in the following description of the method according to an advantageous embodiment of the invention , a wireless communication device with a receiver 2 and a transmitter 3 for radio - frequency communication will be used as an example of an electronic device 1 . as the modulation method , m - ary phase shift keying m psk will be used , such as 8 psk ( m = 8 ). in such phase shift keying , eight different phase shift values are available for symbol formation , wherein one symbol can be used to transmit the information of three bits ( 2 3 ). generally in phase shift keying , the letter m indicates the involution of two 2 n , in which n is greater than 0 . such an 8 psk modulated signal has been proposed for example for the gsm - edge system , but it is obvious that the invention can also be applied in connection with other phase shift modulation methods and other communication systems applying phase shift modulation . the receiver 2 comprises a high - frequency part 4 , in which the received high - frequency signal is converted either to an intermediate - frequency signal or directly to the baseband . the signal formed in the high - frequency part 4 is converted to digital format by sampling in an analog - to - digital converter 5 . these samples are stored in a memory 10 for further processing steps . on the basis of these samples , the receiver 2 is synchronized with the received signal in a synchronization block 6 . here , so - called training sequences can be utilized , which are transmitted with payload information to the receiver 2 . such a training sequence and its location in relation to the payload information is also known in the receiver 2 , wherein the receiver can search for these training sequences in the received signal e . g . by a correlation method . the synchronization block 6 can also be used to control the timing of the analog - to - digital converter 5 . an automatic amplification control block 8 can be used to control the amplification of the high - frequency part 4 , if necessary , wherein variations in signal strength can be compensated for , to at least some extent . the received , digitized signal is transmitted to a channel equalizer 7 in which the received signal is subjected to equalizing operations to compensate , as well as possible , for changes in signal propagation due to the radio channel . the channel equalizer 7 also comprises a channel estimator 17 ( fig2 a ) for estimating the properties of the communication channel , i . e . for finding out the transfer function of the communication channel . subsequently , the channel - coded signal is directed to a decoder 9 for decoding . the function of the decoder 9 is to find out from the received signal the original information transmitted in the signal , after which the received information is led to further processing steps in a way known as such . the following is a mathematical description of the computing algorithm to be used in the method according to the invention . for clarity , it is assumed in the description that 8 psk modulation ( m = 3 ) is involved , wherein the symbol value is determined by three bits , but the principles to be presented below can also be applied in other m psk encodings . the purpose of the computing algorithm is to minimize the function f ( b ) presented in formula ( 3 ) in an iterative manner . to facilitate the description , two terms are added in the cost function ( 3 ), resulting in the cost function f ⁡ ( b ) = 1 2 ⁢ ∑ t = 0 t ⁢ ⁢ ( ⁢  r t - ∑ s = 0 h - 1 ⁢ ⁢ h s ⁢ s ⁡ ( b t - s )  2 - ∑ s = 0 h - 1 ⁢ ⁢ h _ s ⁢ h s ⁢ s _ ⁡ ( b t - s ) ⁢ s ⁡ ( b t - s ) + ∑ k = 1 m ⁢ ⁢ ( b t , k - 1 2 ) 2 ) ( 4 ) in which the reference x indicates the complex conjugate of x . since the terms b are binary , they have a constant value . thus , the minimum of the function ( 4 ) corresponds to the minimum of the function ( 3 ). taking the derivative of f ( b ) according to formula ( 4 ) with respect to b l , k ( l = t − s ), we get that δ ⁢ ⁢ f ⁡ ( b ) δ ⁢ ⁢ b l , k = b l , k - 1 2 + ∑ t = l l + h - 1 ⁢ ⁢ ( - re ⁢ { r _ t ⁢ h t - l ⁢ ⁢ δs ⁡ ( b l ) δ ⁢ ⁢ b l , k } + re ⁢ { h _ t - l ⁢ δ ⁢ s _ ⁡ ( b l ) δ ⁢ ⁢ b l , k ⁢ ∑ q = 0 , t - q ≠ l h - 1 ⁢ ⁢ h q ⁢ s ⁡ ( b t - q ) } ) ( 5 ) now , using the gradient descent and requiring that b is either 0 or 1 , we get that b l , k ⁡ ( j + 1 ) = g h ⁡ ( b l ⁢ ⁢ k ⁡ ( j ) - α ⁢ ⁢ δf ⁡ ( b l ) δ ⁢ ⁢ b l , k ) ( 6 ) where j refers to iteration number and g h ( x )= 1 , if x & gt ; 0 . 