AI-driven Convergent Channel Allocation for 7G Mobile Networks: A Study
Keywords:
7G, Artificial intelligence, Channel allocation, Mobile networks, Terahertz spectrumAbstract
The advent of 7G mobile networks heralds an era of unprecedented demands for ultra-high data rates (Tbps), near-zero latency (microseconds), and massive, ubiquitous connectivity, encompassing holographic communication, pervasive AI, and Integrated Sensing-Communication-Computation (ISCC). Traditional, rigid channel allocation schemes are fundamentally inadequate to manage the hyper-dynamic, heterogeneous, and multi-band (THz, mmWave, sub-6GHz, VLC, NTN) environments characteristic of 7G. This study explores and proposes a paradigm shift towards AI-driven convergent channel allocation, leveraging advanced machine learning techniques such as Deep Reinforcement Learning (DRL), Federated Learning (FL), and Graph Neural Networks (GNNs). Our envisioned framework enables proactive, cognitive, and intent-driven resource management, dynamically optimising spectral efficiency, minimising interference across diverse spectrum bands, ensuring fairness, and enhancing energy efficiency in real-time. By fostering a self-organising and self-optimising network fabric, AI-based allocation moves beyond reactive adjustments to anticipate traffic spikes, propagation changes, and user mobility patterns. These abstract highlights the imperative of AI in orchestrating seamless spectrum access, facilitating dynamic spectrum sharing, and supporting adaptive beamforming for intelligent surfaces, thereby paving the way for truly intelligent, immersive, and resilient 7G ecosystems.
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