the RSM-2000. A Jim M-75 low noise amplifier was inserted in front of HP 8560A spectrum analyzer to decrease its noise figure. The GPIB bus was used for logging the trace data onto the hard disk of a PC and for transferring control command sequences to the spectrum analyzer. Table 1 lists the configuration parameters of spectrum analyzer for scanning 100-1500 MHz. Justifications will be given in the next section.

> 6SHFWUXP\$QDO\]HU +3\$

> > \*3,%

'UHVVOHU\$5\$

section.

560

0LQL&LUFXLWV 6+3

Table 1. Configuration parameters for scanning 100-1500 MHz

Fig. 2. Monitoring system for 100-1500 MHz spectrum

Frequency span (MHz)/ sweep 6 Resolution bandwidth (kHz) 10

> Sweep time (s) 6 Detection mode Sample RF attenuator (dB) 10 Reference level (dBm) -20

The monitoring system for 1500-2500 MHz spectrum was configured as in Figure 3. Instead of an omnidirectional antenna, an antenna array consisted of 4 directional Log-periodic antennas which enable the detection of signal incident directions. An RF switch controlled by the PC parallel port was used to choose a given antenna. Table 2 lists the configuration parameters of spectrum analyzer for scanning 1500-2500 MHz. Justifications will be given in the next

Model HP 8560A

of Spectrum Occupancy 5

Table 2. Configuration parameters for scanning 1500-2500 MHz

#### **3. Statistics of spectrum occupancy**

It is often helpful to make some simple characterization of the data in terms of summary statistics and graphics. The spectrum measurements contained in this chapter can only be used to assess the feasibility of using alternate services or systems under restricted conditions. Extrapolation of data in this paper to general spectrum occupancy for spectrum sharing requires consideration of additional factors. These include spectrum management regulations, types of missions performed in the bands and new spectrum requirements in the development and procurement stages. Also, measurement area, measurement site, and measurement system parameters should be considered Freedman et al. (2007).

Highly dynamic bands where occupancy changes rapidly include those used by mobile radios (land, marine, and airborne) and airborne radars. These bands should be assigned a high priority and be measured often during a spectrum survey in order to maximize opportunities for signal detection. Bands that are not very dynamic in their occupancy such as those occupied by commercial radio and television signals or fixed emitters such as air traffic control radars need not be observed as often, because the same basic occupancy profile will be generated every time. Such bands should be given a low priority and less measurement time. An extreme case is that of the common carrier bands, which are essentially non-dynamic.

Boxplot in Figure 4 also known as a box-and-whisker diagram Freedman et al. (2007) is a convenient way of graphically depicting groups of numerical data. A boxplot shows a measure of central location (the median), two measures of dispersion (*Q*1 and *Q*3 1 and inter-quantile range IQR), the skewness (from the orientation of the median relative to the quartiles) and potential outliers (marked individually). Boxplots are especially useful when comparing two or more sets of data. Figure 5 shows overall occupancy statistics of each band in the frequency range 100 MHz to 2500 MHz, where the threshold was set to -100 dBm. Figures 6 to 16 describe spectrum occupancy measurements and statistics of each band in the frequency range 100 MHz to 2500 MHz in Durham area during the period of 27/06/07 – 03/07/07. The spectrum occupancy in the *frequency domain* is shown in the top panel. This panel shows *Average* with time, in which the power values of each 10 kHz channel are linearly averaged during the measurement period, and *Maximum*, in which the result for any given

1 The quantile function is the inverse of the cumulative distribution function. The *p*-quantile is the value with the property that there is probability *p* of getting a value less than or equal to it. *Q*1 is 25-quantile, *Q*3 is 75-quantile.

of Spectrum Occupancy 7

Measurement and Statistics of Spectrum Occupancy 137

\$(521\$87,&\$/5\$',21\$9,\*\$7,210+]

Fig. 5. Occupancy statistics of spectrum

0(7(252/2\*,&\$/6\$7(//,7(27+(50+] ),;('5\$',2\$6752120<0+] ),;('0\$5,7,0(02%,/(0+] 02%,/(H[FHSWDHURQDXWLFDOPRELOH0+]

\$(521\$87,&\$/5\$',21\$9,\*\$7,210+]

\$(521\$87,&\$/02%,/(0+]

%52\$'&\$67,1\*0+] ),;('02%,/(0+] ),;('02%,/(0+]

),;('02%,/(0+]

),;('02%,/(0+] ),;('02%,/(0+] %52\$'&\$67,1\*0+]

%52\$'&\$67,1\*0+] 02%,/(0+] 02%,/(27+(50+= 02%,/(0+]

