Joint Power, Bandwidth, and Subchannel Allocation in a UAV-Assisted DFRC Network

作     者：Zhang, Haowei Liu, Weijian Zhang, Qun Zhang, Lin Liu, Baobao Xu, He-Xiu 

作者机构：Air Force Engn Univ Air Def & Missile Def Coll Xian 710051 Shaanxi Peoples R China Wuhan Elect Informat Inst Wuhan 430019 Hubei Peoples R China Air Force Engn Univ Informat & Nav Inst Xian 710077 Shaanxi Peoples R China Xian Polytech Univ Sch Comp Sci & Technol Xian 710048 Shaanxi Peoples R China 

出 版 物：《IEEE INTERNET OF THINGS JOURNAL》 (IEEE Internet Things J.)

年 卷 期：2025年第12卷第9期

页      面：11633-11651页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [62071482, 62471348] Shaanxi Association of Science and Technology Youth Talent Support Program Project 

主　　题：Radar Resource management Interference Target tracking Radar tracking Bandwidth Optimization models Autonomous aerial vehicles Signal to noise ratio Channel allocation Alternating ascent-descent method (AADM) dual-function radar and communication (DFRC) system joint resource allocation strategy uncrewed aerial vehicle (UAV)-assisted Internet of Things (IoT) 

摘      要：Uncrewed aerial vehicle (UAV)-assisted joint radar and communication (JRC) systems are widely adopted in Internet of Things applications. This is due to their convenience, affordability, and space and resource-saving. A joint power, bandwidth, and subchannel allocation (JPBSA) strategy is proposed for a UAV-assisted dual-function radar and communication (DFRC) network. The predicted posterior Cram & eacute;r-Rao lower bound (PCRLB) is utilized to measure the target tracking accuracy. The optimization model is established as minimizing the sum of weighted predicted PCRLBs while meeting the communication data ratio (CDR) requirements, total power and bandwidth budget. It is shown that the JPBSA problem involves a mixed-integer programming (MIP) problem. Even worse, the bandwidth and subchannel are coherent. A three-stage alternating optimization method (TSAOM) is constructed for the solution. By integrating the radar and communication power allocation as a whole, the alternating ascent-descent method (AADM) is employed to solve the power allocation. Then, two propositions are proposed to provide the upper bounds for bandwidth allocation. Finally, the subchannel allocation is solved using a greedy search-based method. Simulation results confirm the effectiveness and efficiency of the proposed method, compared with the state-of-the-art methods. It also shows that using the PCRLB as the optimization metric is better than the signal-to-interference-plus-noise ratio (SINR) and mutual information (MI).