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

Kürşat Tekbıyık*, Okan Yurduseven, Gunes Karabulut Kurt

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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In this letter, 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 multi-channel 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.
Original languageEnglish
Pages (from-to)562-566
Number of pages5
JournalIEEE Communications Letters
Issue number3
Publication statusPublished - 13 Dec 2021


  • Wireless communication
  • channel characterization
  • compressive sensing
  • metasurface
  • millimeter wave
  • antenna
  • Deep learning (DL)
  • machine learning


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