Optimizing resource allocation in 5G networks through enhanced Particle Swarm Optimization leveraging deep deterministic policy gradient techniques

Abstract

The development of telecommunications technologies from 4G to the much-hyped 5G meant that unthinkably high data speeds with ultra-low latencies and a high level of connectivity among numerous devices would be offered. The complex and turbulent nature of 5G networks brings very challenging problems in resource allocation needing newer solutions due to fluctuations in user demands with limited resources. Indeed, the complexities have often rendered conventional resource management methodologies inefficient, hence suboptimality and, by extension, compromised network performance. Particle Swarm Optimization has established itself, emulating the evolutionary social behavior of a particular dynamic system by effectively and quickly setting solutions to a variety of optimization problems within telecommunications industries. Particle Swarm Optimization simulates the balance between flying particles in an intelligent swarm when dozens, hundreds, or thousands of particles’ combined knowledge are used to fly through extreme solution spaces during resource allocation. The most recent state of the art in deep reinforcement learning, Deep Deterministic Policy Gradients (DDPG) presents a solid framework appropriate for the current decision-making in continuous action space environments, notably applicable to the kind of dynamic scenarios present in 5G networks. In this paper a new hybrid approach between PSO and DDPG is proposed, with the objective to increase resource allocation efficiency by bringing in adaptive learning with real-time decisions based on feedback from the environment. Illuminating the awareness of the role DDPG takes in perfecting the process of resource allocation and how PSO enters into resource allocation is an objective of this study, looking forward to drawing examples in which, collectively, techniques might deal with challenging issues that are hounding resource management in the 5G network in terms of energy consumption, resource utility optimization, and strengthening the network by answering the requirements, in the future to come inter-connected.