),;('02%,/(0+]

02%,/(0+] ),;('02%,/(0+]

02%,/(6\$7(//,7(0+] ),;('02%,/(0+] 02%,/(0+]

),;('02%,/(6\$7(//,7(0+]

02%,/(63\$&(5(6(\$5&+0+] ),;('5DGLRORFDWLRQ0+]

02%,/(6\$7(//,7(27+(50+]

\$(521\$87,&\$/5\$',21\$9,\*\$7,210+]

\$(521\$87,&\$/5\$',21\$9,\*\$7,210+] (\$57+(;3/25\$7,216\$7(//,7(27+(50+]

63\$&(23(5\$7,2102%,/(6\$7(//,7(0+]

2FFXSDQF\

Fig. 3. Monitoring system for 1500-2500 MHz spectrum

Fig. 4. Box-and-whisker diagram

channel is the maximum power ever observed in that channel in 7-day time. Together, the *Average* and *Maximum* results provide a simple characterization of the temporal behavior of a channel. For example, when the results are equal, it suggests a single transmitter which is always on and which experiences no fading. At the other extreme, a large difference between the mean and maximum measurements suggests intermittent use of the channel.

The middle panel shows the band occupancy in the *time domain* with thresholds -95 dBm and -100 dBm during the measurement period of 27/06/07 – 03/07/07. For example, the occupancy rate of the Air band shown in Figure 6 was calculated in each time point in a given threshold -95 dBm and -100 dBm respectively. Total 168 time points for each hour of 7-day were plotted in this panel.

6 Will-be-set-by-IN-TECH

0LQL&LUFXLWV =./5

> \$GMXVWDEOH DWWHQXDWRU

6SHFWUXP\$QDO\]HU +3\$

\*3,%

0LQL&LUFXLWV 6+3

0HGLDQ

 4 4 4 ,45 4 ,45

,45

the mean and maximum measurements suggests intermittent use of the channel.

channel is the maximum power ever observed in that channel in 7-day time. Together, the *Average* and *Maximum* results provide a simple characterization of the temporal behavior of a channel. For example, when the results are equal, it suggests a single transmitter which is always on and which experiences no fading. At the other extreme, a large difference between

The middle panel shows the band occupancy in the *time domain* with thresholds -95 dBm and -100 dBm during the measurement period of 27/06/07 – 03/07/07. For example, the occupancy rate of the Air band shown in Figure 6 was calculated in each time point in a given threshold -95 dBm and -100 dBm respectively. Total 168 time points for each hour of 7-day

5)6ZLWFK

Fig. 3. Monitoring system for 1500-2500 MHz spectrum

Fig. 4. Box-and-whisker diagram

were plotted in this panel.

/RJSHULRGLFDQWHQQDDUUD\

of Spectrum Occupancy 7

Fig. 5. Occupancy statistics of spectrum

of Spectrum Occupancy 9

Measurement and Statistics of Spectrum Occupancy 139

\$YDLODELOLW\

2FFXSDQF\

\$PSOLWXGHG%P

0D[LPXQ \$YHUDJH

7KUHVKROG G%P

7KUHVKROG G%P

7KUHVKROG G%P 7KUHVKROG G%P

0LG%DQG0+]

Fig. 7. Occupancy statistics of Mid Band

G%P G%P

7LPHKRXU

:KLWHEDQGN+]

0LQ VW4XDQWLOH 0HGLDQ 0HDQ UG4XDQWLOH 0D[ D

E

F

)UHTXHQF\0+]

Fig. 6. Occupancy statistics of Air Band

8 Will-be-set-by-IN-TECH

D

E

F

)UHTXHQF\0+]

G%P G%P

7LPHKRXU

:KLWHEDQGN+]

0LQ

4XDQWLOH 0HGLDQ 0HDQ UG4XDQWLOH 0D[

\$YDLODELOLW\

2FFXSDQF\

\$PSOLWXGHG%P

0D[LPXQ \$YHUDJH

7KUHVKROG G%P

7KUHVKROG G%P

7KUHVKROG G%P 7KUHVKROG G%P

\$LU%DQG0+]

VW

Fig. 6. Occupancy statistics of Air Band

of Spectrum Occupancy 9

Fig. 7. Occupancy statistics of Mid Band

of Spectrum Occupancy 11

Measurement and Statistics of Spectrum Occupancy 141

\$YDLODELOLW\

2FFXSDQF\

\$PSOLWXGHG%P

0D[LPXQ \$YHUDJH

7KUHVKROG G%P

7KUHVKROG G%P

7KUHVKROG G%P 7KUHVKROG G%P

%DQG,,,0+]

