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Title: Sensing-based spectrum sharing in cognitive radio networks
Authors: Kang, X. 
Liang, Y.-C.
Garg, H.K. 
Zhang, L.
Keywords: Cognitive radio (CR)
Convex optimization
Dual decomposition
Ergodic capactiy
Fading channels
Opportunistic spectrum access
Optimal power control
Spectrum sharing
Issue Date: 2009
Citation: Kang, X., Liang, Y.-C., Garg, H.K., Zhang, L. (2009). Sensing-based spectrum sharing in cognitive radio networks. IEEE Transactions on Vehicular Technology 58 (8) : 4649-4654. ScholarBank@NUS Repository.
Abstract: In this paper, a new spectrum-sharing model called sensing-based spectrum sharing is proposed for cognitive radio networks. This model consists of two phases: In the first phase, the secondary user (SU) listens to the spectrum allocated to the primary user (PU) to detect the state of the PU; in the second phase, the SU adapts its transit power based on the sensing results. If the PU is inactive, the SU allocates the transmit power based on its own benefit. However, if the PU is active, the interference power constraint is imposed to protect the PU. Under this new model, the evaluation of the ergodic capacity of the SU is formulated as an optimization problem over the transmit power and the sensing time. Due to the complexity of this problem, two simplified versions, which are referred to as the perfect sensing case and the imperfect sensing case, are studied in this paper. For the perfect sensing case, the Lagrange dual decomposition is applied to derive the optimal power allocation policy to achieve the ergodic capacity. For the imperfect sensing case, an iterative algorithm is developed to obtain the optimal sensing time and the corresponding power allocation strategy. Finally, numerical results are presented to validate the proposed studies. It is shown that the SU can achieve a significant capacity gain under the proposed model, compared with that under the opportunistic spectrum access or the conventional spectrum sharing model. © 2009 IEEE.
Source Title: IEEE Transactions on Vehicular Technology
ISSN: 00189545
DOI: 10.1109/TVT.2009.2018258
Appears in Collections:Staff Publications

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