The EVM calculation of the FPGA-based prototype is made possible by comparing the files captured in the equalization block with the equivalent ones of the ideal MATLAB receiver. To achieve this we have to feed the MATLAB model with all the different test vectors captured in the post AGC stage of the FPGA prototype and produce the same amount of files at the output of the equalization stage. For each OFDM symbol in each captured frame, we would apply the following MATLAB operations (a mean value for

	- [17] O. Font-Bach, N. Bartzoudis, A. Pascual-Iserte, and D. López Bueno. Prototying Processing-Demanding Physical Layer Systems Featuring Single Or Multi-Antenna Schemes. In *Proc. European Signal Processing Conference (EUSIPCO)*, September 2011.

**DVB-S2 Model in Matlab:**

**Issues and Impairments**

Bahman Azarbad and Aduwati Binti Sali

http://dx.doi.org/10.5772/46473

**1. Introduction**

Additional information is available at the end of the chapter

maintain the growth rate required by the various market sectors.

these tools are often incomplete or too simplistic.

above mentioned capabilities.

Geostationary (GEO) satellite as a broadcasting media.

For the past two decades, the wide coverage and the reliable bandwidth offered by the broadband satellite systems has made them a promising media for IP streaming. Meanwhile, the demand for exploring new solutions concerning this industry has kept increasing to

**Chapter 10**

In order to minimize the design time as well as implementation costs, the initial steps in researches concerning satellite systems are often taken in simulation environments. In contrast with the actual satellite system test-beds which are expensive or sometimes not available to the academic research community, the simulation software packages like Mathworks MATLAB are widely used at the early stages of modeling and design process.However, such packages are mostly designed as general purpose tools and therefore, the built-in models provided by

The main objective of this research is to use MATLAB simulator for study of issues and impairments of Digital Video Broadcasting-Second generation (DVB-S2) standard in

In this study we model DVB-S2 standard using Matlab communication toolbox and built in DVB-S2 model in Simulink. However, it is noted that the model in Matlab does not include higher order modulations: 16APSK and 32APSK and also the main novelty of DVB-S2 which is the ACM algorithm. Therefore we need to rebuild the model using toolboxes to add the

In the following sections, we first provide background of DVB-S2 Standard and in the next section a brief overview on DVB-S2 Model in Matlab will be presented , and then we will go through the model improvement on the following section and afterwards we will analyze presented solution by means of BER performance of the system and the provided ACM

> ©2012 Azarbad and Binti Sali, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original

©2012 Azarbad and Binti Sali, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

engine. Finally, in the last section ,we will discuss the results and future work.

work is properly cited.

216 MATLAB – A Fundamental Tool for Scienti c Computing and Engineering Applications – Volume 2 **Chapter 0 Chapter 10**

## **DVB-S2 Model in Matlab: Issues and Impairments**

Bahman Azarbad and Aduwati Binti Sali

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/46473

## **1. Introduction**

30 Will-be-set-by-IN-TECH

[17] O. Font-Bach, N. Bartzoudis, A. Pascual-Iserte, and D. López Bueno. Prototying Processing-Demanding Physical Layer Systems Featuring Single Or Multi-Antenna Schemes. In *Proc. European Signal Processing Conference (EUSIPCO)*, September 2011.

[19] IEEE 802.16e-2005. IEEE Standard for Local and Metropolitan Area Networks. Part 16: Air Interface for Fixed Broadband Wireless Access Systems. Amendment 2: Physical and Medium Access Control Layer for Combined Fixed and Mobile Operation in Licensed

[18] *CTTC, GEDOMIS® testbed*. http://engineering.cttc.es/gedomis, 2012.

Bands, 2005.

For the past two decades, the wide coverage and the reliable bandwidth offered by the broadband satellite systems has made them a promising media for IP streaming. Meanwhile, the demand for exploring new solutions concerning this industry has kept increasing to maintain the growth rate required by the various market sectors.

In order to minimize the design time as well as implementation costs, the initial steps in researches concerning satellite systems are often taken in simulation environments. In contrast with the actual satellite system test-beds which are expensive or sometimes not available to the academic research community, the simulation software packages like Mathworks MATLAB are widely used at the early stages of modeling and design process.However, such packages are mostly designed as general purpose tools and therefore, the built-in models provided by these tools are often incomplete or too simplistic.

The main objective of this research is to use MATLAB simulator for study of issues and impairments of Digital Video Broadcasting-Second generation (DVB-S2) standard in Geostationary (GEO) satellite as a broadcasting media.

In this study we model DVB-S2 standard using Matlab communication toolbox and built in DVB-S2 model in Simulink. However, it is noted that the model in Matlab does not include higher order modulations: 16APSK and 32APSK and also the main novelty of DVB-S2 which is the ACM algorithm. Therefore we need to rebuild the model using toolboxes to add the above mentioned capabilities.

In the following sections, we first provide background of DVB-S2 Standard and in the next section a brief overview on DVB-S2 Model in Matlab will be presented , and then we will go through the model improvement on the following section and afterwards we will analyze presented solution by means of BER performance of the system and the provided ACM engine. Finally, in the last section ,we will discuss the results and future work.

©2012 Azarbad and Binti Sali, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ©2012 Azarbad and Binti Sali, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **2. Background**

The evolution of digital multimedia broadcasting over broadband communication systems in early 90's ,initiated the first stages of standardizing the digital TV broadcasting.In 1993, European Telecommunication Standards Institute (ETSI) started a project named Digital Video Broadcasting(DVB).The main objective of this standard was to introduce standards for digital multimedia services. DVB-S,the first standard for satellite video broadcasting over GEO satellite systems hired an outer shortened Reed Solomon(RS) and inner variable length convolutional code as its Forward Error Correction(FEC) technique[1].

The satellite communication channel mostly suffers from signal fading mainly resulted from atmospheric rain attenuation and ionospheric scintillation.Rain attenuation affects these signals severely by absorbing properties of the water,while amplitude and phase of the received signal will be distorted frequently by scintillation in ionosphere caused by electron

Since scintillation effects rapidly occurs cannot be compensated by mode adaptation because of the long propagation delay in GEO satellite systems. But rain attenuation time scale is larger comparing to scintillation, hence deploying mode adaptation techniques is convincing to tackle these effects.ACM threshold offsets define thresholds for every ModCod a certain level of safe SNR to guarantee a quasi-zero BER transmission.Making the decision of switching among ModCods is the Gateways's responsibility. In one hand,this adaptation is crucial to tolerate fade attenuations by choosing an adequate ModCod according to the safe threshold defined in the ACM table,on the other hand very frequent changes in channel states due to scintillation may cause in recurrent crossing of thresholds subsequently frequent switchings

Provided that with 11 coding rate and 4 modulation order available in the standard , there will be 44 possible combinations. Only 28 of them are covered by DVB-S2 standard.Considering complexity of terminal design and limited usage of some of these ModCods,there is some proposals suggesting a reduced subset of ModCods.A subset of less than one forth(5 or 7 out of 28)has been investigated in[7].They showed a close to optimum performance can be achieved using these reduced subset and consequently complexity of the terminal will be decreased.This reduced set of ModCods avoid system oscillations and will increase systems

The tradeoff between terminal design complexity and achievable spectral efficiency has also been investigated in[9].In this research they proposed subsets of 8 and 5 Modcods with equidistant separation of adjacent ModCods.A simulated time series of one month was their

they assumed that a frame is corrupted if the SNR of received signal is less than the threshold offset and it means that comparison between sent and received signal has not been carried out.They obviously ignored the PSNR of received signal and effects of error concealment

The design of different ACM thresholds and safety margins also play a significant role in the performance of the system.Although the zero-quasi BER offered by DVB-S2 standard is excellent,recent advances in audio and video codecs reduced their need for such outstanding BER level.About 15% packet loss can be accommodated without severe degradation in speech and also modern video codecs like MPEG-4 are capable of concealing errors in input stream by hiring error correction techniques (eg.interframe interpolation)they can accept a higher BER providing acceptable video quality[10].Hence BER rates as high as given below can be

Considering the variation in application requirements of different threshold offsets can be implemented in ACM tables.As mentioned above , some applications can tolerate higher BER, so it is reasonable to decrease quality of the transmission in terms of allowing higher BER

*BER* =≤ 10−5 (1)

DVB-S2 Model in Matlab: Issues and Impairments 219

*BER* =≤ 10−3 (2)

density irregularities[5].

which is not desirable[6].

