Evaluation of Intelligent 6G wireless networks with unification of Massive MIMO, mm Wave, and Deep Reinforcement Learning

Abstract

The current wireless communication is experiencing rising data rate demands, extremely low latency, and efficient connections in a wide range of applications, which leads to a viable migration from the fifth-generation (5G) communication system to the sixth-generation (6G) system. The paper explores the potential of 6G networks to enhance the speed of data transfer through an analysis of Multi-Input Multi-Output (MIMO), Millimeter Wave (mmWave), and Artificial Intelligence (AI). Massive MIMO increases spectral efficiency with the use of massive arrays of antennas. Using high frequencies that have a large bandwidth, mmWave communication provides speedier communication. Even so, mmWave has to deal with issues due to path loss and blockage of the signal. These will be dealt with using beamforming and AI. In this research work, these limitations are overcome by integrating Deep Reinforcement Learning (DRL) using a Deep Q-Network (DQN), Tools for dynamic management of network resources, to better predict traffic and optimize real-time network slicing. When these technologies work together, the combination will significantly improve networks in 6G networks applications for autonomous systems, immersive virtual and augmented reality, holographic communication, etc. This is done in a hybrid setting where MATLAB/Simulink is used to analyze Massive MIMO and beamforming analysis, and the NS-3 tool is used for the mmWave module to evaluate end-to-end 6G network performance. The outcomes confirm that the proposed 6G framework has a 35 to 40 percent increase in spectral efficiency, and 25-30% decrease in end-to-end latency as compared to a 5G communication system. The results demonstrate that Massive MIMO, mmWave, and DQN-based optimization can be jointly applied to provide a robust architecture that can support new 6G applications, including autonomous vehicles, immersive extended reality, and holographic communications. The research presents the improvements in terms of performance, defines the barriers to the implementation, and suggests the possible solutions, which are a valuable input in the design of intelligent and future-wise wireless networks.