A Joint Radar-Communication Precoding Design Based on Cramér-Rao Bound Optimization

Fan Liu; Ya Feng Liu; Christos Masouros; Ang Li; Yonina C. Eldar

doi:10.1109/RadarConf2248738.2022.9764187

A Joint Radar-Communication Precoding Design Based on Cramér-Rao Bound Optimization

Fan Liu, Ya Feng Liu, Christos Masouros, Ang Li, Yonina C. Eldar

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper investigates joint radar-communication (JRC) transmission, where a JRC precoder is designed to simultaneously perform target sensing and information signaling. We minimize the Cramér-Rao Bound (CRB) for target estimation, while guaranteeing the per-user signal-to-interference-plus-noise ratio (SINR) in the downlink. While the formulated problem is non-convex in general, we propose an efficient successive convex approximation (SCA) method, which solves a second-order cone program (SOCP) subproblem at each iteration. Numerical results demonstrate the effectiveness of the proposed JRC precoding design, showing that the SCA algorithm is able to approach the convex relaxation bound, which significantly outperforms conventional benchmark solvers in terms of both complexity and performance.

Original language	English
Journal	Proceedings of the IEEE Radar Conference
DOIs	https://doi.org/10.1109/RadarConf2248738.2022.9764187
State	Published - 2022
Externally published	Yes
Event	2022 IEEE Radar Conference, RadarConf 2022 - New York City, United States Duration: 21 Mar 2022 → 25 Mar 2022

Keywords

Cramér-Rae bound
Joint radar-communication
semidefinite relaxation
successive convex approximation

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Signal Processing
Instrumentation

Access to Document

10.1109/RadarConf2248738.2022.9764187

Cite this

@article{78c23006e69e435b8d30c16bd4d10da4,

title = "A Joint Radar-Communication Precoding Design Based on Cram{\'e}r-Rao Bound Optimization",

abstract = "This paper investigates joint radar-communication (JRC) transmission, where a JRC precoder is designed to simultaneously perform target sensing and information signaling. We minimize the Cram{\'e}r-Rao Bound (CRB) for target estimation, while guaranteeing the per-user signal-to-interference-plus-noise ratio (SINR) in the downlink. While the formulated problem is non-convex in general, we propose an efficient successive convex approximation (SCA) method, which solves a second-order cone program (SOCP) subproblem at each iteration. Numerical results demonstrate the effectiveness of the proposed JRC precoding design, showing that the SCA algorithm is able to approach the convex relaxation bound, which significantly outperforms conventional benchmark solvers in terms of both complexity and performance.",

keywords = "Cram{\'e}r-Rae bound, Joint radar-communication, semidefinite relaxation, successive convex approximation",

author = "Fan Liu and Liu, {Ya Feng} and Christos Masouros and Ang Li and Eldar, {Yonina C.}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Radar Conference, RadarConf 2022 ; Conference date: 21-03-2022 Through 25-03-2022",

year = "2022",

doi = "10.1109/RadarConf2248738.2022.9764187",

language = "الإنجليزيّة",

journal = "Proceedings of the IEEE Radar Conference",

issn = "1097-5764",

}

TY - JOUR

T1 - A Joint Radar-Communication Precoding Design Based on Cramér-Rao Bound Optimization

AU - Liu, Fan

AU - Liu, Ya Feng

AU - Masouros, Christos

AU - Li, Ang

AU - Eldar, Yonina C.

PY - 2022

Y1 - 2022

N2 - This paper investigates joint radar-communication (JRC) transmission, where a JRC precoder is designed to simultaneously perform target sensing and information signaling. We minimize the Cramér-Rao Bound (CRB) for target estimation, while guaranteeing the per-user signal-to-interference-plus-noise ratio (SINR) in the downlink. While the formulated problem is non-convex in general, we propose an efficient successive convex approximation (SCA) method, which solves a second-order cone program (SOCP) subproblem at each iteration. Numerical results demonstrate the effectiveness of the proposed JRC precoding design, showing that the SCA algorithm is able to approach the convex relaxation bound, which significantly outperforms conventional benchmark solvers in terms of both complexity and performance.

AB - This paper investigates joint radar-communication (JRC) transmission, where a JRC precoder is designed to simultaneously perform target sensing and information signaling. We minimize the Cramér-Rao Bound (CRB) for target estimation, while guaranteeing the per-user signal-to-interference-plus-noise ratio (SINR) in the downlink. While the formulated problem is non-convex in general, we propose an efficient successive convex approximation (SCA) method, which solves a second-order cone program (SOCP) subproblem at each iteration. Numerical results demonstrate the effectiveness of the proposed JRC precoding design, showing that the SCA algorithm is able to approach the convex relaxation bound, which significantly outperforms conventional benchmark solvers in terms of both complexity and performance.

KW - Cramér-Rae bound

KW - Joint radar-communication

KW - semidefinite relaxation

KW - successive convex approximation

UR - http://www.scopus.com/inward/record.url?scp=85146198882&partnerID=8YFLogxK

U2 - 10.1109/RadarConf2248738.2022.9764187

DO - 10.1109/RadarConf2248738.2022.9764187

M3 - مقالة من مؤنمر

SN - 1097-5764

JO - Proceedings of the IEEE Radar Conference

JF - Proceedings of the IEEE Radar Conference

T2 - 2022 IEEE Radar Conference, RadarConf 2022

Y2 - 21 March 2022 through 25 March 2022

ER -

A Joint Radar-Communication Precoding Design Based on Cramér-Rao Bound Optimization

Abstract

Keywords

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this