Journal
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume 20, Issue 10, Pages 6343-6355Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2021.3073570
Keywords
NOMA; Wireless networks; Optimization; Resource management; Trajectory optimization; Unmanned aerial vehicles; Base stations; UAV; energy efficiency; user scheduling; power allocation; trajectory optimization; matching theory; successive convex optimization
Funding
- National Key Research and Development Program of China [2020YFB1708800]
- National Natural Science Foundation of China [61822104, 61771044]
- Fundamental Research Funds for the Central Universities [FRF-TP-19-002C1, RC1631]
- Beijing Top Discipline for Artificial Intelligent Science and Engineering, University of Science and Technology Beijing
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This paper investigates the use of replacing base stations with unmanned aerial vehicles for communication, proposing a joint resource allocation and UAV trajectory optimization algorithm to maximize total energy efficiency. The algorithm is validated through numerical results, demonstrating its rationality.
Replacing base stations with unmanned aerial vehicles (UAVs) to serve the communication of ground users has attracted a lot of attention recently. In this paper, we study the joint resource allocation and UAV trajectory optimization for maximizing the total energy efficiency in UAV-based non-orthogonal multiple access (NOMA) downlink wireless networks with the quality of service (QoS) requirements. To handle the user scheduling problem, a heuristic algorithm based on matching and swapping theory is proposed first to allocate users that access UAV in each subperiod, then the transmit power allocation problem which considers the maximum transmit power and minimum user date rate is transformed to a convex optimization problem using logarithmic approximation. Meanwhile, the successive convex optimization is used in UAV trajectory optimization problem and a joint optimization algorithm is presented with the algorithm's convergence and computational complexity. Finally, numerical results are provided to support the rationality of the proposed algorithm.
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