Allocation of computing resources based on multi-objective strategy and performance improvement in 5G networks

Abstract

The increase in mobile communication has escalated network load, necessitating more intelligent resource management in 5G and forthcoming networks. This research introduces a novel methodology to enhance network performance by integrating bird swarm optimization (BSO) with deep learning (DL). We investigate a configuration in which several users inside a single cell solicit services from proximate edge servers and remote cloud servers, employing Non-Orthogonal Multiple Access (NOMA) to optimize radio spectrum utilization. BSO, influenced by avian flocking patterns, aggregates users and distributes workloads among servers to reduce energy consumption, service latency, and operational expenses. Simultaneously, DL examines historical network data to forecast traffic and user requirements, enabling BSO to make immediate, educated decisions. We utilize queuing theory to model this system, addressing a complicated optimization problem that concurrently reduces energy, latency, and costs. In contrast to conventional solutions, our technology dynamically adjusts to fluctuating network conditions, providing an effective remedy for the requirements of 5G. Simulations demonstrate that the integrated BSO-DL approach diminishes energy usage, delays, and expenses by around 54 %, substantiating its efficacy in improving broadband performance. This research facilitates the development of more efficient and sustainable 5G networks, addressing the increasing demands of contemporary mobile communication through a scalable and intelligent approach.