Integrated Sensing and Communications in Downlink FDD MIMO without CSI Feedback

2024-12-17

arXiv.org

AI Summary

In FDD MIMO ISAC systems, the absence of downlink channel state information (CSI) feedback hinders effective trade-offs between communication and sensing performance. Method: We propose a feedback-free joint precoding framework: (i) reconstruct downlink CSI leveraging uplink training signals and partial channel reciprocity; (ii) estimate the CSI error covariance via observed Fisher information; and (iii) model CSI mismatch as an optimization variable, integrating rate-splitting multiple access (RSMA) with a nonlinear eigenvalue optimization—driven by Karush–Kuhn–Tucker (KKT) conditions and explicitly dependent on eigenvectors—to jointly enhance spectral efficiency and sensing beam accuracy. Contribution/Results: The framework achieves high-fidelity beam pattern control under mean-square-error (MSE) constraints while maximizing ergodic sum spectral efficiency, outperforming existing FDD ISAC schemes significantly.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Applications

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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 mitigate interference caused by imperfect DL CSI reconstruction and sensing operations, we adopt rate-splitting multiple access (RSMA). We observe that the error covariance matrix of the reconstructed channel effectively compensates for CSI imperfections, affecting both communication and sensing performance. To obtain this, we devise an observed Fisher information-based estimation technique. We then optimize the precoder by solving the Karush-Kuhn-Tucker (KKT) conditions, jointly updating the precoding vector and Lagrange multipliers, and 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.

Problem

• Maximize spectral efficiency in FDD MIMO ISAC systems
• Reconstruct downlink CSI without feedback using partial reciprocity
• Optimize precoding to balance sensing and communication performance

Innovation

• Reconstructs DL CSI from UL signals
• Uses Fisher info for error covariance
• Optimizes RSMA precoder for SE