Unmanned aerial vehicles (UAVs) facilitate information collection greatly in the Internet-of-Things (IoT) systems due to their superior flexibility and mobility. On the other hand, nonorthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in fifth-generation networks. The integration of NOMA into UAV-Assisted wireless networks shows great potential, but how to determine the user grouping and power allocation in NOMA according to the high mobility of UAV is challenging. In this article, we propose a general NOMA-enabled UAV-Assisted data collection (NUDC) protocol to maximize the sum rate of a wireless sensor network (WSN), where the location of UAV, sensor grouping, and power control are jointly considered. Moreover, a joint signal-To-interference ratio (SIR) hypergraph-based grouping and power control (SHG-PC) NOMA scheme is provided to obtain the appropriate sensor grouping and the optimal power control solutions efficiently, in which the hypergraph and the greedy coloring algorithm are exploited to find out the optimized group relationships. Extensive simulation results demonstrate the efficiency of our proposed protocol.
Bibliographical noteFunding Information:
Manuscript received March 10, 2020; revised May 25, 2020; accepted June 19, 2020. Date of publication June 26, 2020; date of current version December 21, 2020. This work was supported in part by the National Key Research and Development Project under Grant 2019YFB2102300 and Grant 2019YFB2102301; in part by the National Natural Science Foundation of China under Grant 61936014 and Grant 61901302; in part by the Scientific Research Project of Shanghai Science and Technology Committee under Grant 19511103302; in part by the Fundamental Research Funds for the Central Universities; in part by the Open Research Fund from Shandong Provincial Key Laboratory of Wireless Communication Technologies under Grant SDKLWCT-2019-02; in part by the Open Research Fund from Shenzhen Research Institute of Big Data under Grant 2019ORF01014; and in part by the National Science Foundation under Grant CPS-1932413 and Grant ECCS-1935915. (Corresponding authors: Shengjie Zhao; Rongqing Zhang.) Weichao Chen is with the Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, China (e-mail: email@example.com).
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- Data collection
- nonorthogonal multiple access (NOMA)
- unmanned aerial vehicle (UAV)