This paper aims to optimize the different network parameters in a distributed manner for delay-constrained flying ad hoc networks (FANETs) without the global network topology information. To this end, each Unmanned Aerial Vehicle (UAV) calculates the average interference level during a certain time period to indicate the channel states. Next, we formulate the distributed optimization problem as a utility maximization problem, which jointly optimizes power control, rate allocation and delay-constrained routing. To obtain a distributed solution, a dual method is proposed to eliminate the link capacity constraint, and a primal decomposition method is employed to decouple the end-to-end delay constraint. Built on these two methods above, a distributed optimization algorithm is proposed in this work by considering the estimated one-hop delay for each transmission, which only uses the local channel information to optimize the sub-problems and limit the end-to-end delay. Finally, we deduce the relationship between the primal and dual solutions to underpin the advantage of the proposed algorithm. Experiments on simulate (and real) data demonstrate that the proposed algorithm effectively can improve network performances in terms of energy efficiency, packet timeout ratio and network throughput.

本文旨在针对没有全局网络拓扑信息的时延受限的飞行自组织网络（FANET），以分布式方式优化不同的网络参数。为此，每个无人飞行器（UAV）计算特定时间段内的平均干扰水平以指示信道状态。接下来，我们将分布式优化问题表述为效用最大化问题，以共同优化功率控制，速率分配和时延约束路由。为了获得分布式解决方案，提出了一种对偶方法来消除链路容量约束，并采用原始分解方法来解耦端到端延迟约束。基于以上两种方法，在这项工作中，通过考虑每次传输的估计单跳延迟，提出了一种分布式优化算法，该算法仅使用本地信道信息来优化子问题并限制端到端延迟。最后，我们推导了原始解和对偶解之间的关系，以支持所提出算法的优势。在模拟（和真实）数据上的实验表明，该算法在能效，数据包超时率和网络吞吐量方面可以有效地提高网络性能。