Application of Sphere Decoding to MSDD

In multiple-symbol differential detection (MSDD) for powe rfficient transmission over 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 i s therefore (complex) N −1 dimensional, and MSDD quickly becomes computationally intractable as N grows. Sphere decoding (SD) is a widely used approach to find the ML estimate in such high-dimensional spaces. In this pap er, we devise the application of SD to accomplish MSDD, and we refer to the resulting technique as multiple-sy mbol differential sphere decoding (MSDSD). We present an efficient algorithm for MSDSD, whose excellent pe rformance versus complexity trade-off is verified by various simulation results and by comparisons with other, s uboptimum approaches known from the literature.

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