A Comparison of Reinforcement Learning Algorithms in Fairness-Oriented OFDMA Schedulers

• Published in: Information (Basel) Vol. 10; no. 10; p. 315
• Main Authors: Comșa, Ioan-Sorin, Zhang, Sijing, Aydin, Mehmet, Kuonen, Pierre, Trestian, Ramona, Ghinea, Gheorghiță
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2019
• Subjects: Access control; Adaptation; Algorithms; Complexity; Decision making; feed forward neural networks; Machine learning; Neural networks; ofdma; Optimization; Packets (communication); Parameterization; Provisioning; Quality of service; Radio; radio resource management; reinforcement learning; Resource management; Resource scheduling; Scheduling; scheduling optimization; Wireless networks
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info10100315

Due to large-scale control problems in 5G access networks, the complexity of radio resource management is expected to increase significantly. Reinforcement learning is seen as a promising solution that can enable intelligent decision-making and reduce the complexity of different optimization problems for radio resource management. The packet scheduler is an important entity of radio resource management that allocates users’ data packets in the frequency domain according to the implemented scheduling rule. In this context, by making use of reinforcement learning, we could actually determine, in each state, the most suitable scheduling rule to be employed that could improve the quality of service provisioning. In this paper, we propose a reinforcement learning-based framework to solve scheduling problems with the main focus on meeting the user fairness requirements. This framework makes use of feed forward neural networks to map momentary states to proper parameterization decisions for the proportional fair scheduler. The simulation results show that our reinforcement learning framework outperforms the conventional adaptive schedulers oriented on fairness objective. Discussions are also raised to determine the best reinforcement learning algorithm to be implemented in the proposed framework based on various scheduler settings.