Reinforcement learning-based model for the prevention of beam-forming vector attacks on massive MIMO system

Massive multiple-input multiple-output (MMIMO) is a WiFi access technique studied and investigated in response to the worldwide bandwidth bottleneck in the WiFi telecommunication industry. Massive MIMO, which brings multiple antennae to transmission and reception to deliver excellent spectrum and power effectiveness with comparatively simple computation, is among the leading fundamental technologies for next-generation networking. For such a practical implementation of 5G—and further, that networks will realize many implementations of the smart sensor device—it is essential to gain a greater understanding of such a massive MIMO model to address its underlying problems. Because of the significant achievements of reinforcement learning (RL) and deep learning (DL), new and potent techniques are now available to help MIMO telecommunication networks deal with problems. This paper presents a thorough analysis of the convergence among the two fields, emphasizing RL and DL methods for MIMO networks. Throughout this article, a framework for RL-based beam-forming vector assault defense has been presented (reinforcement learning). Its outcomes demonstrated acceptable efficiency as well as the anticipated outcome.