Application of Intelligent Antenna Selection Based on Reinforcement Learning in 5G Communication

With the rapid development of 5G communication technology, smart antenna selection has become a key technology to improve network performance and resource utilization. However, in complex wireless environments, due to factors such as user distribution, interference, and channel changes, how to efficiently select the appropriate antenna remains a difficult problem that needs to be solved. To this end, this paper proposes a reinforcement learning-based approach to achieve dynamic smart antenna selection, aiming to improve the coverage, throughput, and spectral efficiency of 5G networks. This study constructs a reinforcement learning model based on Q-learning, designs a reward function for different antenna configurations and channel states, and optimizes the antenna selection strategy by training the model. The specific steps include: first, evaluating the system status based on real-time channel information and user needs; then, using the Q-learning algorithm to select the best antenna configuration based on the current status; and then, evaluating the selection results and adjusting the strategy based on actual performance feedback. The effectiveness of this method is verified through simulation experiments. Compared with the traditional rule-based antenna selection method, the antenna selection strategy based on reinforcement learning improves the system throughput by 20.36%, the network coverage by 2.5%, and the average data rate of users by 22.18%. This method shows good adaptability under different channel conditions and can cope with complex wireless environments and dynamic changes of users. It can be concluded that the antenna selection method based on reinforcement learning can significantly improve the performance of 5G communication systems and has strong application potential. Especially in highly dynamic and large-scale network environments, it can effectively solve the limitations that traditional methods cannot adapt to, and provide a feasible solution for future 5G and its evolved networks.