Machine learning and IoT based MIMO-UWB Antenna Integrated with Ku-Band for Seamless Wireless Communication

Abstract

The implementation of machine learning algorithms with advanced antenna technologies, such as Multiple Input Multiple Output Ultra-Wideband -UWB- and Cup-Ban, has emerged as a powerful method for improving wireless communication systems. This research focuses on the effectiveness of applying three machine learning algorithms, which are Deep Q-Networks -DQN- , Label Propagation, and Model-Agnostic Meta-Learning (MAML). Using several experiments, we analyzed the throughput, latency, and packet loss of every algorithm. These were part of the project designed on a wireless signal combined with MIMO-UWB and Ku-Band. Nevertheless, MAML has the highest average throughput of 261.3 Mbps, and the other algorithms follow one by one: DQN with 252.5 Mbps and Label Propagation with 240.6 Mbps. As to latency, the lowest average is 9.0 ms and 11.0 ms for DQN and 14.2 ms for Label Propagation. Finally, MAML proved the least packet losses with the average value of 0.18% and DQN with 0.39% and 0.73% of Label Propagation .The results presented above indicate that by adapting more quickly to changing wireless environments, MAML effectively optimizes communication performance in terms of throughput, latency, and packet loss. Specifically, through its ability to accelerate the allocation of resources and management of networks as a whole, more frequent and rapid learning with MAML results in better system reliability and data integrity.