Adaptive Windowed Statistical Selection Rake for Long Ultra-Wideband Multipath Channels


Chung, Gwo Chin and Sandar, T. Su and Alias, Mohamad Yusoff (2018) Adaptive Windowed Statistical Selection Rake for Long Ultra-Wideband Multipath Channels. Wireless Personal Communications, 98 (1). pp. 453-466. ISSN 1572-834X

[img] Text
Restricted to Repository staff only

Download (792kB)


Under ultra-wideband indoor channels, the characteristic of fine multipath resolution allows Rake collectors to combine the multipaths as well as to maximize the signal-to-noise ratio effectively. However, the presence of long multipaths in ultra-wideband channels usually gives rise to severe interference at the receiver end. Hence, the performance of the received signals cannot be improved further because the conventional Rake receivers do not take into account the long multipath effect. In this paper, we propose to shorten the long multipath propagation by minimizing the variance of the distribution of the received signals. The new proposed statistical selection Rake receiver is capable of selecting the most significant combining paths in order to achieve a minimum variance distribution. Further improvement has been done to reduce the computational complexity by introducing two different type of adaptive windowed statistical selection schemes. The performance of all proposed schemes has been simulated under ultra-wideband IEEE P802.15.3a standard channel models. From the results, the proposed Rake receivers are able to minimize the long multipath effect significantly by achieving more than 50% reduction of the variance distribution as compared to the other Rake receivers.

Item Type: Article
Uncontrolled Keywords: Statistical selection, Rake, Ultra-wideband, Multipath, Lagrange multiplier
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7871 Electronics--Materials
Divisions: Faculty of Engineering (FOE)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 08 Nov 2020 12:20
Last Modified: 08 Nov 2020 12:20


Downloads per month over past year

View ItemEdit (login required)