Optimal Linear Precoder Design for MIMO-OFDM Integrated Sensing and Communications Based on Bayesian Cramér-Rao Bound

Xinyang Li; Vlad C. Andrei; Ullrich J. Mönich; Holger Boche

doi:10.1109/GLOBECOM54140.2023.10437293

Optimal Linear Precoder Design for MIMO-OFDM Integrated Sensing and Communications Based on Bayesian Cramér-Rao Bound

Xinyang Li, Vlad C. Andrei, Ullrich J. Mönich, Holger Boche

Chair of Theoretical Information Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

In this paper, we investigate the fundamental limits of MIMO-OFDM integrated sensing and communications (ISAC) systems based on a Bayesian Cramér-Rao bound (BCRB) analysis. We derive the BCRB for joint channel parameter estimation and data symbol detection, in which a performance trade-off between both functionalities is observed. We formulate the optimization problem for a linear precoder design and propose the stochastic Riemannian gradient descent (SRGD) approach to solve the non-convex problem. We analyze the optimality conditions and show that SRGD ensures convergence with high probability. The simulation results verify our analyses and also demonstrate a fast convergence speed. Finally, the performance trade-off is illustrated and investigated.

Original language	English
Title of host publication	GLOBECOM 2023 - 2023 IEEE Global Communications Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1314-1319
Number of pages	6
ISBN (Electronic)	9798350310900
DOIs	https://doi.org/10.1109/GLOBECOM54140.2023.10437293
State	Published - 2023
Event	2023 IEEE Global Communications Conference, GLOBECOM 2023 - Kuala Lumpur, Malaysia Duration: 4 Dec 2023 → 8 Dec 2023

Publication series

Name	Proceedings - IEEE Global Communications Conference, GLOBECOM
ISSN (Print)	2334-0983
ISSN (Electronic)	2576-6813

Conference

Conference	2023 IEEE Global Communications Conference, GLOBECOM 2023
Country/Territory	Malaysia
City	Kuala Lumpur
Period	4/12/23 → 8/12/23

Keywords

Bayesian Cramér-Rao bound
Integrated sensing and communications
MIMO-OFDM

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10.1109/GLOBECOM54140.2023.10437293

Cite this

Li, X., Andrei, V. C., Mönich, U. J., & Boche, H. (2023). Optimal Linear Precoder Design for MIMO-OFDM Integrated Sensing and Communications Based on Bayesian Cramér-Rao Bound. In GLOBECOM 2023 - 2023 IEEE Global Communications Conference (pp. 1314-1319). (Proceedings - IEEE Global Communications Conference, GLOBECOM). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GLOBECOM54140.2023.10437293

Li, Xinyang ; Andrei, Vlad C. ; Mönich, Ullrich J. et al. / Optimal Linear Precoder Design for MIMO-OFDM Integrated Sensing and Communications Based on Bayesian Cramér-Rao Bound. GLOBECOM 2023 - 2023 IEEE Global Communications Conference. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1314-1319 (Proceedings - IEEE Global Communications Conference, GLOBECOM).

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title = "Optimal Linear Precoder Design for MIMO-OFDM Integrated Sensing and Communications Based on Bayesian Cram{\'e}r-Rao Bound",

abstract = "In this paper, we investigate the fundamental limits of MIMO-OFDM integrated sensing and communications (ISAC) systems based on a Bayesian Cram{\'e}r-Rao bound (BCRB) analysis. We derive the BCRB for joint channel parameter estimation and data symbol detection, in which a performance trade-off between both functionalities is observed. We formulate the optimization problem for a linear precoder design and propose the stochastic Riemannian gradient descent (SRGD) approach to solve the non-convex problem. We analyze the optimality conditions and show that SRGD ensures convergence with high probability. The simulation results verify our analyses and also demonstrate a fast convergence speed. Finally, the performance trade-off is illustrated and investigated.",

keywords = "Bayesian Cram{\'e}r-Rao bound, Integrated sensing and communications, MIMO-OFDM",

