NDLI: Compressed sensing channel estimation in massive MIMO

Content Provider	IET Digital Library
Author	Pramanik, Ankita Maity, Santi P. Farheen, Zeba
Abstract	Massive multiple-input–multiple-output (MIMO) emerges as a promising technology to meet the ever growing demand on a large volume of data transmission in an energy efficient manner in the upcoming fifth generation networks. This explores an integrated system model of improved wireless channel estimation (CE) and its consequent effect on image reconstruction, both done in compressed sensing (CS) framework. A massive MIMO channel exhibits sparse nature and has been modelled as a CS problem. Two CS-MIMO-CE algorithms, discrete cosine transform-based stagewise orthogonal matching pursuit and improved stagewise orthogonal matching pursuit (iStOMP), are suggested here. Low density parity check codes are also integrated to serve three-fold purposes, as a pilot signal, sensing matrix, and error control code, while Daubechies transform offers a sparse representation of the MIMO channel and transmitted image. The proposed iStOMP MIMO-CE algorithm offers ∼ 6 dB gain in CE over the existing works and gain in 2 dB on image visual quality at 75% CS measurements.
Starting Page	3145
Ending Page	3152
Page Count	8
ISSN	17518628
Volume Number	13
e-ISSN	17518636
Issue Number	Issue 19, Dec (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/13/19
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2019.0126
Journal	IET Communications
Publisher Date	2019-06-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	75% CS Measurements Channel Estimation Code Communication Channel Equalisation And Identification Compressed Sensing Compressed Sensing Channel Estimation Compressed Sensing Framework Computer Vision And Image Processing Technique Consequent Effect CS Problem CS-MIMO-CE Algorithm Data Transmission Discrete Cosine Transform Energy Efficient Manner Error Control Code Error Correction Code Growing Demand Image Reconstruction Improved Wireless Channel Estimation Integrated System Model Interpolation And Function Approximation Iterative Method Low Density Parity Check Code Massive MIMO Channel Exhibits Sparse Nature MIMO Communication MIMO-CE Algorithm Multiple-input–multiple-output Noise Figure 2.0 DB Noise Figure 6.0 DB Numerical Analysis Optical, Image And Video Signal Processing Parity Check Code Radio Link And Equipment Signal Processing And Detection Stagewise Orthogonal Matching Pursuit Statistics Upcoming Fifth Generation Network Wireless Channel
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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