5G NetOptima: AI-Driven Real-Time Latency and Bandwidth Optimization for Intelligent Resource Allocation in Next-Generation Networks

Abstract

With the advent of fifth-generation (5G) networks, the world has become wireless thanks to ultra-low latency, high bandwidth, and massive connectivity that have become an essential part of applications like autonomous vehicles, industrial IoT, smart cities, and real-time multimedia streaming. Conventional fixed or intuitive-based resource allocation schemes fail to adjust well to changing network states and user demands with varied needs leading to delay of service, congestion and the poor use of spectrum. In this paper, 5G NetOptima, which is an AI-based real-time resource allocation framework, is introduced and optimizes both bandwidth and latency at the same time. The suggested system uses machine learning models predicting the intelligent allocation decisions by analyzing network parameters such as user density, traffic type, channel quality, and the priority of services continuously. Dynamic priorities are given to latency sensitive and mission critical services, whereas bandwidth utilization is optimized over the entire bandwidth to improve the Quality of Service (QoS) and Quality of Experience (QoE). The vast simulations show that 5G NetOptima is more efficient than traditional methods of the allocation, as the 5G system facilitates the reduction of latency by the significant degree, enhanced the throughput, and enhanced the load balancing throughout the network. The most important novelty of the work is related to its combined AI-oriented structure which adjusts to all network parameters in real time, provides an efficient, scalable, and intelligent solution to 5G networks of the next generation.