Relay Selection and Deployment for NOMA-Enabled Multi-AAV-Assisted WSN

• Published in: IEEE sensors journal Vol. 25; no. 9; pp. 16235 - 16249
• Main Authors: Zheng, Kechen, Fu, Junsheng, Liu, Xiaoying
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autonomous aerial vehicles; autonomous aerial vehicles (AAVs); Device-to-device (D2D) communication; Device-to-device communication; Greedy algorithms; NOMA; Nonorthogonal multiple access; nonorthogonal multiple access (NOMA); Numerical models; Optimization; Protocols; Relay; Relays; Remote control; Resource management; Three-dimensional displays; Throughput; Unmanned aerial vehicles; Wireless sensor networks; wireless sensor networks (WSNs)
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2025.3551916

With the rapid development of wireless sensor networks (WSNs), there has been a massive increase in the number of sensor nodes (SNs), making it increasingly difficult to provide satisfactory service for them. To tackle this problem, we study the multiautonomous aerial vehicle (AAV)-assisted WSN, where AAVs provide relay services for the remote control communication from the base station (BS) to SNs and the transmission of D2D pairs under the proposed time switching nonorthogonal multiple access (TS-NOMA) protocols. We formulate the total throughput maximization problem by jointly optimizing the relay selection, time allocation, and relay deployment, subject to the quality-of-service (QoS) constraint. As the relay selection and time allocation could hardly be solved separately due to the information causality between them, we divide the formulated problem into the relay selection and time allocation subproblem, and relay deployment subproblem, and solve it by solving the two subproblems alternately. For the first subproblem, we propose a stochastic learning-based relay selection and greedy time allocation (SRSGTA) algorithm with strong global search capability. For the second subproblem, we propose an improved whale optimization (IWO) algorithm with advantages in both convergence speed and global search capability. Numerical results validate the superiority of the proposed TS-NOMA protocol and the proposed algorithms in terms of the total throughput.