Abstract
In this paper, sphere decoding algorithms are proposed for both hard detection and soft processing in multi-input multi-output (MIMO) systems. Both algorithms are based on the complex tree structure to reduce the complexity of searching the unique minimum Euclidean distance and multiple Euclidean distances, and obtain the corresponding transmit symbol vectors. The novel complex hard sphere decoder for MIMO detection is presented first, and then the soft processing of a novel sphere decoding algorithm for list generation is discussed. The performance and complexity of the proposed techniques are demonstrated via simulations in terms of bit error rate (BER), the number of nodes accessed and floating-point operations (FLOPS).
Highlights
To achieve a high spectral efficiency, maximum likelihood (ML) detection should be employed with high-order constellations
This is because the proposed Complex sphere decoder (CSD) significantly reduces the required number of times for performing enumeration and the span of the detection tree
3.2.1 Search strategy Compared to conventional SE-CSD, the novel search strategy first performs successive interference cancellation (SIC) to obtain the nulling-cancelling points and the full path metric (FPM) without calculating the partial path metric (PPM) of other constellation points and sorting for each layer, and the radius pSD may be updated by FPM, i.e., pNt, once the search reaches the bottom layer
Summary
To achieve a high spectral efficiency, maximum likelihood (ML) detection should be employed with high-order constellations. Another efficient ordering and pruning scheme is studied in [18], which performs the horizontal pruning and vertical pruning with a novel tight lower limit for the path metric These two schemes discussed above could be used in the complex-valued SDs with simple modifications. The results in [14] illustrate the hardware implementation of LSD with four candidates, which may not be considered as an implementation of approximate MAP detector [19] This is because the list size is too small to achieve near-capacity performance. An efficient CSD has been proposed that approaches the linear complexity for practical large-scale MIMO systems This is because the proposed CSD significantly reduces the required number of times for performing enumeration and the span of the detection tree. The CSDs with MMSE-SQRD can achieve the ML performance at the expense of very low complexity
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