DYNAMIC SPECTRUM MANAGEMENT: TRADITIONAL APPROACHES VS. AI-DRIVEN REAL-TIME ALLOCATION IN MILITARY SYSTEMS

Abstract

Efficient spectrum management is critical to address the growing demands of wireless communications, particularly in congested and contested environments such as military operations. Traditional spectrum management methods, based on fixed allocations and centralized control, have proven insufficient due to underutilization, inefficiency, and limited adaptability. This paper presents a comprehensive comparison between traditional and AI-driven approaches to Dynamic Spectrum Management (DSM), with emphasis on real-time adaptability and interference mitigation. We review classical allocation models, early cognitive radio techniques, and recent AI- based advances in spectrum sensing, allocation, and interference management. The core contribution of this work is introduction of a novel methodology for AI-driven real-time spectrum allocation in military systems, integrating supervised learning for spectrum prediction and reinforcement learning for autonomous decision-making. We propose specific AI-based strategies, including multi-agent coordination, proactive interference prediction, adaptive power control, and adversarial robust learning, to enhance resilience against both unintentional interference and hostile jamming. This research strongly suggests that AI-driven DSM can significantly improve spectrum utilization, reduce interference, and provide mission-critical reliability in military communication systems, while offering a roadmap for broader adoption in civilian and regulatory contexts.