Integrated Sensing and Communications in Downlink FDD MIMO without CSI Feedback

Abstract

We present a comprehensive framework for integrated sensing and communications (ISAC) in downlink frequency-division duplex (FDD) MIMO systems that operates without requiring channel state information (CSI) feedback. This work combines rate-splitting multiple access (RSMA) techniques with sophisticated error covariance estimation methods to address the complex non-linear eigenvalue problems inherent in joint sensing and communication optimization.

Key Contributions

• ISAC Framework: Novel integrated sensing and communications approach for FDD MIMO systems
• RSMA Integration: Seamless integration of rate-splitting multiple access with sensing capabilities
• Error Covariance Estimation: Advanced techniques for covariance matrix estimation without CSI feedback
• Non-linear Eigenvalue Solutions: Novel approaches to solving complex non-linear eigenvalue problems in ISAC
• OFDM Integration: Efficient integration with orthogonal frequency-division multiplexing (OFDM) systems

Technical Highlights

• Joint Optimization: Simultaneous optimization of sensing and communication performance
• Array Signal Processing: Advanced array signal processing techniques for enhanced sensing capabilities
• Interference Management: Sophisticated interference mitigation strategies
• Precoding Design: Novel precoding techniques for dual-purpose sensing and communication

Keywords

MIMO, Integrated sensing and communication, Rate-splitting multiple access, Error covariance estimation, Non-linear eigenvalue problem, OFDM, Array signal processing, Precoding, Interference management

Applications

This research has significant implications for future wireless systems, particularly in applications requiring simultaneous communication and sensing capabilities such as autonomous vehicles, smart cities, and IoT networks.