Performance evaluation of a novel WLAN receiver performance in highly dispersive nonlinear environment

Our novel work presents an intelligent, unsupervised organizing neural network based, high power amplifier (HPA) characteristics independent, adaptive method for compensation of nonlinear distortions for orthogonal frequency division multiplexing (OFDM) based wireless communication system. Major elements that contribute to multipath fading effects, like Doppler Effect, interference effects, phase noise and frequency offset are also considered and to account for major degradation categories, Rayleigh and Rician channel models are considered in an OFDM system. Two unsupervised neural networks used are self organizing feature map (SOFM) and parameter-less self organizing map (PLSOM). System performance is tested for OFDM system with and without neural block and it is proved through spectral regrowth as well as bit error rate (BER) plots that the proposed model brings an overall improvement in OFDM system in highly dispersive environment.