Analyzing Performance of Eigenvalue-based Spectrum Sensing within LoRaCog Framework

Abstract

The CR technology enhances spectrum utilization by allowing access to unused licensed channels, while spectrum sensing allows secondary users to verify channel availability before the transmission. This study relies on the LoRaCog framework, a solution integrating the CR technology with LoRa LPWAN networks, to evaluate the performance of eigenvalue-based detection algorithms, such as maximum eigenvalue detection (MED), maximum to minimum eigenvalue (MME), energy-to-minimum eigenvalue (EME) and maximum-to-mean eigenvalue detection (MMED), with the comparisons based on energy detection (ED). The said algorithms were evaluated under three scenarios characterized by an increasing degree of complexity. These included the following: an ideal additive white Gaussian noise (AWGN) channel, followed by a multipath fading channel with noise uncertainty using a SISO receiver and, finally, a SIMO multiantenna receiver system. The simulation results for the AWGN channel showed that the ED algorithm achieved the best detection probability and the lowest sensing time. When multipath fading and noise uncertainty were introduced, eigenvalue-based algorithms achieved higher detection probabilities while maintaining comparable detection times. The MME algorithm achieved the highest detection probability when used with the SIMO multi-antenna reception system.