Joint CSIT Acquisition Based on Low-Rank Matrix Completion for FDD Massive MIMO Systems

Abstract

Channel state information at the transmitter (CSIT) is essential for frequency-division duplexing (FDD) massive MIMO systems, but conventional solutions involve overwhelming overhead both for downlink channel training and uplink channel feedback. In this letter, we propose a joint CSIT acquisition scheme to reduce the overhead. Particularly, unlike conventional schemes where each user individually estimates its own channel and then feed it back to the base station (BS), we propose that all scheduled users directly feed back the pilot observation to the BS, and then joint CSIT recovery can be realized at the BS. We further formulate the joint CSIT recovery problem as a low-rank matrix completion problem by utilizing the low-rank property of the massive MIMO channel matrix, which is caused by the correlation among users. Finally, we propose a hybrid low-rank matrix completion algorithm based on the singular value projection to solve this problem. Simulations demonstrate that the proposed scheme can provide accurate CSIT with lower overhead than conventional schemes.

References in corpus (5)

• Scaling up MIMO: Opportunities and Challenges with Very Large Arrays
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• Distributed Compressive CSIT Estimation and Feedback for FDD Multi-user Massive MIMO Systems
• Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training with Memory
• Guaranteed Rank Minimization via Singular Value Projection

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• Channel Training for Analog FDD Repeaters: Optimal Estimators and Cramér-Rao Bounds
• Multiuser Precoding and Channel Estimation for Hybrid Millimeter Wave MIMO Systems
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