Optimization Method for Communication Security in Millimeter Wave and Fiber Hybrid Networks Based on Machine Learning

With 5G and 6G's rise, millimeter wave and fiber hybrid networks are gaining popularity for their high data rates and low latency. Yet, they confront significant security issues during transmission. This paper presents a machine learning-based communication security optimization method for these networks. It involves creating a channel model and applying ML algorithms to enhance channel prediction and optimization, thereby boosting transmission security and efficiency. Experiments demonstrate the method's effectiveness in lowering bit error rates, increasing signal-to-noise ratios, and bolstering the system's anti-interference capabilities.