Low-Complexity Detection Algorithms for Spatial Modulation MIMO Systems

Abstract

In this paper, the authors propose three low-complexity detection schemes for spatial modulation (SM) systems based on the modified beam search (MBS) detection. e MBS detector, which splits the search tree into some subtrees, can reduce the computational complexity by decreasing the nodes retained in each layer. However, the MBS detector does not take into account the effect of subtree search order on computational complexity, and it does not consider the effect of layers search order on the biterror-rate (BER) performance. e ost-MBS detector starts the search from the subtree where the optimal solution is most likely to be located, which can reduce total searches of nodes in the subsequent subtrees.us, it can decrease the computational complexity. When the number of the retained nodes is fixed, which nodes are retained is very important. at is, the different search orders of layers have a direct influence on BER. Based on this, we propose the oy-MBS detector. e ost-oy-MBS detector combines the detection order of ost-MBS and oy-MBS together. e algorithm analysis and experimental results show that the proposed detectors outstrip MBS with respect to the BER performance and the computational complexity.

Optimum Low-Complexity Decoder for Spatial Modulation

“…floating point operations (flops). Since the ML decoder costs 9MN r N t flops, the maximum complexity reduction that can be achieved from [34] when compared with the ML decoder in high SNR is 87.2% and 88.1% Another recent low-complexity algorithm that provides a near-ML BER performance is proposed in [17] by dividing the tree-search into N t subtrees with 2N r levels (for the real-form representation of (3)) and M branches. The transmit and receive antennas are ordered to reach the solution faster.…”

“…10, 11 and 12). Thus, the m-M algorithm provides a lower decoding complexity than the algorithm in [17] without losing the optimality of BER performance.…”

“…In theory, the error in channel estimation decreases as the SNR increases [30], [31]; therefore, we can consider σ 2 e = 1/snr in case of variable σ 2 e where snr denotes the signal-to-noise ratio in linear scale (i.e., SNR = 10log 10 (snr)). d i,j in (17) in the case of imperfect-CSIR is denoted by di,j and given as…”

“…Low-latency communications and energy-efficient transmission techniques are among the next generation (5G) requirements [10]; one solution to achieve this is the design of low-complexity decoding algorithms for the SM system. Recently, low-complexity decoding algorithms have been proposed for the SM system in [11]- [17], and surveyed in [18]. In [11]- [13], the sphere decoding (SD) concept of [19], [20] is exploited to provide a low-complexity detection at the BER level of the brute-force ML detector.…”

“…However, further reduction in the decoding complexity can be attained. The authors in [17] have proposed an algorithm with near-ML performance, which reduces the computational complexity of the SM decoders based on modified beam search and ordering concepts, by splitting the tree-search into sub-trees. It should be noted that the algorithms in [11]- [17] consider perfect knowledge of the channel state information at the receiver side (CSIR), and no study is presented in the case of imperfect CSIR.…”