Energy-Efficient Resource Allocation in Multiple UAVs-assisted Energy Harvesting-Powered Two-Hop Cognitive Radio Network

• Published in: IEEE sensors journal Vol. 23; no. 7; p. 1
• Main Authors: Xiao, He, Wu, Chun, Jiang, Hong, Deng, Li-Ping, Luo, Ying, Zhang, Qiu-Yun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Base stations; Batteries; Cognitive radio; end-to-end energy efficiency; Energy efficiency; Energy harvesting; Frank-Wolfe; inter-cell interference; Interference; Linear programming; Multiple UAVs; Optimization; Polynomials; Radio networks; Relays; Resource allocation; Resource management; Sensors; two-hop CRN; Unmanned aerial vehicles; Wireless communications; Wireless networks; Wireless sensor networks
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2023.3247436

The UAV-assisted energy harvesting-based cognitive wireless networks can solve the problems of spectrum scarcity, energy shortage, and challenging layout of communication facilities faced by traditional wireless networks, envisioned as a vital frontier communication technology in the 5G/6G era. In this paper, we consider a UAVs-assisted two-hop cognitive radio network with energy harvesting (UAVs-EH-DHCRN), where multiple UAVs are employed to act as air access points (AAPs) and relay nodes (RNs). Since all energy harvesting-powered secondary users (SUs) can simultaneously associate with AAPs by sharing the licensed spectrum of primary users (PUs), inter-cell interference (ICI) occurs among AAPs. Our goal is to maximize the end-to-end energy efficiency (EE) of the UAVs-EH-DHCRN while considering constraints like ICI, energy, minimum data rate, and interference to PUs. However, the formulated end-to-end EE maximization problem is a typical non-convex optimization problem with complex constraints that off-the-shelf optimization strategies cannot solve. Therefore, we propose a resource allocation algorithm called approximate convex policy concerning associations, power allocation, and placements (ACP-APP). Specifically, in ACP-APP, we introduce additional parameters to convert the fractional objective function into a polynomial function. Then, the optimal AAPs' placements, optimal associations between SUs and AAPs, and powers allocation are obtained alternately by leveraging the proposed circular-based recursive random search (CRRS), one-dimensional linear programming, and Frank-Wolfe (FW). Simulation results show that our proposed strategy can converge quickly to obtain the approximate optimal solution. Moreover, the ACP-APP can obtain significant end-to-end EE gains compared with benchmark schemes.