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Radiofrequency signal processing with a memristive system-on-a-chip

Nature Electronics volume 8, pages 587–596 (2025)Cite this article

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Abstract

The development of wireless communication technology and the Internet of Things requires radiofrequency communication systems with higher frequencies and faster communication speeds. However, traditional digital processing platforms—which involve high-speed analogue-to-digital converters, intensive data movement and complex digital computation in software-defined radio systems—suffer from high energy consumption and latency. Signal processing in the analogue domain using non-volatile memristive devices can reduce data movement and energy consumption, but the development of system-level designs remains limited. Here we report a radiofrequency signal processing system that is based on analogue in-memory computing within a multicore memristive system-on-a-chip. With the approach, we demonstrate an analogue discrete Fourier transform for spectrum analysis, a mixer-free demodulator for in-phase and quadrature demodulation, and analogue neural networks for radiofrequency transmitter identification and anomaly detection. The memristive system-on-a-chip offers an identification accuracy of over 90% and is up to 6.8 times more energy efficient and up to 6.2 times faster than traditional digital processing platforms.

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Fig. 1: RF signal processing system via a memristive SoC.
Fig. 2: Memristive SoC and evaluation kit.
Fig. 3: Experimental results of 64-point analogue DFT.
Fig. 4: Experimental results of mixer-free in-phase and quadrature (I/Q) demodulator.
Fig. 5: RF fingerprint identification and anomaly detection.

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Data availability

The datasets for the RF transmitter identification and anomaly detection are available via GitHub at https://github.com/gxhen/LoRa_RFFI/tree/main/Openset_RFFI_TIFS and at https://cores.ee.ucla.edu/downloads/datasets/wisig/. Further data that support other finding of this study are available from the corresponding authors upon reasonable request.

Code availability

The code that supports the operation of the integrated chip for the demonstrated RF application and the plot within this Article is available from the corresponding authors upon reasonable request.

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Acknowledgements

Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-23–2–0014 (Q.X.), Office of Naval Research (N00014-23-1-2021, Q.X.), and the Texas A&M University System’s Chancellor’s Research Initiative (CRI) (L.K.). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the US Government. The US Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

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Author notes

  1. These authors contributed equally: Yi Huang, Chaoyi He.

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA, USA

    Yi Huang, Yunzhi Ling & Qiangfei Xia

  2. Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA

    Chaoyi He & Linda Katehi

  3. TetraMem Inc., San Jose, CA, USA

    Ning Ge, J. Joshua Yang, Miao Hu & Qiangfei Xia

  4. Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA

    J. Joshua Yang

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Contributions

Q.X. and L.K. conceived and led the project. Y.H. and C.H. conducted the experimental and simulation work with help from Y.L. Q.X., N.G., J.J.Y. and M.H. contributed to the evaluation kit (hardware and software). Y.H., C.H., Q.X. and L.K. wrote the article. All authors edited the paper before submission.

Corresponding authors

Correspondence to Miao Hu, Linda Katehi or Qiangfei Xia.

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Competing interests

Q.X. and J.J.Y. are co-founders and paid consultants of TetraMem. The other authors declare no competing interests.

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Nature Electronics thanks Yixiang Li, Zaichen Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Huang, Y., He, C., Ling, Y. et al. Radiofrequency signal processing with a memristive system-on-a-chip. Nat Electron 8, 587–596 (2025). https://doi.org/10.1038/s41928-025-01409-y

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