A Q-Learning-Based Approach for Distributed Beam Scheduling in mmWave Networks

Conference · Thu Dec 15 04:00:00 EST 2022

DOI:https://doi.org/10.1109/DySPAN53946.2021.9677150· OSTI ID:1885963

Zhang, Xiang ^[1]; Sarkar, Shamik ^[1]; Kasera, Sneha ^[1]; Ji, Mingyue ^[1]; Bhuyan, Arupjyoti ^[2]

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1. University of Utah
2. Idaho National Laboratory

We consider the problem of distributed downlink beam scheduling and power allocation for millimeter-Wave (mmWave) cellular networks where multiple base stations (BSs) belonging to different service operators share the same unlicensed spectrum with no centralized control or explicit coordination among them. Our goal is to design efficient distributed beam scheduling and power allocation schemes such that the network-level payoff, defined as the weighted sum of the total throughput and a power penalization term, can be maximized. To this end, we propose a distributed scheduling approach to power allocation and adaptation for efficient interference management over the shared spectrum by modeling each BS as an independent Q-learning agent. As a baseline, we compare the proposed approach to the state-of-the-art non-cooperative game-based approach which was previously developed for the same problem. We conduct extensive experiments under various scenarios to verify the effect of multiple factors on the performance of both approaches. Experiment results show that the proposed approach adapts well to different interference conditions by learning from experience and thus can achieve higher payoff than the game-based approach. The proposed approach can also be integrated into our previously developed Lyapunov stochastic optimization framework for the purpose of network utility maximization with optimality guarantee. As a result, the weights in the payoff function can be automatically and optimally determined by the virtual queue values from the sub-problems derived from the Lyapunov optimization framework.

• Research Organization: Idaho National Laboratory (INL), Idaho Falls, ID (United States)
• Sponsoring Organization: LDRD; USDOE Office of Nuclear Energy (NE)
• DOE Contract Number: AC07-05ID14517
• OSTI ID: 1885963
• Report Number(s): INL/CON-21-64906-Rev000
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
Distributed Scheduling
Q-learning
Reinforcement Learning