Complex Neural Network based Joint AoA and AoD Estimation for Bistatic ISAC

Salmane Naoumi, Ahmad Bazzi, Roberto Bomfin, Marwa Chafii

Research output: Contribution to journalArticlepeer-review

Abstract

Integrated sensing and communication (ISAC) in wireless systems has emerged as a promising paradigm, offering the potential for improved performance, efficient resource utilization, and mutually beneficial interactions between radar sensing and wireless communications, thereby shaping the future of wireless technologies. In this work, we present two novel methods to address the joint angle of arrival and angle of departure estimation problem for bistatic ISAC systems. Our proposed methods consist of a deep learning (DL) solution leveraging complex neural networks, in addition to a parameterized algorithm. By exploiting the estimated channel matrix and incorporating a preprocessing step consisting of a coarse timing estimation, we are able to notably reduce the input size and improve the computational efficiency. In our findings, we emphasize the remarkable potential of our DL-based approach, which demonstrates comparable performance to the parameterized method that explicitly exploits the multiple-input multiple-output (MIMO) model, while exhibiting significantly lower computational complexity.

Original languageEnglish (US)
Pages (from-to)1-15
Number of pages15
JournalIEEE Journal on Selected Topics in Signal Processing
DOIs
StateAccepted/In press - 2024

Keywords

  • angle of arrival (AoA) estimation
  • angle of departure (AoD) estimation
  • bistatic radar
  • DL
  • Integrated sensing and communication (ISAC)

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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