In this paper, we introduce a reinforcement learning (RL) framework aimed at optimizing power allocation within 5G Radio Access Network (RAN) systems. The framework addresses critical issues such as high path loss, interference, and restricted coverage in high-frequency bands. Utilizing an Actor-Critic model, our approach allows base stations to dynamically modify their transmission power based on real-time environmental conditions. This technique improves network performance by balancing the objectives of increasing throughput and minimizing power consumption. Experimental results reveal a 19% increase in throughput across multiple base stations during rainy conditions, highlighting the efficacy of our solution. To bolster the security and integrity of the RL-driven optimization, we incorporate blockchain technology into the framework. This addition safeguards the RL model against potential cyber threats, ensuring that power allocation decisions are secure and tamper-proof. The proposed framework is compatible with various modern RAN architectures, including cloud, virtual, and open RAN systems (CRAN, VRAN, and O-RAN), making it a scalable and adaptable solution for future communication networks. By integrating reinforcement learning with blockchain, our approach provides a robust, efficient, and secure method for optimizing power distribution in next-generation wireless networks.

https://ieeexplore.ieee.org/document/11101552