An End-To-End Neuromorphic Radio Classification System with an Efficient Sigma-Delta-Based Spike Encoding Scheme

Wenzhe Guo, Kuilian Yang, Haralampos G. Stratigopoulos, Hassan Aboushady, Khaled Nabil Salama

Research output: Contribution to journalArticlepeer-review


Rapid advancements in 5G communication and the Internet of Things have prompted the development of cognitive radio sensing for spectrum monitoring and malicious attack detection. An end-to-end radio classification system is essential to realize efficient real-time monitoring at the edge. This work presents an end-to-end neuromorphic system enabled by an efficient spiking neural network (SNN) for radio classification. A novel hardware-efficient spiking encoding method is proposed leveraging the Sigma-Delta modulation mechanism in analog-to-digital converters. It requires no additional hardware components, simplifies the system design, and helps reduce conversion latency. Following a designed hardware-emulating conversion process, the classification performance is verified on two benchmark radio modulation datasets. A comparable accuracy to an artificial neural network (ANN) baseline with a difference of 0.30% is achieved on the dataset RADIOML 2018 with more realistic conditions. Further analysis reveals that the proposed method requires less power-intensive computational operations, leading to 22× lower computational energy consumption. Additionally, this method exhibits more than 99% accuracy on the dataset when the signal-to-noise ratio is above zero dB. The SNN-based classification module is realized on FPGA with a heterogeneous streaming architecture, achieving high throughput and low resource utilization. Therefore, this work demonstrates a promising solution for constructing an efficient high-performance end-to-end radio classification system.

Original languageEnglish (US)
Pages (from-to)1-14
Number of pages14
JournalIEEE Transactions on Artificial Intelligence
StateAccepted/In press - 2023

Bibliographical note

Publisher Copyright:


  • Artificial intelligence
  • Cognitive radio
  • Cognitive radio classification
  • deep learning
  • Encoding
  • Hardware
  • IoT
  • Modulation
  • modulation recognition
  • Monitoring
  • sigma-delta modulation
  • spectrum monitoring
  • spiking neural network
  • Wireless sensor networks

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence


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