In this study, the authors propose two low-complexitynear-maximum-likelihood (ML) detection algorithms for spatial modulation (SM) systems, employing the new multiple-ring star-M-ary quadrature amplitude modulation (...
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In this study, the authors propose two low-complexitynear-maximum-likelihood (ML) detection algorithms for spatial modulation (SM) systems, employing the new multiple-ring star-M-ary quadrature amplitude modulation (NR-STAR-MQAM) constellation. The proposed detectors exploit the specific orientation of NR-STAR-MQAM, in order to avoid searching across all constellation points. As a result, the computational complexity is independent of both the constellation size and the number of rings presented in NR-STAR-MQAM. In addition, these detectors are generalized and can be applied to the entire star-MQAM family. The Monte Carlo simulation results demonstrate that the proposed detection algorithms achieve the same average bit error rate (ABER) as ML detection for SM but at a much lower computational complexity. For example, in a 4 x 4, 2R-STAR-16QAM aided SM system, the proposed optimal and sub-optimal detectors achieve an 88.8% and 90.5% reduction in computational complexity, respectively, compared to the ML detector. Furthermore, the simulation results are supported by a closed-form union-bound theoretical ABER expression.
In this contribution, a novel soft-output ant colony optimization (SO-ACO)-based multiuser detector (MUD)-namely the MUlti-input-Approximation (MUA) assisted SO-ACO-based MUD-is proposed for the synchronous direct-seq...
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In this contribution, a novel soft-output ant colony optimization (SO-ACO)-based multiuser detector (MUD)-namely the MUlti-input-Approximation (MUA) assisted SO-ACO-based MUD-is proposed for the synchronous direct-sequence code-division-multiple-access (DS-CDMA) uplink (UL). The previously proposed conventional ACO based MUDs were unable to provide soft log-likelihood ratio (1,1,11) values for the channel decoder. Hence, to solve this open problem, we commence by proposing the maximum-Approximation (MAA) assisted SO-ACO algorithm, leading to a novel MUA assisted SO-ACO algorithm, which subsumes the MAA algorithm as a particular case and outperforms the MAA algorithm. More explicitly, at a signal-to-noise ratio (SNR) of 13 dB, the BER performance of the convolutional coding (CC) aided CDMA UL employing the MAA SO-ACO is improved from 5.2 . 10(-6) to 2.7 . 10(-6) by employing the MUA SO-ACO. Our numerical results also demonstrate that the MUA assisted SO ACO-MUD is capable of approaching the optimum performance of the Bayesian detector, when K = 32 UL users are supported with the aid of 31-chip Gold codes, while the complexity of the former is a fraction of 10(-8) lower than that of the latter.
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