SNR pattern targeting a BER at :

resulting in higher spectrum efficiency in the system.

provided by other layers .

stability[8]

acceptable:

For interactive applications and adaptive transmissions presence of a return link is compulsory.One return link can be established using terrestrial networks which has its own disadvantages like higher cost and unavailability of the link. The more interesting option would be using the same antenna at the receiver for the return link via satellite.This was the motivation for defining a new standard called Digital Video Broadcasting Return channel satellite (DVB-RCS).At the same time that ETSI was providing this standard , they had another project runing for second generation of satellite broadcasting,DVB-S2. In 2003, DVB-S2 specifications was provided by ETSI.

DVB-S2 standard exploits 30% channel efficiency improvement using same power and bandwidth as its ancestor DVB-S[2]. This improvement was resulted from the usage of new FEC techniques: Low Density Parity Check (LDPC) and Bose-Chaudhuri-Hochquenghem (BCH), higher order modulations: 16APSK, 32APSK and a dynamic selection of coding and modulations using instantaneous Channel State Information (CSI) so called Adaptive Coding and Modulation (ACM).

Fade mitigation techniques (FMT) are widely applied on satellite communication to tackle transmission errors and losses caused by atmospheric effects on these systems. Time variant nature of channel states in a Ka band channel affects severely performance of a satellite link,hence it necessitates a dynamic adjustment of Modulation and Coding (ModCods)used by physical layer for transmission. This dynamic adjustment is performed by an adaptive coding and modulation algorithm augmented in DVB-S2 systems deploying a feedback link via DVB-RCS terminals. ACM algorithm uses CSI sent by DVB-RCS return link from individual Earth Stations (ES) to Gate-way ,so called Network Control Center (NCC). NCC is responsible for making the proper selection of ModCods aiming best spectrum efficiency and the desired Bit Error Rate (BER) at the same time[3].

To overcome influences of fading events on transmission quality one of the Well-Known techniques among FMT's is ACM.ACM algorithm will make the proper switching in physical layer transmission mode to maintain channel quality.Different combinations of coding rates and modulation orders are given by ETSI standard.A low coding rate and modulation order will increase robustness of transmission in poor channel quality conditions with a lower spectral efficiency indeed. In case of clear sky by choosing higher coding rate and higher modulation orders spectral efficiency will be increased and system provides a higher throughput.The trade-off between spectral efficiency and BER is crucial in system performance and also transmission cost.In one hand, keeping in to account that although a spectral efficiency higher than appropriate according to channel state will increase system throughput ,it will introduce excessive BER to the system as well. On the other hand, if a ModCod with too low spectral efficiency is selected when the channel quality is in a good condition then capacity of the system will be wasted[4].

The satellite communication channel mostly suffers from signal fading mainly resulted from atmospheric rain attenuation and ionospheric scintillation.Rain attenuation affects these signals severely by absorbing properties of the water,while amplitude and phase of the received signal will be distorted frequently by scintillation in ionosphere caused by electron density irregularities[5].

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

The evolution of digital multimedia broadcasting over broadband communication systems in early 90's ,initiated the first stages of standardizing the digital TV broadcasting.In 1993, European Telecommunication Standards Institute (ETSI) started a project named Digital Video Broadcasting(DVB).The main objective of this standard was to introduce standards for digital multimedia services. DVB-S,the first standard for satellite video broadcasting over GEO satellite systems hired an outer shortened Reed Solomon(RS) and inner variable length

For interactive applications and adaptive transmissions presence of a return link is compulsory.One return link can be established using terrestrial networks which has its own disadvantages like higher cost and unavailability of the link. The more interesting option would be using the same antenna at the receiver for the return link via satellite.This was the motivation for defining a new standard called Digital Video Broadcasting Return channel satellite (DVB-RCS).At the same time that ETSI was providing this standard , they had another project runing for second generation of satellite broadcasting,DVB-S2. In 2003, DVB-S2

DVB-S2 standard exploits 30% channel efficiency improvement using same power and bandwidth as its ancestor DVB-S[2]. This improvement was resulted from the usage of new FEC techniques: Low Density Parity Check (LDPC) and Bose-Chaudhuri-Hochquenghem (BCH), higher order modulations: 16APSK, 32APSK and a dynamic selection of coding and modulations using instantaneous Channel State Information (CSI) so called Adaptive Coding

Fade mitigation techniques (FMT) are widely applied on satellite communication to tackle transmission errors and losses caused by atmospheric effects on these systems. Time variant nature of channel states in a Ka band channel affects severely performance of a satellite link,hence it necessitates a dynamic adjustment of Modulation and Coding (ModCods)used by physical layer for transmission. This dynamic adjustment is performed by an adaptive coding and modulation algorithm augmented in DVB-S2 systems deploying a feedback link via DVB-RCS terminals. ACM algorithm uses CSI sent by DVB-RCS return link from individual Earth Stations (ES) to Gate-way ,so called Network Control Center (NCC). NCC is responsible for making the proper selection of ModCods aiming best spectrum efficiency and

To overcome influences of fading events on transmission quality one of the Well-Known techniques among FMT's is ACM.ACM algorithm will make the proper switching in physical layer transmission mode to maintain channel quality.Different combinations of coding rates and modulation orders are given by ETSI standard.A low coding rate and modulation order will increase robustness of transmission in poor channel quality conditions with a lower spectral efficiency indeed. In case of clear sky by choosing higher coding rate and higher modulation orders spectral efficiency will be increased and system provides a higher throughput.The trade-off between spectral efficiency and BER is crucial in system performance and also transmission cost.In one hand, keeping in to account that although a spectral efficiency higher than appropriate according to channel state will increase system throughput ,it will introduce excessive BER to the system as well. On the other hand, if a ModCod with too low spectral efficiency is selected when the channel quality is in a good

convolutional code as its Forward Error Correction(FEC) technique[1].

**2. Background**

specifications was provided by ETSI.

the desired Bit Error Rate (BER) at the same time[3].

condition then capacity of the system will be wasted[4].

and Modulation (ACM).

Since scintillation effects rapidly occurs cannot be compensated by mode adaptation because of the long propagation delay in GEO satellite systems. But rain attenuation time scale is larger comparing to scintillation, hence deploying mode adaptation techniques is convincing to tackle these effects.ACM threshold offsets define thresholds for every ModCod a certain level of safe SNR to guarantee a quasi-zero BER transmission.Making the decision of switching among ModCods is the Gateways's responsibility. In one hand,this adaptation is crucial to tolerate fade attenuations by choosing an adequate ModCod according to the safe threshold defined in the ACM table,on the other hand very frequent changes in channel states due to scintillation may cause in recurrent crossing of thresholds subsequently frequent switchings which is not desirable[6].

