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

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Tekbiyik K., Yurduseven O., Kurt G. K.

IEEE COMMUNICATIONS LETTERS, vol.26, no.3, pp.562-566, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 26 Issue: 3
  • Publication Date: 2022
  • Doi Number: 10.1109/lcomm.2021.3135325
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.562-566
  • Keywords: Direction-of-arrival estimation, Estimation, Aperture antennas, Channel estimation, Transfer functions, Antenna radiation patterns, Receiving antennas, Metasurface, compressive sensing, coded-aperture, graph attention networks, direction-of-arrival estimation, CHANNEL CHARACTERIZATION, COMMUNICATION
  • Istanbul Technical University Affiliated: Yes


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.