UAV-Assisted Data Collection With Nonorthogonal Multiple Access

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.

Automated summary

This article proposes a general NOMA-enabled UAV-assisted data collection (NUDC) protocol to maximize the sum rate of a wireless sensor network (WSN) by considering the UAV location, sensor grouping, and power control. A joint signal-to-interference ratio (SIR) hypergraph-based grouping and power control (SHG-PC) NOMA scheme is provided to efficiently obtain appropriate sensor grouping and optimal power control solutions, utilizing hypergraph and greedy coloring algorithm to find optimized group relationships. Extensive simulation results demonstrate the efficiency of the proposed protocol.