Provided that with 11 coding rate and 4 modulation order available in the standard , there will be 44 possible combinations. Only 28 of them are covered by DVB-S2 standard.Considering complexity of terminal design and limited usage of some of these ModCods,there is some proposals suggesting a reduced subset of ModCods.A subset of less than one forth(5 or 7 out of 28)has been investigated in[7].They showed a close to optimum performance can be achieved using these reduced subset and consequently complexity of the terminal will be decreased.This reduced set of ModCods avoid system oscillations and will increase systems stability[8]

The tradeoff between terminal design complexity and achievable spectral efficiency has also been investigated in[9].In this research they proposed subsets of 8 and 5 Modcods with equidistant separation of adjacent ModCods.A simulated time series of one month was their SNR pattern targeting a BER at :

$$BER = \le 10^{-5} \tag{1}$$

they assumed that a frame is corrupted if the SNR of received signal is less than the threshold offset and it means that comparison between sent and received signal has not been carried out.They obviously ignored the PSNR of received signal and effects of error concealment provided by other layers .

The design of different ACM thresholds and safety margins also play a significant role in the performance of the system.Although the zero-quasi BER offered by DVB-S2 standard is excellent,recent advances in audio and video codecs reduced their need for such outstanding BER level.About 15% packet loss can be accommodated without severe degradation in speech and also modern video codecs like MPEG-4 are capable of concealing errors in input stream by hiring error correction techniques (eg.interframe interpolation)they can accept a higher BER providing acceptable video quality[10].Hence BER rates as high as given below can be acceptable:

$$BER = \le 10^{-3} \tag{2}$$

Considering the variation in application requirements of different threshold offsets can be implemented in ACM tables.As mentioned above , some applications can tolerate higher BER, so it is reasonable to decrease quality of the transmission in terms of allowing higher BER resulting in higher spectrum efficiency in the system.

## **3. DVB-S2 model in Matlab**

## **3.1. Introduction**

Matlab includes some of important digital video transmission models such as Digital Video Broadcasting-Terrestrial (DVB-T), Digital Video Broadcasting-Cable (DVB-C) and Digital Video Broadcasting-Satellite Second Generation (DVB-S2). Representation of these models are all based on European Telecommunication Standardization Institute (ETSI), in our case of study EN 302 307 [11].

.Information bits or called Data FieLd (DFL)can be calculated as given in below formula:

Number of MPEG packets that can be fitted in one BBFRAME can be shown as:

Kldpc. The structure of whole FECFRAME is shown in the Figure 3.

*Numbero f Packets* = [*Kbch* − 80

To fulfill BBFRAME size to match BCH encoder input ,post zero padding will be applied.At the receiver side Unbuffering block is responsible for excluding added zero pads and the BBHeader to generate MPEG packets from received frame.Number of zero pad added can

One of DVB-S2 standard advances is the forward error correction which is deployed to reduce BER in transmissions is BCH error correction. Output of BBFrame buffering block at the sender side, as above mentioned ,are frames of kbch bits where a BCH(Nbch,Kbch) error correction with the correcting power of t will be applied to them . For each of 11 rate of coding presented in the standard Kbch and Nbch values are defined including the t-error correcting parameter. In tables 1 and 2 these values are shown for normal and short frames respectively. The output of BCH encoder called BCHFEC frame will be created by adding parity check bits to make a frame with Nbch size.Nbch is the input of inner LDPC encoder which is also named

Nbch,the BCH encoder out put as the input of inner FEC encoder will be processed at LDPC encoder to be protected from error with parity bits.The number of parity bits are given in

LDPC encoder supports 11 coding rates.These coding rates are the ratio between information bits(Nbch bits) and LDPC coded block bits which is the FECFRAME. Fore example for rate

*ZeroPadNo* = *Kbch* − ((*Numbero f Packets* ∗ 1504) + 80) (5)

*numbero f LDPCparitybits* = *Nldpc* − *Nbch* (6)

size.Structure of a BBFRAME is shown In Figure 2.

**Figure 2.** BBFRAME Structure[11]

be shown as:

*3.2.3. BCH encoder/decoder*

*3.2.4. LDPC encoder/decoder*

tables 1 and 2, as :

Where Kbch is the size of outer FEC Encoder BCH input,and 80 is the BBFrame header

*DataField* = *Kbch* − 80 (3)

1504 ] (4)

DVB-S2 Model in Matlab: Issues and Impairments 221

Simulink function blocks are hired for this implementation which will be discussed in detail in the following sections and lack of some parts will be mentioned indeed.

## **3.2. Matlab DVB-S2 model hierarchy**

Figure 1 shows block diagram of DVB-S2 model in Matlab . Each block and its functionalities are given in subsequent sections.

**Figure 1.** DVB-S2 Block Diagram in Matlab

## *3.2.1. Bernoulli sequence generator*

The very first block is responsible for generating a balanced, in terms of probability of incidents, random binary sequence. Bernoulli sequence is a distribution of zeroes and ones by probabilities of p and (p-1) respectively. In this model, p equals to 0.5 resulting in equal probability of happening for 0 and 1. Output of this block is frame based with the same size as a MPEG-TS packet which is 188 bytes of 8 bits making it 1504 bits.

### *3.2.2. BBFRAME buffering/unbuffering*

Output of packet source generator is buffered to make a Base Band Frame (BBFRAME).The size of this frame is related to the coding rate being used ,equal to BCH encoder input size .Information bits or called Data FieLd (DFL)can be calculated as given in below formula:

$$DataField = K\_bch - 80\tag{3}$$

Where Kbch is the size of outer FEC Encoder BCH input,and 80 is the BBFrame header size.Structure of a BBFRAME is shown In Figure 2.

#### **Figure 2.** BBFRAME Structure[11]

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

Matlab includes some of important digital video transmission models such as Digital Video Broadcasting-Terrestrial (DVB-T), Digital Video Broadcasting-Cable (DVB-C) and Digital Video Broadcasting-Satellite Second Generation (DVB-S2). Representation of these models are all based on European Telecommunication Standardization Institute (ETSI), in our case of

Simulink function blocks are hired for this implementation which will be discussed in detail

Figure 1 shows block diagram of DVB-S2 model in Matlab . Each block and its functionalities

The very first block is responsible for generating a balanced, in terms of probability of incidents, random binary sequence. Bernoulli sequence is a distribution of zeroes and ones by probabilities of p and (p-1) respectively. In this model, p equals to 0.5 resulting in equal probability of happening for 0 and 1. Output of this block is frame based with the same size

Output of packet source generator is buffered to make a Base Band Frame (BBFRAME).The size of this frame is related to the coding rate being used ,equal to BCH encoder input size

as a MPEG-TS packet which is 188 bytes of 8 bits making it 1504 bits.

in the following sections and lack of some parts will be mentioned indeed.

**3. DVB-S2 model in Matlab**

**3.2. Matlab DVB-S2 model hierarchy**

are given in subsequent sections.

**Figure 1.** DVB-S2 Block Diagram in Matlab

*3.2.1. Bernoulli sequence generator*

*3.2.2. BBFRAME buffering/unbuffering*

**3.1. Introduction**

study EN 302 307 [11].

