Abstract
In this paper, we investigate an unmanned aerial vehicle (UAV)-enabled non-orthogonal multiple access (NOMA) systems, where UAV acts as a full-duplex (FD) relay to help the communication between the base station (BS) and two NOMA users. Assume that the UAV follows a circular trajectory and applies decode-and-forward (DF) strategy. Using simultaneous wireless information and power transfer (SWIPT), the UAV harvests energy from the BS in the first time slot and self-interference due to FD mode in the second time slot. By the joint optimization of beamforming and time allocation ratio, we aim at maximizing sum throughput of the whole system and harvested energy at UAV. To solve two highly non-convex problem, we propose the corresponding algorithms based on inner approximation method, respectively, which can converge to at least optimal solutions in few steps. In terms of two different system performances, numerical results can verify that the effectiveness of the proposed scheme. We also find the optimal azimuth angle of UAV’s circular trajectory by simulation.
Highlights
With high mobility deployment flexibility and costeffectiveness, the application of unmanned aerial vehicles (UAVs) in wireless communication has drawn wide attention in academia, research industries, and government [1,2,3]
In the first time slot αT, the UAV harvests the energy from the base station (BS)
2.4.1 Sum data rate maximization Given the trajectory of UAV, we aim at maximizing sum throughput of system by the joint optimization of four beamforming vectors (w1, w2, s1, s2) and time allocation coefficient α in this paper
Summary
With high mobility deployment flexibility and costeffectiveness, the application of unmanned aerial vehicles (UAVs) in wireless communication has drawn wide attention in academia, research industries, and government [1,2,3]. Yin et al [41] investigated the end-to-end throughput maximization for UAV-assisted cooperative communication system, where the UAV act as a dedicated relay to help data transmission between the BS and the destination by employing PS protocol. To solve this problem, the author alternately solved the two subproblems by the joint optimization of the UAV’s power profile, power-splitting ratio profile, and trajectory. We pay attention to the time switching mechanism to enable the UAV to harvest energy and relay data in two phases respectively such that communication reliability is improved. IN denotes the N × N identity matrix; CN×M and HN+ denote the N × M complex matrices and N × N Hermitian matrices, respectively. · 2 means the Euclidean norm of a vector while · F means the Frobenius norm of a matrix; null (·) denotes the null space of a vector or matrix. x ∼ CN (μ, ) means the vector x is a complex Gaussian variable with mean μ and covariance
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