7th International Conference on Cognitive Radio Oriented Wireless Networks

Research Article

Jointly Optimal Sensing and Resource Allocation for Multiuser Interweave Cognitive Radios

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  • @INPROCEEDINGS{10.4108/icst.crowncom.2012.248459,
        author={Luis Lopez-Ramos and Antonio Marqu\^{e}s and Javier Ramos},
        title={Jointly Optimal Sensing and Resource Allocation for Multiuser Interweave Cognitive Radios},
        proceedings={7th International Conference on Cognitive Radio Oriented Wireless Networks},
        publisher={IEEE},
        proceedings_a={CROWNCOM},
        year={2012},
        month={7},
        keywords={cognitive radios dynamic programming imperfect channel state information resource management},
        doi={10.4108/icst.crowncom.2012.248459}
    }
    
  • Luis Lopez-Ramos
    Antonio Marqués
    Javier Ramos
    Year: 2012
    Jointly Optimal Sensing and Resource Allocation for Multiuser Interweave Cognitive Radios
    CROWNCOM
    IEEE
    DOI: 10.4108/icst.crowncom.2012.248459
Luis Lopez-Ramos1,*, Antonio Marqués1, Javier Ramos1
  • 1: King Juan Carlos University
*Contact email: luismiguel.lopez@urjc.es

Abstract

Successful deployment of cognitive radios requires efficient sensing of the spectrum and dynamic adaptation of the available resources according to the sensed (imperfect) information. While most works design these two tasks separately, in this paper the sensing and resource allocation schemes are jointly designed. We investigate an interweave CR with multiple secondary users that access orthogonally a set frequency bands originally devoted to primary users. The schemes are designed to optimize the performance of the secondary users while limiting the ``probability of interfering'' the primary users. The joint design is addressed using dynamic programming and nonlinear optimization techniques. A two-step strategy that first finds the optimal resource allocation for any sensing scheme and then uses that solution as input to solve for the optimal sensing policy is implemented. The two-step strategy is optimal and entails a computational complexity much lower than that required to solve the original formulation.