NDLI: Weighted compressive sensing based uplink channel estimation for time division duplex massive multi-input multi-output systems

Content Provider	IET Digital Library
Author	Nan, Yang Zhang, Li Sun, Xin
Abstract	In this study, the channel estimation problem for the uplink massive multi-input multi-output (MIMO) system is considered. Motivated by the observations that the channels in massive MIMO systems may exhibit sparsity and the channel support changes slowly over time, the authors propose one efficient channel estimation method under the framework of compressive sensing (CS). By exploiting the channel impulse response (CIR) estimated from the previous orthogonal frequency division multiplexing symbol, they firstly estimate the probabilities that the elements in the current CIR are non-zero. Then, they propose the probability-weighted subspace pursuit algorithm exploiting these probability information to efficiently reconstruct the uplink massive MIMO channel. Moreover, noting that the massive MIMO systems also share a common support within one channel matrix due to the shared local scatterers in the physical propagation environment, an antenna collaborating method is exploited for the proposed method to further enhance the channel estimation performance. Simulation results show that compared to the existing CS methods, the proposed methods could achieve higher spectral efficiency as well as more reliable performance over time-varying channel.
Starting Page	355
Ending Page	361
Page Count	7
ISSN	17518628
Volume Number	11
e-ISSN	17518636
Issue Number	Issue 3, Feb (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/11/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2016.0625
Journal	IET Communications
Publisher Date	2017-02-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Antenna Array Antenna Collaborating Method Channel Estimation Channel Impulse Response Estimation CIR Estimation Communication Channel Equalisation And Identification Compressed Sensing CS Method Massive MIMO System Matrix Algebra MIMO Communication Orthogonal Frequency Division Multiplexing Symbol Physical Propagation Environment Probability Probability-weighted SubSpace Pursuit Algorithm Radio Link And Equipment Signal Processing And Detection Statistics Time Division Duplex Massive Multiinput Multioutput System Time-varying Channel Uplink Channel Estimation Problem Weighted Compressive Sensing Wireless Channel
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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