A method of detecting sequences of digitally modulated symbols, said modulated symbols being transmitted by multiple transmitting sources and received by multiple receiving elements and grouped as a received vector of sequences of digitally modulated symbols, wherein said method includes the step of: - receiving a channel state information matrix from a first module and processing said channel state information matrix, said processing including a triangularization of said channel state information matrix, - identifying a set of values for at least one reference transmit symbol, the possible values representing candidate values; - determining for each candidate value a set of sequences of transmitted symbols obtained by: a) estimating for each candidate value a set of sequences of estimated values for the remaining transmitted symbols, and b) grouping said candidate values and said estimated values; - calculating a metric for each of said sequence of transmitted symbols, - selecting the sequence of transmitted symbols that maximizes said metric, one for each candidate value, and - storing the value of the related metric and the associated selected sequence, characterized in that: - said method receives a-priori information ( L a) on said modulated symbols from a second module, - said calculating a metric for each said sequences of transmitted symbols includes calculating an a-posteriori probability metric for each of said sequence of transmitted symbols, - said set of sequences of estimated values for the remaining transmitted symbols is estimated for each candidate value by estimating the remaining transmit symbols in turn one at a time, in a successive detection fashion, by: a) calculating a partial Euclidean distance term associated with the transmit symbol to be estimated; b) selecting as one candidate transmitted symbol the transmitted symbol with the smallest value of said partial Euclidean distance term; c) selecting as further candidate transmitted symbol the transmit symbol with the largest a-priori information ( L a) of said symbol; d) calculating a partial a-posteriori probability metric associated with the transmit symbol to be estimated; e) selecting as estimated transmit symbol the symbol that maximizes said partial a-posteriori probability metric, and - said method includes calculating a-posteriori bit soft output information ( L p) for said selected sequences of transmitted symbols from the related metrics for said selected sequences of transmitted symbols. - receiving a channel state information matrix from a first module and processing said channel state information matrix, said processing including a triangularization of said channel state information matrix, - identifying a set of values for at least one reference transmit symbol, the possible values representing candidate values; - determining for each candidate value a set of sequences of transmitted symbols obtained by: a) estimating for each candidate value a set of sequences of estimated values for the remaining transmitted symbols, and b) grouping said candidate values and said estimated values; a) estimating for each candidate value a set of sequences of estimated values for the remaining transmitted symbols, and b) grouping said candidate values and said estimated values; - calculating a metric for each of said sequence of transmitted symbols, - selecting the sequence of transmitted symbols that maximizes said metric, one for each candidate value, and - storing the value of the related metric and the associated selected sequence, - said method receives a-priori information ( L a) on said modulated symbols from a second module, - said calculating a metric for each said sequences of transmitted symbols includes calculating an a-posteriori probability metric for each of said sequence of transmitted symbols, - said set of sequences of estimated values for the remaining transmitted symbols is estimated for each candidate value by estimating the remaining transmit symbols in turn one at a time, in a successive detection fashion, by: a) calculating a partial Euclidean distance term associated with the transmit symbol to be estimated; b) selecting as one candidate transmitted symbol the transmitted symbol with the smallest value of said partial Euclidean distance term; c) selecting as further candidate transmitted symbol the transmit symbol with the largest a-priori information ( L a) of said symbol; d) calculating a partial a-posteriori probability metric associated with the transmit symbol to be estimated; e) selecting as estimated transmit symbol the symbol that maximizes said partial a-posteriori probability metric, and a) calculating a partial Euclidean distance term associated with the transmit symbol to be estimated; b) selecting as one candidate transmitted symbol the transmitted symbol with the smallest value of said partial Euclidean distance term; c) selecting as further candidate transmitted symbol the transmit symbol with the largest a-priori information ( L a) of said symbol; d) calculating a partial a-posteriori probability metric associated with the transmit symbol to be estimated; e) selecting as estimated transmit symbol the symbol that maximizes said partial a-posteriori probability metric, and - said method includes calculating a-posteriori bit soft output information ( L p) for said selected sequences of transmitted symbols from the related metrics for said selected sequences of transmitted symbols.