Distributed Compressive CSIT Estimation and Feedback for FDD Multi-user Massive MIMO Systems
arXiv:1405.2786 · doi:10.1109/TSP.2014.2324991
Abstract
To fully utilize the spatial multiplexing gains or array gains of massive MIMO, the channel state information must be obtained at the transmitter side (CSIT). However, conventional CSIT estimation approaches are not suitable for FDD massive MIMO systems because of the overwhelming training and feedback overhead. In this paper, we consider multi-user massive MIMO systems and deploy the compressive sensing (CS) technique to reduce the training as well as the feedback overhead in the CSIT estimation. The multi-user massive MIMO systems exhibits a hidden joint sparsity structure in the user channel matrices due to the shared local scatterers in the physical propagation environment. As such, instead of naively applying the conventional CS to the CSIT estimation, we propose a distributed compressive CSIT estimation scheme so that the compressed measurements are observed at the users locally, while the CSIT recovery is performed at the base station jointly. A joint orthogonal matching pursuit recovery algorithm is proposed to perform the CSIT recovery, with the capability of exploiting the hidden joint sparsity in the user channel matrices. We analyze the obtained CSIT quality in terms of the normalized mean absolute error, and through the closed-form expressions, we obtain simple insights into how the joint channel sparsity can be exploited to improve the CSIT recovery performance.
16 double-column pages, accepted for publication in IEEE Transactions on Signal Processing
References in corpus (2)
Cited by in corpus (13)
- Distributed Compressive CSIT Estimation and Feedback for FDD Multi-user Massive MIMO Systems
- Channel Estimation in Massive MIMO Systems
- Super-Resolution mmWave Channel Estimation using Atomic Norm Minimization
- Beamspace Channel Estimation in mmWave Systems via Cosparse Image Reconstruction Technique
- Efficient Downlink Channel Probing and Uplink Feedback in FDD Massive MIMO Systems
- Channel Estimation for TDD/FDD Massive MIMO Systems with Channel Covariance Computing
- Truncated Polynomial Expansion Downlink Precoders and Uplink Detectors for Massive MIMO
- High-Dimensional CSI Acquisition in Massive MIMO: Sparsity-Inspired Approaches
- Efficient Feedback Mechanisms for FDD Massive MIMO under User-level Cooperation
- Low-Rank Covariance-Assisted Downlink Training and Channel Estimation for FDD Massive MIMO Systems
- Sparse Channel Estimation for Massive MIMO with 1-bit Feedback per Dimension
- Atomic Norm Denoising-Based Joint Channel Estimation and Faulty Antenna Detection for Massive MIMO
- Cell Coverage Extension with Orthogonal Random Precoding for Massive MIMO Systems