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Article detail · 2020

DeepMUSIC: Multiple Signal Classification via Deep Learning

Journal

IEEE Sensors Letters
OpenAlex Open access · green SJR Q2 Citations 253 Top 1% Percentile 99.4% FWCI 13.86
Year
2020
DOI
10.1109/lsens.2020.2980384
Type
article

Data source split

  • YÖKSİS venue IEEE Sensors Letters
  • OpenAlex OpenAlex enrichment (abstract, citations, topics)

OpenAlex record

Abstract

OpenAlex · English

This letter introduces a deep learning (DL) framework for the classification of multiple signals in direction finding (DF) scenario via sensor arrays. Previous works in DL context mostly consider a single or two target scenario, which is a strong limitation in practice. Hence, in this letter, we propose a DL framework called DeepMUSIC for multiple signal classification. We design multiple deep convolutional neural networks (CNNs), each of which is dedicated to a subregion of the angular spectrum. Each CNN learns the MUltiple SIgnal Classification (MUSIC) spectra of the corresponding angular subregion. Hence, it constructs a nonlinear relationship between the received sensor data and the angular spectrum. We have shown, through simulations, that the proposed DeepMUSIC framework has superior estimation accuracy and exhibits less computational complexity in comparison with both DL- and non-DL-based techniques.

Topics

Citations

OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.

253 citations

OpenAlex cited_by_count (cache / database)

13 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).

  1. Terahertz-Band Joint Ultra-Massive MIMO Radar-Communications: Model-Based and Model-Free Hybrid Beamforming 2021 Citations 180 · OpenAlex
  2. Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO 2022 Citations 145 · OpenAlex
  3. Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO 2022 Citations 145 · OpenAlex
  4. Federated Learning for Channel Estimation in Conventional and IRS-Assisted Massive MIMO 2020 Citations 49 · OpenAlex
  5. Sparse Array Selection Across Arbitrary Sensor Geometries With Deep Transfer Learning 2021 Citations 32 · OpenAlex
  6. Antenna Selection With Beam Squint Compensation for Integrated Sensing and Communications 2024 Citations 19 · OpenAlex
  7. Millimeter-Wave Radar Beamforming with Spatial Path Index Modulation Communications 2023 Citations 12 · OpenAlex
  8. Deep Sparse Array Design for Integrated Sensing and Communications 2023 Citations 5 · OpenAlex
  9. Sparse Array Design for Direction Finding Using Deep Learning 2023 Citations 3 · OpenAlex
  10. Federated Learning for Wireless Communications 2024 Citations 2 · OpenAlex

Authors

No author information.