Abstract
The unknown noise variance and time-variant fading channels make the spectrum sensing design a challenging task for cognitive radios. Most existing sensing methods suffer from the information uncertainty and can hardly acquire promising performances in the adverse situations. To address this challenge, in this paper, we first formulate a dynamic state-space model for spectrum sensing, in which the unknown noise variance and time-variant flat fading channels are all taken into considerations. The dynamic behaviors of both primary user states and fading channels are characterized by two discrete state Markov chains. Based on this model, a novel spectrum sensing scheme is designed to recursively estimate the occupancy state of primary users, by estimating the time-variant fading channel gain and noise parameters jointly. The joint estimation is primarily premised on a maximum a posteriori probability criterion and the marginal particle filtering schemes. Simulation results are provided to demonstrate the advantages of our proposed method, which can significantly improve the sensing performance over time-variant flat fading channels, even with unknown noise variance.
Highlights
Due to limited availability of spectrum resources, the conventional paradigm of static spectrum management will not be able to accommodate the ever-growing demands of future wireless communications [1]
Cognitive radio (CR) supports the secondary users (SUs) to utilize the spectrum assigned to the primary users (PUs) opportunistically [2]–[6], it has the potential to alleviate the spectrum scarcity problem
We focus on the spectrum sensing design for cognitive radio (CR) with unknown noise variance as well as time-variant channel in this paper, and we propose an novel sensing method, which will suppress the information uncertainty and improve the sensing performance of matched filtering detection (MFD)
Summary
Due to limited availability of spectrum resources, the conventional paradigm of static spectrum management will not be able to accommodate the ever-growing demands of future wireless communications [1]. While this method manages to jointly estimate PU’s occupancy states and time-variant fading channel, the noise variance is assumed to be perfectly known in [19] To combat these imperfections, we focus on the spectrum sensing design for CRs with unknown noise variance as well as time-variant channel in this paper, and we propose an novel sensing method, which will suppress the information uncertainty and improve the sensing performance of MFD. On this basis, a joint-estimation based sensing paradigm is designed, and an iterative algorithm is proposed.
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