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

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 b...

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Vydané v:IEEE Global Communications Conference (Online) s. 1314 - 1319
Hlavní autori: Li, Xinyang, Andrei, Vlad C., Monich, Ullrich J., Boche, Holger
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 04.12.2023
Predmet:
Bayes methods
Bayesian Cramér-Rao bound
Convergence
Integrated sensing and communications
MIMO-OFDM
Parameter estimation
Sensors
Simulation
Symbols
System analysis and design
ISSN:2576-6813
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Popis
Shrnutí: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.
ISSN:2576-6813
DOI:10.1109/GLOBECOM54140.2023.10437293