论文标题
无人机启用双重增长的Noma的自适应解码机制
Adaptive Decoding Mechanisms for UAV-enabled Double-Uplink Coordinated NOMA
论文作者
论文摘要
在本文中,我们提出了一种新型的自适应解码机制(ADM),用于无人驾驶飞机(UAV)启用的上行链路(UL)非正交多重访问(NOMA)通信。具体而言,考虑到一个苛刻的无人机环境,在地面到地面链接通常无法使用,拟议的ADM巩固了传统的UL-NOMA系统的挑战性问题,其性能对发射机的统计渠道状态信息和接收器的解码顺序敏感。为了评估ADM的性能,我们得出了系统中断概率(OP)和系统吞吐量的封闭形式表达式。在“绩效分析”部分中,我们为实用的空对面和地面渠道提供了新颖的表达,同时考虑到UL-NOMA中不完善的连续干扰取消(SIC)的实际实施。此外,可以采用获得的表达来表征在基于伽马(MG)的基于分布的褪色通道的混合物下的各种系统的OP。接下来,我们提出了一种基于梯度下降的算法,以获得功率分配系数,从而导致相对于无人机轨迹上每个位置的最大吞吐量。为了确定拟议的ADM在非组织环境中的重要性,我们认为地面用户和无人机是根据随机路点移动性(RWM)和参考点组迁移率(RPGM)模型移动的。还提供了距离分布的准确公式。数值解决方案表明,ADM增强的NOMA不仅优于正交多访问(OMA),而且还可以提高具有无人机的UL-Noma的性能,即使在移动环境中也是如此。
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.