Leveraging Bi-Directional Channel Reciprocity for Robust Ultra-Low-Rate Implicit CSI Feedback with Deep Learning

2025-07-16

AI Summary

Implicit channel state information (CSI) feedback in massive MIMO systems suffers from degraded reconstruction accuracy and poor environmental adaptability under ultra-low feedback rates. Method: We propose an uplink-aided deep learning feedback framework. It incorporates a bidirectional correlation enhancement module and an input-format alignment module to explicitly model uplink-downlink CSI reciprocity and ensure consistent data distribution between encoder and decoder. Additionally, we integrate angular-delay domain sparsity priors with a Transformer-based architecture to achieve efficient feature-space projection and compression. Contribution/Results: The framework enhances cross-scenario robustness without incurring additional overhead. Compared to state-of-the-art methods, it reduces feedback overhead by 85% while significantly improving CSI reconstruction accuracy—especially in unseen environments. This work establishes a new, highly efficient, and generalizable paradigm for ultra-low-rate implicit CSI feedback.

Technology Category

• Machine Learning: Multimodal Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Deep learning-based implicit channel state information (CSI) feedback has been introduced to enhance spectral efficiency in massive MIMO systems. Existing methods often show performance degradation in ultra-low-rate scenarios and inadaptability across diverse environments. In this paper, we propose Dual-ImRUNet, an efficient uplink-assisted deep implicit CSI feedback framework incorporating two novel plug-in preprocessing modules to achieve ultra-low feedback rates while maintaining high environmental robustness. First, a novel bi-directional correlation enhancement module is proposed to strengthen the correlation between uplink and downlink CSI eigenvector matrices. This module projects highly correlated uplink and downlink channel matrices into their respective eigenspaces, effectively reducing redundancy for ultra-low-rate feedback. Second, an innovative input format alignment module is designed to maintain consistent data distributions at both encoder and decoder sides without extra transmission overhead, thereby enhancing robustness against environmental variations. Finally, we develop an efficient transformer-based implicit CSI feedback network to exploit angular-delay domain sparsity and bi-directional correlation for ultra-low-rate CSI compression. Simulation results demonstrate successful reduction of the feedback overhead by 85% compared with the state-of-the-art method and robustness against unseen environments.

Problem

• Enhance spectral efficiency in massive MIMO systems
• Address performance degradation in ultra-low-rate scenarios
• Improve adaptability across diverse environments

Innovation

• Bi-directional correlation enhancement for CSI feedback
• Input format alignment without extra overhead
• Transformer-based network exploiting angular-delay sparsity