PERFORMANCE EVALUATION OF INDEPENDENT COMPONENT ANALYSIS ALGORITHMS FOR DS-CDMA DETECTION - Parmar, Sargam (International Journal of Psychosocial Rehabilitation, 2020)
Direct Sequence Code Division Multiple Access (DS-CDMA) systems are inherently limited by multi-user interference, particularly in dense cellular deployments. Independent Component Analysis (ICA) offers a blind preprocessing approach for interference suppression without requiring prior knowledge of spreading codes or channel parameters. This paper presents a quantitative performance evaluation of three widely used ICA algorithms—Cardoso’s Joint Approximate Diagonalization of Eigen-matrices (JADE), Hyvärinen’s FastICA fixed-point algorithm, and Comon’s mutual-information-based algorithm—for symbol detection in DS-CDMA downlink systems. Simulation results are compared against conventional Single User Detection (SUD), standalone ICA detection, and a combined SUD–ICA detection scheme under additive white Gaussian noise (AWGN) and colored (pink) noise conditions. Performance is assessed using symbol error rate (SER), convergence behavior, and robustness across signal-to-noise ratio (SNR). The results demonstrate that ICA-based detection provides measurable SER reductions relative to SUD, with JADE consistently achieving the best performance across all examined scenarios.