Performance Study of MB-OFDM Ultra-Wideband Signals over Multimode Fiber

Author(s):  
Y.X. Guo ◽  
Viet Hung Pham ◽  
M.L. Yee ◽  
L.C. Ong ◽  
B. Luo
2017 ◽  
Vol 10 (2) ◽  
pp. 141-148
Author(s):  
Abdelmadjid Maali ◽  
Geneviève Baudoin ◽  
Ammar Mesloub

In this paper, we propose a novel energy detection (ED) receiver architecture combined with time-of-arrival (TOA) estimation algorithm, compliant to the IEEE 802.15.4a standard. The architecture is based on double overlapping integrators and a sliding correlator. It exploits a series of ternary preamble sequences with perfect autocorrelation property. This property ensures coding gain, which allows an accurate estimation of power delay profile (PDP). To improve TOA estimation, the interpolation of PDP samples is proposed and the architecture is validated by using an ultra-wideband signals measurements platform. These measurements are carried out in line-of-sight and non-line-of-sight multipath environments. The experimental results show that the ranging performances obtained by the proposed architecture are higher than those obtained by the conventional architecture based on a single-integrator in both LOS and NLOS environments.


2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Muzaffer Kanaan ◽  
Memduh Suveren

Results about the problem of accurate ranging within the human body using ultra-wideband signals are shown. The ability to accurately measure the range between a sensor implanted in the human body and an external receiver can make a number of new medical applications such as better wireless capsule endoscopy, next-generation microrobotic surgery systems, and targeted drug delivery systems possible. The contributions of this paper are twofold. First, we propose two novel range estimators: one based on an implementation of the so-called CLEAN algorithm for estimating channel profiles and another based on neural networks. Second, we develop models to describe the statistics of the ranging error for both types of estimators. Such models are important for the design and performance analysis of localization systems. It is shown that the ranging error in both cases follows a heavy-tail distribution known as the Generalized Extreme Value distribution. Our results also indicate that the estimator based on neural networks outperforms the CLEAN-based estimator, providing ranging errors better than or equal to 3.23 mm with 90% probability.


Author(s):  
J. George ◽  
D. Thelen ◽  
A. Chamarti ◽  
A. Ng’oma ◽  
M. Sauer

2011 ◽  
Vol 23 (15) ◽  
pp. 1055-1057 ◽  
Author(s):  
Marta Beltran ◽  
Jesper Bevensee Jensen ◽  
Roberto Llorente ◽  
Idelfonso Tafur Monroy

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