Sensing-Assisted Channel Estimation for Bistatic OFDM ISAC Systems: Framework, Algorithm, and Analysis

TL;DR

This paper introduces a sensing-assisted channel estimation scheme for bistatic OFDM ISAC systems, combining a low-complexity sensing algorithm with a robust LMMSE estimator, validated through simulations to outperform existing methods especially at high SNRs.

Contribution

It proposes a novel sensing-assisted channel estimation framework that integrates low-complexity sensing with a robust LMMSE algorithm for real-time ISAC system applications.

Findings

Superior NMSE performance at high SNRs

Effective handling of sensing errors with tolerance factors

Low computational complexity compared to existing methods

Abstract

Integrated sensing and communication (ISAC) has garnered significant attention in recent years. In this paper, we delve into the topic of sensing-assisted communication within ISAC systems. More specifically, a novel sensing-assisted channel estimation scheme is proposed for bistatic orthogonal-frequency-division-multiplexing (OFDM) ISAC systems. A framework of sensing-assisted channel estimator is first developed, integrating a tailored low-complexity sensing algorithm to facilitate real-time channel estimation and decoding. To address the potential sensing errors caused by low-complexity sensing algorithms, a sensing-assisted linear minimum mean square error (LMMSE) estimation algorithm is then developed. This algorithm incorporates tolerance factors designed to account for deviations between estimated and true channel parameters, enabling the construction of robust correlation matrices for LMMSE estimation. Additionally, we establish a systematic mechanism for determining these tolerance factors. A comprehensive analysis of the normalized mean square error (NMSE) performance and computational complexity is finally conducted, providing valuable insights into the selection of the estimator's parameters. The effectiveness of our proposed scheme is validated by extensive simulations. Compared to existing methods, our proposed scheme demonstrates superior performance, particularly in high signal-to-noise ratio (SNR) regions or with large bandwidths, while maintaining low computational complexity.