Fig. 9. Occupancy statistics of Band III

G%P G%P

7LPHKRXU

:KLWHEDQGN+]

0LQ VW4XDQWLOH 0HGLDQ 0HDQ UG4XDQWLOH 0D[ D

E

F

)UHTXHQF\0+]

Fig. 8. Occupancy statistics of High Band

10 Will-be-set-by-IN-TECH

\$YDLODELOLW\

2FFXSDQF\

\$PSOLWXGHG%P

0D[LPXQ \$YHUDJH

7KUHVKROG G%P

7KUHVKROG G%P

7KUHVKROG G%P 7KUHVKROG G%P

+LJK%DQG0+]

VW

Fig. 8. Occupancy statistics of High Band

G%P G%P

7LPHKRXU

:KLWHEDQGN+]

0LQ

4XDQWLOH 0HGLDQ 0HDQ UG4XDQWLOH 0D[ D

E

F

)UHTXHQF\0+]

of Spectrum Occupancy 11

Fig. 9. Occupancy statistics of Band III

of Spectrum Occupancy 13

Measurement and Statistics of Spectrum Occupancy 143

D

E

F

)UHTXHQF\0+]

G%P G%P

7LPHKRXU

:KLWHEDQGN+]

0LQ VW4XDQWLOH 0HGLDQ 0HDQ UG4XDQWLOH 0D[

\$YDLODELOLW\

2FFXSDQF\

\$PSOLWXGHG%P

0D[LPXQ \$YHUDJH

7KUHVKROG G%P

7KUHVKROG G%P

7KUHVKROG G%P 7KUHVKROG G%P

8+) %DQG0+]

Fig. 11. Occupancy statistics of UHF1&2 Band

Fig. 10. Occupancy statistics of NATO Band

12 Will-be-set-by-IN-TECH

\$YDLODELOLW\

2FFXSDQF\

\$PSOLWXGHG%P

0D[LPXQ \$YHUDJH

7KUHVKROG G%P

7KUHVKROG G%P

7KUHVKROG G%P 7KUHVKROG G%P

1\$72%DQG0+]

VW

Fig. 10. Occupancy statistics of NATO Band

G%P G%P

7LPHKRXU

:KLWHEDQGN+]

0LQ

4XDQWLOH 0HGLDQ 0HDQ UG4XDQWLOH 0D[ D

E

F

)UHTXHQF\0+]

of Spectrum Occupancy 13

Fig. 11. Occupancy statistics of UHF1&2 Band

of Spectrum Occupancy 15

Measurement and Statistics of Spectrum Occupancy 145

\$YDLODELOLW\

2FFXSDQF\

\$PSOLWXGHG%P

0D[LPXQ \$YHUDJH

7KUHVKROG G%P

7KUHVKROG G%P

7KUHVKROG G%P 7KUHVKROG G%P

\*60 PRELOH0+]

Fig. 13. Occupancy statistics of GSM & mobile Band

G%P G%P

7LPHKRXU

:KLWHEDQGN+]

0LQ VW4XDQWLOH 0HGLDQ 0HDQ UG4XDQWLOH 0D[ D

E

F

)UHTXHQF\0+]

Fig. 12. Occupancy statistics of TV, Band IV & V

14 Will-be-set-by-IN-TECH

)UHTXHQF\0+]

G%P G%P

7LPHKRXU

:KLWHEDQGN+]

0LQ

4XDQWLOH 0HGLDQ 0HDQ UG4XDQWLOH 0D[ D

E

F

\$YDLODELOLW\

2FFXSDQF\

\$PSOLWXGHG%P

0D[LPXQ \$YHUDJH

7KUHVKROG G%P

7KUHVKROG G%P

7KUHVKROG G%P 7KUHVKROG G%P

79%DQG,9 90+]

VW

Fig. 12. Occupancy statistics of TV, Band IV & V

of Spectrum Occupancy 15

Fig. 13. Occupancy statistics of GSM & mobile Band

of Spectrum Occupancy 17

Measurement and Statistics of Spectrum Occupancy 147

D

E

F

)UHTXHQF\0+]

G%P G%P

7LPHKRXU

:KLWHEDQGN+]

0LQ VW4XDQWLOH 0HGLDQ 0HDQ UG4XDQWLOH 0D[

\$YDLODELOLW\

2FFXSDQF\

\$PSOLWXGHG%P

0D[LPXQ \$YHUDJH

7KUHVKROG G%P

7KUHVKROG G%P

7KUHVKROG G%P 7KUHVKROG G%P

/%DQGXSSHU0+]

