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Source localization of primary users (PUs) is a spectrum awareness feature that can be very useful in enhancing the functionality of cognitive radios (CRs). When the cooperating CRs have limited information about the PU, weighted centroid localization (WCL) based on received signal strength (RSS) measurements represents an attractive low-complexity solution. This paper proposes a new analytical framework to accurately calculate the performance of WCL based on the statistical distribution of the ratio of two quadratic forms in normal variables. In particular, we derive an analytical expression for the root mean square error (RMSE) and an exact expression for the cumulative distribution function (CDF) of the two-dimensional location estimate. The proposed framework accounts for the presence of independent and identically distributed (i.i.d.) shadowing as well as correlated shadowing with distance-dependent intensity. The methodology is general enough to include the analysis of the one-dimensional error, which leads also to the evaluation of the bias of the position estimate. Numerical results confirm that the analytical framework is able to predict the performance of WCL capturing all the essential aspects of propagation as well as CR network spatial topology.

Source localization of primary users (PUs) is a geolocation spectrum awareness feature that can be very useful in enhancing the functionality of cognitive radios (CRs). When the cooperating CRs have limited information about the PU, weighted centroid localization (WCL) based on received signal strength (RSS) measurements represents an attractive low-complexity solution. In this paper, we propose a new analytical framework to calculate the exact performance of WCL in the presence of shadowing, based on results of the ratio of two quadratic forms in normal variables. In particular, we derive an exact expression for the root mean square error (RMSE) of the two-dimensional location estimate. Numerical results confirm that the derived framework is able to predict the performance of WCL capturing all the essential aspects of propagation as well as CR network spatial topology.

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Weighted centroid localization (WCL) based on received signal strength (RSS) measurements is an attractive lowcomplexity solution that enables cognitive radios (CRs) to have a geolocation awareness of the radio environment. In this paper, we propose a new analytical framework to accurately calculate the performance of WCL based on the statistical distribution of the ratio of two quadratic forms in normal variables. In particular, we derive an exact expression for the cumulative distribution function (CDF) of the two-dimensional location estimation in the presence of independent and identically distributed (i.i.d.) as well as correlated shadowing. Numerical results confirm that the analytical framework is able to predict the performance of WCL capturing all the essential aspects of propagation as well as CR network spatial topology.

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