NOMA-Aided UAV Data Collection System: Trajectory Optimization and Communication Design

Abstract

Unmanned aerial vehicle (UAV) communication has been deemed as a promising technology to collect data for the Internet of Things (IoT) in inaccessible areas. However, due to the limited UAV flight time, traditional UAV communication may not be competent for large-scale IoT data collection. This paper considers integrating non-orthogonal multiple access (NOMA) into UAV communication systems to collect data for large-scale IoT devices within UAV flight time. We aim to minimize the total energy consumption of IoT devices while ensuring data collection, by jointly optimizing UAV trajectory, IoT device scheduling and transmit power. The formulated problem is a mixed integer non-convex problem, which is challenging to solve in general. We propose a data collection optimization algorithm (DCOA) to solve it by applying the Generalized Benders Decomposition (GBD) and successive convex approximation (SCA) techniques. Then, a greedy algorithm (GA) is also proposed to reduce complexity by simplifying the optimization of UAV trajectory and IoT device scheduling. Finally, the numerical results demonstrate that, compared with traditional UAV communication systems, the NOMA-aided UAV system performs better in terms of data collection and lower total energy consumption of IoT devices can be achieved by DCOA. INDEX TERMS Unmanned aerial vehicle (UAV) communication, non-orthogonal multiple access (NOMA), data collection, Internet of Things (IoT), energy consumption minimization.

UAV-Assisted Data Collection for Internet of Things: A Survey

“…Aiming to minimizing the total energy consumption of sensors while ensuring data collection through joint optimization of trajectory of UAV, data transmission scheduling of sensors and transmit power. Zhao et al [102] innovatively use non-orthogonal multiple-access (NOMA) in UAV data collection. In addition, the generalized benders decomposition (GBD) is used to decouple sensers scheduling and transmit power.…”

Energy-Efficiency Optimization for D2D Communications Underlaying UAV-Assisted Industrial IoT Networks With SWIPT

“…In [17], the authors investigated the time sharing (TS) NOMA networks, and developed a resource allocation algorithm to maximize the EE of the UAV-TS-NOMA networks. Besides, the energy consumption minimization problem for the NOMAaided UAV data collection system was investigated in [18]. To guarantee the quality of service (QoS), the authors developed a data collection optimization algorithm using the generalized benders decomposition methods.…”

Deep Reinforcement Learning-Based UAV Data Collection and Offloading in NOMA-Enabled Marine IoT Systems

“…Nonorthogonal multiple access (NOMA) technology is considered a promising technology in the 5G era [12]. Compared with orthogonal multiple access (OMA) technology, NOMA greatly improves the spectrum efficiency in the presence of limited spectrum resources by allowing multiple users to access simultaneously in the same channel and relying on power domain multiplexing and successive interference cancellation (SIC) decoding technology [13][14][15].…”

“…Recently, much research has applied NOMA technology to UAV-assisted wireless communication system. Zhao et al in [13] investigated a NOMA-assisted UAV large-scale IoT data collection system and proposed a data collection optimization algorithm. The results show that, compared with the traditional UWCS, the NOMA-based UWCS has better performance in data collection.…”