Self-Organizing Networks: A Packet Scheduling Approach for Coverage/Capacity Optimization in 4G Networks Using Reinforcement Learning

The next generation mobile networks LTE and LTE-A are all-IP based networks. In such IP based networks, the issue of Quality of Service (QoS) is becoming more and more critical with the increase in network size and heterogeneity. In this paper, a Reinforcement Learning (RL) based framework for QoS enhancement is proposed. The framework achieves the coverage/capacity optimization by adjusting the scheduling strategy. The proposed self-optimization algorithm uses coverage/capacity compromise in Packet Scheduling (PS) to maximize the capacity of an eNB subject to the condition that minimum coverage constraint is not violated. Each eNB has an associated agent that dynamically changes the scheduling parameter value of an eNB. The agent uses the RL technique of Fuzzy Q-Learning (FQL) to learn the optimal scheduling parameter. The learning framework is designed to operate in an environment with varying traffic, user positions, and propagation conditions. A comprehensive analysis on the obtained simulation results is presented, which shows that the proposed approach can significantly improve the network coverage as well as capacity in terms of throughput.