Eliminating the Barriers: Demystifying Wi-Fi Baseband Design and Introducing the PicoScenes Wi-Fi Sensing Platform
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Abstract (arXiv)
The research on Wi-Fi sensing has been thriving over the past decade but the process has not been smooth. Three barriers always hamper the research: unknown baseband design and its influence, inadequate hardware, and the lack of versatile and flexible measurement software. This paper tries to eliminate these barriers through the following work. First, we present an in-depth study of the baseband design of the Qualcomm Atheros AR9300 (QCA9300) NIC. We identify a missing item of the existing CSI model, namely, the CSI distortion, and identify the baseband filter as its origin. We also propose a distortion removal method. Second, we reintroduce both the QCA9300 and software-defined radio (SDR) as powerful hardware for research. For the QCA9300, we unlock the arbitrary tuning of both the carrier frequency and bandwidth. For SDR, we develop a high?performance software implementation of the 802.11a/g/n/ac/ax baseband, allowing users to fully control the baseband and access the complete physical-layer information. Third, we release the PicoScenes software, which supports concurrent CSI measure?ment from multiple QCA9300, Intel Wireless Link (IWL5300) and SDR hardware. PicoScenes features rich low-level controls, packet injection and software baseband implementation. It also allows users to develop their own measurement plugins. Finally, we report state-of-the-art results in the extensive evaluations of the PicoScenes system, such as the >2 GHz available spectrum on the QCA9300, concurrent CSI measurement, and up to 40 kHz and 1 kHz CSI measurement rates achieved by the QCA9300 and SDR. PicoScenes is available at https://ps.zpj.io.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Oct 2020.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 5.
10 papers.
- 2025A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospectscited by 9
- 2023Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitationscited by 8
- 2022Hands-on Wireless Sensing with Wi-Fi: A Tutorialcited by 2
- 2023Deakin RF-Sensing: Experiments on Correlated Knowledge Distillation for Monitoring Human Postures with Radioscited by 2
- 2025Cross-Domain Continual Learning for Edge Intelligence in Wireless ISAC Networkscited by 2
- 2025Noise-Robust Radio Frequency Fingerprint Identification Using Denoise Diffusion Modelcited by 2
- 2025Meta-SimGNN: Adaptive and Robust WiFi Localization Across Dynamic Configurations and Diverse Scenarioscited by 2
- 2024SAWEC: Sensing-Assisted Wireless Edge Computingcited by 0
- 2024Towards Battery-Free Wireless Sensing via Radio-Frequency Energy Harvestingcited by 0
- 2025Cross-Domain Multi-Person Human Activity Recognition via Near-Field Wi-Fi Sensingcited by 0
- 2025Meta-SimGNN: Adaptive and Robust WiFi Localization Across Dynamic Configurations and Diverse Scenarios18 Nov 2025
- 2025Cross-Domain Multi-Person Human Activity Recognition via Near-Field Wi-Fi Sensing27 Sep 2025
- 2025A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects11 Mar 2025
- 2025Noise-Robust Radio Frequency Fingerprint Identification Using Denoise Diffusion Model7 Mar 2025
- 2025Cross-Domain Continual Learning for Edge Intelligence in Wireless ISAC Networks18 Feb 2025
- 2024Towards Battery-Free Wireless Sensing via Radio-Frequency Energy Harvesting26 Aug 2024
- 2024SAWEC: Sensing-Assisted Wireless Edge Computing15 Feb 2024
- 2023Deakin RF-Sensing: Experiments on Correlated Knowledge Distillation for Monitoring Human Postures with Radios24 May 2023
- 2023Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations2 Feb 2023
- 2022Hands-on Wireless Sensing with Wi-Fi: A Tutorial20 Jun 2022