# Attention-Refined Unrolling for Sparse Sequential micro-Doppler Reconstruction

arXiv: https://arxiv.org/abs/2306.14233 · HTML: https://mlsanity.com/paper/2306.14233/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 59 references that are in the map.
- First posted on arXiv 25 Jun 2023.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing](https://mlsanity.com/paper/1912.10557/) (2019)
- [Maximal Sparsity with Deep Networks?](https://mlsanity.com/paper/1605.01636/) (2016)
- [An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing](https://mlsanity.com/paper/2102.12780/) (2021)
- [Unsupervised Learning for Human Sensing Using Radio Signals](https://mlsanity.com/paper/2207.02370/) (2022)
- [Data-driven Estimation of Sinusoid Frequencies](https://mlsanity.com/paper/1906.00823/) (2019)
- [Toward Unobtrusive In-home Gait Analysis Based on Radar Micro-Doppler Signatures](https://mlsanity.com/paper/1809.06653/) (2018)
- [Multi-Person Continuous Tracking and Identification from mm-Wave micro-Doppler Signatures](https://mlsanity.com/paper/2003.03571/) (2020)
- [DISC: a Dataset for Integrated Sensing and Communication in mmWave Systems](https://mlsanity.com/paper/2306.09469/) (2023)
- [SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network](https://mlsanity.com/paper/2106.05490/) (2021)
- [Integration of Communication and Sensing in 6G: a Joint Industrial and Academic Perspective](https://mlsanity.com/paper/2106.13023/) (2021)
- [RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing](https://mlsanity.com/paper/2109.04819/) (2021)
- [SPARCS: A Sparse Recovery Approach for Integrated Communication and Human Sensing in mmWave Systems](https://mlsanity.com/paper/2205.03263/) (2022)
- [Adaptive and Fast Combined Waveform-Beamforming Design for mmWave Automotive Joint Communication-Radar](https://mlsanity.com/paper/2009.02633/) (2020)
- [Framework for an Innovative Perceptive Mobile Network Using Joint Communication and Sensing](https://mlsanity.com/paper/1702.06531/) (2017)
- [Designing recurrent neural networks by unfolding an l1-l1 minimization algorithm](https://mlsanity.com/paper/1902.06522/) (2019)
- [Green Joint Communications and Sensing Employing Analog Multi-Beam Antenna Arrays](https://mlsanity.com/paper/2208.09782/) (2022)

## Built on by (most cited first, 2 of 2)

- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)
- [StarBOA: Real-Time Mamba State-Space Unrolling for Sparse Radar Micro-Doppler in ISAC Networks](https://mlsanity.com/paper/2609.33408/) (2026)

## Abstract (arXiv)

The reconstruction of micro-Doppler signatures of human movements is a key enabler for fine-grained activity recognition wireless sensing. In Joint Communication and Sensing (JCS) systems, unlike in dedicated radar sensing systems, a suitable trade-off between sensing accuracy and communication overhead has to be attained. It follows that the micro-Doppler has to be reconstructed from incomplete windows of channel estimates obtained from communication packets. Existing approaches exploit compressed sensing, but produce very poor reconstructions when only a few channel measurements are available, which is often the case with real communication patterns. In addition, the large number of iterations they need to converge hinders their use in real-time systems. In this work, we propose and validate STAR, a neural network that reconstructs micro-Doppler sequences of human movement even from highly incomplete channel measurements. STAR is based upon a new architectural design that combines a single unrolled iterative hard-thresholding layer with an attention mechanism, used at its output. This results in an interpretable and lightweight architecture that reaps the benefits of both model-based and data driven solutions. STAR is evaluated on a public JCS dataset of 60 GHz channel measurements of human activity traces. Experimental results show that it substantially outperforms state-of-the-art techniques in terms of the reconstructed micro-Doppler quality. Remarkably, STAR enables human activity recognition with satisfactory accuracy even with 90% of missing channel measurements, for which existing techniques fail.