Design of a Hybrid Deep Learning and Reinforcement Learning Model for Enhancing the Performance of Modern Communication Networks

Abstract

Sophisticated and adaptive resource-management functions are critical to ensure high performance in dynamic traffic and network conditions of modern communication networks. In this paper, we present a hybrid Deep Learning (DL) and Reinforcement Learning (RL) framework on, specifically constructed through the use of Deep Q-Network (DQN), for optimal resource management solutions in 5G and beyond-5G communication networks. To provide a usable framework, we combine Deep Learning applied to network-state analysis feature extraction and performance prediction with DQN utilized for adaptive decision-making and resource allocation. The assessment includes throughput latency packet loss resource consumption Quality of Service (QoS) and forecasting accuracy along with low medium and high traffic volumes. We compare our framework with canonical DL-only and RL-only baselines. For example, the simulated estimates from the illustrative analysis indicate that the hybrid approach can provide improvements in terms of throughput reduce latency and packet loss improve resource utilization and maintain higher quality of service (QoS) under different traffic patterns. Statistical Analysis The differences between the studied approaches are also evaluated through statistical analysis. The suggested framework serves as a theoretically sound foundation for smart resource management and adaptation while results from an actual simulator-generated model should be used for empirical validation.