Improved Channel Equalization using Deep Reinforcement Learning and Optimization

EAI Endorsed Transactions on Scalable Information Systems

article

Swati Katwal¹  Vinay Bhatia² 

[1] Department of ECE, Baddi University of Emerging Sciences and Technology;Department of ECE, Chandigarh Group of Colleges

关键词: Digital communication;    Inter Symbol Interference (ISI);    Channel equalization;    Whale Optimization Algorithm (WOA);    and Reinforcement Learning (RL);   

DOI  :  10.4108/eai.28-10-2021.171685

学科分类：社会科学、人文和艺术（综合）

来源: Bern Open Publishing

【 摘 要 】

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.

【 授权许可】

CC BY