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 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, 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, 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 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 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, 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, 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 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.