# Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Model

arXiv: https://arxiv.org/abs/2104.10378 · HTML: https://mlsanity.com/paper/2104.10378/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 21 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles](https://mlsanity.com/paper/2103.03786/) (2021)
- [Joint Radar and Communication Design: Applications, State-of-the-art, and the Road Ahead](https://mlsanity.com/paper/1906.00789/) (2019)
- [Doppler-Resilient 802.11ad-Based Ultra-Short Range Automotive Joint Radar-Communications System](https://mlsanity.com/paper/1902.01306/) (2019)

## Built on by (most cited first, 9 of 9)

- [Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI](https://mlsanity.com/paper/2207.00969/) (2022)
- [Task-Oriented Over-the-Air Computation for Multi-Device Edge AI](https://mlsanity.com/paper/2211.01255/) (2022)
- [Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication](https://mlsanity.com/paper/2201.08512/) (2022)
- [Accelerating Edge Intelligence via Integrated Sensing and Communication](https://mlsanity.com/paper/2107.09574/) (2021)
- [Collaborative Edge AI Inference over Cloud-RAN](https://mlsanity.com/paper/2404.06007/) (2024)
- [Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach](https://mlsanity.com/paper/2107.09621/) (2021)
- [Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning](https://mlsanity.com/paper/2306.02990/) (2023)
- [Task-oriented Over-the-air Computation for Edge-device Co-inference with Balanced Classification Accuracy](https://mlsanity.com/paper/2407.00955/) (2024)
- [Multi-Point Integrated Sensing and Communication: Fusion Model and Functionality Selection](https://mlsanity.com/paper/2208.07592/) (2022)

## Abstract (arXiv)

Human motion recognition (HMR) based on wireless sensing is a low-cost technique for scene understanding. Current HMR systems adopt support vector machines (SVMs) and convolutional neural networks (CNNs) to classify radar signals. However, whether a deeper learning model could improve the system performance is currently not known. On the other hand, training a machine learning model requires a large dataset, but data gathering from experiment is cost-expensive and time-consuming. Although wireless channel models can be adopted for dataset generation, current channel models are mostly designed for communication rather than sensing. To address the above problems, this paper proposes a deep spectrogram network (DSN) by leveraging the residual mapping technique to enhance the HMR performance. Furthermore, a primitive based autoregressive hybrid (PBAH) channel model is developed, which facilitates efficient training and testing dataset generation for HMR in a virtual environment. Experimental results demonstrate that the proposed PBAH channel model matches the actual experimental data very well and the proposed DSN achieves significantly smaller recognition error than that of CNN.