Fig. 15. Occupancy statistics of L (upper) Band

Fig. 14. Occupancy statistics of L (lower) Band

16 Will-be-set-by-IN-TECH

\$YDLODELOLW\

2FFXSDQF\

\$PSOLWXGHG%P

0D[LPXQ \$YHUDJH

7KUHVKROG G%P

7KUHVKROG G%P

7KUHVKROG G%P 7KUHVKROG G%P

/%DQGORZHU0+]

VW

Fig. 14. Occupancy statistics of L (lower) Band

G%P G%P

7LPHKRXU

:KLWHEDQGN+]

0LQ

4XDQWLOH 0HGLDQ 0HDQ UG4XDQWLOH 0D[ D

E

F

)UHTXHQF\0+]

of Spectrum Occupancy 17

Fig. 15. Occupancy statistics of L (upper) Band

of Spectrum Occupancy 19 The bottom panel shows the statistical distributions of the *white spectrum*. *White spectrum* can be defined as the continuous idle spectrum in a given bandwidth and in a given threshold which can be used for communications. For example, in Figure 12, for -100 dBm threshold we can find about 60% white band with 1000 kHz bandwidth distributed in the total spectrum, if we divide the TV band (470 - 854 MHz) into 384 sub-bands with 1000 kHz bandwidth.

Measurement and Statistics of Spectrum Occupancy 149

The statistics table of each figure shows the minimum and maximum occupancy rates, 1*st*, 3*rd* quartile values, and mean and median values. A median is described as the number separating the higher half of a sample, a population, or a probability distribution, from the

The statistics presented in these figures shows that the spectrum occupancies are really spares in the measuring locations and period. The average occupancies for most bands are less than 20%. Except the GSM and CDMA communication bands, the average occupancies in the 1 GHz to 2.5 GHz spectrum are less than 5%. The good propagation characteristics of this range of spectrum, in terms of propagating distance and data rates, make it an excellent candidate for cognitive radio technology. The data shows that, without additional spectrum, there are a great amount of spectrum resources for accommodating the cognitive radio systems if the

There are generally positive findings in this project with respect to the prospects for cognitive radio. Statistics show that the spectral occupancies are spare indeed. Occupancy rates of most bands in VHF and UHF are less than 10% overall and the distribution of the white band indicates that the bands are capable of running wideband wireless communications. Of course, the current static spectrum allocation policies and spectrum management strategies have to be modified to motivate applications of the cognitive radio technology. While this study is to identify the low utilizations of bands, long term studies are crucial in developing

Above all, radio spectrum occupancies in time, frequency and space domains observed in this project are sparse indeed. This suggests that cognitive radio technologies have great prospects in the future wireless communication infrastructure, if current telecommunication policies

I am indebted to Durham University and British Telecom for their funding of my studies. I would like to record my gratitude to Sana Salous for giving valuable guidance and suggestions for improving the work, and Stuart Feeney for sharing his RF knowledge, Roger Lewenz and Peter Baxendale for their contributions in the development of the data acquisition

Ashley, S. (2006). Cognitive radio, *Scientific American Magazine* pp. 35– 42. Carr, J. J. (1999). *Practical Radio Frequency Test and Measurement*, Newnes, Oxford.

Freedman, D., Pisani, R. & Purves, R. (2007). *Statistical*, Oxford.

current communication regulations could be changed.

spectrum sharing technologies and for spectrum management.

lower half.

**4. Conclusion**

and regulations are modified.

ITU (n.d.). *ICT regulation toolkit*, ITU.

**5. Acknowledgement**

software.

**6. References**

Fig. 16. Occupancy statistics of S (lower) Band

The bottom panel shows the statistical distributions of the *white spectrum*. *White spectrum* can be defined as the continuous idle spectrum in a given bandwidth and in a given threshold which can be used for communications. For example, in Figure 12, for -100 dBm threshold we can find about 60% white band with 1000 kHz bandwidth distributed in the total spectrum, if we divide the TV band (470 - 854 MHz) into 384 sub-bands with 1000 kHz bandwidth.

of Spectrum Occupancy 19

The statistics table of each figure shows the minimum and maximum occupancy rates, 1*st*, 3*rd* quartile values, and mean and median values. A median is described as the number separating the higher half of a sample, a population, or a probability distribution, from the lower half.

