Version 1
: Received: 14 July 2024 / Approved: 15 July 2024 / Online: 16 July 2024 (05:16:10 CEST)
How to cite:
Bhuyan, M.; Sarma, K. K.; Misra, D. D.; Guha, K.; Iannacci, J. Adaptive Hybrid Beamforming Codebook Design using Multi-Agent Reinforcement Learning for Multiuser MIMO Systems. Preprints2024, 2024071166. https://doi.org/10.20944/preprints202407.1166.v1
Bhuyan, M.; Sarma, K. K.; Misra, D. D.; Guha, K.; Iannacci, J. Adaptive Hybrid Beamforming Codebook Design using Multi-Agent Reinforcement Learning for Multiuser MIMO Systems. Preprints 2024, 2024071166. https://doi.org/10.20944/preprints202407.1166.v1
Bhuyan, M.; Sarma, K. K.; Misra, D. D.; Guha, K.; Iannacci, J. Adaptive Hybrid Beamforming Codebook Design using Multi-Agent Reinforcement Learning for Multiuser MIMO Systems. Preprints2024, 2024071166. https://doi.org/10.20944/preprints202407.1166.v1
APA Style
Bhuyan, M., Sarma, K. K., Misra, D. D., Guha, K., & Iannacci, J. (2024). Adaptive Hybrid Beamforming Codebook Design using Multi-Agent Reinforcement Learning for Multiuser MIMO Systems. Preprints. https://doi.org/10.20944/preprints202407.1166.v1
Chicago/Turabian Style
Bhuyan, M., Koushik Guha and Jacopo Iannacci. 2024 "Adaptive Hybrid Beamforming Codebook Design using Multi-Agent Reinforcement Learning for Multiuser MIMO Systems" Preprints. https://doi.org/10.20944/preprints202407.1166.v1
Abstract
This paper presents a novel approach to designing beam codebooks for downlink multiuser hybrid multiple input multiple output (MIMO) wireless communication systems, leveraging multi-agent reinforcement learning (MARL). The primary objective is to develop an environment-specific beam codebook composed of non-interfering beams, learned by cooperative agents within the MARL framework. Machine learning (ML) based beam codebook design for downlink communications have been based on channel state information (CSI) feedback or only reference signal received power (RSRP) consisting of an offline training and user clustering phase. In massive MIMO, the full CSI feedback data is of large size and is resource-intensive to process, making it challenging to implement efficiently. RSRP alone for a stand-alone base station is not a good marker of the position of a receiver. Hence, in this work, uplink CSI estimated at the base station along with feedback of RSRP and binary acknowledgment of the accuracy of received data is utilized to design the beamforming codebook at the base station. Simulations using sub-array antenna and ray-tracing channel demonstrate the proposed system’s ability to learn topography-aware beam codebook for arbitrary beams serving multiple user groups simultaneously. The proposed method extends beyond mono-lobe and fixed beam architectures by dynamically adapting arbitrary shaped beams to avoid inter-beam interference, enhancing overall system performance. This work leverages MARL’s potential in creating efficient beam codebooks for hybrid MIMO systems, paving the way for enhanced multiuser communication in future wireless networks.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
