A method for estimating a channel in a communication system comprising a plurality of communication channels (C 1 , C i , C j , C N ), from a plurality of noisy values respectively representative (ξ i ) of the transmission through said communication channels (C 1 , C i , C j , C N ), comprising the following steps: - based on a physical model of the communication system having at least one parameter (θ), constructing (E2) a set of vectors ( e (θ)) associated with a plurality of values of said at least one parameter (θ), a vector ( e (θ)) associated with a given value of said at least on parameter (θ) comprising values respectively representative of the transmission through said communication channels (C 1 , C i , C j , C N ) according to said physical model for said given value of said at least one parameter (θ); - initializing (E4), as a function of the constructed vectors ( e (θ)), columns of weighting coefficients defining a part at least of an artificial neural network , said part of artificial neural network being configured to determine the column of weighting coefficients that is the best correlated with a vector received as an input of the artificial neural network and to produce as an output a vector that is colinear with the best correlated column; - applying (E10), as an input of said part of the artificial neural network, a vector ( x ) determined as a function of said noisy values (ξ i ) in such a way as to produce as an output of the artificial neural network a vector ( ĥ ) comprising estimated values respectively representative of the transmission through said communication channels (C 1 , C i , C j , C N ); - updating (E14) the weighting coefficients of said part of the artificial neural network by a learning technique reducing a cost function that increases as a function a distance between the vector ( x ) applied as an input of the artificial neural network and the vector ( ĥ ) produced as an output of the artificial neural network . - based on a physical model of the communication system having at least one parameter (θ), constructing (E2) a set of vectors ( e (θ)) associated with a plurality of values of said at least one parameter (θ), a vector ( e (θ)) associated with a given value of said at least on parameter (θ) comprising values respectively representative of the transmission through said communication channels (C 1 , C i , C j , C N ) according to said physical model for said given value of said at least one parameter (θ); - initializing (E4), as a function of the constructed vectors ( e (θ)), columns of weighting coefficients defining a part at least of an artificial neural network , said part of artificial neural network being configured to determine the column of weighting coefficients that is the best correlated with a vector received as an input of the artificial neural network and to produce as an output a vector that is colinear with the best correlated column; - applying (E10), as an input of said part of the artificial neural network, a vector ( x ) determined as a function of said noisy values (ξ i ) in such a way as to produce as an output of the artificial neural network a vector ( ĥ ) comprising estimated values respectively representative of the transmission through said communication channels (C 1 , C i , C j , C N ); - updating (E14) the weighting coefficients of said part of the artificial neural network by a learning technique reducing a cost function that increases as a function a distance between the vector ( x ) applied as an input of the artificial neural network and the vector ( ĥ ) produced as an output of the artificial neural network . <EOS>