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Bayesian Reinforcement Learning for Beam Forming in Millimetre-Wave Networks for Multi-Target Detection in 5G Radio Resource Allocation

International Journal of Electronics and Communication Engineering
© 2025 by SSRG - IJECE Journal
Volume 12 Issue 1
Year of Publication : 2025
Authors : Phaneendra Jonnabhatla, T. Vimala
10.14445/23488549/IJECE-V12I1P114
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How to Cite?

Phaneendra Jonnabhatla, T. Vimala, "Bayesian Reinforcement Learning for Beam Forming in Millimetre-Wave Networks for Multi-Target Detection in 5G Radio Resource Allocation," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 1, pp. 174-192, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I1P114

Abstract:

Millimeter wave (mmWave) beam forming has long been explored as a key enabler of Fifth-Generation (5G) technologies. Wireless network providers face numerous challenges due to the increasing density of wireless systems, including, but not limited to, the high costs associated with fiber construction and site acquisition. To address these challenges and take advantage of the maturity of mmWave communication for wireless backhaul links, Integrated Access and Backhaul (IAB) systems have been developed for 5G networks. In the context of 5G radio distribution, Bayesian Reinforcement Learning (BRL) introduces a novel approach to beamforming in mmWave systems, optimizing multi-target identification. Optimal beamforming is crucial for maximizing network capacity and ensuring effective utilization of the radio spectrum, as mmWave frequencies offer significant capacity and high-resolution capabilities. The proposed BRL framework improves beamforming strategies by probabilistically adapting to changing network conditions and user requirements through Bayesian estimation, thereby enhancing the accuracy of multi-target identification. The system dynamically adjusts beam patterns and resource allocation in real time based on immediate feedback, optimizing both signal quality and resource utilization. Experimental results demonstrate that BRL outperforms existing methods in resource utilization and target identification reliability, thereby increasing the capacity of mmWave systems in the 5G era. Extensive simulations and numerical results show that BRL-mmWave significantly improves efficiency and accelerates training compared to previous approaches.

Keywords:

Bayesian reinforcement learning, Beamforming, Millimeter-wave networks, Multi-target detection, 5G radio resource allocation, Adaptive beamforming, Network optimization, Resource utilization.

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