A Swarm of Solutions: Tunicate-Inspired Adaptive Equalization for Distortion-Free Digital Communication

In the realm of digital communication, signal dispersion can hinder successful transmission, resulting in Inter-Symbol Interference (ISI) distortion. Adaptive channel equalization is pivotal for mitigating ISI's impact. This study focuses on reducing ISI effects within FIR channels using an adaptive equalizer approach. Coefficients of the equalizer are refined using the truncated swarm algorithm. The equalization process is framed as an optimization challenge, aiming to iteratively minimize Mean Square Error (MSE) between transmitted and equalized signals. MATLAB is utilized for implementation, demonstrating efficacy through comparative analysis encompassing MSE, learning rate, Bit Error Rate (BER), convergence rate, and computational efficiency. Moreover, the proposed algorithm is benchmarked against the Bat algorithm, Slime Module Algorithm (SMA), and Harris Hawks Optimization (HHO), showcasing its superior optimization performance.