Near-Optimum Multiuser Detectors Using Soft-Output Ant-Colony-Optimization for the DS-CDMA Uplink

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 (LLR) 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 from5.2· 10⁻⁶to2.7· 10⁻⁶by 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 of10⁻⁸lower than that of the latter.