Utility Maximization for Multi-UAV-Assisted IoT Sensor Systems With NOMA

In this article, we consider a multi-unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) sensor system with nonorthogonal multiple access (NOMA) and aim to maximize the sum utility of bit rates of all IoT sensors through the joint optimization of user association, 3-D UAV placement, deco...

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Veröffentlicht in:IEEE sensors journal Jg. 24; H. 17; S. 28233 - 28250
Hauptverfasser: Tang, Rui, Zhou, Wenli, Zhang, Ruizhi, Xu, Yongjun, Li, Xingwang, Zhang, Haibo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.09.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Autonomous aerial vehicles
Convexity
Decoding
Genetic algorithms
Greedy algorithms
Internet of Things
Machine learning
Mathematical programming
Mixed integer
NOMA
Nonconvex optimization
Nonorthogonal multiple access
nonorthogonal multiple access (NOMA)
Optimization
Placement
Quadratic programming
Resource allocation
Resource management
Sensor systems
Sensors
unmanned aerial vehicle (UAV) swarm
Unmanned aerial vehicles
ISSN:1530-437X, 1558-1748
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Zusammenfassung:In this article, we consider a multi-unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) sensor system with nonorthogonal multiple access (NOMA) and aim to maximize the sum utility of bit rates of all IoT sensors through the joint optimization of user association, 3-D UAV placement, decoding ordering, and power allocation. To cope with the formulated mixed-integer nonconvex problem, we decompose it into four subproblems relating to each dimension of radio resources under the alternating optimization framework. For the user association or decoding ordering subproblems involving only binary variables, a novel algorithm is proposed by leveraging variable relaxation, fractional programming, and efficient bounding. For the nonconvex 3-D UAV placement subproblem, the sequential quadratic programming (SQP) algorithm is applied to obtain an efficient suboptimal solution by solving a sequence of quadratic programming problems that are iteratively modified. For the power allocation subproblem, the global-optimal solution is achieved based on auxiliary variables, variable transformation, and convex optimization. Simulation results show that the proposed resource allocation strategy outperforms the state-of-the-art benchmarks that are based on the greedy algorithm, the K-means (KM) algorithm, the geometric center-based algorithm, and the deep reinforcement learning, and strikes a good balance between performance and complexity in contrast to those based on the genetic algorithm and the Brute-force (BF) search.
Bibliographie:ObjectType-Article-1
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3432665