Number of MPEG packets that can be fitted in one BBFRAME can be shown as:

$$Number of Packets = \left[\frac{K\_bch - 80}{1504}\right] \tag{4}$$

To fulfill BBFRAME size to match BCH encoder input ,post zero padding will be applied.At the receiver side Unbuffering block is responsible for excluding added zero pads and the BBHeader to generate MPEG packets from received frame.Number of zero pad added can be shown as:

$$Zero\text{PadNo} = K\_bch - ((NumberofPackets \* 1504) + 80) \tag{5}$$

#### *3.2.3. BCH encoder/decoder*

One of DVB-S2 standard advances is the forward error correction which is deployed to reduce BER in transmissions is BCH error correction. Output of BBFrame buffering block at the sender side, as above mentioned ,are frames of kbch bits where a BCH(Nbch,Kbch) error correction with the correcting power of t will be applied to them . For each of 11 rate of coding presented in the standard Kbch and Nbch values are defined including the t-error correcting parameter. In tables 1 and 2 these values are shown for normal and short frames respectively.

The output of BCH encoder called BCHFEC frame will be created by adding parity check bits to make a frame with Nbch size.Nbch is the input of inner LDPC encoder which is also named Kldpc. The structure of whole FECFRAME is shown in the Figure 3.

#### *3.2.4. LDPC encoder/decoder*

Nbch,the BCH encoder out put as the input of inner FEC encoder will be processed at LDPC encoder to be protected from error with parity bits.The number of parity bits are given in tables 1 and 2, as :

$$numberof\text{LDPC}\_{\text{parity}}bits = \text{N}\_{\text{l}}dpc - \text{N}\_{\text{b}}ch\tag{6}$$

LDPC encoder supports 11 coding rates.These coding rates are the ratio between information bits(Nbch bits) and LDPC coded block bits which is the FECFRAME. Fore example for rate

**Figure 3.** FECFRAME Structure[11]

**Table 3.** Bit Interleaver structure

*3.2.5. Interleaver/deinterleaver*

input.

later.The interleaver structure is given in table 3.

processes are shown in figures 4 and 5.

1/4 in a normal frame it shows:

Modulation Rows(for nldpc=64800) Rows(for nldpc=16200) Columns 8PSK 21600 5400 3 16APSK 16200 4050 4 32APSK 12960 3240 5

*Nbch*

*nldpc* = 16200

This means that for every 1 bit of information sent from outer FEC coder(BCH),there will be 3 bits of parity checks added in LDPC encoder. The lower this ratio the more protection of data against error has been carried out in LDPC encoder. This will result in more robust data transmission,and it will reduce system throughput indeed. At the receiver side,LDPC decoder will check the received sequence till the parity checks are satisfied up to 50 iterations.This error correction uses the sparse parity-check matrices with a hard decision making algorithm.

Interleaving process is the next step in DVB-S2 for modulations 8PSK,16APSK and 32APSK. Interleaving on QPSK is not going to be done and as for DVB-S2 model in Matlab ,16APSK and 32APSK modulations are not included so we will discuss them in our proposed model

Interleaver block in Matlab will make this on 8PSK by writing column wise serially the output of LDPC encoder in a 3 by nldpc(21600 for 8PSK) matric and then will read it out row wise.The MSB of BBHeader will be read-out first since for rate 3/5 it will be read-out as third.These

Interleaving process creates rows in a matric from the LDPC encoder output according to the modulation order M, so each row will contain a symbol ready to be mapped in the next block ,modulation.At the receiver side Deinterleaver block will receive the output of demodulator block as input and will apply the reverse process to create a serial output for the LDPCdecoder

64800 = 1

4 (7)

DVB-S2 Model in Matlab: Issues and Impairments 223

**Table 1.** Coding parameters for normal FECFRAMEnldpc=64800

**Table 2.** Coding parameters for short FECFRAMEnldpc=16200

**Figure 3.** FECFRAME Structure[11]

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

LDPC BCH Uncoded BCH coded block Nbch BCH t-error LDPC Coded Block

LDPC BCH Uncoded BCH coded block Nbch BCH Effective LDPC Coded Code Block Kbch LDPC Uncoded t-error LDPC Rate Block identifier Block kldpc correction kldpc/16 200 nldpc 1/4 3072 3240 12 1/5 16200 1/3 5232 5400 12 1/3 16200 2/5 6312 6480 12 2/5 16200 1/2 7032 7200 12 4/9 16200 3/5 9552 9720 12 3/5 16200 2/3 10632 10800 12 2/3 16200 3/4 11712 11880 12 11/15 16200 4/5 12432 12600 12 7/9 16200 5/6 13152 13320 12 37/45 16200 8/9 14232 14400 12 8/9 16200 9/10 NA NA NA NA NA

Code Block Kbch LDPC Uncoded Block kldpc correction nldpc 1/4 16008 16200 12 64800 1/3 21408 21600 12 64800 2/5 25728 25920 12 64800 1/2 32208 32400 12 64800 3/5 38688 38880 12 64800 2/3 43040 43200 10 64800 3/4 48408 48600 12 64800 4/5 51648 51840 12 64800 5/6 53840 54000 10 64800 8/9 57472 57600 8 64800 9/10 58192 58320 8 64800

**Table 1.** Coding parameters for normal FECFRAMEnldpc=64800

**Table 2.** Coding parameters for short FECFRAMEnldpc=16200

**Table 3.** Bit Interleaver structure

1/4 in a normal frame it shows:

$$\frac{N\_bch}{n\_ldpc} = \frac{16200}{64800} = \frac{1}{4} \tag{7}$$

This means that for every 1 bit of information sent from outer FEC coder(BCH),there will be 3 bits of parity checks added in LDPC encoder. The lower this ratio the more protection of data against error has been carried out in LDPC encoder. This will result in more robust data transmission,and it will reduce system throughput indeed. At the receiver side,LDPC decoder will check the received sequence till the parity checks are satisfied up to 50 iterations.This error correction uses the sparse parity-check matrices with a hard decision making algorithm.

#### *3.2.5. Interleaver/deinterleaver*

Interleaving process is the next step in DVB-S2 for modulations 8PSK,16APSK and 32APSK. Interleaving on QPSK is not going to be done and as for DVB-S2 model in Matlab ,16APSK and 32APSK modulations are not included so we will discuss them in our proposed model later.The interleaver structure is given in table 3.

Interleaver block in Matlab will make this on 8PSK by writing column wise serially the output of LDPC encoder in a 3 by nldpc(21600 for 8PSK) matric and then will read it out row wise.The MSB of BBHeader will be read-out first since for rate 3/5 it will be read-out as third.These processes are shown in figures 4 and 5.

Interleaving process creates rows in a matric from the LDPC encoder output according to the modulation order M, so each row will contain a symbol ready to be mapped in the next block ,modulation.At the receiver side Deinterleaver block will receive the output of demodulator block as input and will apply the reverse process to create a serial output for the LDPCdecoder input.

8 Will-be-set-by-IN-TECH 224 MATLAB – A Fundamental Tool for Scienti c Computing and Engineering Applications – Volume 2 DVB-S2 Model in Matlab: Issues and Impairments 9

*Phaseo f f set* = *π*

At the receiver side,demodulator will analyze the received signal from the channel and will

approximately calculate LLR's using soft decision making LLR algorithm.Demodulator needs noise variance of the signal or at least an estimation of it. Noise variance calculation is given

*Noisevariance* = *SignalPower* ∗ *SymbolTime*

Additive Gaussian White Noise(AWGN) block in Matlab comply with Transmission channel in Ka band.This block adds a noise to the complex signal sent from modulator based on a

*SampleTime* ∗ 10

( *Es No* ) 10

(10)

**Figure 6.** Bit mapping into QPSK constellation[11]

**Figure 7.** Bit mapping into 8PSK constellation[11]

in below formula:

*3.2.7. AWGN channel*

4 (9)

