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Spatial modulation (SM) is a low complexity, highly spectral efficient multiple-input -multiple-output scheme that has been proposed in the literature. The authors apply adaptive modulation to conventional SM in order to maximise the average throughput of the scheme. For this to be possible, the equivalent received signal-to-noise ratio (SNR) needs to be defined and is done so via two proposed approaches: using the first-order statistics SNR and using the average statistics SNR. Average theoretical bit-error-rate (BER) bounds are derived for both of these SNR approaches. Also, adaptive M-ary quadrature amplitude spatial modulation switching levels are determined to maximise the throughput while meeting the average target BER. The Monte Carlo simulation results successfully validate the derived theoretical BER bounds and also prove that the average throughput is improved in comparison to conventional SM. The two proposed definitions for the equivalent received SNR are confirmed to yield comparable BER and throughput performances via simulations, implying that the equivalent received SNR can be defined using either approach.

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 (𝑀) of at least 64, i.e. 𝑀 ≥ 64. 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.

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