Optimizing Network Performance in $\kappa-\mu$ Fading and Co-Channel Interference Environments using Machine Learning and Experimental Design

ML and RL are one of the innovative solutions to optimize network performance in K-J.1 fading and co-channel interference scenarios where traditional methods often fail. ML employs data driven, adaptive methods to minimize signal degradation and interference while significantly enhancing throughput, SNR (signal-to-noise ratio) and overall system performance. The manner in which the transmission power and the antenna configuration should be set can be indeed determined by factorial experiments (FEs) additionally to RSM [together with what it is called, response surface methodology. RL algorithms make use of the RL for dynamic parameter updates and maximize cumulative rewards at each time-step. My main takeaway from this work is that machine learning can be used to facilitate reliable and efficient communication in challenging wireless environments, which could be applicable beyond the setting considered here. Additional insights into scalability will be needed to generalize ML-driven optimization across different network scenarios.