Integrated Sensing and Communications in Downlink FDD MIMO without CSI Feedback

TL;DR

This paper introduces a novel precoding framework for FDD MIMO systems that reconstructs downlink CSI from uplink signals, optimizing spectral efficiency and sensing performance without CSI feedback.

Contribution

It proposes a CSI reconstruction method from uplink signals, combined with a rate-splitting precoder optimization, to enhance FDD ISAC system performance.

Findings

Achieves precise beam pattern control.

Maximizes spectral efficiency.

Improves sensing-communication trade-off.

Abstract

In this paper, we propose a precoding framework for frequency division duplex (FDD) integrated sensing and communication (ISAC) systems with multiple-input multiple-output (MIMO). Specifically, we aim to maximize ergodic sum spectral efficiency (SE) while satisfying a sensing beam pattern constraint defined by the mean squared error (MSE). Our method reconstructs downlink (DL) channel state information (CSI) from uplink (UL) training signals using partial reciprocity, eliminating the need for CSI feedback. To obtain the error covariance matrix of the reconstructed DL CSI, we devise an observed Fisher information-based estimation technique. Leveraging this, to mitigate interference caused by imperfect DL CSI reconstruction and sensing operations, we propose a rate-splitting multiple access (RSMA) aided precoder optimization method. This method jointly updates the precoding vector and Lagrange multipliers by solving the nonlinear eigenvalue problem with eigenvector dependency to maximize SE. The numerical results show that the proposed design achieves precise beam pattern control, maximizes SE, and significantly improves the sensing-communication trade-off compared to the state-of-the-art methods in FDD ISAC scenarios.