UAV-Enabled Uplink Non-Orthogonal Multiple Access System: Joint Deployment and Power Control

In order to overcome the inherent latency in multi-user orthogonal multiple access (OMA) unmanned aerial vehicle (UAV) networks. In this paper, we investigate a UAV-enabled uplink non-orthogonal multiple access (NOMA) network, where a UAV is deployed to collect the messages transmitted by the ground...

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Veröffentlicht in:IEEE transactions on vehicular technology Jg. 69; H. 9; S. 10090 - 10102
Hauptverfasser: Lu, Jinhui, Wang, Yuntian, Liu, Tingting, Zhuang, Zhihong, Zhou, Xiaobo, Shu, Feng, Han, Zhu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.09.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Complexity
Computational geometry
Convex analysis
Convexity
Iterative algorithms
Iterative methods
Mixed integer
Network latency
NOMA
Nonorthogonal multiple access
Optimization
Penalty function
Power control
Silicon carbide
Trajectory
UAV deployment
UAV networks
Unmanned aerial vehicles
Uplink
uplink NOMA
ISSN:0018-9545, 1939-9359
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Zusammenfassung:In order to overcome the inherent latency in multi-user orthogonal multiple access (OMA) unmanned aerial vehicle (UAV) networks. In this paper, we investigate a UAV-enabled uplink non-orthogonal multiple access (NOMA) network, where a UAV is deployed to collect the messages transmitted by the ground users. To maximize the users' sum-rate, we formulate a optimization problem, in which the UAV deployment position and the power control are jointly optimized subject to the transmission power constraints and the quality of service (QoS) constraints. However, this problem is a mixed integer non-convex optimization problem and thus it is difficult to solve directly. Therefore, we first transform the non-convex optimization problem into a convex one based on the first-order approximation technique and the penalty function method. Then, a successive convex approximate (SCA) technique based iterative algorithm as well as its initialization scheme are developed to find a suboptimal solution to the original problem. Afterwards, a low-complexity approximate algorithm is proposed to reduce the high computational complexity of the iterative algorithm. Numerical results show that our proposed iterative algorithm and low-complexity approximate algorithm can achieve a similar performance, and they can efficiently improve the sum-rate compared with OMA scheme and conventional NOMA scheme.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2020.3005732