AI-Driven Adaptive Beamforming and Resource Management for 6G Wireless Networks: A Multimodal Machine Learning Approach to Ultra-Reliable Low-Latency Communication

Abstract

The advent of 6G wireless networks introduces unprecedented challenges in delivering ultra-high throughput, ultra-reliable low-latency communication (URLLC), and massive connectivity, particularly in millimeter-wave (mmWave) and terahertz (THz) frequency bands. Traditional RF engineering approaches, while foundational, face limitations in adapting quickly to the dynamic and complex wireless environments inherent to 6G. This paper presents a novel integration of advanced machine learning techniques focusing on reinforcement learning and multimodal deep neural networks with cutting-edge RF beamforming and resource allocation strategies. Leveraging real-world and high-fidelity synthetic datasets, including DeepMIMO channel models and ns-3-based network simulations, we develop and evaluate an AI-driven adaptive beamforming framework that dynamically optimizes antenna array configurations and spectrum resources in response to environmental variability. Experimental results demonstrate significant improvements in throughput, latency reduction, and energy efficiency compared to baseline heuristics, achieving up to 30% enhancement in communication reliability under realistic channel conditions. This work not only bridges the gap between theoretical AI methodologies and practical RF systems but also offers a scalable, interpretable solution poised to accelerate the deployment of intelligent 6G networks. The findings provide a roadmap for future research in AI-native wireless communications and establish a foundation for ultra-reliable, intelligent resource management for next-generation connectivity.