Enhancing Signal Transmission in Multi-Band Communication Networks Using Optimized Deep Reinforcement Learning

The rapid expansion of multi-band communication networks necessitates efficient resource management strategies to optimize signal transmission and ensure reliable communication. This paper presents an innovative approach for enhancing signal transmission in multi-band communication networks through the integration of optimized Deep Reinforcement Learning (DRL) for band assignment and beam management. DRL is employed to dynamically allocate frequency bands and manage beamforming techniques in real-time, with objective of minimizing interference and maximizing data throughput. Phase Shift Keying (PSK) is a digital modulation and demodulation technique used in modern communication systems. PSK modulation is used for its bandwidth efficiency and resilience to noise. PSK demodulation is used to recover the transmitted data by estimating the phase of received signal in each band. Particle Swarm Optimization (PSO) is a strong and effective optimization, and it improves many parts of multi-band communication systems, such as channel equalization, interference management, power allocation, and resource allocation. The simulation is implemented in MATLAB Software, and it achieved the lowest Bit Error Rate (BER) of 0.01% in comparison to traditional optimization techniques.