Utilization-Oriented Spectrum Allocation in an Underlay Cognitive Radio Network

Spectrum access and assignment in cognitive radio networks (CRNs) are hot topics in wireless communications. Dynamic spectrum access and assignment could greatly improve spectrum resource efficiency and help to satisfy the explosively increasing communication demands of wireless devices. The problem of spectrum access and assignment in an underlay CRN is considered in this paper. The problem is modeled as a global optimization problem by considering the interference between primary and secondary users, the interference between secondary users and the utilization of the entire network. The utilization of the underlay CRN is maximized in the optimization model. To effectively solve this combinatorial optimization problem, a modified binary artificial bee colony algorithm is proposed. Numerical experiments are conducted to simulate the network and verify the proposed assignment method. The simulation results show that the proposed assignment method offers good performance in improving the spectrum usage efficiency and reducing the interference among primary and secondary users. Furthermore, the proposed algorithm is also very effective in achieving optimal allocation solutions compared with other methods.

[1]  Fuhui Zhou,et al.  Resource Allocation in Wireless Powered Cognitive Radio Networks Based on a Practical Non-Linear Energy Harvesting Model , 2017, IEEE Access.

[2]  Nirwan Ansari,et al.  Maximizing Network Capacity of Cognitive Radio Networks by Capacity-Aware Spectrum Allocation , 2015, IEEE Transactions on Wireless Communications.

[3]  Jiasong Mu,et al.  Optimization of relay placement and power allocation for decode-and-forward cooperative relaying over correlated shadowed fading channels , 2014, EURASIP J. Wirel. Commun. Netw..

[4]  Xin Zhang,et al.  Adaptive multiclass support vector machine for multimodal data analysis , 2017, Pattern Recognit..

[5]  Feng Zhao,et al.  Joint Beamforming and Power Allocation for Cognitive MIMO Systems Under Imperfect CSI Based on Game Theory , 2013, Wireless Personal Communications.

[6]  Sha Pang,et al.  Particle swarm optimization algorithm for multi-salesman problem with time and capacity constraints , 2013 .

[7]  Guoping He,et al.  Parallel Variable Distribution Algorithm for Constrained Optimization with Nonmonotone Technique , 2013, J. Appl. Math..

[8]  Feng Zhao,et al.  Optimal Time Allocation for Wireless Information and Power Transfer in Wireless Powered Communication Systems , 2016, IEEE Transactions on Vehicular Technology.

[9]  Rui Liu,et al.  Device-to-Device Communications in Unlicensed Spectrum: Mode Selection and Resource Allocation , 2016, IEEE Access.

[10]  Feng Zhao,et al.  Interference alignment and game-theoretic power allocation in MIMO Heterogeneous Sensor Networks communications , 2016, Signal Process..

[11]  Edgar Alfredo Portilla-Flores,et al.  Hybrid Metaheuristic for Designing an End Effector as a Constrained Optimization Problem , 2017, IEEE Access.

[12]  Zhongpei Zhang,et al.  Optimal Resource Allocation for Harvested Energy Maximization in Wideband Cognitive Radio Network With SWIPT , 2017, IEEE Access.

[13]  Chen Xiao-hong,et al.  Distributed channel allocation algorithm based on classifying of services and channels in cognitive radio networks , 2012 .

[14]  Shiu Yin Yuen,et al.  A directional mutation operator for differential evolution algorithms , 2015, Appl. Soft Comput..

[15]  Bin Liu,et al.  Buffer-Aware Resource Allocation Scheme With Energy Efficiency and QoS Effectiveness in Wireless Body Area Networks , 2017, IEEE Access.

[16]  G. He,et al.  Parallel algorithms for large-scale linearly constrained minimization problem , 2014, Acta Mathematicae Applicatae Sinica, English Series.

[17]  AKHIL GUPTA,et al.  A Survey of 5G Network: Architecture and Emerging Technologies , 2015, IEEE Access.

[18]  Jie Jia,et al.  A genetic approach on cross-layer optimization for cognitive radio wireless mesh network under SINR model , 2015, Ad Hoc Networks.

[19]  Geoffrey Ye Li,et al.  Joint Mode Selection and Resource Allocation for Device-to-Device Communications , 2014, IEEE Transactions on Communications.

[20]  Zhou Wu,et al.  Dynamic battery equalization with energy and time efficiency for electric vehicles , 2017 .

[21]  Jing Gao,et al.  End-to-End Delay Analysis in Cognitive Radio Ad Hoc Networks with Different Traffic Models , 2015, Mob. Inf. Syst..

[22]  Haibo Mei,et al.  Multi-Layer Cloud-RAN With Cooperative Resource Allocations for Low-Latency Computing and Communication Services , 2017, IEEE Access.

[23]  Feng Zhao,et al.  Group buying spectrum auction algorithm for fractional frequency reuse cognitive cellular systems , 2017, Ad Hoc Networks.

[24]  Xin Zhang,et al.  A binary artificial bee colony algorithm for constructing spanning trees in vehicular ad hoc networks , 2017, Ad Hoc Networks.

[25]  Xing Zhang,et al.  Optimization of Resource Allocation and User Association for Energy Efficiency in Future Wireless Networks , 2017, IEEE Access.

[26]  Zhu Han,et al.  Spectrum Allocation and Power Control for Non-Orthogonal Multiple Access in HetNets , 2017, IEEE Transactions on Wireless Communications.

[27]  Li Guo,et al.  A bio-inspired approach for cognitive radio networks , 2012 .

[28]  Tie Qiu,et al.  An Interference Coordination-Based Distributed Resource Allocation Scheme in Heterogeneous Cellular Networks , 2017, IEEE Access.

[29]  Xianneng Li,et al.  Artificial bee colony algorithm with memory , 2016, Appl. Soft Comput..

[30]  Navrati Saxena,et al.  Next Generation 5G Wireless Networks: A Comprehensive Survey , 2016, IEEE Communications Surveys & Tutorials.

[31]  Yongli Wang,et al.  A decomposition method for large-scale box constrained optimization , 2014, Appl. Math. Comput..

[32]  Ben Y. Zhao,et al.  Utilization and fairness in spectrum assignment for opportunistic spectrum access , 2006, Mob. Networks Appl..