NDLI: Compressed Sensing: A new approach to analyze the recovery algorithms based on UWB channel estimation

Content Provider	IEEE Xplore Digital Library
Author	Nguyen Thanh Son Nguyen Vu Quynh Pham Van Toan Le Phuong Truong
Copyright Year	2014
Description	Author affiliation: Dept. of Electromech. & Electron., LacHong Univ., LacHong, Vietnam (Nguyen Thanh Son; Nguyen Vu Quynh; Pham Van Toan; Le Phuong Truong)
Abstract	Compressed Sensing (CS) is a new mathematical concept, which can reconstruct the original signal accurately with lower Nyquist sampling. Besides, multipath arrivals in an Ultra-wideband (UWB) channel have a long time intervals between clusters and rays where the signal takes on zero or negligible values. It is precisely this signal sparsity of the impulse response of the UWB channel that is suitable for the application of Compressed Sensing theory. However, these multipath arrivals mainly depend on the channel models that generate different sparse levels (low-sparse or high-sparse) of the UWB channels according to which, the authors have analysed and chosen the best recovery algorithms which are suitable to the sparse level for each type of channel environment. Criteria for evaluating the algorithms are based on computational complexity, ability to reduce the sampling rate and processing time. In addition, the results of this study are an open topic for further research aimed at creating a optimal algorithm specially for application of CS based UWB systems.
Sponsorship	IEEE Vietnam Sect.
Starting Page	46
Ending Page	51
File Size	919351
Page Count	6
File Format	PDF
e-ISBN	9781479929030
DOI	10.1109/ComManTel.2014.6825576
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-04-27
Publisher Place	Vietnam
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Ultra-wideband Multipath channel Noise Channel estimation Signal processing algorithms Estimation Channel model Bandwidth Sparse level Channel models Compressed sensing Recovery algorithm
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Channel estimation in UWB channels using compressed sensing

Impact of channel models on compressed sensing recovery algorithms-based ultra-wideband channel estimation

A joint sparse signal reconstruction and channel estimation for UWB communication system

Sparse channel estimation based on compressed sensing for ultra wideband systems

Ultra-wideband channel estimation based on Bayesian compressive sensing

Bayesian compressive sensing for ultra-wideband channel models

Sparse multipath channel estimation using ds algorithm in wideband communication systems

A Compressed Sensing Approach for IR-UWB Communication

Compressed sensing for OFDM UWB systems

Compressed Sensing: A new approach to analyze the recovery algorithms based on UWB channel estimation

Similar Documents

Channel estimation in UWB channels using compressed sensing

Impact of channel models on compressed sensing recovery algorithms-based ultra-wideband channel estimation

A joint sparse signal reconstruction and channel estimation for UWB communication system

Sparse channel estimation based on compressed sensing for ultra wideband systems

Ultra-wideband channel estimation based on Bayesian compressive sensing

Bayesian compressive sensing for ultra-wideband channel models

Sparse multipath channel estimation using ds algorithm in wideband communication systems

A Compressed Sensing Approach for IR-UWB Communication

Compressed sensing for OFDM UWB systems

Compressed Sensing: A new approach to analyze the recovery algorithms based on UWB channel estimation