Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems

By Xiongbin Rao, Vincent Kin Nang Lau. IEEE Transactions on Signal Processing, 2014

669 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/221f0b5455de014314e0a8b18ae6789bdfa43138

How researchers see this paper

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In Biomedical Engineering

1. Geoffrey Y. Li
2. Zhen Gao
3. F. Gao
4. Shun Zhang
5. David Neumann
6. Gye-Tae Gil
7. Chao-Kai Wen
8. Thomas Wiese

In Computational Mechanics

1. Zhijin Qin
2. Deniz Gündüz
3. Geoffrey Y. Li
4. Zhen Gao
5. F. Gao
6. Shun Zhang
7. Zhiguo Ding
8. David Neumann

In Signal Processing

1. Zhijin Qin
2. Linglong Dai
3. Shi Jin
4. Chao-Kai Wen
5. Geoffrey Y. Li
6. F. Gao
7. Zhen Gao
8. Shun Zhang

Citations in the corpus

• Channel Estimation via Orthogonal Matching Pursuit for Hybrid MIMO Systems in Millimeter Wave Communications (2016, methodology): “Rademacher random variables [12], [13].”
• Deep Learning for Massive MIMO CSI Feedback (2017, methodology): “This concept has inspired the establishment of CSI feedback protocols based on CS [3] and distributed compressive channel estimation [4].”
• Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO (2015, methodology): “Both the J-OMP based CSI acquisition scheme [15] and the DSAMP algorithm used the fixed G = 15.”
• A Unified Transmission Strategy for TDD/FDD Massive MIMO Systems With Spatial Basis Expansion Model (2017, methodology): “matrices, whereas the one in [17] is based on the sparsity pattern.”
• Convolutional Neural Network-Based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis (2019, background): “In [14], a hidden joint sparsity structure in the user channel matrices has been found and exploited due to the shared local scatterers.”
• A Comprehensive Survey of Pilot Contamination in Massive MIMO—5G System (2016, methodology): “Several schemes have been proposed for possible deployment of massive MIMO using FDD [16], [53], [71], [76], [79]–[82] but are not discussed here because it is outside the scope of this survey.”
• Machine Learning in the Air (2019, background): “More recently, compressive sensing has been considered to exploit the sparse structure of the underlying channel in a transform domain [39], [40].”
• Deep Learning-Based CSI Feedback Approach for Time-Varying Massive MIMO Channels (2018, methodology): “These methods sparsify CSI under certain bases to apply CS for feedback and reconstruction [2] or distributed compressive channel estimation [4].”
• Nonorthogonal Multiple Access for 5G and Beyond (2017, methodology): “By exploiting the sparsity of the user’s channel matrix, compressive sensing (CS) can be invoked for reducing the pilot-overhead [142], as well as for reducing the feedback overhead transmitted from the downlink receivers to the BS.”
• Learning the MMSE Channel Estimator (2017, methodology): “For example, in [4] and [9], methods from the area of compressive sensing are used to approximate the channel vector as a linear combination of k steering vectors where k is much smaller than the number of antennas M .”
• Cascaded Channel Estimation for Large Intelligent Metasurface Assisted Massive MIMO (2019, background): “ayleigh fading assumption, the LIM-user channel matrix Gis generated from CN(0;1). The array structures at the BS and the LIM are assumed to be a half-wavelength uniform linear array (ULA). Following [12], [13], [18], the BS-LIM channel matrix His generated by H= XR r=1 ra L( r)a H B (# r) (25) wher”