Adaptive channel estimation for sparse ultra wideband systems

Abstract

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.