NDLI: Low-Complexity Cyclostationarity Feature Detection Scheme of Localized SC-FDMA Uplink System for Application to Detect and Avoid

Content Provider	Springer Nature Link
Author	Zhang, Wensheng Sanada, Yukitoshi
Copyright Year	2011
Abstract	This paper proposes a low-complexity cyclostationarity feature detection scheme for detect and avoid (DAA) of Ultra-Wideband (UWB) system in order to solve the coexistence issues between UWB system and Long Term Evolution-Advanced (LTE-Advanced) system. The proposed scheme is suitable for the detection of a localized Single-carrier Frequency Division Multiple Access (SC-FDMA) signal utilized in the uplink of LTE-Advanced system. Compared with conventional cyclostationarity feature detection, the proposed scheme utilizes all possible cyclic-spectrums located in a distributed window function, which is decided by the frequency distribution of the Primary User (PU) signal. The computational complexity of the proposed scheme is low, due to only one window width instead of all occupied spectrum interval will be searched for the possible cyclic-spectrums. On the other hand, the proposed scheme can also avoid the estimation of the cyclic-spectrums when the type of PU signal is unclear or the cyclic-spectrums are hard to estimate. Simulation results indicate that the proposed scheme can make a tradeoff between detection performance and computational complexity. The low-complexity cyclostationarity feature detection also provides a substitute for the energy detection when the later approach suffers from the noise uncertainty and cannot distinguish the target signal type.
Starting Page	307
Ending Page	319
Page Count	13
File Format	PDF
ISSN	09296212
Journal	Wireless Personal Communications
Volume Number	66
Issue Number	2
e-ISSN	1572834X
Language	English
Publisher	Springer US
Publisher Date	2011-05-17
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cyclostationarity feature detection SC-FDMA DAA Signal, Image and Speech Processing Communications Engineering, Networks Computer Communication Networks
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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