RT Journal Article
T1 Online detection and SNR estimation in cooperative spectrum sensing
A1 Pérez, Jesús
A1 Vía, Javier
A1 Vielva, Luis
A1 Ramírez García, David
AB Cooperative spectrum sensing has proved to be an effective method to improve the detection performance in cognitive radio systems. This work focuses on centralized cooperative schemes based on the soft fusion of the energy measurements at the cognitive radios (CRs). In these systems, the likelihood ratio test (LRT) is the optimal detection rule, but the sufficient statistic depends on the local signal-to-noise ratio (SNR) at the CRs, which are unknown in most practical cases. Therefore, the detection problem becomes a composite hypothesis test. The generalized LRT is the most popular approach in those cases. Unfortunately, in mobile environments, its performance is well below the LRT because the local energies are measured under varying SNRs. In this work, we present a new algorithm that jointly estimates the instantaneous SNRs and detects the presence of primary signals. Due to its adaptive nature, the algorithm is well suited for mobile scenarios where the local SNRs are time-varying. Simulation results show that its detection performance is close to the LRT in realistic conditions.
PB IEEE
SN 1536-1276
YR 2022
FD 2022-04-01
LK https://hdl.handle.net/10016/34567
UL https://hdl.handle.net/10016/34567
LA eng
NO This work was supported in part by the Ministerio de Ciencia, Innovación y Universidades, jointly with European Commission [European Regional Development Fund (ERDF)], under Grant TEC2017-86921-C2-1-R and Grant TEC2017-86921-C2-2-R (CAIMAN) and in part by The Comunidad de Madrid under Grant Y2018/TCS-4705 (PRACTICO-CM).
DS e-Archivo
RD 1 sept. 2024