sis 18: e16

Research Article

Improved Channel Equalization using Deep Reinforcement Learning and Optimization

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  • @ARTICLE{10.4108/eai.28-10-2021.171685,
        author={Swati Katwal and Vinay Bhatia},
        title={Improved Channel Equalization using Deep Reinforcement Learning and Optimization},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={10},
        keywords={Digital communication, Inter Symbol Interference (ISI), Channel equalization, Whale Optimization Algorithm (WOA), and Reinforcement Learning (RL)},
        doi={10.4108/eai.28-10-2021.171685}
    }
    
  • Swati Katwal
    Vinay Bhatia
    Year: 2021
    Improved Channel Equalization using Deep Reinforcement Learning and Optimization
    SIS
    EAI
    DOI: 10.4108/eai.28-10-2021.171685
Swati Katwal1,*, Vinay Bhatia2
  • 1: Department of ECE, Baddi University of Emerging Sciences and Technology, Solan, Himachal Pradesh, India
  • 2: Department of ECE, Chandigarh Group of Colleges, Landran, Mohali (Punjab), India
*Contact email: engg.swati@yahoo.co.in

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.