Improved Channel Equalization using Deep Reinforcement Learning and Optimization

Abstract

INTRODUCTION: Data transmission through channels observe large distortions arising due to the channel's dispersive nature challenged with inter-symbol interference. OBJECTIVES: The paper serves twin tasks, firstly addresses the challenges of signal interference using RL based model and secondly evaluates its effectiveness using different communication channels. METHODS: The author proposes an improvement in channel equalization with the implementation of Whale Optimization Algorithm (WOA) followed by the Q-Learning model for Reinforcement Learning (RL) to identify the most suitable bit streams that will offer least interference. RESULTS: Simulation analysis is performed against four existing works in terms of Bit Error Rate (BER), reflecting 20 to 30% improvement. The performance evaluation is executed using AWGN, Rician, Rayleigh, and Nakagami channels to evaluate BER against SNR, Eb/No, and Es/No. CONCLUSION: Overall, the proposed work offers high-speed data transfer through a reliable communication channel with least BER under different scenarios.

Keywords

• digital communication
• inter symbol interference (isi)
• channel equalization
• whale optimization algorithm (woa)
• and reinforcement learning (rl)