4.7 Article Proceedings Paper

Joint Trajectory and Precoding Optimization for UAV-Assisted NOMA Networks

期刊

IEEE TRANSACTIONS ON COMMUNICATIONS
卷 67, 期 5, 页码 3723-3735

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCOMM.2019.2895831

关键词

Interference avoidance; non-orthogonal multiple access; precoding; trajectory optimization; unmanned aerial vehicle

资金

  1. National Natural Science Foundation of China [61871065]
  2. Open Research Fund of State Key Laboratory of Integrated Services Networks [ISN19-02]
  3. Fundamental Research Funds for the Central Universities [DUT17JC43, 7215433803]
  4. Xinghai Scholars Program
  5. Key Project of National Natural Science Foundation of China (NSFC) [61631015]
  6. UK EPSRC [EP/N005597/1]
  7. NSFC [61728101]
  8. H2020-MSCA-RISE-2015 [690750]
  9. EPSRC [EP/N005597/1] Funding Source: UKRI

向作者/读者索取更多资源

The explosive data traffic and connections in 5G networks require the use of non-orthogonal multiple access (NOMA) to accommodate more users. Unmanned aerial vehicle (UAV) can be exploited with NOMA to improve the situation further. In this paper, we propose a UAV-assisted NOMA network, in which the UAV and base station (BS) cooperate with each other to serve ground users simultaneously. The sum rate is maximized by jointly optimizing the UAV trajectory and the NOMA precoding. To solve the optimization, we decompose it into two steps. First, the sum rate of the UAV-served users is maximized via alternate user scheduling and UAV trajectory with its interference to the BS-served users below a threshold. Then, the optimal NOMA precoding vectors are obtained using two schemes with different constraints. The first scheme intends to cancel the interference from the BS to the UAV-served user, while the second one restricts the interference to a given threshold. In both schemes, the non-convex optimization problems are converted into tractable ones. An iterative algorithm is designed. Numerical results are provided to evaluate the effectiveness of the proposed algorithms for the hybrid NOMA and UAV network.

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