DVB-S2 Model in Matlab: Issues and Impairments 225

**Figure 4.** Bit Interleaving scheme for 8PSK and normal FECFRAME length (all rates except 3/5)[11]

**Figure 5.** Bit Interleaving scheme for 8PSK and normal FECFRAME length (rate 3/5 only)[11]

#### *3.2.6. Modulator/demadulator*

Modulation block will process the interleaved vector by first mapping each row to a symbol which in our case is a gray mapping,then the mapped symbols will be assigned to constellations.Constellation and mapping for QPSK and 8PSK are given in figures 6 and 7 respectively.Modulator for QPSK and 8psk both orders have a phase offset given below and the average energy for normalized constellation per symbol should correspond to :

$$
\rho^2 = 1 \tag{8}
$$

#### 224 MATLAB – A Fundamental Tool for Scienti c Computing and Engineering Applications – Volume 2 DVB-S2 Model in Matlab: Issues and Impairments 9 DVB-S2 Model in Matlab: Issues and Impairments 225

$$PhaseOffset = \frac{\pi}{4} \tag{9}$$

At the receiver side,demodulator will analyze the received signal from the channel and will

**Figure 6.** Bit mapping into QPSK constellation[11]

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

**Figure 4.** Bit Interleaving scheme for 8PSK and normal FECFRAME length (all rates except 3/5)[11]

**Figure 5.** Bit Interleaving scheme for 8PSK and normal FECFRAME length (rate 3/5 only)[11]

the average energy for normalized constellation per symbol should correspond to :

Modulation block will process the interleaved vector by first mapping each row to a symbol which in our case is a gray mapping,then the mapped symbols will be assigned to constellations.Constellation and mapping for QPSK and 8PSK are given in figures 6 and 7 respectively.Modulator for QPSK and 8psk both orders have a phase offset given below and

*ρ*2 = 1 (8)

*3.2.6. Modulator/demadulator*

**Figure 7.** Bit mapping into 8PSK constellation[11]

approximately calculate LLR's using soft decision making LLR algorithm.Demodulator needs noise variance of the signal or at least an estimation of it. Noise variance calculation is given in below formula:

$$\text{Noisevariance} = \frac{\text{SignalPower} \ast \text{SymbolTime}}{\text{SampleTime} \ast 10^{\frac{\left(\frac{\text{Es}}{\text{No}}\right)}{\text{10}}}} \tag{10}$$

#### *3.2.7. AWGN channel*

Additive Gaussian White Noise(AWGN) block in Matlab comply with Transmission channel in Ka band.This block adds a noise to the complex signal sent from modulator based on a manual parameter being set by the user.This parameter can be defined as Es/No,Eb/No or SNR,any how the block will calculate the noise variance and will add a noise with zero mean to the signal.The relation between Es/No,Eb/No and SNR is as follows:

$$\frac{E\_s}{N\_o} = \left(\frac{T\_s \text{sym}}{T\_s amp}\right) \* SNR \tag{11}$$

**4.1. MPEG-TS input/output blocks**

estimation[12].MSE computation is expressed as:

**4.2. PSNR calculation**

**4.3. Interleaver/deinterleaver**

**4.4. Modulator/demodulator**

band signal for transmission.

*4.4.1. 16 APSK modulator*

In Matlab DVB-S2 model as mentioned earlier they use a random Bernoulli sequence generator for input of the model.To analyze the effect of ACM algorithm on the quality of transmission of a real MPEG stream, we used MPEG-TS stored in a matrix with 188 columns (one MPEG packet) and 100000 rows(number of packets).This is used as the input of the

Peak Signal to Noise Ratio(PSNR)which is a well-known metric for quality measurement of received video, is added to the proposed model.The computation of PSNR is in decibels and uses the Mean Square Error(MSE) which is also an error metric often used for video quality

*N* ∑(*Ps*

where N represents the number of pixels in the image while P and P are the original image

*PSNR* = 10*log* (2*n* − 1)2

Interleaving for 16 and 32 APSK are two blocks included to the model based on ETSI instructions.The number of rows and columns of this interleaver are given in table 3.In figure 9 the structure of Interleaver for 16 APSK is given , the only difference with 32 APSK is the number of columns which is 5 instead of 4, consequently number of rows also will be different.

Higher order modulations presented by DVB-S2,provides higher spectrum efficiency comparing to the earlier generation,DVB-S.To analyze the BER performance of DVB-S2 and also adding the ACM algorithm to our model, we include Modulations for 16 and 32 APSK. Modulation block is responsible for mapping and constellation generation to prepare the base

The modulation constellation for 16 APSK as described in ETSI uses two concentric rings R1 and R2.These rings are uniformly spaced with 4 and 12 points.The constellation is given in the

> *γ* = *R*2 *R*1

*i* − *Pr*

*i* )2 (13)

DVB-S2 Model in Matlab: Issues and Impairments 227

*MSE*(*I*) (14)

(15)

*MSE* = 1

and received image respectively.The PSNR then is computed as follows:

where n is the number of luminance bits used in the image.

Figure 10.The ratio between R1 and R2,gamma,is defined as:

system and at receiver side will be again stored for PSNR calculation.

$$\frac{E\_s}{N\_o} = \left(\frac{E\_b}{N\_o}\right) + 10\log(k)\tag{12}$$

Where Es is the signal energy in Joules ,Eb is bit energy in Joules, No is noise power spectral density,Tsym is the symbol period ,K is the information bits per symbol and Tsamp is the sample time.

## **4. Proposed DVB-S2 model**

The most important motivation of this project was to implement a DVB-S2 link layer model in matlab which includes ACM algorithm with a complete set of ModCods and also video quality measurement system . To do so we added modulations for 16APSK and 32APSK and PSNR calculation to the system.Specificaly, we used communication toolbox in Matlab for this approach.The model is depicted in Figure 8.The model is defined in the following sections.

**Figure 8.** DVB-S2 proposed model

#### **4.1. MPEG-TS input/output blocks**

In Matlab DVB-S2 model as mentioned earlier they use a random Bernoulli sequence generator for input of the model.To analyze the effect of ACM algorithm on the quality of transmission of a real MPEG stream, we used MPEG-TS stored in a matrix with 188 columns (one MPEG packet) and 100000 rows(number of packets).This is used as the input of the system and at receiver side will be again stored for PSNR calculation.

#### **4.2. PSNR calculation**

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

manual parameter being set by the user.This parameter can be defined as Es/No,Eb/No or SNR,any how the block will calculate the noise variance and will add a noise with zero mean

Where Es is the signal energy in Joules ,Eb is bit energy in Joules, No is noise power spectral density,Tsym is the symbol period ,K is the information bits per symbol and Tsamp is the

The most important motivation of this project was to implement a DVB-S2 link layer model in matlab which includes ACM algorithm with a complete set of ModCods and also video quality measurement system . To do so we added modulations for 16APSK and 32APSK and PSNR calculation to the system.Specificaly, we used communication toolbox in Matlab for this approach.The model is depicted in Figure 8.The model is defined in the following sections.

*Tsamp* ) ∗ *SNR* (11)

) + 10 log(*k*) (12)

= ( *Tsym*

= ( *Eb No*

to the signal.The relation between Es/No,Eb/No and SNR is as follows:

*Es No*

*Es No*

sample time.

**4. Proposed DVB-S2 model**

**Figure 8.** DVB-S2 proposed model

Peak Signal to Noise Ratio(PSNR)which is a well-known metric for quality measurement of received video, is added to the proposed model.The computation of PSNR is in decibels and uses the Mean Square Error(MSE) which is also an error metric often used for video quality estimation[12].MSE computation is expressed as:

$$MSE = \frac{1}{N} \sum (P\_i^s - P\_i^r)^2 \tag{13}$$

where N represents the number of pixels in the image while P and P are the original image and received image respectively.The PSNR then is computed as follows:

$$PSNR = 10 \log \frac{(2^n - 1)^2}{MSE(I)} \tag{14}$$

where n is the number of luminance bits used in the image.

