Eigenbased Multi-Antenna Spectrum Sensing: Experimental Validation on a Software-Defined Radio Testbed

Spectrum Sensing (SS) is expected to play a crucial role in forthcoming 6G Cognitive Radio Networks (CRNs), where unlicensed users will be able to dynamically access the spectrum and perform opportunistic transmissions without causing interference to licensed users. In this work, we investigate multiple-antenna SS techniques by analyzing the performance of several widely used detection schemes—namely, Roy’s Largest Root Test (RLRT), Generalized Likelihood Ratio Test (GLRT), Eigenvalue Ratio Detector (ERD), and Energy Detector (ED)—under varying false alarm probabilities and signal-to-noise ratios (SNRs). The study assumes a fixed number of sensors at the secondary user receiver, equal to four. To evaluate the behavior of these detectors in realistic conditions, we developed a software-defined radio (SDR) testbed using Universal Software Radio Peripherals (USRPs), enabling both primary user signal transmission and secondary user data acquisition. The experimental results, illustrated through Receiver Operating Characteristic (ROC) and performance curves, are compared with simulation outcomes. The analysis is complemented by a detailed state-of-the-art listing of the available analytical characterizations of the false alarm probabilities, for the considered SS schemes. In particular, the GLRT false alarm probability, previously unavailable in explicit form for a four antenna equipped receiver, is computed as well. These results validate the superior detection capability of RLRT over the other tested schemes, confirming its effectiveness not only in theoretical analysis but also in practical SDR-based implementations.