Sphere-constrained ML detection for frequency-selective channels

Abstract

Maximum-likelihood (ML) detection problem for channels with memory is investigated. The Viterbi algorithm provides an elegant solution, but is computationally inefficient when employed for detection on long channels. On the other hand, sphere decoding solves the ML detection problem in polynomial expected time over a wide range of SNRs. The sphere-constrained search strategy of sphere decoding is combined with the dynamic programming principles of the Viterbi algorithm. The resulting algorithm has the worst-case complexity of the Viterbi algorithm, but significantly lower expected complexity.

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