author = "Xinyang Li and Andrei, {Vlad C.} and M{\"o}nich, {Ullrich J.} and Holger Boche",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Global Communications Conference, GLOBECOM 2023 ; Conference date: 04-12-2023 Through 08-12-2023",

year = "2023",

doi = "10.1109/GLOBECOM54140.2023.10437293",

language = "English",

series = "Proceedings - IEEE Global Communications Conference, GLOBECOM",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1314--1319",

booktitle = "GLOBECOM 2023 - 2023 IEEE Global Communications Conference",

}

Li, X, Andrei, VC, Mönich, UJ & Boche, H 2023, Optimal Linear Precoder Design for MIMO-OFDM Integrated Sensing and Communications Based on Bayesian Cramér-Rao Bound. in GLOBECOM 2023 - 2023 IEEE Global Communications Conference. Proceedings - IEEE Global Communications Conference, GLOBECOM, Institute of Electrical and Electronics Engineers Inc., pp. 1314-1319, 2023 IEEE Global Communications Conference, GLOBECOM 2023, Kuala Lumpur, Malaysia, 4/12/23. https://doi.org/10.1109/GLOBECOM54140.2023.10437293

Optimal Linear Precoder Design for MIMO-OFDM Integrated Sensing and Communications Based on Bayesian Cramér-Rao Bound. / Li, Xinyang; Andrei, Vlad C.; Mönich, Ullrich J. et al.
GLOBECOM 2023 - 2023 IEEE Global Communications Conference. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1314-1319 (Proceedings - IEEE Global Communications Conference, GLOBECOM).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Optimal Linear Precoder Design for MIMO-OFDM Integrated Sensing and Communications Based on Bayesian Cramér-Rao Bound

AU - Li, Xinyang

AU - Andrei, Vlad C.

AU - Mönich, Ullrich J.

AU - Boche, Holger

PY - 2023

Y1 - 2023

N2 - In this paper, we investigate the fundamental limits of MIMO-OFDM integrated sensing and communications (ISAC) systems based on a Bayesian Cramér-Rao bound (BCRB) analysis. We derive the BCRB for joint channel parameter estimation and data symbol detection, in which a performance trade-off between both functionalities is observed. We formulate the optimization problem for a linear precoder design and propose the stochastic Riemannian gradient descent (SRGD) approach to solve the non-convex problem. We analyze the optimality conditions and show that SRGD ensures convergence with high probability. The simulation results verify our analyses and also demonstrate a fast convergence speed. Finally, the performance trade-off is illustrated and investigated.

AB - In this paper, we investigate the fundamental limits of MIMO-OFDM integrated sensing and communications (ISAC) systems based on a Bayesian Cramér-Rao bound (BCRB) analysis. We derive the BCRB for joint channel parameter estimation and data symbol detection, in which a performance trade-off between both functionalities is observed. We formulate the optimization problem for a linear precoder design and propose the stochastic Riemannian gradient descent (SRGD) approach to solve the non-convex problem. We analyze the optimality conditions and show that SRGD ensures convergence with high probability. The simulation results verify our analyses and also demonstrate a fast convergence speed. Finally, the performance trade-off is illustrated and investigated.

KW - Bayesian Cramér-Rao bound

KW - Integrated sensing and communications

KW - MIMO-OFDM

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T3 - Proceedings - IEEE Global Communications Conference, GLOBECOM

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BT - GLOBECOM 2023 - 2023 IEEE Global Communications Conference

PB - Institute of Electrical and Electronics Engineers Inc.

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Li X, Andrei VC, Mönich UJ, Boche H. Optimal Linear Precoder Design for MIMO-OFDM Integrated Sensing and Communications Based on Bayesian Cramér-Rao Bound. In GLOBECOM 2023 - 2023 IEEE Global Communications Conference. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1314-1319. (Proceedings - IEEE Global Communications Conference, GLOBECOM). doi: 10.1109/GLOBECOM54140.2023.10437293

Optimal Linear Precoder Design for MIMO-OFDM Integrated Sensing and Communications Based on Bayesian Cramér-Rao Bound

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