1. A method for maximum-likelihood decoding of convolutional codes on a network of microprocessors interconnected as a hypercube in which a sequence of received symbols to be decoded is broadcast to every processor and each processor is adapted to compare the accumulated metrics and select survivors during about 5m, stages of an m-state trellis (where m is a whole integer and each processor is fixably assigned to one state of said trellis), said method comprising the steps of: connecting each one of a plurality of 2m processors, equipped with a decoding algorithm means, in an n-cube configuration having bidirectional communication links along the edges only of said cube and certain processors thereof not having a direct communication link between other processors of said n-cube configuration; mapping an equivalent trellis for said n-cube configuration wherein processors represent more than one state, and in that remapped state, have direct communication links between other processors of said n-cube configuration on said equivalent trellis; computing by the decoding algorithm t each processor, that processor's local accumulated metrics; transmitting that accumulated metric to that processor's neighboring processors as identified by said equivalent trellis; determining the current branch metric at each of said processors; updating, based on each received symbol, the locally-accumulated metrics by adding the current branch metrics thereto; comparing in each processor the updated locally-accumulated metrics with the updated accumulated metrics from a neighboring processor; locally storing the result of said comprising step in a local storage; tracing-back paths by interrogating local storage of each processor; and sending the decoded data resulting from this tracing-back step to the origin node, and then to a user.