NDLI: Compressed channel estimation for FDD massive MIMO systems without prior knowledge of sparse channel model

Content Provider	IET Digital Library
Author	Azizipour, Mohammad Javad Pour, Kamal Mohamed
Abstract	In this study, a new greedy-based algorithm for sparse channel estimation in frequency-division duplexing massive multiple-input multiple-output (MIMO) systems is introduced. The proposed algorithm is able to acquire the channel with no information about the channel model such as the sparsity order, statistical bound on sparsity support, and special features of the channel. In other words, the authors assume that the massive channel is totally unknown and then exploit the inherent property of correlation between measurements and the sensing matrix to estimate the channel, which is available at the user side. By utilising this property, a halting parameter is defined as the halting threshold instead of known prior information assumed in the conventional greedy algorithms. Furthermore, the lower and upper bounds of the halting parameter are obtained that helps the algorithm to take the suitable values of this parameter and by considering the lower bound as the values of the halting parameter, the algorithm can prevent from missing the original channel bins. Simulation results demonstrate that the proposed algorithm outperforms its counterparts in terms of normalised mean square error while it faces a completely unknown massive MIMO channel.
Starting Page	657
Ending Page	663
Page Count	7
ISSN	17518628
Volume Number	13
e-ISSN	17518636
Issue Number	Issue 6, Apr (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/13/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2018.5134
Journal	IET Communications
Publisher Date	2018-12-13
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Channel Estimation Communication Channel Equalisation And Identification Completely Unknown Massive MIMO Channel Compressed Channel Estimation Conventional Greedy Algorithm FDD Massive MIMO System Frequency Division Multiplexing Frequency-division Duplexing Massive Multiple-input Multiple-output System Greedy Algorithm Greedy-based Algorithm Halting Parameter Halting Threshold Interpolation And Function Approximation Known Prior Information Lower Bounds Massive Channel Mean Square Error Method MIMO Communication Numerical Analysis Optimisation Technique Original Channel Bins Prior Knowledge Radio Link And Equipment Signal Processing And Detection Sparse Channel Estimation Sparse Channel Model Sparsity Order Sparsity Support Statistics Upper Bound Utilising This Property
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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