A PPO-Based Deep Reinforcement Learning Framework for Dynamic Resource Allocation and Network Slicing in 5G Mobile Networks
Abstract
Abstract-This study proposes a new intelligent framework to cope with the challenges involved with dynamic resource allocation in the 5G network environment based on Proximal Policy Optimization (PPO), which is one of the most successful Deep Reinforcement Learning (DRL) techniques. We have reformulated resource allocation as a Markov Decision Process (MDP). Here, the "state" represents the current status of the network in terms of demand, interference, and channel quality. At the same time, the "Action" represents the allocation decision made for each service slice in terms of spectrum, capacity, and time. The proposed model focuses on balanced dynamic resource allocation across three main segments: eMBB, URLLC, and mMTC, through ensuring that QoS requirements for each segment are met without impact to the overall system performance. Our simulation results have demonstrated excellent performance by the proposed algorithm when compared to traditional algorithms (i.e., GA, PSO, Q-Learning and Round Robin). In our results, we showed a throughput increase of approximately 180 Mbps, energy efficiency of 0.91 bps/joule, a Fairness Index of 0.88 overall performance improvement between 12% to 15%. As a result of the simulation results, we believe that the PPO-MDP Framework is a good, realistic option for optimizing the use of resources within a dynamically segmented environment, thus improving the ability of a 5G system to efficiently and sustainably respond to a variety of service demands.
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