5 , else 0 . setting α = 1 , we get the update rule for the bit b l , k , ( k = 1 , 2 , 3 ; l is an index ): b l , k ⁡ ( j + 1 ) = f h ⁡ ( ∑ t = l l + h - 1 ⁢ ⁢ re ⁢ { r _ t ⁢ h t - l ⁢ ⁢ δs ⁡ ( b l ) δ ⁢ ⁢ b l , k } - re ⁢ { h _ t - l ⁢ δ ⁢ s _ ⁡ ( b l ) δ ⁢ ⁢ b l , k ⁢ ∑ q = 0 , t - q ≠ l h - 1 ⁢ ⁢ h q ⁢ s ⁡ ( b t - q ) } ) ( 7 ) where b l =└ b l , 1 , b l , 2 , b l , 3 ┘ are three bits at time l = u + lδu , s ( b l ) is the corresponding symbol , s ( b l ) is the derivative with respect to k bits , h indicates the communication channel , of which h channel taps are estimated ( h - path channel ), and f h ( x ) is a hard limit function which receives the value 1 , if x & gt ; 0 , else 0 . in the general case , b l =└ b l , 1 , b l , 2 , . . . , b l , m ┘. the symbols s are complex numbers , and re { } indicates taking the real part . to compute the iteration step to be taken by the update rule according to this function , it is possible to select how long a signal is to be examined . for example , a period of 5h can be examined , i . e . l = 0 , . . . , 5h − 1 . the description s is defined , as presented in formula ( 1 ), in such a way that s ( b l ) = a 2 ( 1 − b l , 1 ) b l , 2 b l , 3 + a 4 ( 1 − b l , 1 ) b l , 2 ( 1 − b l , 3 ) + a 6 ( 1 − b l , 1 )( 1 − b l , 2 )( 1 − b l , 3 ) + a 8 ( 1 − b l 1 )( 1 b l , 2 ) b l , 3 + a 10 b l , 1 ( 1 − b l , 2 ) b l , 3 + a 12 b l , 1 ( 1 − b l , 2 )( 1 − b 1 , 3 ) + a 14 b l , 1 b l , 2 ( 1 − b l , 3 ) + a 16 b l , 1 b l , 2 b l , 3 ( 8 ) where a = exp ( i2π 1 / 16 ). the bit values are initialized for example randomly . because binary optimization is involved , the method may be stuck in a local minimum . however , this can be prevented by adding noise whose variance is reduced during convergence , to the iteration . furthermore , several initializations can be used . the structure of a channel equalizer 7 complying with a preferred embodiment of the invention is shown in the appended fig2 a . to start the operation , the states b l , k are set for example randomly . the sampled signal r which is delayed in delay blocks 15 a – 15 d of a delay line , is led to the channel equalizer 7 . furthermore , channel coefficients are estimated from the signal in the channel estimator 17 which forms a vector h . after the required number ( in this example 5h ) of samples has been led to the channel equalizer 7 , the iteration step is started . thus , the values of the vector h as well as the sample values of the sampled signal r , delayed in the delay blocks 15 a – 15 d , are input in computing units 16 a – 16 e , one of which is shown in more detail in fig2 b . further , the bit decisions b of the neighbouring computing units , whose values at the first iteration time are the values determined at the initialization stage , are input in the computing unit . the computing units 16 a – 16 e contain a number corresponding to the term m of iteration blocks , in this example three iteration blocks 18 a , 18 b , 18 c , each of which implements the iteration step according to the above formula ( 7 ), except for the hard limit function f h , that is b ~ l , k ⁡ ( j + 1 ) = ∑ t = l l + h - 1 ⁢ ⁢ re ⁢ { r _ t ⁢ h t - l ⁢ ⁢ δs ⁡ ( b l ) δ ⁢ ⁢ b l , k } - re ⁢ { h _ t - l ⁢ δ ⁢ s _ ⁡ ( b l ) δ ⁢ ⁢ b l , k ⁢ ∑ q = 0 , t - q ≠ l h - 1 ⁢ ⁢ h q ⁢ s ⁡ ( b t - q ) } + awgn ( 9 ) where k = 1 , 2 , 3 and awgn refers to gaussian noise . the hard limit function is implemented by means of hard limit blocks 19 a , 19 b , 19 c . in the references of fig2 b , the character ˜ on top of a symbol indicates the decision value before performing