#### **4.3. Interleaver/deinterleaver**

Interleaving for 16 and 32 APSK are two blocks included to the model based on ETSI instructions.The number of rows and columns of this interleaver are given in table 3.In figure 9 the structure of Interleaver for 16 APSK is given , the only difference with 32 APSK is the number of columns which is 5 instead of 4, consequently number of rows also will be different.

#### **4.4. Modulator/demodulator**

Higher order modulations presented by DVB-S2,provides higher spectrum efficiency comparing to the earlier generation,DVB-S.To analyze the BER performance of DVB-S2 and also adding the ACM algorithm to our model, we include Modulations for 16 and 32 APSK. Modulation block is responsible for mapping and constellation generation to prepare the base band signal for transmission.

#### *4.4.1. 16 APSK modulator*

The modulation constellation for 16 APSK as described in ETSI uses two concentric rings R1 and R2.These rings are uniformly spaced with 4 and 12 points.The constellation is given in the Figure 10.The ratio between R1 and R2,gamma,is defined as:

$$
\gamma = \frac{R\_2}{R\_1} \tag{15}
$$

12 Will-be-set-by-IN-TECH 228 MATLAB – A Fundamental Tool for Scienti c Computing and Engineering Applications – Volume 2 DVB-S2 Model in Matlab: Issues and Impairments 13

**Figure 9.** Bit Intereaving scheme for 16APSK and normal FECFRAME length

**Figure 10.** 16APSK Signal Constellation[11]

#### *4.4.2. 32 APSK modulator*

Three uniformly spaced rings are defined in the standard using 4 ,12 and 16 PSK points for 32 APSK modulator constellation :R1 ,R2 and R3.The outer ring R3 should be equal to one and gamma1 and gamma2 will define the ratio between rings as follows:

$$
\gamma\_1 = \frac{R\_2}{R\_1} \tag{16}
$$

The symbol order and constellation defined by ETSI is given in Figure 11.

DVB-S2 Model in Matlab: Issues and Impairments 229

At the receiver side,demodulator will receive the signal from AWGN channel which is the noisy signal.To demodulate the signal ,demodulator needs noise variance of the channel for computation of the signal LLR's. For this computation we used approximate LLR algorithm.

In the current DVB-S2 model in Matlab,a Constant Coding and Modulation strategy has been developed .This strategy is based on the worst case adjustment for physical layer which will waste spectral efficiency at most of the time .This waste is costly and can be avoided by using state of the art ACM algorithm ,so any individual user can be treated according to its present

The last step of design in the proposed model is the implementation of the ACM engine.ACM algorithm included in this model is based on the standard required Es/No by ETSI to support a Quasi Error Free transmission.As depicted in table 4,for all 28 combinations of ModCods there is a required level of Es/No.To make decisions among ModCods threshold offsets are provided as equidistance thresholds between adjacent ModCods. The SNR of received signal at the receiver will be sent to ACM block to be used for next frame mode of transmission.

We run simulations for different values of Es/No ranging from -3 to 17 dB and for each ModCod. The LDPC decoder was set to 50 iterations and normal frame size (64,800 bits)

**Figure 11.** 32APSK Signal Constellation[11]

*4.4.3. Demodulator*

**4.5. ACM algorithm**

**5. Simulation and results**

channel state.

$$
\gamma\_2 = \frac{R\_3}{R\_1} \tag{17}
$$

The symbol order and constellation defined by ETSI is given in Figure 11.

**Figure 11.** 32APSK Signal Constellation[11]

### *4.4.3. Demodulator*

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

Three uniformly spaced rings are defined in the standard using 4 ,12 and 16 PSK points for 32 APSK modulator constellation :R1 ,R2 and R3.The outer ring R3 should be equal to one and

> *γ*1 = *R*2 *R*1

> *γ*2 = *R*3 *R*1

(16)

(17)

**Figure 9.** Bit Intereaving scheme for 16APSK and normal FECFRAME length

gamma1 and gamma2 will define the ratio between rings as follows:

**Figure 10.** 16APSK Signal Constellation[11]

*4.4.2. 32 APSK modulator*

At the receiver side,demodulator will receive the signal from AWGN channel which is the noisy signal.To demodulate the signal ,demodulator needs noise variance of the channel for computation of the signal LLR's. For this computation we used approximate LLR algorithm.

### **4.5. ACM algorithm**

In the current DVB-S2 model in Matlab,a Constant Coding and Modulation strategy has been developed .This strategy is based on the worst case adjustment for physical layer which will waste spectral efficiency at most of the time .This waste is costly and can be avoided by using state of the art ACM algorithm ,so any individual user can be treated according to its present channel state.

The last step of design in the proposed model is the implementation of the ACM engine.ACM algorithm included in this model is based on the standard required Es/No by ETSI to support a Quasi Error Free transmission.As depicted in table 4,for all 28 combinations of ModCods there is a required level of Es/No.To make decisions among ModCods threshold offsets are provided as equidistance thresholds between adjacent ModCods. The SNR of received signal at the receiver will be sent to ACM block to be used for next frame mode of transmission.

## **5. Simulation and results**

We run simulations for different values of Es/No ranging from -3 to 17 dB and for each ModCod. The LDPC decoder was set to 50 iterations and normal frame size (64,800 bits)

Simulation Parameters Settings

LDPC Decoder output Type Information Part LDPC Decoder Decision Input Type Hard Decision

LDPC Encoder Input Type Bit

Modulator Input Type Bit Modulator Symbol Order Gray Demodulator Output Type Bit Demodulator Decision Type Soft Decision

Channel Noise Factor SNR

is used. Simulation parameters and settings are presented in Table 5. At the LDPC decoder

To evaluate performance of the presented model in terms of BER we run simulation for all ModCods .The results are shown in Figure 12.It can be perceived,that the model performs

In order to analyze the video quality ,instead of sending a Bernoulli sequence , we send MPEG-TS and considering PSNR of received video and QEF requirements.In Figure 13, we showed that deploying the standardized ACM thresholds the model can perform the

transmission with the desired quality with almost 0.3 dB less Es/No required.

**Table 5.** Simulation Parameters and Settings

**Figure 12.** BER Vs. Es/No For Different ModCods

output we calculate BER and number of violated parity checks.

transmissions with the expected BER levels for all ModCods.

LDPC Decoder Number Of Iterations 50

Frame size 64800(Normal)

DVB-S2 Model in Matlab: Issues and Impairments 231

LLR Algorithm Approximate LLR

**Table 4.** Es/No performance at Quasi Error Free PER

**Table 5.**  Es/No performance at Quasi Error Free PER

for FECFRAME Length 64800

is used. Simulation parameters and settings are presented in Table 5. At the LDPC decoder output we calculate BER and number of violated parity checks.

To evaluate performance of the presented model in terms of BER we run simulation for all ModCods .The results are shown in Figure 12.It can be perceived,that the model performs transmissions with the expected BER levels for all ModCods.

**Figure 12.** BER Vs. Es/No For Different ModCods

In order to analyze the video quality ,instead of sending a Bernoulli sequence , we send MPEG-TS and considering PSNR of received video and QEF requirements.In Figure 13, we showed that deploying the standardized ACM thresholds the model can perform the transmission with the desired quality with almost 0.3 dB less Es/No required.

**6. Conclusion**

**Acknowledgement**

codes.

**Author details**

**7. References**

services., 1994.

