Multiuser ML sequence estimator for convolutionally coded asynchronous DS-CDMA systems

The optimal multiuser sequence estimator is formulated for an asynchronous direct-sequence code-division multiple-access (DS-CDMA) system where each user employs convolutional coding to improve its performance on a nondispersive additive white Gaussian noise (AWGN) channel, It is shown that the decoder may be implemented efficiently using a Viterbi algorithm which operates on a time-varying trellis with a number of states which is exponential in the product of the number of users in the system and the constraint length of the codes used (for the rate 1/2 code case), The asymptotic efficiency of this receiver relative to an uncoded coherent binary phase shift keying (BPSK) receiver (termed asymptotic multiuser coding gain, or AMCG) is then upper and lower bounded, The AMCG parameter unifies the asymptotic coding gain parameter and the asymptotic multiuser efficiency parameter which are traditional figure of merit parameters for single-user coded systems and multiuser uncoded systems, respectively, Finally, some simulations are presented to illustrate the performance of the maximum likelihood sequence estimator (MLSE) at moderate and low bit error rates.