AI-Enhanced FPGA-Based Software Defined Radio for Real-Time Spectrum Sensing and Adaptive Jamming Mitigation

Abstract Current wireless environments are characterized by severe spectrum congestion and deliberate interference, exposing the limitations of traditional radio systems. This article presents an AI-enhanced software-defined radio (SDR) architecture implemented entirely on a field-programmable gate array and deployed on the PYNQ-Z2 platform, designed to perform real-time spectrum sensing and adaptive jamming mitigation. The proposed architecture implements an end-to-end processing chain comprising a 2048-point fast Fourier transform (FFT), log-power feature extraction, and a low-precision quantized neural network executed on the programmable logic with minimal processor involvement. Experimental results show an end-to-end latency of 2.1 ms, a classification accuracy of 94.5% at 5 dB signal-to-noise ratio, a jammer detection probability of 96.3% at a false-alarm rate of 0.05, and an energy consumption of 1.8 mJ per inference. Additional hardware security measures further enhance resilience against timing side-channel attacks and unauthorized access. These findings confirm that the proposed solution constitutes a compact, energy-efficient, and autonomous radio platform suitable for next-generation embedded defense systems.