Abstract
Due to large-scale control problems in 5G access networks, the complexity of radioresource management is expected to increase significantly. Reinforcement learning is seen as apromising solution that can enable intelligent decision-making and reduce the complexity of differentoptimization problems for radio resource management. The packet scheduler is an importantentity of radio resource management that allocates users’ data packets in the frequency domainaccording to the implemented scheduling rule. In this context, by making use of reinforcementlearning, we could actually determine, in each state, the most suitable scheduling rule to be employedthat could improve the quality of service provisioning. In this paper, we propose a reinforcementlearning-based framework to solve scheduling problems with the main focus on meeting the userfairness requirements. This framework makes use of feed forward neural networks to map momentarystates to proper parameterization decisions for the proportional fair scheduler. The simulation resultsshow that our reinforcement learning framework outperforms the conventional adaptive schedulersoriented on fairness objective. Discussions are also raised to determine the best reinforcement learningalgorithm to be implemented in the proposed framework based on various scheduler settings.
Highlights
In next-generation wireless access networks, the Quality of Service (QoS) provisioning is more challenging due to much tighter application requirements and very high heterogeneity of use cases, services, and functionalities [1]
Simulation results are obtained by using the Resource Management (RRM)-Scheduler simulator [4], a C++ tool that inherits the Long Term Evolution Simulator (LTE-Sim) [35] by implementing additional functions such as advanced state-of-the-art Orthogonal Frequency Division Multiple Access (OFDMA) schedulers, Reinforcement Learning (RL) algorithms used in different scheduling problems, neural network approximation for RL decisions, and Channel Quality Indicator (CQI) compression schemes
This paper proposes a reinforcement learning framework able to adapt the generalized proportional scheduling rule in each state with the purpose of improving the fraction of scheduling time when the qualitative Next Generation of Mobile Networks (NGMN) fairness requirement is met
Summary
In next-generation wireless access networks, the Quality of Service (QoS) provisioning is more challenging due to much tighter application requirements and very high heterogeneity of use cases, services, and functionalities [1]. Radio Resource Management (RRM) will reach a substantial proportion [2] To this extent, machine learning-based hybrid solutions are seen as very promising tools that can enable intelligent and flexible RRM decisions in order to meet the complexity requirements and to enhance the quality of decision-making for a wide variety of networking conditions [3]. At each Transmission Time Interval (TTI), the packet scheduler allocates, in the frequency domain, the user’s packets in order to meet the stringent QoS requirements in terms of bit rate, delay, and packet loss rate [4] Among these targets, the fairness performance is an important objective of QoS provisioning which is not explored properly in the literature. Delivering the requested services to mobile users subject to given fairness constraints remains an important aspect to be investigated [5]
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