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Recently, a multiple symbol differential (MSD) sphere decoding (SD) algorithm for unitary space-time modulation over quasi-static channel has been proved to achieve the performance of maximum-likelihood (ML) detection with relatively low complexity. However, an error floor occurs if the algorithm is applied over rapid-fading channels. Based on the assumption of continuous fading, a multiple symbol differential automatic sphere decoding (MSDASD) algorithm is developed by incorporating a recursive form of an ML metric into automatic SD (ASD) algorithm. Furthermore, two algorithms, termed as MSD approximate ASD (MSDAASD) and MSD pruning ASD (MSDPASD), are proposed to reduce computational complexity and the number of comparisons, respectively. Compared with the existing typical algorithms, i.e., multiple symbol differential feedback detection (MS-DFD) and noncoherent sequence detection (NSD), the performance of the proposed algorithms is much superior to that of MS-DFD and a little inferior to that of NSD, while the complexity is lower than that of MS-DFD in most cases and significantly lower than that of NSD.
Recently, a multiple symbol differential (MSD) sphere decoding (SD) algorithm for unitary space-time modulation over quasi-static channel has been proved to achieve the performance of maximum-likelihood (ML) detection with relatively low complexity. However, an error based on the assumption of continuous fading, a multiple symbol differential automatic sphere decoding (MSDASD) algorithm is developed by incorporating a recursive form of an ML metric into automatic SD (ASD) algorithm . Two algorithms, termed as MSD approximate ASD (MSDAASD) and MSD pruning ASD (MSDPASD), are proposed to reduce computational complexity and the number of comparisons, respectively. Compared with the existing typical algorithms, ie, multiple symbol differential feedback detection (MS-DFD) and noncoherent sequence detection (NSD), the performance of the proposed algorithms is much superior to that of MS-DFD and a little inferio r to that of NSD, while the complexity is lower than that of MS-DFD in most cases and significantly lower than that of NSD.