4.7 Article Proceedings Paper

Energy Efficiency Optimization for NOMA UAV Network With Imperfect CSI

Journal

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
Volume 38, Issue 12, Pages 2798-2809

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2020.3005489

Keywords

Resource management; NOMA; Optimization; Scheduling; Unmanned aerial vehicles; 5G mobile communication; Fading channels; Unmanned aerial vehicle; energy efficiency; 5G; resource allocation; imperfect channel state information

Funding

  1. National Natural Science Foundation of China [61822104, 61771044]
  2. National Key R&D Program of China [2019YFB1803304]
  3. Beijing Natural Science Foundation [L172025, L172049]
  4. 111 Project [B170003]
  5. Fundamental Research Funds for the Central Universities [FRF-TP-19-002C1, RC1631]
  6. Beijing Top Discipline for Artificial Intelligent Science and Engineering
  7. University of Science and Technology Beijing

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Unmanned aerial vehicles (UAVs) are developing rapidly owing to flexible deployment and access services as air base stations. However, the channel errors of low-altitude communication links formed by mobile deployment of UAVs cannot be ignored. And the energy efficiency of the UAVs communication with imperfect channel state information (CSI) hasnt been well studied yet. Therefore, we focus on system performance optimization in non-orthogonal multiple access (NOMA) UAV network considering imperfect CSI between the UAV and users. A suboptimal resource allocation scheme including user scheduling and power allocation is designed for maximizing energy efficiency. Because of the nonconvexity of optimization function with an probability constraint for imperfect CSI, the original problem is converted into a non-probability problem and then decoupled into two convex subproblems. First, a user scheduling method is applied in the two-side matching of users and subchannels by the difference of convex programming. Then based on user scheduling, the energy efficiency in UAV cells is optimized through a suboptimal power allocation algorithm by successive convex approximation method. The simulation results prove that the proposed algorithm is effective compared with existing resource allocation schemes.

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