Bahman Azarbad and Aduwati Binti Sali *University Putra Malaysia, Malaysia*

Radiotelevisione Italiana, 2004.

*Workshop on*, pages 13 –17, oct. 2008.

*Networking*, 2007:10, 2007.

pages 334–356, portland usa, september 2003.

*International Conference on*, pages 1 –5, may 2010.

In this study, we proposed a complete DVB-S2 model for GEO satellite communication system implemented in Matlab.Specifically, we added high order modulation schemes, 16APSK and 32APSK.On top of that, we include ACM algorithm with standard threshold offsets provided by ETSI.Instead of random inputs we used MPEG-TS and we analyzed the quality of the received video streams.By running extensive simulation runs, we showed that the model corresponds to the standard in terms of BER performance.Then we investigate the model performance according to the PSNR of received video and we compared the results with some results in the literature.The results show a 0.3 dB improvement in the required Es/No defined in ACM offsets by ETSI.For future work, we address the study of different ACM thresholds

DVB-S2 Model in Matlab: Issues and Impairments 233

and their performance in terms of spectral efficiency versus PSNR of received video.

This project was funded by Malaysian Ministry of Science, Technology and Innovation (MOSTI) under its eScience fund program '3D Video Transmission over GEO Satellite Networks for Disaster Management Applications' (Project code: 01-01-04-SF1047). We would also take this opportunity to thank Mr Bernhard Shcmidt for his kind cooperation in some

[1] ETSI. Ets 200 421 v1.2.2 digital broadcasting system for television, sound, and data

[2] Alberto Morello and Vittoria Mignone. Dvb-s2 ready for lift off. Technical report, RAI,

[3] B. Azarbad, A. Sali, B.M. Ali, and H.A. Karim. Study of ber in dvb-s2 satellite implemented in matlab. In *Space Science and Communication (IconSpace), 2011 IEEE International Conference on DOI - 10.1109/IConSpace.2011.6015887*, pages 221–224, 2011. [4] J.E. Barcelo, G. Giambene, and M. Castro. Cross-layer optimization of tcp throughput for dvb-s2 links. In *Satellite and Space Communications, 2008. IWSSC 2008. IEEE International*

[5] S. Datta-Barua, P. H. Doherty, S. H. Delay, T. Dehel, and J. A. Klobuchar. Ionospheric scintillation effects on single and dual frequency gps positioning. In *16th international technical meeting of the satellite division of the institute of navigation(ION GPS/GNSS'03)*,

[6] Matteo Berioli, Christian Kissling, and Rémi Lapeyre. Capacity versus bit error rate trade-off in the dvb-s2 forward link. *EURASIP Journal on Wireless Communications and*

[7] V. Boussemart, H. Brandt, and M. Berioli. Subset optimization of adaptive coding and modulation schemes for broadband satellite systems. In *Communications (ICC), 2010 IEEE*

16 Will-be-set-by-IN-TECH 232 MATLAB – A Fundamental Tool for Scienti c Computing and Engineering Applications – Volume 2 DVB-S2 Model in Matlab: Issues and Impairments 17

**Figure 13.** Es/No Comparison with ETSI Standard

In[8],the authors proposed SNR intervals for different ModCods based on ETSI standard.We also compared SNR performance of our proposed model with their intervals.We consider the PSNR of received video in conjunction with BER.The comparison result is given in Figure 14.

**Figure 14.** SNR Comparison with D.Moad et. al Offsets

## **6. Conclusion**

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

In[8],the authors proposed SNR intervals for different ModCods based on ETSI standard.We also compared SNR performance of our proposed model with their intervals.We consider the PSNR of received video in conjunction with BER.The comparison result is given in Figure 14.

**Figure 13.** Es/No Comparison with ETSI Standard

**Figure 14.** SNR Comparison with D.Moad et. al Offsets

In this study, we proposed a complete DVB-S2 model for GEO satellite communication system implemented in Matlab.Specifically, we added high order modulation schemes, 16APSK and 32APSK.On top of that, we include ACM algorithm with standard threshold offsets provided by ETSI.Instead of random inputs we used MPEG-TS and we analyzed the quality of the received video streams.By running extensive simulation runs, we showed that the model corresponds to the standard in terms of BER performance.Then we investigate the model performance according to the PSNR of received video and we compared the results with some results in the literature.The results show a 0.3 dB improvement in the required Es/No defined in ACM offsets by ETSI.For future work, we address the study of different ACM thresholds and their performance in terms of spectral efficiency versus PSNR of received video.

## **Acknowledgement**

This project was funded by Malaysian Ministry of Science, Technology and Innovation (MOSTI) under its eScience fund program '3D Video Transmission over GEO Satellite Networks for Disaster Management Applications' (Project code: 01-01-04-SF1047). We would also take this opportunity to thank Mr Bernhard Shcmidt for his kind cooperation in some codes.

## **Author details**

Bahman Azarbad and Aduwati Binti Sali *University Putra Malaysia, Malaysia*

## **7. References**

	- [8] D. Moad, Y. Hadjadj-Aoul, and F. Nait-Abdesselam. Predictive channel estimation for optimized resources allocation in dvb-s2 networks. In *Personal, Indoor and Mobile Radio Communications, 2009 IEEE 20th International Symposium on*, pages 2608 –2612, sept. 2009.
	- [9] M. Smolnikar, T. Javornik, and M. Mohorcic. Target ber driven adaptive coding and modulation in hap based dvb-s2 system. In *Advanced Satellite Mobile Systems, 2008. ASMS 2008. 4th*, pages 262 –267, aug. 2008.
	- [10] ETSI. Tr 126 975 v10.0.0 "digital cellular telecommunications system (phase 2+);universal mobile telecommunications system (umts);lte;performance characterization of the adaptive multi-rate (amr) speech codec" (3gpp tr 26.975 version 10.0.0 release 10), 04 2011.
	- [11] ETSI. En 302 307 v1.2.1 (2009-08)-digital video broadcasting (dvb);second generation framing structure, channel coding and modulation systems for broadcasting,interactive services, news gathering and other broadband satellite applications (dvb-s2), 2009.
	- [12] D. Pradas, Lei Jiang, M.A. Vazquez Castro, P. Barsocchi, and F. Potorti. Satellite phy-layer selector design for video applications in tropical areas. In *Satellite and Space Communications, 2009. IWSSC 2009. International Workshop on*, pages 407 –411, sept. 2009.

© 2012 Sánchez-Salas et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Sánchez-Salas licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

2

(1)

**Wireless Channel Model with**

**Markov Chains Using MATLAB**

José Luis Cuevas-Ruíz and Miguel González-Mendoza

Emerging technology developed in last years is the result of the necessity of human to communicate in an effective and fast way from any point of the world. Since some type of loss is always present in a wireless link, new transmission and reception techniques for wireless

Channel modeling is the characterization of the wireless channel; it describes how the characteristics of the sent signal can be affected or what the conditions of the environment are, i.e. frequency, obstacles in the path, etc. The channel model helps to evaluate the performance of the system and to compare different techniques to mitigate the perturbations

The simplest model is the free space loss which considers no obstacles between transmitter and receptor. (Parsons, 2000) However this is an ideal model that does not exist in real scenario because there are other losses in the wireless channel. Another option to describe a channel is by using statistical models which are based on probability density functions (pdf). Although they do not describe the behavior of the sent signal in a complete way, they give a good approximation of the conditions of the channel in a certain moment. (Nakagami, 1960; Abouraddy & Elnoubi, 2000) The main statistical channels and their corresponding pdf are:

Additive White Gaussian Noise, AWGN. This channel only includes the sum of the

2

σ 2π 2σ

p x = exp -

1 x-μ

white noise that follows a normal or Gaussian density.

communication systems require the study and characterization of the wireless channel.

so the best fitted solution can be implemented according to the presented problem.

