IEEE/ACM Transactions on Networking (TON)
Efficient fair queueing using deficit round-robin
IEEE/ACM Transactions on Networking (TON)
Fair scheduling in wireless packet networks
IEEE/ACM Transactions on Networking (TON)
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Neuro-Dynamic Programming
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Enhancing throughput over wireless LANs using channel state dependent packet scheduling
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 3
Soft QoS provisioning using the token bank fair queuing scheduling algorithm
IEEE Wireless Communications
Providing quality of service over a shared wireless link
IEEE Communications Magazine
Opportunistic transmission scheduling with resource-sharing constraints in wireless networks
IEEE Journal on Selected Areas in Communications
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This paper studies the packet scheduling problem in Broadband Wireless Access (BWA) networks. The key difficulties of the BWA scheduling problem lie in the high variability of wireless channel capacity and the unknown model of packet arrival process. It is difficult for traditional heuristic scheduling algorithms to handle the situation and guarantee satisfying performance in BWA networks. In this paper, we introduce learning-based approach for a better solution. Specifically, we formulate the packet scheduling problem as an average cost Semi-Markov Decision Process (SMDP). Then, we solve the SMDP by using reinforcement learning. A feature-based linear approximation and the Temporal-Difference learning technique are employed to produce a near optimal solution of the corresponding SMDP problem. The proposed algorithm, called Reinforcement Learning Scheduling (RLS), has in-built capability of self-training. It is able to adaptively and timely regulate its scheduling policy according to the instantaneous network conditions. Simulation results indicate that RLS outperforms two classical scheduling algorithms and simultaneously considers: (i) effective QoS differentiation, (ii) high bandwidth utilization, and (iii) both short-term and longterm fairness.