Adaptive Channel Equalization Using Seagull Optimization with Various Initialization Strategies

Abstract

In a digital communication channel, the transmitted signal may be dispersive causing the information to not be transmitted as same. Due to the distortion, the communication channel is affected known as Inter-Symbol Interference (ISI). To reducing the ISI effect, adaptive channel equalization plays an important role in digital communication. In the proposed method, the Finite Impulse Response (FIR) channel ISI effect is reduced by the proposed optimization algorithm. The FIR channel weight or coefficients are optimized by the proposed Seagull Optimization Algorithm (SOA) with different initialization strategies. Normally, SOA has random initialization of population but to improve the process of adaptive channel equalization, Random Number Generation (RNG), Opposition based learning (OBL) and Quasi-Opposition based learning (QBL) methods are utilized for initialization. The objective function of the equalization process is minimization error values where the error value is estimated based on the desired signal and channel output signal. The experiment is carried out on the MATLAB platform. The proposed method's effectiveness is shown to compare the various initialization methods of RNG, OBL and QBL with SOA. Based on different SNRs, the error value is computed and shows the convergence results.