eprintid: 10204693
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/20/46/93
datestamp: 2025-02-13 09:38:00
lastmod: 2025-02-13 09:38:00
status_changed: 2025-02-13 09:38:00
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Zhang, Jianjun
creators_name: Masouros, Christos
creators_name: Liu, Fan
creators_name: Huang, Yongming
creators_name: Swindlehurst, A Lee
title: Low-Complexity Joint Radar-Communication Beamforming: From Optimization to Deep Unfolding
ispublished: inpress
divisions: UCL
divisions: B04
divisions: F46
keywords: Dual-functional radar-communication, symbollevel precoding, recursive optimization, sequential optimization,
low-complexity design, integrated sensing and communication
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: By sharing the same hardware platform, spectral resource as well as transmit waveform, dual-functional radar-communication (DFRC) systems have been envisioned as a key technology for the future wireless networks. However, advanced signal processing algorithms for DFRC, which can achieve better performance, tradeoff or other design goals, often suffer from prohibitive computational complexity. This motivates us to design low-complexity joint radar sensing and communication beamforming algorithms in this paper, so as to achieve better energy efficiency, communication-sensing tradeoff, and so on. First, we formulate the problem of joint radar-communication beamforming based on symbol-level precoding (SLP) by incorporating constructive interference so as to improve the energy efficiency. To address the formulated problem, we tailor highly parallelizable iterative optimization algorithms that are shown to converge to stationary (or locally optimal) points. To achieve better performance, we propose efficient recursive optimizations that monotonically improve the performance metric of interest. Simulation results indicate that the proposed iterative algorithms outperform the previous approaches. Finally, to further reduce the complexity, we employ deep unfolding to design efficient learning-based algorithms. Besides parallelizability, the learning-based algorithms also enjoy appealing advantages of scalability in the number of served users, the number of transmit antennas and the length of the radar pulse.
date: 2025-01-13
date_type: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
official_url: https://doi.org/10.1109/jstsp.2024.3522787
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2358011
doi: 10.1109/JSTSP.2024.3522787
lyricists_name: Masouros, Christos
lyricists_id: CMASO14
actors_name: Masouros, Christos
actors_id: CMASO14
actors_role: owner
full_text_status: public
publication: IEEE Journal of Selected Topics in Signal Processing
pagerange: 1-16
issn: 1932-4553
citation:        Zhang, Jianjun;    Masouros, Christos;    Liu, Fan;    Huang, Yongming;    Swindlehurst, A Lee;      (2025)    Low-Complexity Joint Radar-Communication Beamforming: From Optimization to Deep Unfolding.                   IEEE Journal of Selected Topics in Signal Processing     pp. 1-16.    10.1109/JSTSP.2024.3522787 <https://doi.org/10.1109/JSTSP.2024.3522787>.    (In press).    Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10204693/1/J-STSP-SPISC-00082-2023.R2_Proof_hi.pdf