Performance Analysis of Adaptive Channel Equalizer using Population based Update Algorithms

Digital channel is modeled as a low pass filter; thereby the data transmitted through this band limited channel suffers from distortions. Adaptive Channel equalization involves compensation for an unknown time-varying channel which is achieved with the help of an adaptive algorithm. This update algorithm is primarily used to update the equalizer coefficients. In the present work channel equalization is formulated as an optimization problem and minimization of squared error, which serves as objective function, is achieved iteratively using two population based algorithms namely Bacterial foraging optimization (BFO) and Differential Evolution (DE) and its different variants. Finally, these approaches are compared with respect to MSE convergence and bit error rate.