Artificial Intelligence in Software Defined Radio: A Survey
Shadman Rahman Doha, Ahmed Abdelhadi · IEEE Access · 2026
Software-defined radio (SDR) is widely used in wireless research because it allows communication algorithms to be rapidly developed, tested, and validated over the air (OTA) using flexible hardware and software platforms, enabling rapid prototyping across the physical (PHY) and medium access control (MAC) layers. As wireless environments become increasingly dynamic and adversarial, artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to enhance adaptability and resilience in SDR-based systems, particularly in cognitive radio (CR) and wireless security. However, a gap remains in the literature: few surveys link AI/ML methods with their specific SDR implementations and provide insights into what works or does not work in practice. This survey addresses that gap by focusing on two broad domains: (i) CR, where we trace the evolution from classical SDR prototypes (energy/cyclostationary/eigenvalue sensing, spectrum mobility, MAC design) to AI-enabled approaches (shallow learning, deep networks, and reinforcement learning for access and handoff); and (ii) cybersecurity for SDR-enabled radios, where we synthesize jamming, spoofing, and eavesdropping studies from classical toolchains to AI/ML-based detection and mitigation. We compile representative SDR testbeds and toolkits, providing a structured taxonomy that emphasizes what AI/ML can alleviate (such as manual thresholding and moderate hardware drift) and what remains challenging (including wideband real-time scanning on commodity SDRs, resilience to correlated noise and impairments, as well as the burdens of dataset management and retraining). The outcome is a practical roadmap for AI in SDR, guiding researchers and practitioners from initial concepts to reproducible, hardware-supported experiments.