Additional information is available at the end of the chapter

Diana Alejandra Sánchez-Salas,

http://dx.doi.org/10.5772/3338

**1. Introduction**


**RNA INTERFERENCE**

Edited by **Ibrokhim Y. Abdurakhmonov**

#### **RNA Interference**

http://dx.doi.org/10.5772/60631 Edited by Ibrokhim Y. Abdurakhmonov

#### **Contributors**

Gyung Whan Kim, Kyoungjoo Cho, Utpal Bhadra, Debabani Roy Chowdhury, Manika Pal Bhadra, Constanca Figueiredo, Rainer Blasczyk, Qiuju Han, Zhaohua Hou, Cai Zhang, Jian Zhang, Devi Singh, Preeti Sirohi, Rajendra Kumar, Sarika Chaudhary, Kamiya Mehla, Pankaj Kumar Singh, Shashi Kumar, Pooran Chand, Tetsuya Tanaka, Remil Galay, Rika Umemiya-Shirafuji, Kozo Fujisaki, Masami Mochizuki, Emine Salva, Ceyda Ekentok, Suna Özbaş Turan, Jülide Akbuğa, Omar Bagasra, Zhabiz Golkar, Donald Gene Pace, Elena Chernolovskaya, Olga Gvozdeva, Yuxia Yang, Mengyu Hu, Qiankun Ni, Bin Yang, Cheng Yang, Santosh Kumar Upadhyay, Nidhi Thakur, Jaspreet Kaur Mundey, Thais B. Rodrigues, Antonio Figueira, Anne Meinhardt, Barbara Seliger, Doerte Falke, Thomas Böldicke, Oliver Backhaus, Covadonga Paneda, Wenyi Gu, Taisen Iguchi, Kenji Toyota, Wayne Hunter, Eduardo Andrade, Ibrokhim Y. Abdurakhmonov

#### **© The Editor(s) and the Author(s) 2016**

The moral rights of the and the author(s) have been asserted.

All rights to the book as a whole are reserved by INTECH. The book as a whole (compilation) cannot be reproduced, distributed or used for commercial or non-commercial purposes without INTECH's written permission. Enquiries concerning the use of the book should be directed to INTECH rights and permissions department (permissions@intechopen.com).

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Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

First published in Croatia, 2016 by INTECH d.o.o. eBook (PDF) Published by IN TECH d.o.o. Place and year of publication of eBook (PDF): Rijeka, 2019. IntechOpen is the global imprint of IN TECH d.o.o. Printed in Croatia

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Bauer Sabine

than the ventrally located structures. A further example in the field of medicine is studying

It is clearly pointed out that modeling has to be understood as an approximation to the reality. In near future, it will probably not be possible that all influencing factors can be simulated completely as in reality. Often, input parameters based on literature data representing the average of a specific cohort. Because each person has a very specific anthropometric and morphological characteristic, the standard deviations of such investigations may be relatively large. For example, the standard deviation for geometric parameters that describe the sagittal alignment of the spine is partially enormous. The deviation from average of the pelvic incidence is partly more than 20% [31], of the pelvic tilt 53% [32] respectively 70% [33], and of the total lumbosacral lordosis 19% [34], etc. But based on sensitivity analysis, the exact influence of individual parameters can be determined. However, because of the complexity of the models, it is very important to know the exact configuration of the model and the limita‐ tions of its input parameters. Therefore, the modeling and the validation process should to be

Opportunities for improvement and expansion of the different model components will be

In the presented models, the biomechanics of the intervertebral disc is defined by a force law, which can be understood as an initial approach. To precise the biomechanical properties of the annulus fibrosis and nucleus pulposus, a 3D hybrid model consisting of MBS and FE units will be built. The aim is to analyze whether the implementation of FE-disc features contributes to a significant gain of knowledge concerning load distribution of the intervertebral discs and

Currently, a highly accurate modeled individual cartilaginous contact layer for all facet joints is in development. These individually formed layers of cartilage (**Figure 17**) allow us to do a

the effects of different body weights on selected surgical procedures.

44 Numerical Simulation - From Brain Imaging to Turbulent Flows

**5. Meaningfulness and limitation of MBS modeling**

understood as an evolving process and will be advanced in future research.

summarized in the following:

the adjacent spinal structures.

3D calculation of the resulting forces.

**Figure 17.** Facet joints with a 3D layer of cartilage.

Address all correspondence to<EMAIL_ADDRESS>
MTI Mittelrhein, Institute for Medical Engineering and Information Processing, University Koblenz-Landau, Campus Koblenz, Germany


**6. Conclusion**

In this chapter, a despeckling method, based on a 2D directional non separable transform known as contourlet transform is presented. Conventional 2D wavelet transform is separable and thus cannot sparsely represent non separable structures of the image, such as directional curves. It is found that pyramidal directional filter bank feature of contourlet transform makes it a good choice for representation of curves and edges in the image. But, the contourlet transform, one of the recent geometrical image transforms, lacks the feature of translation invariance due to sub sampling in its filter bank structure. In cycle spinning, CT is improved by averaging the estimation of all translations of the degraded image. The Gibbs effect is considerably reduced by the contourlet transform with cycle spinning, because the average of different estimations of the image reduces the oscillations. In the literature, the authors [33,41,45,54,55,61] have considered ultrasound images (natural/synthetic) with artificially added speckle noise content and have proposed methods for despeckling such images. However, in the present study, we considered ultrasound images captured by the ultrasound equipment which contain inherent speckle noise and have proposed methods for removing the speckle noise more effectively.

When the noise characteristics of the images are unknown, it is proposed to denoise by a linear regression model, which is cost effective compared to the other methods. We have proposed a novel linear regression model for Gaussian noise estimation and removal in despeckling medical ultrasound images. The experimental results demonstrate its efficacy both in terms of speckle reduction and computational time required for denoising. Further, the proposed regression model is simple, generic and computationally inexpensive. Hence, it is easily amenable for building embedded system software for ultrasound imaging equipments in order to display the high quality images, which help the medical experts for speedy accurate image analysis and diagnosis. Further, the proposed regression model is simple, generic and computationally inexpensive.


**Aaron Greener**

W.F. Albright Institute of Archaeological Research in Jerusalem, Salah e-Din St 26, 91190 Jerusalem, Israel<EMAIL_ADDRESS>
Received: 28 February 2019; Accepted: 2 April 2019; Published: 9 April 2019

**Abstract:** Dozens of temples were excavated in the Canaanite city-states of the Late Bronze Age. These temples were the focal points for the Canaanites' cultic activities, mainly sacrifices and ceremonial feasting. Numerous poetic and ritual texts from the contemporary city of Ugarit reveal the rich pantheon of Canaanite gods and goddesses which were worshiped by the Canaanites. Archaeological remains of these rites include burnt animal bones and many other cultic items, such as figurines and votive vessels, which were discovered within the temples and sanctuaries. These demonstrate the diverse and receptive character of the Canaanite religion and ritual practices. It seems that the increased Egyptian presence in Canaan towards the end of the period had an influence on the local belief system and rituals in some areas, a fact which is demonstrated by the syncretic architectural plans of several of the temples, as well as by glyptic and votive items. Late Bronze Age religious and cultic practices have attracted much attention from Biblical scholars and researchers of the religion of Ancient Israel who are searching for the similarities and influences between the Late Bronze Age and the following Iron Age.

**Keywords:** Late Bronze Age; Canaan; religion; cult; temples; Egypt