ChatGPT “Hybrid Angle of Arrival and Received Signal Strength Approach for Detecting and Isolating Primary User Emulators in Cognitive Radio Networks”

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Cognitive Radio (CR) technology is a promising solution to address the issue of spectrum scarcity in wireless networks. However, its implementation faces significant security challenges, with Primary User Emulation Attack (PUEA) being the most severe among them. This study focuses on detecting Primary User Emulators (PUEs) through a range-based localization approach, which offers enhanced accuracy compared to range-free methods.

The range-based localization category comprises two major techniques: Angle of Arrival (AOA) and Received Signal Strength (RSS). AOA utilizes angular measurements to locate the PUE, while RSS relies solely on distance information. To optimize the performance of range-based methods, this research proposes a hybrid scheme that combines AOA and RSS techniques for PUE localization in television (TV) white spaces. In these TV white spaces, the location of the Primary User (PU) is known, allowing for the computation of both AOA and distance from RSS measurements to determine the position of the PU’s signal transmitter. By comparing this position with the known PU location, the true source of the signal is identified, thus detecting the PUE.

Computer simulations demonstrate the superiority of the hybrid scheme, which achieves a significantly faster and more accurate estimation of the PUE’s position, with a Root Mean Square Error (RMSE) of 5.00×10-3 after 20 iterations. This outperforms the individual RSS and AOA methods, which achieve RMSEs of 2.00×10-1 and 1.00×10-2, respectively, after 50 iterations.

Furthermore, the study investigates the selection of the most suitable pair of Secondary Users (SUs) for the detection process. It is found that a pair of SUs from the same communication environment, exhibiting close RSS values, achieves better PUE localization (RMSE of 4.7×10-3 after 20 iterations) compared to a pair with higher RSS values from different communication environments (RMSE of 6.0×10-3 after 70 iterations).

The significance of these results lies in their implications for efficient cognitive radio operation, particularly concerning speed, accuracy, and energy efficiency. These factors are crucial, especially in the context of the current global energy crises faced by wireless systems. Additionally, by isolating detected PUEs from the Cognitive Radio Network (CRN), more spectrum holes become available to accommodate newer wireless technologies, facilitating effective communication. Furthermore, Secondary Users (SUs) benefit from increased transmission time, improved quality of service (QoS), connection reliability, higher throughput, and overall enhancement of the cognitive radio network’s performance.

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