# Spectrum Shortage for Radio Sensing? Leveraging Ambient 5G Signals for Human Activity Detection

arXiv: https://arxiv.org/abs/2603.03579 · HTML: https://mlsanity.com/paper/2603.03579/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 3 Mar 2026.

## Builds on (most cited first, 20 of 20)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [DeepPose: Human Pose Estimation via Deep Neural Networks](https://mlsanity.com/paper/1312.4659/) (2013)
- [Associative Embedding: End-to-End Learning for Joint Detection and Grouping](https://mlsanity.com/paper/1611.05424/) (2016)
- [HRFormer: High-Resolution Transformer for Dense Prediction](https://mlsanity.com/paper/2110.09408/) (2021)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [Learning Feature Pyramids for Human Pose Estimation](https://mlsanity.com/paper/1708.01101/) (2017)
- [Person-in-WiFi: Fine-grained Person Perception using WiFi](https://mlsanity.com/paper/1904.00276/) (2019)
- [SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing](https://mlsanity.com/paper/2207.07859/) (2022)
- [AdaPose: Towards Cross-Site Device-Free Human Pose Estimation with Commodity WiFi](https://mlsanity.com/paper/2309.16964/) (2023)
- [Sensing as a Service in 6G Perceptive Networks: A Unified Framework for ISAC Resource Allocation](https://mlsanity.com/paper/2202.09969/) (2022)
- [Full-Duplex Communication for ISAC: Joint Beamforming and Power Optimization](https://mlsanity.com/paper/2211.00229/) (2022)
- [Waveform Design and Performance Analysis for Full-Duplex Integrated Sensing and Communication](https://mlsanity.com/paper/2108.06449/) (2021)
- [Target Localization in Cooperative ISAC Systems: A Scheme Based on 5G NR OFDM Signals](https://mlsanity.com/paper/2403.02028/) (2024)
- [An Overview of Resource Allocation in Integrated Sensing and Communication](https://mlsanity.com/paper/2305.08465/) (2023)

## Built on by (most cited first, 1 of 1)

- [Rascene: High-Fidelity 3D Scene Imaging with mmWave Communication Signals](https://mlsanity.com/paper/2604.02603/) (2026)

## Abstract (arXiv)

Radio sensing in the sub-10 GHz spectrum offers unique advantages over traditional vision-based systems, including the ability to see through occlusions and preserve user privacy. However, the limited availability of spectrum in this range presents significant challenges for deploying largescale radio sensing applications. In this paper, we introduce Ambient Radio Sensing (ARS), a novel Integrated Sensing and Communications (ISAC) approach that addresses spectrum scarcity by repurposing over-the-air radio signals from existing wireless systems (e.g., 5G and Wi-Fi) for sensing applications, without interfering with their primary communication functions. ARS operates as a standalone device that passively receives communication signals, amplifies them to illuminate surrounding objects, and captures the reflected signals using a self-mixing RF architecture to extract baseband features. This hardware innovation enables robust Doppler and angular feature extraction from ambient OFDM signals. To support downstream applications, we propose a cross-modal learning framework focusing on human activity recognition, featuring a streamlined training process that leverages an off-the-shelf vision model to supervise radio model training. We have developed a prototype of ARS and validated its effectiveness through extensive experiments using ambient 5G signals, demonstrating accurate human skeleton estimation and body mask segmentation applications.