Optimization of Wireless QoS using Deep Learning in Multi-User MIMO Environments

Anil Premji Bhushan

Title:
Optimization of Wireless QoS using Deep Learning in Multi-User MIMO Environments

Authors:
Anil Premji Bhushan

Cite This Article :

Anil Premji Bhushan "Optimization of Wireless QoS using Deep Learning in Multi-User MIMO Environments" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-2, April 2025, pp.1304-1313, URL: https://www.ijtsrd.com/papers/ijtsrd79798.pdf

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Abstract :
In the era of next-generation wireless communication, the demand for enhanced data rates, reliability, and Quality of Service (QoS) has catalyzed the adoption of Multi-Input Multi-Output (MIMO) systems. This paper presents a comprehensive performance analysis of MIMO systems using various modulation schemes over Additive White Gaussian Noise (AWGN) and Rician fading channels. Furthermore, it integrates a deep learning-based optimization framework to enhance QoS in multi-user MIMO environments. Simulation results demonstrate how different modulation techniques BPSK, QPSK, 16-QAM, and 64-QAM perform under both AWGN and Rician channels, and how neural networks can predict optimal resource allocation to minimize Bit Error Rate (BER) and latency. The work offers valuable insights into modulation performance and deep learning-driven QoS optimization in modern wireless networks.

Keywords :
MIMO, AWGN Channel, Rician Channel, Modulation Schemes, Deep Learning, Wireless QoS, Multi-User MIMO, BER, Neural Networks

Publication Details:

Unique Identification Number : IJTSRD79798

Published In : Volume-9 | Issue-2, April 2025

Page Number(s) : 1304-1313

Publisher Name : IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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