Machine Learning-Assisted Hybrid Beamforming for Spectral Efficiency and Interference Reduction in 5G and Beyond Wireless Networks

Abstract

The development of 5G and higher wireless communications systems has posed a great need on intelligent transmission techniques capable of supporting ultra-high data rates, low latency, high spectral efficiency, and dependable connectivity in dynamic networked environments. Beamforming is one of the emerging technologies that is critical in improving signal directionality and reducing interference, especially in millimeter-wave and massive multiple-input multiple-output (MIMO) communication systems. The traditional methods of beamforming are however usually challenged by issues associated with a high computational complexity, high energy usage and low adaptability to the fast-varying channel conditions. Through the research, a Hybrid Beaming Technique to enhance the aspect of performance in 5G and above communication networks using Machine Learning, combining Artificial Neural Networks (ANN) and Deep Convolutional Neural Networks (DNN) to optimize the beam selection, channel estimation, and adaptive resource allocation in the 5G and above communication networks. Within the framework proposed, ANN is used as a predictive analysis of channel state information and intelligent beam weight optimization, and CNN is used as a feature extraction of complex spatial channel patterns and interference mapping, in order to make accurate decisions of beam steering in real time. Incorporating the energy efficiency of analog beam control with the flexibility and precision of digital beam processing, the hybrid beamforming architecture forms a robust and scalable communication model. The results of the simulations have shown that the proposed ANN-DNN-based hybrid beaming technique is significantly better in terms of throughput, signal-to-interference-plus-noise ratio (SINR), spectral efficiency, and coverage reliability than traditional beamforming techniques. The framework also exhibits less beam misalignment and greater flexibility in case of user mobility and dense deployment. The proposed intelligent hybrid beaming model presents a bright solution to next-generation wireless systems, such as 5G networks, to support high-capacity, low-latency, and energy-efficient communication infrastructures to future smart and connected environments.