Machine Learning–Guided Cognitive RF System with Dynamic FFT Resolution and Multiplier Reconfiguration for Adaptive Anti-Jamming Communication - Soham Das; A S Rassel (International Journal of Radio Frequency Innovations, 2026)
This paper presents a hierarchical adaptive RF communication system that integrates signal quality-based pre- processing with machine learning-driven signal classification to achieve robust and resource-efficient operation in dynamic, interference-prone environments. Unlike prior art that addresses adaptive RF, ML classification, or anti-jamming individually, this work uniquely combines real-time SNR/RSSI-based signal strength estimation with dynamic FFT size selection (64-, 256- , or 512-point) and arithmetic-level multiplier reconfiguration (CORDIC, Distributed Arithmetic, and hybrid Booth-Wallace) prior to ML inference. A quantized 1D-CNN model deployed on an MCU classifies signals into normal, narrowband jammer, wideband jammer, or noise categories, and triggers adaptive notch filtering when jamming is detected. Experimental results on an SX-1278 LoRa-based hardware prototype demonstrate signifi- cant BER reduction under low-SNR conditions, high classification accuracy across all signal classes, and effective jammer frequency attenuation, with real-time prediction latencies as low as 1.2 ms. For low-power embedded deployments, the suggested architecture minimises hardware resource utilisation, lowers computing complexity, and further improves spectral efficiency. Furthermore, the adaptive communication architecture strengthens resistance to spectrum congestion, channel interference, and signal distortion, which makes it ideal for real-time adaptive RF monitoring applications in challenging operating environments, IoT security, and next-generation intelligent wireless communication with increased scalability and dependability.
Keywords: 1D- CNN; Adaptive RF Communication; Anti-Jamming; Booth- Wallace Multiplier; CORDIC; Cognitive Radio; Embedded DSP; FFT Reconfiguration; Machine Learning; distributed arithmetic