Joint power and admission control based on hybrid users in cognitive radio network

Abstract Hybrid users or primary-secondary users (PSUs) are advanced primary users that are able to access the secondary network using their cognitive functions. Hybrid users can appear as a new type of cognitive users that would be promising for future cognitive radio networks (CRNs). Regarding the unique capabilities of such users and due to the lack of considerable research on this subject, we study the problem of joint power and admission control in spectrum underlay CRN based on hybrid users. In order to fully take the advantages of cognitive capability, an advanced cognitive radio network (ACRN) is proposed by employing the PSUs, and the corresponding power and signal-to-interference-plus-noise ratio (SINR) are derived. Then, feasibility checking mechanism is investigated and the optimal value of interference temperature limit for PSUs is also obtained. A new formulation to maximize the number of admitted secondary users (SUs) in ACRN is presented. Moreover, two power and admission control algorithms are proposed which significantly improve the network performance not only in the number of admitted SUs but also in transmit power consumption. For a feasible network, the problem of aggregate throughput maximization is solved using successive geometric programming. Afterwards, it is proved that our proposed ACRN can improve the aggregate throughput of SUs. The superior efficiency of ACRN in terms of the number of admitted SUs, transmit power consumption and aggregate throughput is verified by simulation results for different scenarios.

[1]  Daniel Pérez Palomar,et al.  Power Control By Geometric Programming , 2007, IEEE Transactions on Wireless Communications.

[2]  Bernard Fong,et al.  Low-complexity centralized joint power and admission control in cognitive radio networks , 2009, IEEE Communications Letters.

[3]  Mohammed Nafie,et al.  Admission and Power Control for Spectrum Sharing Cognitive Radio Networks , 2011, IEEE Transactions on Wireless Communications.

[4]  Ekram Hossain,et al.  Resource allocation for spectrum underlay in cognitive radio networks , 2008, IEEE Transactions on Wireless Communications.

[5]  Dong In Kim,et al.  Optimization of OFDMA-Based Cellular Cognitive Radio Networks , 2010, IEEE Transactions on Communications.

[6]  Ali Jamshidi,et al.  Defence against primary user emulation attack using statistical properties of the cognitive radio received power , 2017, IET Commun..

[7]  Ekram Hossain,et al.  On Characterization of Feasible Interference Regions in Cognitive Radio Networks , 2016, IEEE Transactions on Communications.

[8]  Sherali Zeadally,et al.  Spectrum Assignment in Cognitive Radio Networks: A Comprehensive Survey , 2013, IEEE Communications Surveys & Tutorials.

[9]  Insoo Koo,et al.  Throughput Maximization for a Primary User with Cognitive Radio and Energy Harvesting Functions , 2014, KSII Trans. Internet Inf. Syst..

[10]  Ioannis Mitliagkas,et al.  Joint Power and Admission Control for Ad-Hoc and Cognitive Underlay Networks: Convex Approximation and Distributed Implementation , 2011, IEEE Transactions on Wireless Communications.

[11]  Dong In Kim,et al.  Joint rate and power allocation for cognitive radios in dynamic spectrum access environment , 2008, IEEE Transactions on Wireless Communications.

[12]  Ana I. Pérez-Neira,et al.  Distributed power control with received power constraints for time-area-spectrum licenses , 2016, Signal Process..

[13]  Octavia A. Dobre,et al.  Radio Resource Allocation Techniques for Efficient Spectrum Access in Cognitive Radio Networks , 2016, IEEE Communications Surveys & Tutorials.

[14]  Koduvayur P. Subbalakshmi,et al.  Dynamic Spectrum Access with QoS and Interference Temperature Constraints , 2007, IEEE Trans. Mob. Comput..

[15]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[16]  Daniel K. C. So,et al.  Hybrid Overlay/Underlay Cognitive Radio Network With MC-CDMA , 2014, IEEE Transactions on Vehicular Technology.

[17]  Wei-Ping Zhu,et al.  A pth order moment based spectrum sensing for cognitive radio in the presence of independent or weakly correlated Laplace noise , 2017, Signal Process..

[18]  Ekram Hossain,et al.  On Joint Power and Admission Control in Underlay Cellular Cognitive Radio Networks , 2015, IEEE Transactions on Wireless Communications.

[19]  F AkyildizIan,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks , 2006 .

[20]  Walaa Hamouda,et al.  Resource Allocation for Underlay Cognitive Radio Networks: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[21]  A. Allahyari,et al.  A centralized cooperative power control algorithm for cognitive radio networks , 2012, 20th Iranian Conference on Electrical Engineering (ICEE2012).

[22]  Ali Jamshidi,et al.  Probabilistic spectrum sensing data falsification attack in cognitive radio networks , 2017, Signal Process..

[23]  Wolfgang Utschick,et al.  Spatial interference shaping for underlay MIMO cognitive networks , 2017, Signal Process..