Adaptive channel estimation for sparse ultra wideband systems

Increased research in ultra wideband (UWB) systems in the last two decades has established it as a technology for high-speed, short-range applications. UWB also offers low power consumption, immunity to multipath fading, increased security, and low interference in multipath environments. Unfortunate...

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Main Author:	Nunoo, Solomon
Format:	Thesis
Language:	English
Published:	2015
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/54877/1/SolomonNunooPFKE2015.pdf
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spelling	my-utm-ep.548772017-10-08T09:18:23Z Adaptive channel estimation for sparse ultra wideband systems 2015-08 Nunoo, Solomon TK Electrical engineering. Electronics Nuclear engineering Increased research in ultra wideband (UWB) systems in the last two decades has established it as a technology for high-speed, short-range applications. UWB also offers low power consumption, immunity to multipath fading, increased security, and low interference in multipath environments. Unfortunately, it is a great challenge to obtain accurate channel state information at the receiver side of UWB systems, especially in time-varying applications. Consequently, this research deals with the design of an adaptive channel estimation algorithm for sparse UWB systems. Using measurement data, this thesis considers the estimation of a long sparse multipath channel in a mobile UWB system. Recent advances in Compressive Sensing (CS) applications in signal processing make CS to be a legitimate candidate for processing sparse signals. Among the broad application areas of CS is channel estimation. Based on the objectives of the research, the contributions of this thesis are in three parts. Firstly, channel measurements usually provide accurate Channel Impulse Response (CIR), which helps to accurately model any channel behaviour. Thus, this thesis provides channel measurements in various mobile line-of-sight scenarios to precisely measure the efficacy of the proposed channel estimation algorithm. Secondly, traditional channel estimation algorithms like the Least Mean Square (LMS) and Normalised LMS (NLMS) algorithms do not consider the structural information of the channel. In addition, CS-based LMS and NLMS algorithms do not consider the use of the channel sparsity to control the algorithm performance. Therefore, this thesis also proposes a number of Sparseness-Controlled (SC) LMS and NLMS algorithms for estimating sparse UWB channels. Lastly, the thesis presents an analysis of the performance of the proposed estimators in terms of the Mean Square Error (MSE), steady-state excess MSE, convergence speed, robustness, and computational complexity. Simulation results show that unlike traditional algorithms, the proposed estimators perform better to improve the estimation of the CIR of sparse UWB channels. Even though, for all the scenarios considered, compared to the SC-l0-Norm NLMS (SC-L0-NLMS) algorithm, the SC-reweighted zero-attracting NLMS (SCRZA- NLMS) algorithm provides excellent performance, the SC-ZA-NLMS algorithm is less computationally complex than both and it performs in close proximity to both at higher SNR. For the sparse channel, when SNR is 30 dB, the SC-ZA-NLMS algorithm converges faster with better MSE of -38.2391 dB compared to the SC-RZALMS algorithm, which converges at -33.9805 dB. Therefore, the SC-ZA-NLMS algorithm is the most suitable for accurately estimating the sparse UWB channel. 2015-08 Thesis http://eprints.utm.my/id/eprint/54877/ http://eprints.utm.my/id/eprint/54877/1/SolomonNunooPFKE2015.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Nunoo, Solomon Adaptive channel estimation for sparse ultra wideband systems
description	Increased research in ultra wideband (UWB) systems in the last two decades has established it as a technology for high-speed, short-range applications. UWB also offers low power consumption, immunity to multipath fading, increased security, and low interference in multipath environments. Unfortunately, it is a great challenge to obtain accurate channel state information at the receiver side of UWB systems, especially in time-varying applications. Consequently, this research deals with the design of an adaptive channel estimation algorithm for sparse UWB systems. Using measurement data, this thesis considers the estimation of a long sparse multipath channel in a mobile UWB system. Recent advances in Compressive Sensing (CS) applications in signal processing make CS to be a legitimate candidate for processing sparse signals. Among the broad application areas of CS is channel estimation. Based on the objectives of the research, the contributions of this thesis are in three parts. Firstly, channel measurements usually provide accurate Channel Impulse Response (CIR), which helps to accurately model any channel behaviour. Thus, this thesis provides channel measurements in various mobile line-of-sight scenarios to precisely measure the efficacy of the proposed channel estimation algorithm. Secondly, traditional channel estimation algorithms like the Least Mean Square (LMS) and Normalised LMS (NLMS) algorithms do not consider the structural information of the channel. In addition, CS-based LMS and NLMS algorithms do not consider the use of the channel sparsity to control the algorithm performance. Therefore, this thesis also proposes a number of Sparseness-Controlled (SC) LMS and NLMS algorithms for estimating sparse UWB channels. Lastly, the thesis presents an analysis of the performance of the proposed estimators in terms of the Mean Square Error (MSE), steady-state excess MSE, convergence speed, robustness, and computational complexity. Simulation results show that unlike traditional algorithms, the proposed estimators perform better to improve the estimation of the CIR of sparse UWB channels. Even though, for all the scenarios considered, compared to the SC-l0-Norm NLMS (SC-L0-NLMS) algorithm, the SC-reweighted zero-attracting NLMS (SCRZA- NLMS) algorithm provides excellent performance, the SC-ZA-NLMS algorithm is less computationally complex than both and it performs in close proximity to both at higher SNR. For the sparse channel, when SNR is 30 dB, the SC-ZA-NLMS algorithm converges faster with better MSE of -38.2391 dB compared to the SC-RZALMS algorithm, which converges at -33.9805 dB. Therefore, the SC-ZA-NLMS algorithm is the most suitable for accurately estimating the sparse UWB channel.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Nunoo, Solomon
author_facet	Nunoo, Solomon
author_sort	Nunoo, Solomon
title	Adaptive channel estimation for sparse ultra wideband systems
title_short	Adaptive channel estimation for sparse ultra wideband systems
title_full	Adaptive channel estimation for sparse ultra wideband systems
title_fullStr	Adaptive channel estimation for sparse ultra wideband systems
title_full_unstemmed	Adaptive channel estimation for sparse ultra wideband systems
title_sort	adaptive channel estimation for sparse ultra wideband systems
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2015
url	http://eprints.utm.my/id/eprint/54877/1/SolomonNunooPFKE2015.pdf
_version_	1747817745394696192

Adaptive channel estimation for sparse ultra wideband systems

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