In this paper, we propose a novel adaptive decoding mechanism (ADM) for the unmanned aerial vehicle (UAV)-enabled uplink (UL) non-orthogonal multiple access (NOMA) communications. Specifically, considering a harsh UAV environment, where ground-to-ground links are regularly unavailable, the proposed ADM overcomes the challenging problem of conventional UL-NOMA systems whose performance is sensitive to the transmitter's statistical channel state information and the receiver's decoding order. To evaluate the performance of the ADM, we derive closed-form expressions for the system outage probability (OP) and system throughput. In the performance analysis section, we provide novel expressions for practical air-to-ground and ground-to-air channels, while taking into account the practical implementation of imperfect successive interference cancellation (SIC) in UL-NOMA. Moreover, the obtained expression can be adopted to characterize the OP of various systems under a Mixture of Gamma (MG) distribution-based fading channels. Next, we propose a sub-optimal Gradient Descent-based algorithm to obtain the power allocation coefficients that result in maximum throughput with respect to each location on UAV's trajectory. To determine the significance of the proposed ADM in nonstationary environments, we consider the ground users and the UAV to move according to the Random Waypoint Mobility (RWM) and Reference Point Group Mobility (RPGM) models, respectively. Accurate formulas for the distance distributions are also provided. Numerical solutions demonstrate that the ADM-enhanced NOMA not only outperforms Orthogonal Multiple Access (OMA), but also improves the performance of UAV-enabled UL-NOMA even in mobile environments.

翻译：本文提出了一种新颖的自适应解码机制（ADM），用于无人机（UAV）赋能的上行链路（UL）非正交多址接入（NOMA）通信。具体而言，考虑到严苛的无人机环境（其中地对地链路通常不可用），所提出的ADM克服了传统UL-NOMA系统的关键问题——其性能对发射机的统计信道状态信息和接收机的解码顺序敏感。为评估ADM的性能，我们推导了系统中断概率（OP）和系统吞吐量的闭式表达式。在性能分析部分，我们针对实际空对地与地对空信道提出了新颖表达式，并考虑了UL-NOMA中不完美连续干扰消除（SIC）的实际实现。此外，所得表达式可适用于表征基于混合Gamma（MG）分布衰落信道的多种系统的中断概率。随后，我们提出了一种基于梯度下降的次优算法，以获取能针对无人机轨迹上每个位置实现最大吞吐量的功率分配系数。为确定所提ADM在非平稳环境中的重要性，我们分别采用随机路点移动性（RWM）和参考点组移动性（RPGM）模型来模拟地面用户和无人机的移动，并提供了距离分布的精确公式。数值解表明，增强ADM的NOMA不仅优于正交多址接入（OMA），而且在移动环境下也能提升无人机赋能UL-NOMA的性能。