Dynamic Bandwidth Allocation in MIMO 5G Networks

The advent of 5 G technology has ushered in a new era of wireless communication, characterized by its promise of high data rates, low latency, and enhanced connectivity. In this context, Multiple-Input Multiple-Output (MIMO) systems have emerged as a key enabler, leveraging advanced antenna arrays to simultaneously serve multiple users with increased spectral efficiency. This paper investigates the dynamic resource allocation problem in a MIMO 5 G environment, where each user possesses distinct bandwidth requirements. The focus is on optimizing user allocation while considering the limited bandwidth and user capacity of base stations. By harnessing the power of deep learning techniques, the proposed solution aims to efficiently manage the allocation of users to base station antennas, thereby maximizing overall network performance while accommodating heterogeneous user demands.