Multi-agent DRL for distributed codebook design in RIS-aided cell-free massive MIMO networks
Multi-agent DRL for distributed codebook design in RIS-aided cell-free massive MIMO networks
This paper proposes an innovative approach for enhancing network capacity and coverage by integrating cell-free massive multiple-input multiple-output (CF-mMIMO) networks with reconfigurable intelligent surfaces (RISs). A significant challenge in leveraging RIS-assisted CF-mMIMO lies in the cooperative beam training across multiple access points (APs) and RISs, complicated by the passive nature of reflective elements and the complexity channel state information (CSI) acquisition in millimeter wave mMIMO systems. To address these challenges, we develop a multi-agent deep reinforcement learning (MA-DRL) framework that jointly designs beamforming and reflection codebooks for distributed APs and RISs, eliminating the need for CSI and relying solely on received power measurements feedback. The joint beamforming and reflection codebook design problem is decomposed into two sub-problems: one for beam codebook design at APs and another for sequential reflection codebook design at RISs. We employ transfer learning to speed up learning convergence and reduce computational complexity for training multiple RISs. Additionally, we introduce an AP and RIS selection scheme that improves overall energy efficiency and reduces backhaul overhead. Extensive simulations demonstrate that our proposed MA-DRL approach curtails number of beams significantly, thereby outperforming the widely adopted discrete Fourier transform (DFT) codebooks by achieving an 84% reduction in beam training overhead. Our findings suggest that increasing the number of passive RISs allows putting more APs into idle mode, leading to substantial savings in hardware and energy costs.
cell-free, Deep reinforcement learning, massive MIMO, mmWave, multi-agent, transfer learning
3283-3297
Abdallah, Asmaa
86b80268-48be-4bc8-9577-c989e496e459
Celik, Abdulkadir
f8e72266-763c-4849-b38e-2ea2f50a69d0
Mansour, Mohammad M.
d26c1cf6-ff88-4871-9999-624781b0de3b
Eltawil, Ahmed M.
5eb9e965-5ec8-4da1-baee-c3cab0fb2a72
May 2025
Abdallah, Asmaa
86b80268-48be-4bc8-9577-c989e496e459
Celik, Abdulkadir
f8e72266-763c-4849-b38e-2ea2f50a69d0
Mansour, Mohammad M.
d26c1cf6-ff88-4871-9999-624781b0de3b
Eltawil, Ahmed M.
5eb9e965-5ec8-4da1-baee-c3cab0fb2a72
Abdallah, Asmaa, Celik, Abdulkadir, Mansour, Mohammad M. and Eltawil, Ahmed M.
(2025)
Multi-agent DRL for distributed codebook design in RIS-aided cell-free massive MIMO networks.
IEEE Transactions on Communications, 73 (5), .
(doi:10.1109/TCOMM.2024.3483041).
Abstract
This paper proposes an innovative approach for enhancing network capacity and coverage by integrating cell-free massive multiple-input multiple-output (CF-mMIMO) networks with reconfigurable intelligent surfaces (RISs). A significant challenge in leveraging RIS-assisted CF-mMIMO lies in the cooperative beam training across multiple access points (APs) and RISs, complicated by the passive nature of reflective elements and the complexity channel state information (CSI) acquisition in millimeter wave mMIMO systems. To address these challenges, we develop a multi-agent deep reinforcement learning (MA-DRL) framework that jointly designs beamforming and reflection codebooks for distributed APs and RISs, eliminating the need for CSI and relying solely on received power measurements feedback. The joint beamforming and reflection codebook design problem is decomposed into two sub-problems: one for beam codebook design at APs and another for sequential reflection codebook design at RISs. We employ transfer learning to speed up learning convergence and reduce computational complexity for training multiple RISs. Additionally, we introduce an AP and RIS selection scheme that improves overall energy efficiency and reduces backhaul overhead. Extensive simulations demonstrate that our proposed MA-DRL approach curtails number of beams significantly, thereby outperforming the widely adopted discrete Fourier transform (DFT) codebooks by achieving an 84% reduction in beam training overhead. Our findings suggest that increasing the number of passive RISs allows putting more APs into idle mode, leading to substantial savings in hardware and energy costs.
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More information
e-pub ahead of print date: 17 October 2024
Published date: May 2025
Keywords:
cell-free, Deep reinforcement learning, massive MIMO, mmWave, multi-agent, transfer learning
Identifiers
Local EPrints ID: 505742
URI: http://eprints.soton.ac.uk/id/eprint/505742
ISSN: 0090-6778
PURE UUID: 148010ab-38af-4902-a528-1e784c3ba0a2
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Date deposited: 17 Oct 2025 16:35
Last modified: 18 Oct 2025 02:18
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Contributors
Author:
Asmaa Abdallah
Author:
Abdulkadir Celik
Author:
Mohammad M. Mansour
Author:
Ahmed M. Eltawil
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