Optimal and Robust Waveform Design for MIMO-OFDM Channel Sensing: A Cram\'er-Rao Bound Perspective

TL;DR

This paper develops optimal and robust waveform design strategies for MIMO-OFDM channel sensing, maximizing estimation accuracy and robustness to uncertainties using advanced optimization techniques.

Contribution

It introduces a novel waveform design framework based on the Fisher information matrix and proposes the REPMS and SREPMS algorithms for robust optimization.

Findings

REPMS achieves similar results to SDR with faster computation.

Robust waveforms perform well under channel uncertainties.

The proposed methods improve channel parameter estimation accuracy.

Abstract

Wireless channel sensing is one of the key enablers for integrated sensing and communication (ISAC) which helps communication networks understand the surrounding environment. In this work, we consider MIMO-OFDM systems and aim to design optimal and robust waveforms for accurate channel parameter estimation given allocated OFDM resources. The Fisher information matrix (FIM) is derived first, and the waveform design problem is formulated by maximizing the log determinant of the FIM. We then consider the uncertainty in the parameters and state the stochastic optimization problem for a robust design. We propose the Riemannian Exact Penalty Method via Smoothing (REPMS) and its stochastic version SREPMS to solve the constrained non-convex problems. In simulations, we show that the REPMS yields comparable results to the semidefinite relaxation (SDR) but with a much shorter running time. Finally, the designed robust waveforms using SREMPS are investigated, and are shown to have a good performance under channel perturbations.