TY  - INPR
A1  - Zhang, Jianjun
A1  - Masouros, Christos
A1  - Liu, Fan
A1  - Huang, Yongming
A1  - Swindlehurst, A Lee
KW  - Dual-functional radar-communication
KW  -  symbollevel precoding
KW  -  recursive optimization
KW  -  sequential optimization
KW  - 
low-complexity design
KW  -  integrated sensing and communication
JF  - IEEE Journal of Selected Topics in Signal Processing
UR  - https://doi.org/10.1109/jstsp.2024.3522787
PB  - Institute of Electrical and Electronics Engineers (IEEE)
SN  - 1932-4553
N2  - 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.
ID  - discovery10204693
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
AV  - public
Y1  - 2025/01/13/
SP  - 1
EP  - 16
TI  - Low-Complexity Joint Radar-Communication Beamforming: From Optimization to Deep Unfolding
ER  -