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ORIGINAL RESEARCH article

Front. Comms. Net.
Sec. Signal Processing for Communications
Volume 5 - 2024 | doi: 10.3389/frcmn.2024.1477270
This article is part of the Research Topic Emerging Optimization, Learning and Signal Processing for Next Generation Wireless Communications and Networking View all articles

Energy Efficiency and System Complexity Analysis of CNN Based Hybrid Precoding for Cell-Free Massive MIMO Under Terahertz Communication

Provisionally accepted
Yitbarek Anbese Yitbarek Anbese 1*Tadele Abera Tadele Abera 2*Binyam Gebremedhin Binyam Gebremedhin 3Naol Worku Naol Worku 3
  • 1 Department of Electrical and Computer Engineering, Dire Dawa University, Dire Dawa, Ethiopia
  • 2 Department of Electrical and Computer Engineering, Mattu University, Mattu, 318, Ethiopia, Mattu, Ethiopia
  • 3 Department of Electrical and Computer Engineering, Dire Dawa, Ethiopia

The final, formatted version of the article will be published soon.

    The integration of terahertz (THz) communication with cell-free massive multiple-input multiple-output (CFMM) systems presents a promising strategy to enhance energy efficiency and reduce system complexity in future wireless networks. However, this integration faces significant challenges, such as dynamic and unpredictable channel behavior. Traditional channel estimation techniques are inadequate for handling these dynamic conditions. To address these issues, a convolutional neural network (CNN)-based hybrid precoding scheme is proposed for CFMM systems operating at THz frequencies. This method leverages CNN to predict optimal precoding weights, significantly improving the adaptability of hybrid precoding. The CNN-based model not only mitigates pilot contamination (PC) but also enhances channel estimation by capturing temporal and spatial dynamics. Simulation results indicate that the CNN-based approach achieves superior energy efficiency and lower system complexity compared to conventional techniques. At a signal-to-noise ratio (SNR) of 30 dB, it achieves 1.2 bits per joule and reduces system complexity to 1400 FLOPs, demonstrating better scalability and resource optimization. These findings highlight the potential of CNN-based hybrid precoding to revolutionize THz communication in next-generation wireless networks by optimizing energy efficiency and system complexity.

    Keywords: Cell-free massive MIMO, Convolutional Neural Networks, energy efficiency, hybrid precoding, Terahertz communication

    Received: 07 Aug 2024; Accepted: 04 Nov 2024.

    Copyright: © 2024 Anbese, Abera, Gebremedhin and Worku. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yitbarek Anbese, Department of Electrical and Computer Engineering, Dire Dawa University, Dire Dawa, Ethiopia
    Tadele Abera, Department of Electrical and Computer Engineering, Mattu University, Mattu, 318, Ethiopia, Mattu, Ethiopia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.