the hard limit function . the iteration blocks 18 a , 18 b , 18 c are coupled to each other in such a way that their output is led to the other iteration blocks 18 a , 18 b , 18 c in the same computing unit 16 a – 16 e preferably through the hard limit blocks 19 a , 19 b , 19 c , but not to the input of the same iteration block ; that is , there is no feedback in the iteration blocks 18 a – 18 c . the block n forms random noise which is input in the iteration blocks 18 a , 18 b , 18 c of the computing unit , if necessary . preferably , the variance of the added noise should reduce during the step of stabilization . furthermore , the output of all the three limiting blocks 19 a , 19 b , 19 c in the computing block is preferably input in at least one , preferably neighbouring computing unit 16 a – 16 e . the number of computing units 16 a – 16 e in which the output is input , depends on the number of channel taps . after the computing blocks 16 a – 16 e have computed the bit value of one iteration step , the hard limit function f h is computed in the hard limit block 19 a , 19 b , 19 c which makes the bit decisions b of the computing blocks at the respective iteration round . after this , it is examined , for example in the control block 11 , whether the state of the channel equalizer 7 is stabilized , or if there is still a need to iterate the computation of the bit decisions . if it is noticed that the state is not yet sufficiently stable , a new round of iteration computing is performed , in which the bit decisions formed in the previous iteration round are now used as the bit decisions b . stabilization can be examined for example by comparing the bit decisions of the previous iteration round to the bit decisions of the latest iteration round , wherein if the bit decisions have remained the same , it can be assumed that the channel equalizer 7 is stabilized . after the channel equalizer has been stabilized , a channel - equalized binary signal can be obtained from the hard limit blocks of the channel equalizer , for further processing . thus , the output of each hard limit block contains the bit decision b l which is a vector containing the channel - equalized binary values corresponding to the symbol r l . from the computing units 16 a – 16 e it is also possible to take so - called soft decisions { tilde over ( b )} 0 by using an activation function f a which can be for example a sigmoid function . soft decisions are advantageous in decoding of bits . the channel equalizer 7 shown in fig2 a and 2 b can be primarily implemented by programming e . g . in a digital signal processor ( dsp ) which can be e . g . a part of the control block 11 . furthermore , a memory is needed in the operation of the channel equalizer 7 , for storing the bit decisions made at the iteration rounds . the channel equalizer 7 can also be implemented as an integrated circuit . further , fig3 shows simulation results comparing the method according to an advantageous embodiment of the invention with methods of prior art . the method according to an advantageous embodiment of the invention has been tested with a complex 4 - path channel . the channel coefficient h is estimated from a training sequence of 100 bits . also , parameters of a delayed decision - feedback sequence estimator ( ddfse ) according to prior art are estimated from the corresponding sequence . noise has been added during the iteration . the result shows that in this test , the new method ( curve a ) is clearly better than the reference method ( curve b ). the number of iterations was 50 . the dotted line ( curve c ) shows the result obtained with the method according to the invention by initializing the bit values b with the real values , by eliminating the added noise and letting the equalizer stabilize . this is very close to the performance of the viterbi algorithm . it is obvious that the present invention is not limited solely to the above - presented embodiments , but it can be modified within the scope of the appended claims .