Compressive sensing based channel feedback protocols for spatially-correlated massive antenna arrays
By Ping-Heng Kuo, H. T. Kung, Pangan Ting. IEEE Wireless Communications and Networking Conference, 2012
328 citations recorded by the source corpus.
Incorporating wireless transceivers with numerous antennas (such as Massive-MIMO) is a prospective way to increase the link capacity or enhance the energy efficiency of future communication systems. However, the benefits of such approach can be realized only when proper channel information is available at the transmitter. Since the amount of the channel information required by the transmitter is large with so many antennas, the feedback is arduous in practice, especially for frequency division duplexing (FDD) systems. This paper proposes channel feedback reduction techniques based on the theory of compressive sensing, which permits the transmitter to obtain channel information with acceptable accuracy under substantially reduced feedback load. Furthermore, by leveraging properties of compressive sensing, we present two adaptive feedback protocols, in which the feedback content can be dynamically configured based on channel conditions to improve the efficiency.
Subfields: Computational Mechanics, Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/57af75fe168bf370c8b6b99dbc9f43664413e41a
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Citations in the corpus
- 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].”
- Convolutional Neural Network-Based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis (2019, methodology): “In [13], CS has been first applied to CSI feedback in the spatial-frequency domain, which exploits the high spatial correlation of CSI resulting from the limited distance among antennas in massive MIMO.”
- A Unified Transmission Strategy for TDD/FDD Massive MIMO Systems With Spatial Basis Expansion Model (2017, background): “The compressive sensing (CS)-based feedback reduction in [16] and”
- 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].”
- Spatial Modulation for Generalized MIMO: Challenges, Opportunities, and Implementation (2014, background): “For these reasons, researchers are currently investigating the design of FDD–based large–scale MIMO cellular systems with a limited training and feedback overhead [279], [283], [280], and [287].”
- Millimeter-Wave Massive MIMO Communication for Future Wireless Systems: A Survey (2018, background): “and remote radio heads (RRHs) have channel matrix entries with strong spatial correlation [66], and therefore a sparse representation in certain domains other than space [65].”
- Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO (2015, background): “Recent study and experiments have shown that the wireless channels between the BS and users exhibit a small angle spread seen from the BS [4], [16], [17].”