Cognitive Radio Spectrum Sensing on the Edge: A Quantization-Aware Deep Learning Approach

Abstract

Wideband spectrum sensing demands ultra-low latency and high accuracy to detect spectrum holes, yet deploying deep learning (DL)-based models on resource-constrained edge devices is challenging due to high computational costs. This letter proposes quantization-aware training (QAT) to optimize DL-based spectrum sensing models for low-power, low-memory deployment with fast inference. Using a hardware-oriented approach and data-driven quantization scaling, the models retain near-identical performance across varying signal-to-noise ratio (SNR) levels. Real-time deployment on the Sony Spresense shows 72% model size reduction, 51% faster inference, and 7% lower power consumption, confirming the feasibility of QAT-optimized models for spectrum sensing on the edge.