Achievable Transmission Rate of the Secondary User in Cognitive Radio Networks with Hybrid Spectrum Access Strategy

A hybrid spectrum access strategy which is different from traditional underlay or overlay strategy is proposed for cognitive radio networks. In proposed strategy, the secondary users (SU) cooperatively sense the state of the primary user (PU) in a given spectrum band. Based on the sensing results and an additional requirement on the bit error rate, the SU adapts its transmit power and modulation level. Specifically, if the PU is inactive, the SU allocates its power subject to a peak transmit power constraint. Else if the PU is active, the average interference power constraint would be further imposed to protect the primary link. We assume that the maximal ratio combing (MRC) technique is available at the secondary receiver. Imperfect spectrum sensing and imperfect estimations of the channel power gain from the secondary transmitter to primary receiver are also considered. Achievable transmission rate of the SU over Rayleigh fading channel is formulated as an optimization problem and solved by using Lagrange dual decomposition method. Simulation results demonstrate that the transmission rate of the SU with hybrid strategy is significantly improved when compared with the traditional ones.

[1]  Halim Yanikomeroglu,et al.  Access Strategies for Spectrum Sharing in Fading Environment: Overlay, Underlay, and Mixed , 2010, IEEE Transactions on Mobile Computing.

[2]  Alagan Anpalagan,et al.  Opportunistic Spectrum Access in Cognitive Radio Networks: Global Optimization Using Local Interaction Games , 2012, IEEE Journal of Selected Topics in Signal Processing.

[3]  Yuli Yang,et al.  Achievable Data Rate in Spectrum-Sharing Channels with Variable-Rate Variable-Power Primary Users , 2012, IEEE Wireless Communications Letters.

[4]  Alagan Anpalagan,et al.  Opportunistic Spectrum Access in Unknown Dynamic Environment: A Game-Theoretic Stochastic Learning Solution , 2012, IEEE Transactions on Wireless Communications.

[5]  Daniel Pérez Palomar,et al.  A tutorial on decomposition methods for network utility maximization , 2006, IEEE Journal on Selected Areas in Communications.

[6]  Athanasios V. Vasilakos,et al.  Novel overlay/underlay cognitive radio waveforms using SD-SMSE framework to enhance spectrum efficiency- part i: theoretical framework and analysis in AWGN channel , 2009, IEEE Transactions on Communications.

[7]  Mohamed-Slim Alouini,et al.  Power Adaptation for Joint Switched Diversity and Adaptive Modulation Schemes in Spectrum Sharing Systems , 2012, IEEE Communications Letters.

[8]  Yuanan Liu,et al.  Energy Efficiency Optimization for OFDM-Based Cognitive Radio Systems: A Water-Filling Factor Aided Search Method , 2013, IEEE Transactions on Wireless Communications.

[9]  Vahid Asghari,et al.  Adaptive Rate and Power Transmission in Spectrum-Sharing Systems , 2010, IEEE Transactions on Wireless Communications.

[10]  Yonghong Zeng,et al.  Sensing-Throughput Tradeoff for Cognitive Radio Networks , 2008, IEEE Trans. Wirel. Commun..

[11]  Chao Yang,et al.  Energy-Efficient Hybrid Spectrum Access Scheme in Cognitive Vehicular Ad hoc Networks , 2013, IEEE Communications Letters.