This paper studies beam bandwidth partitioning problem in mobile millimeter-wave (mmWave) and multiple antennas networks. The main novelty is to flexibly optimize the beamforming bandwidth with the aim to minimize the energy consumption of the system while guaranteeing the data requirements of all mobile users. We formulate the problem as an integer nonlinear programming problem. To efficiently solve the problem, we design a deep reinforcement learning using the proximal policy optimization approach and train a deep neural network in an on-policy manner. Then, for comparison purposes, we develop low-complexity online iterative accurate solutions. We show that our approach achieves better performance compared to the iterative solutions and is able to achieve at least 4% less energy consumption and more than 12% energy efficiency gains.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Online Energy-Efficient Beam Bandwidth Partitioning in mmWave Mobile Networks


    Contributors:


    Publication date :

    2024-10-07


    Size :

    5357814 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    MmWave Vehicular Beam Alignment Leveraging Online Learning

    Xian, Qingyang / Doufexi, Angela / Armour, Simon | IEEE | 2023




    Robust Beam Tracking Algorithm for mmWave MIMO Systems in Mobile Environments

    Kim, Seonyong / Han, Hyungsik / Kim, Namshik et al. | IEEE | 2019


    System Level Simulation of mmWave Based Mobile Xhaul Networks

    Min, Kyungsik / Jung, Minchae / Shin, Seiyun et al. | IEEE | 2017