Multi-Antenna Communication Security with Deep Learning Network and Internet of Things

In telecommunication, the base station preserves the communication between the network and the mobile user through a radio link. Based on the frequency of the radio link, the communication series is attuned from a few meters to hundreds of kilometers. Numerous protocols provide the communication between the base station and the mobile users, very similar to client server protocols. Based on the (User Equipment’s) UE’s request, a predefined GOB function takes the UE’s request and creates a response table, with the help of a fixed beam. The GOB function has a multifaceted structure with very low performance. This paper focuses on the use of deep learning (DL) and the Internet of Things (IoT) with GOB and multi-antenna technology, for better performance, better carrier distribution, low power consumption, and better downlink and uplink throughputs. Based on artificial intelligence (AI) with a DL network, which can be injected with the base station system, it will be a model with thousands of well-trained parameters and better algorithms, offering anti-optical predict value for the carrier, beam, fault control, etc. The training data will be generated by random models. Once there is live data after deploying the model, the network will be retrained for different scenarios, to achieve better performance.