Optimal Resource Allocation for Underlay Cognitive Radio Networks

In order to improve the effective utilization of available resources in the traditional wireless network, this paper studies the optimization of resource allocation (RA) in the underlay cognitive radio network (CRN). Our goal is to maximize the sum rate of the whole system (e.i., primary users (PUs) and secondary users (SUs)), taking into account the constraints of interference temperature (IT) and minimum rate, and the Quality of Service (QoS) guarantees. A heuristic algorithm is proposed to solve the non-convex non-linear programming optimization problem. Theoretical analysis and simulation results show that this algorithm can effectively reduce the power interference to the PUs, maximize the transmission rate of PUs and SUs, and improve resource utilization of the CRN.

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

[2]  Mehdi Ghamari Adian,et al.  Spectrum sharing and power allocation in multi-input-multi-output multi-band underlay cognitive radio networks , 2013, IET Commun..

[3]  Georgios B. Giannakis,et al.  Resource Allocation for Interweave and Underlay CRs Under Probability-of-Interference Constraints , 2012, IEEE Journal on Selected Areas in Communications.

[4]  Norman C. Beaulieu,et al.  Energy-Efficient Optimal Power Allocation for Fading Cognitive Radio Channels: Ergodic Capacity, Outage Capacity, and Minimum-Rate Capacity , 2016, IEEE Transactions on Wireless Communications.

[5]  Xiaohui Zhao,et al.  Distributed power control for multiuser cognitive radio networks with quality of service and interference temperature constraints , 2015, Wirel. Commun. Mob. Comput..

[6]  Mona Shokair,et al.  Proposed Scheme for Maximization of Minimal Throughput in MIMO Underlay Cognitive Radio Networks , 2017, Wirel. Pers. Commun..

[7]  Feifei Gao,et al.  User Assignment, Power Allocation, and Mode Selection Schemes in Cognitive Radio Networks , 2014, 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall).

[8]  Jian Sun,et al.  Channel allocation and power control scheme over interference channels with QoS constraints , 2017, 2017 13th IEEE International Conference on Control & Automation (ICCA).

[9]  Hyuck M. Kwon,et al.  MIMO Cognitive Radio User Selection With and Without Primary Channel State Information , 2016, IEEE Transactions on Vehicular Technology.

[10]  Luis M. Lopez-Ramos,et al.  Joint sensing and resource allocation for underlay cognitive radios , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).