High Accuracy RF Modulation Recognition using Low-Dimensional Encoder-based SNN

Real-time modulation recognition is crucial for modern communication systems in various cognitive radio tasks. While prior works have employed deep learning techniques to address this challenge, few are feasible for real-time applications. Spiking Neural Networks (SNNs) present a promising alternative to conventional deep learning approaches, enabling low-power hardware implementations. However, existing SNN-based modulation recognition methods often lag behind traditional techniques or necessitate high sample rate implementations. This work introduces an SNN architecture that utilizes a low-resolution quantizer in the receiver and operates at a lower rate than the quantizer, resulting in significant area and power savings when integrated into a system. We experimentally determine the optimal quantizer resolution and the ratio of quantizer-to-SNN rate. The optimized network achieves an average classification accuracy of 68.45% on the RadioML2018.01A dataset, utilizing a 4-bit quantizer and running at a rate 16 times lower than the quantizer. This performance is comparable to conventional neural networks and surpasses that of previous spiking-based methods, especially at low signal-to-noise ratio (SNR) conditions.