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A Power Allocation Framework for Holographic MIMO-Aided Energy-Efficient Cell-Free Networks
  • +3
  • Apurba Adhikary,
  • Avi Deb Raha,
  • Yu Qiao,
  • Yu Min Park,
  • Zhu Han,
  • Choong Seon Hong
Apurba Adhikary
Department of Computer Science and Engineering, Kyung Hee University

Corresponding Author:[email protected]

Author Profile
Avi Deb Raha
Department of Computer Science and Engineering, Kyung Hee University
Yu Qiao
Department of Artificial Intelligence, Kyung Hee University
Yu Min Park
Department of Computer Science and Engineering, Kyung Hee University
Zhu Han
Department of Electrical and Computer Engineering, University of Houston
Choong Seon Hong
Department of Computer Science and Engineering, Kyung Hee University

Abstract

The 6G wireless communication networks need an intelligent networking system to meet the ever-increasing demands of various applications and mobile devices to ensure power savings, energy efficiency (EE), high integration of devices, and mass connection. To achieve these aims, an artificial intelligence (AI)-based holographic MIMO (HMIMO)-aided cell-free (CF) network is suggested to allocate desired power for beamforming by activating the required number of grids from the serving HMIMOs for serving the users. An optimization problem is developed to ensure effective power allocation that maximizes the EE of the system. A Transformer-based AI framework is proposed to solve the formulated NP-hard problem that distributes desired power for serving the users by activating the required number of grids from the required number of serving HMIMOs in the CF network. Finally, simulation results represent that the proposed power allocation framework outperforms the gated recurrent unit and long short-term memory-based mechanisms, achieving a combined power savings of 12.5% and 4.06%, and a combined EE improvement of 14.68% and 8.93%, correspondingly. Therefore, our suggested AI-based framework guarantees effective power allocation for beamforming to serve the users.
04 Jun 2024Submitted to TechRxiv
07 Jun 2024Published in TechRxiv