# Cyclic weighted centroid localization for spectrally overlapped sources in cognitive radio networks

## TL;DR

This paper introduces Cyclic WCL, a novel localization algorithm that leverages cyclostationary features to accurately locate spectrally overlapped sources in cognitive radio networks, outperforming traditional methods.

## Contribution

The paper proposes a new cyclic weighted centroid localization algorithm that exploits cyclostationarity for improved accuracy in localizing overlapped sources.

## Key findings

- Cyclic WCL significantly reduces RMSE compared to traditional WCL.
- Interferer location and modulation affect localization accuracy.
- Performance varies with target and interference power levels.

## Abstract

We consider the problem of localizing spectrally overlapped sources in cognitive radio networks. A new weighted centroid localization algorithm (WCL) called Cyclic WCL is proposed, which exploits the cyclostationary feature of the target signal to estimate its location coordinates. In order to analyze the algorithm in terms of root-mean-square error (RMSE), we model the location estimates as the ratios of quadratic forms in a Gaussian random vector. With analysis and simulation, we show the impact of the interferer location and its modulation scheme on the RMSE. We also study the RMSE performance of the algorithm for different power levels of the target and the interference. Further, the comparison between Cyclic WCL and WCL w/o cyclostationarity is presented. It is observed that the Cyclic WCL provides significant performance gain over WCL.