Residual Energy Analysis in Cognitive Radios with Energy Harvesting UAV under Reliability and Secrecy Constraints

The integration of unmanned aerial vehicles (UAVs) with a cognitive radio (CR) technology can improve the spectrum utilization. However, UAV network services demand reliable and secure communications, along with energy efficiency to prolong battery life. We consider an energy harvesting UAV (e.g., surveillance drone) flying periodically in a circular track around a ground-mounted primary transmitter. The UAV, with limited-energy budget, harvests radio frequency energy and uses the primary spectrum band opportunistically. To obtain intuitive insight into the performance of energy-harvesting, and reliable and secure communications, the closed-form expressions of the residual energy, connection outage probability, and secrecy outage probability, respectively, are analytically derived. We construct the optimization problems of residual energy with reliable and secure communications, under scenarios without and with an eavesdropper, respectively, and the analytical solutions are obtained with the approximation of perfect sensing. The numerical simulations verify the analytical results and identify the requirements of length of sensing phase and transmit power for the maximum residual energy in both reliable and secure communication scenarios. Additionally, it is shown that the residual energy in secure communication is lower than that in reliable communication.

[1]  Xin Liu,et al.  Spectrum Sensing Optimization in an UAV-Based Cognitive Radio , 2018, IEEE Access.

[2]  Phu Tran Tin,et al.  Secrecy Performance Enhancement for Underlay Cognitive Radio Networks Employing Cooperative Multi-Hop Transmission with and without Presence of Hardware Impairments , 2019, Entropy.

[3]  Santi P. Maity,et al.  On Residual Energy Maximization in Energy Harvesting Cognitive Radio Network , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).

[4]  Waqas Khalid,et al.  Sensing and Utilization of Spectrum with Cooperation Interference for Full-Duplex Cognitive Radio Networks , 2019, 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN).

[5]  Md. Arifur Rahman,et al.  Joint Relay Selection and Power Allocation through a Genetic Algorithm for Secure Cooperative Cognitive Radio Networks , 2018, Sensors.

[6]  Heejung Yu,et al.  Physical layer security based on NOMA and AJ for MISOSE channels with an untrusted relay , 2020, Future Gener. Comput. Syst..

[7]  Yang Gao,et al.  An Effective Multi-Objective Optimization Algorithm for Spectrum Allocations in the Cognitive-Radio-Based Internet of Things , 2018, IEEE Access.

[8]  Moon Ho Lee,et al.  Enhancing Security of Primary User in Underlay Cognitive Radio Networks With Secondary User Selection , 2018, IEEE Access.

[9]  Octavia A. Dobre,et al.  Secrecy Performance of Small-Cell Networks With Transmitter Selection and Unreliable Backhaul Under Spectrum Sharing Environment , 2019, IEEE Transactions on Vehicular Technology.

[10]  Yueming Cai,et al.  Physical Layer Security in Cognitive Radio Inspired NOMA Network , 2019, IEEE Journal of Selected Topics in Signal Processing.

[11]  Hui-Ming Wang,et al.  On physical-layer security in underlay cognitive radio networks with full-duplex wireless-powered secondary system , 2016, IEEE Access.

[12]  Zhiguo Ding,et al.  Secure Transmission Design in HARQ Assisted Cognitive NOMA Networks , 2020, IEEE Transactions on Information Forensics and Security.

[13]  Waqas Khalid,et al.  Spatial–Temporal Sensing and Utilization in Full Duplex Spectrum-Heterogeneous Cognitive Radio Networks for the Internet of Things , 2019, Sensors.

[14]  Moussa Ayyash,et al.  Spectrum Assignment in Cognitive Radio Networks for Internet-of-Things Delay-Sensitive Applications Under Jamming Attacks , 2018, IEEE Internet of Things Journal.

[15]  Özgür B. Akan,et al.  On the Utilization of Spectrum Opportunity in Cognitive Radio Networks , 2016, IEEE Communications Letters.

[16]  Heejung Yu,et al.  Residual Energy Analysis with Physical-Layer Security for Energy-Constrained UAV Cognitive Radio Systems , 2020, 2020 International Conference on Electronics, Information, and Communication (ICEIC).

[17]  Heejung Yu,et al.  Optimal primary pilot power allocation and secondary channel sensing in cognitive radios , 2016, IET Commun..

[18]  Danijela Cabric,et al.  Primary user localization in cognitive radio networks using sectorized antennas , 2013, 2013 10th Annual Conference on Wireless On-demand Network Systems and Services (WONS).

[19]  Waqas Khalid,et al.  Optimal sensing performance for cooperative and non-cooperative cognitive radio networks , 2017, Int. J. Distributed Sens. Networks.

[20]  Miroslav Voznák,et al.  Secrecy Performance of Underlay Cooperative Cognitive Network Using Non-Orthogonal Multiple Access with Opportunistic Relay Selection , 2019, Symmetry.

[21]  Bing Chen,et al.  Full Spectrum Sharing in Cognitive Radio Networks Toward 5G: A Survey , 2018, IEEE Access.

[22]  Fan Zhang,et al.  Outage Probability Minimization for Energy Harvesting Cognitive Radio Sensor Networks , 2017, Sensors.

[23]  Runqun Xiong,et al.  DroneTank: Planning UAVs’ Flights and Sensors’ Data Transmission under Energy Constraints , 2018, Sensors.

[24]  Xuemin Shen,et al.  Dynamic Channel Access to Improve Energy Efficiency in Cognitive Radio Sensor Networks , 2016, IEEE Transactions on Wireless Communications.

[25]  Heejung Yu,et al.  Sum Utilization of Spectrum with Spectrum Handoff and Imperfect Sensing in Interweave Multi-Channel Cognitive Radio Networks , 2018, Sustainability.

[26]  Howon Lee,et al.  What is 5G? Emerging 5G Mobile Services and Network Requirements , 2017 .

[27]  Dan Wang,et al.  From IoT to 5G I-IoT: The Next Generation IoT-Based Intelligent Algorithms and 5G Technologies , 2018, IEEE Communications Magazine.

[28]  Zhixiang Deng,et al.  Cognitive Radio Network With Energy-Harvesting Based on Primary and Secondary User Signals , 2018, IEEE Access.