Abstract
In this paper, we develop low complexity Golden code sphere-decoding (SD) algorithms for high-density M-ary quadrature amplitude modulation (M-QAM). We define the high-density M-QAM as having modulation orders (<inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>) of at least 64, i.e. <inline-formula> <tex-math notation="LaTeX">$M\ge 64. $ </tex-math></inline-formula> High-density M-QAM symbols deliver high data rates under good wireless channels. Future wireless systems must deliver high data rates and simultaneously low end-to-end latency. However, higher M-QAM modulation orders increase the Golden code SD search breadth, thus increasing decoding latency. We, therefore, propose two forms of low complexity Golden code SD to achieve low decoding latency while maintaining the near-optimal SD bit-error rate (BER). The proposed low complexity SD algorithms are based on the SD with sorted detection subsets (SD-SDS). The literature shows the SD-SDS to achieve lower detection complexity relative to the Schnorr-Euchner SD (SE-SD). The first form of the proposed Golden code SD is the SD-SDS-Descend algorithm with instantaneously varying subset lengths and a search tree search order sorted based on the worst-first search strategy. The second form of the proposed Golden code SD is an SD-SDS algorithm called SD-SDS-ES-DNN with a deep learning-based early stopping search criterion. Our proposed algorithms achieve at most 57% and 70% reduction in Golden code decoding latency relative to SD-SDS, at low SNR, for 64-QAM and 256-QAM, respectively. At high SNR, the proposed algorithms achieve 40% and 37% in Golden code decoding latency reduction relative to the SD-SDS for 64-QAM and 256-QAM, respectively. The decoding latency reduction is achieved while maintaining near-optimal BER performances.
Highlights
With the high demand for communication services that require high data throughputs and low end-to-end latency, coupled with the sharp increase of mobile devices depending on wireless communications, the wireless communication literature proposes various multiple-input multiple-output (MIMO) architectures to cater to these demands
The candidate symbol subset lengths varied with the instantaneous channel conditions for each estimated M-ary quadrature amplitude modulation (M-QAM) symbol and allowed the search tree search order to be sorted based on the subset lengths
This led to the proposal of the worst-first search strategy, which was employed by the detection algorithm SD with sorted detection subsets (SD-SDS)-Descend
Summary
With the high demand for communication services that require high data throughputs and low end-to-end latency, coupled with the sharp increase of mobile devices depending on wireless communications, the wireless communication literature proposes various multiple-input multiple-output (MIMO) architectures to cater to these demands. We are motivated to propose an SD-SDS search tree early stopping deep learning-based algorithm with a low inference time DNN architecture that is invariant to the M-QAM modulation order. This DNN algorithm prematurely terminates the SD-SDS search under learned channel conditions. Based on the literature survey, none of the research has attempted to reduce the decoding latency of the low complexity Golden code SD-SDS algorithm in a small MIMO environment i.e Nt = 2 and Nr ∈ [Nt :8). SD-SDS-Ascend: Is a Golden code SD algorithm with instantaneously varying subset lengths and a search tree search order sorted based on the best-first search strategy. SD-SDS-ES-DNN: Is a Golden code SD-SDS algorithm with a deep learning-based early stopping search criterion
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