In wireless communications, the transmitted signals show very strong cyclostationary features based on the modulation type, carrier frequency, and data rate, especially when excess bandwidth is utilized. Therefore, identifying the unique set of features of a particular radio signal for a given wireless access system can be used to detect the system based on the cyclostationary analysis at the cognitive radio node. Cyclostationary feature detection based technique has been proposed by many researchers as a key solution for detecting these signals. This paper therefore, took a detail review of the proposals from different authors and pointed out the challenges faced by this method and concluded that it cannot be used to achieve optimality when detecting the presence and absence of primary user. The paper also went further to analyze the principle and model for cyclostationary feature based detection method.
TV Whitespace (TVWS) refers to the unoccupied portions of spectrum in the VHF/UHF terrestrial television frequency bands. When developing a TV White Space (TVWS) system with the available TV spectrum after digital switchover, channel allocation for TVWS devices to avoid interference becomes one of the most challenging problems. Analysis has shown that there exist large quantities of these TVWS in many locations in Nigeria especially with the ongoing digital switch over. Having a proper mechanism for the allocation of these TVWS to devices known as white space devices (WSD) have been a serious concern. Thus, this paper proposes a hybrid model of fuzzy rule based technique and genetic algorithm for optimal allocation of the TVWS. The result shows that the available TVWS were maximally allocation to WSD on a real time basis. The implementation of this model will bring an alternative source for most smart devices that have been competing for the scarce spectrum as raised by the communication industries. These devices can be deployed to these unutilized spectra at low or no cost thereby freeing most of the spectrum bands.
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