RippleSense: Scalable and Efficient Wideband Spectrum Sensing

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Abstract

Dynamic Spectrum Sharing (DSS) is essential for optimizing spectrum utilization in modern wireless systems, but it requires high analog-to-digital converter (ADC) sampling rates, leading to increased costs and power consumption. Existing sub-Nyquist sampling techniques partially address these issues but struggle with dense spectrum adaptability, real-time performance, and efficiency. This paper presents RippleSense, a scalable and efficient wideband spectrum sensing approach capable of capturing GHz of densely occupied spectrum at sub-Nyquist ADC sampling rates. A novel sub-Nyquist sampling method is introduced by injecting distinct signatures into observed signals over different Nyquist zones before sampling, allowing programmatically reconstructing the full spectrum even after the Nyquist zones are folded to baseband due to inadequate ADC sampling rates. To showcase the scalability of this approach, a high-performance, multi-GHz spectrum sensing platform is implemented together with a highly parallelizable reconstruction algorithm that can process the data stream in real-time. Experimental evaluation has shown that the proposed approach supports operating configurations with signal-to-noise ratios as low as –10 dB and time resolutions down to 10 ns, enabling the capture of single radar pulses across bandwidths of up to 10 GHz using our prototype.

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Repository Structure

RippleSense/
├── hardware/
│   ├── rippler/        # Ripple injection circuit (Arduino driver + schematics)
│   └── rfsoc/          # Modified RFSoC4x2 receive pipeline (bitstream + notebook)
└── software/
    ├── simulation/     # End-to-end emulation for process understanding
    ├── overtheair/     # Over-the-air signal reconstruction (DC–10 GHz)
    └── performance/    # Single-core, multi-core, and GPU-accelerated reconstruction

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Hardware

The prototype consists of two components:

1. Rippler — a ripple injection circuit that superimposes distinct low-frequency signatures onto each Nyquist zone prior to ADC sampling. See hardware/rippler/.

2. RFSoC4x2 Receive Pipeline — a custom FPGA bitstream for the AMD RFSoC4x2 board that captures the down-folded spectrum in real-time. See hardware/rfsoc/.

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Software

1. Simulation — a self-contained end-to-end emulation to understand the sensing and reconstruction pipeline. See software/simulation/.

2. Over-the-Air Reconstruction — processes captures from the RFSoC4x2 to reconstruct the full DC–10 GHz spectrum:

   python3 reduce.py
   python3 reconstruct.py
   python3 plot.py

   See software/overtheair/.

3. Performance — benchmarks and optimizations for single-core, multi-core, and GPU execution. See software/performance/.

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Citation

If you use RippleSense in your research, please cite:

@inproceedings{10.1145/3774906.3800487,
author = {Kuster, Andreas and Zhang, Yanbo and Li, Mo},
title = {RippleSense: Scalable and Efficient Wideband Spectrum Sensing},
year = {2026},
isbn = {9798400723094},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3774906.3800487},
doi = {10.1145/3774906.3800487},
abstract = {Dynamic Spectrum Sharing (DSS) is essential for optimizing spectrum utilization in modern wireless systems, but it requires high analog-to-digital converter (ADC) sampling rates, leading to increased costs and power consumption. Existing sub-Nyquist sampling techniques partially address these issues but struggle with dense spectrum adaptability, real-time performance, and efficiency. This paper presents RippleSense, a scalable and efficient wideband spectrum sensing approach capable of capturing GHz of densely occupied spectrum at sub-Nyquist ADC sampling rates. A novel sub-Nyquist sampling method is introduced by injecting distinct signatures into observed signals over different Nyquist zones before sampling, allowing programmatically reconstructing the full spectrum even after the Nyquist zones are folded to baseband due to inadequate ADC sampling rates. To showcase the scalability of this approach, a high-performance, multi-GHz spectrum sensing platform is implemented together with a highly parallelizable reconstruction algorithm that can process the data stream in real-time. Experimental evaluation has shown that the proposed approach supports operating configurations with signal-to-noise ratios as low as –10 dB and time resolutions down to 10 ns, enabling the capture of single radar pulses across bandwidths of up to 10 GHz using our prototype.},
booktitle = {Proceedings of the 2026 ACM/IEEE International Conference on Embedded Artificial Intelligence and Sensing Systems},
pages = {1330–1342},
numpages = {13},
keywords = {Wireless communication, spectrum sensing, signal processing, wideband, signal injection},
location = {},
series = {SenSys '26}
}

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License

This project is licensed under the MIT License. See LICENSE for details.