A statistical model based on a partitioned Markov-chains model has previously been developed to represent time domain behavior of the asynchronous impulsive noise over a broadband power line communication (PLC) network. However, the estimation of its model parameters using the Simplex method can easily trap the final solution at a local optimum. This study proposes an estimation scheme based on the genetic algorithm (GA) to overcome this difficulty. Experimental results show that the proposed scheme yields estimates that more closely match the experimental data statistics.

In this paper, we propose an adaptive multistage fuzzy-based partial parallel interference cancellation (FB-PPIC) multiuser detector for multi-carrier direct-sequence code-division multiple-access (MC-CDMA) communication systems over frequency selective fading channels. The partial cancellation tries to reduce the cancellation error in parallel interference cancellation (PIC) schemes due to the wrong interference estimations in the early stages and thus outperforms the conventional PIC (CPIC) under the heavy load for MC-CDMA systems. Therefore, in this paper, the adaptive cancellation weights are inferred from a proposed multistage fuzzy inference system (FIS) to perform effective PPIC multiuser detection under time-varying frequency selective fading channels in MC-CDMA systems. Simulation results show that the proposed adaptive four-stage FB-PPIC scheme outperforms both CPIC and constant weight PPIC (CW-PPIC) schemes, especially in near-far environments.