The statistics presented in these figures shows that the spectrum occupancies are really spares in the measuring locations and period. The average occupancies for most bands are less than 20%. Except the GSM and CDMA communication bands, the average occupancies in the 1 GHz to 2.5 GHz spectrum are less than 5%. The good propagation characteristics of this range of spectrum, in terms of propagating distance and data rates, make it an excellent candidate for cognitive radio technology. The data shows that, without additional spectrum, there are a great amount of spectrum resources for accommodating the cognitive radio systems if the current communication regulations could be changed.

#### **4. Conclusion**

18 Will-be-set-by-IN-TECH

D

E

F

)UHTXHQF\0+]

G%P G%P

7LPHKRXU

:KLWHEDQGN+]

0LQ

4XDQWLOH 0HGLDQ 0HDQ UG4XDQWLOH 0D[

\$YDLODELOLW\

2FFXSDQF\

\$PSOLWXGHG%P

0D[LPXQ \$YHUDJH

7KUHVKROG G%P

7KUHVKROG G%P

7KUHVKROG G%P 7KUHVKROG G%P

6%DQGORZHU0+]

VW

Fig. 16. Occupancy statistics of S (lower) Band

There are generally positive findings in this project with respect to the prospects for cognitive radio. Statistics show that the spectral occupancies are spare indeed. Occupancy rates of most bands in VHF and UHF are less than 10% overall and the distribution of the white band indicates that the bands are capable of running wideband wireless communications. Of course, the current static spectrum allocation policies and spectrum management strategies have to be modified to motivate applications of the cognitive radio technology. While this study is to identify the low utilizations of bands, long term studies are crucial in developing spectrum sharing technologies and for spectrum management.

Above all, radio spectrum occupancies in time, frequency and space domains observed in this project are sparse indeed. This suggests that cognitive radio technologies have great prospects in the future wireless communication infrastructure, if current telecommunication policies and regulations are modified.

#### **5. Acknowledgement**

I am indebted to Durham University and British Telecom for their funding of my studies. I would like to record my gratitude to Sana Salous for giving valuable guidance and suggestions for improving the work, and Stuart Feeney for sharing his RF knowledge, Roger Lewenz and Peter Baxendale for their contributions in the development of the data acquisition software.

#### **6. References**

Ashley, S. (2006). Cognitive radio, *Scientific American Magazine* pp. 35– 42. Carr, J. J. (1999). *Practical Radio Frequency Test and Measurement*, Newnes, Oxford. Freedman, D., Pisani, R. & Purves, R. (2007). *Statistical*, Oxford. ITU (n.d.). *ICT regulation toolkit*, ITU.

Saakian, A. (2011). *Radio Wave Propagation Fundamentals*, Artech House.

Sanders, F., Ramsey, B. & Lawrence, V. (1996). *Broadband spectrum survey at San Diego, California*, Report, Department of Commerce, U.S.

Staple, G. & Werbach, K. (2004). The end of spectrum scarcity, *Spectrum, IEEE* 41(3): 48– 52.

Wang, Z. & Salous, S. (2009). Journal of signal processing system, *Spectrum occupancy statistics and time series models for cognitive radio* 62: 145– 155.

20 Will-be-set-by-IN-TECH

150 Advances in Cognitive Radio Systems

Sanders, F., Ramsey, B. & Lawrence, V. (1996). *Broadband spectrum survey at San Diego,*

Staple, G. & Werbach, K. (2004). The end of spectrum scarcity, *Spectrum, IEEE* 41(3): 48– 52. Wang, Z. & Salous, S. (2009). Journal of signal processing system, *Spectrum occupancy statistics*

Saakian, A. (2011). *Radio Wave Propagation Fundamentals*, Artech House.

*California*, Report, Department of Commerce, U.S.

*and time series models for cognitive radio* 62: 145– 155.

### *Edited by Cheng-Xiang Wang and Joseph Mitola III*

Cognitive radio technologies are forms of wireless communication with many and varied applications. The contributions in this book will benefit researchers and engineers as they offer cutting-edge knowledge in the field. Subjects include uses of wideband voltage controlled oscillators, control planes for spectrum access and mobility in networks with heterogeneous frequency devices. Other chapters cover cognitive media access control and measurement methods for spectrum occupancy. In addition, there are contributions on delay analysis and channel selection in single-hop networks for delay-sensitive applications, the application of transmission security (TRANSEC) protocols to cognitive radio communication and the use of blind detection, parameters, estimation and the despreading of DS-CDMA signals in multirate, multiuser cognitive radio systems.

Advances in Cognitive Radio Systems

Advances in

Cognitive Radio Systems

*Edited by Cheng-Xiang Wang and Joseph Mitola III*

Photo by simonmayer / iStock