# Graph Attention Network Based Single-Pixel Compressive Direction of Arrival Estimation

arXiv: https://arxiv.org/abs/2109.05466 · HTML: https://mlsanity.com/paper/2109.05466/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 12 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [Channel Estimation for Full-Duplex RIS-assisted HAPS Backhauling with Graph Attention Networks](https://mlsanity.com/paper/2010.12004/) (2020)
- [DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks](https://mlsanity.com/paper/2012.14350/) (2020)
- [Machine Learning for Beam Alignment in Millimeter Wave Massive MIMO](https://mlsanity.com/paper/2002.06362/) (2020)
- [mmRAPID: Machine Learning assisted Noncoherent Compressive Millimeter-Wave Beam Alignment](https://mlsanity.com/paper/2007.12060/) (2020)

## Abstract (arXiv)

In this paper, we present a single-pixel compressive direction of arrival (DoA) estimation technique leveraging a graph attention network (GAT)-based deep-learning framework. The physical layer compression is achieved using a coded-aperture technique, probing the spectrum of far-field sources that are incident on the aperture using a set of spatio-temporally incoherent modes. This information is then encoded and compressed into the channel of the coded-aperture. The coded-aperture is based on a metasurface antenna design and it works as a receiver, exhibiting a single-channel and replacing the conventional multichannel raster scan-based solutions for DoA estimation. The GAT network enables the compressive DoA estimation framework to learn the DoA information directly from the measurements acquired using the coded-aperture. This step eliminates the need for an additional reconstruction step and significantly simplifies the processing layer to achieve DoA estimation. We show that the presented GAT integrated single-pixel radar framework can retrieve high fidelity DoA information even under relatively low signal-to-noise ratio (SNR) levels.