Adaptive decision feedback equaliser based on sparse incremental least mean square/fourth approach

Channel equalisation performs a crucial role in practical wireless communication environment to reduce the consequence of intersymbol interference (ISI), noise and other channel distortions. Different adaptive signal processing algorithms tuned by multiple effective weight update mechanisms have been proposed and verified for designing the channel equaliser. In this paper, a sparse-based reweighted zero attracting incremental least mean square/fourth (ILMS/F) algorithm is anticipated to model an efficient adaptive decision feedback channel equaliser by taking the quadrature amplitude modulation (QAM)-modulated signal as transmitted signal which is passed through a nonlinear channel. The performance of the equaliser based on the proposed sparse-based ILMS/F has been analysed by using software and hardware platforms. The mean square error (MSE) table and bit error rate (BER) plots of the proposed algorithm have been compared with different conventional algorithms using MATLAB simulation and the hardware implementation of the proposed equaliser by using the FPGA platform has been evaluated in this paper. Moreover, the FPGA implementation of the proposed sparse incremental least mean square/fourth (ILMS/F) algorithm validates the robustness of the algorithm.