Multiple-Symbol Differential Sphere Decoding

Abstract

In multiple-symbol differential detection (MSDD) for power-efficient transmission over Rayleigh fading channels without channel state information, blocks of N received symbols are jointly processed to decide on N-1 data symbols. The search space for the maximum-likelihood (ML) estimate is therefore (complex) (N-1)-dimensional, and maximum-likelihood MSDD (ML-MSDD) quickly becomes computationally intractable as N grows. Mackenthun's low-complexity MSDD algorithm finds the ML estimate only for Rayleigh fading channels that are time-invariant over an N symbol period. For the general time-varying fading case, however, low-complexity ML-MSDD is an unsolved problem. In this letter, we solve this problem by applying sphere decoding (SD) to ML-MSDD for time-varying Rayleigh fading channels. The resulting technique is referred to as multiple-symbol differential sphere decoding (MSDSD).