Multiuser detection over generalized-K fading channels using two-stage initialized teaching learning based optimization

Abstract

A multiuser detection (MUD) technique for direct sequence-code division multiple access (DS-CDMA) systems over generalized-K (GK) fading channels using teaching learning based optimization algorithm (TLBO) with two-stage initialization (TSI) is proposed. In DS-CDMA systems, MUD techniques are applied to combat multiple access interference (MAI) and ambient noise. Empirical results proved that the ambient noise is non-Gaussian and impulse in nature which degrades the performance of the system substantially. The DS-CDMA signals are transmitted over channels that introduce impulsive noise, shadowing and fading. In this paper, we develop least-squares (LS), Huber and Hampel M-estimation based MUD technique for joint detection of DS-CDMA signals in the presence of MAI, impulsive noise, modeled by Laplace distribution, and channel fading, modeled by GK distribution. The TLBO with TSI (TLBO-TSI) algorithm is used to minimize a penalty function that is a less rapidly increasing function of residuals. Average bit error rate (BER) is computed to assess the performance of the TLBO-TSI based detector. Obtained results demonstrate that the proposed robust technique offer significant performance gains with increase in signal-to-noise ratio (SNR) and diversity order in the presence of heavy-tailed impulsive noise.