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Indirect Methods for Constructing Radio Environment MapAlfattani, Safwan January 2017 (has links)
To solve the spectrum scarcity problem caused by the high number of wireless applications and users, the concept of cognitive radio (CR) was proposed in the past few years. Cognitive radio networks (CRNs) provide dynamic spectrum access (DSA), where the unlicensed users can access the spectrum without causing unacceptable level of interference to the primary user (PU). DSA was based on conventional spectrum sensing information or geolocation databases. Later,
radio environment map (REM) as an improved geolocation database was introduced to enhance the DSA process. It is a comprehensive map consists of different integrated databases, and the interference field information is one of its databases.
In this thesis, a description of the REM concept and its construction methods will be
presented. The focus will be for the indirect methods for constructing interference map, which represents a layer of the REM. Indirect method refers to the methods that utilize known model information, to first estimate the primary transmitter parameters and then generate REM. Two indirect methods under lognormal shadowing were presented and compared. The better of these two methods is further investigated in different scenarios. These scenarios include different
number of sensors, varied size of measurements, several shadowing spread values, different percentages of error in path-loss exponent, and the effect of the number of moving sensors and their speeds to the REM quality. The performance is evaluated using these metrics: “localization error, signal power error and correct detection zone ratio (CDZR). The results show that performance is enhanced as the number of sensors and the size of measurements increase, whereas clear degradation in REM quality is shown when shadowing spread increases or the model parameters are not well calibrated. Also, as the number of moving sensors or their speeds
increase, the REM performance becomes less effective
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