Enhanced beam attachment recognition for massive MIMO systems in dense distributed renewable energy networks

Abstract

Incorporating large-scale multiple-input multiple-output (MIMO) systems in densely deployed renewable energy systems (RES) represents a significant challenge in developing next-generation wireless networks. This field combines cutting-edge communication technologies with sustainable energy systems to enhance network  communication and energy management in smart grid applications. Furthermore, varying energy availability in RES-based environments and dynamic load profiles make it difficult to achieve optimal beam attachment in mmWave massive MIMO systems. Conventional beam attachment techniques perform poorly in such dynamic conditions, resulting in poor network performance and high latency. This has created the need for better and more versatile  approaches to beam attachment that can address this inherent variability of RES while at the same time providing highly accurate and low-complexity solutions. This paper presents an improved beam attachment recognition system explicitly designed to operate in RES conditions. Thus, the innovative strategy presented in this work is based on ensemble learning, which includes Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers, making the prediction more accurate and the system more stable. The proposed method integrates RES-specific signal strength, interference, traffic load, and renewable energy availability into the choice of the preferred beam. Cohesive simulations support our approach in this case. The Random Forest (RF) classifier test accuracy was 97.56%, and the XGBoost classifier was 97.84% – both of which are higher than conventional methods. Analyzing the feature importance of the problem, it was found that distance, angle, and signal strength were the most significant factors in beam assignment. The performance of the system was also very impressive in terms of scalability, with accuracy rates barely flinching even as the number of samples reached 50,000. Also, the energy efficiency analysis showed that the proposed beam attachment approach could lead to more energy-efficient network operations.