Collaborative Beamforming with DQN for Interference Mitigation in 5G and Beyond Networks

This paper addresses the problem of side lobe interference in 5G networks by proposing a unique collaborative beamforming strategy based on Deep Q-Network (DQN) reinforcement learning. Our method, which operates in the sub-6 GHz band, maximizes beam steering and power management by using a two-antenna system with DQN-controlled phase shifters. We provide an OFDM cellular network environment where inter-cell interference is managed while many base stations serve randomly dispersed customers. In order to reduce interference strength and improve signal-to-interference-plus-noise ratio (SINR), the DQN agent learns to modify the interference angle. Our model integrates experience replay memory with a long short-term memory (LSTM) recurrent neural network for time series prediction to enhance learning stability. The outcomes of our simulations show that our suggested DQN approach works noticeably better than current DQN and Q-learning methods. In particular, our technique reaches a maximum of 29.18 dB and a minimum of 5.15 dB, whereas the other approaches only manage 0.77–27.04 dB. Additionally, we significantly decreased the average interference level to 5.42 dB compared to competing approaches of 38.84 dB and 34.12 dB. The average sum-rate capacity is also increased to 3.90 by the suggested strategy, outperforming previous approaches. These findings demonstrate how well our cooperative beamforming method reduces interference and improves overall network performance in 5G systems.