Unmanned aerial vehicles (UAVs) can be deployed in the air to provide high probabilities of line of sight (LoS) transmission, thus UAVs bring much gain for wireless communication systems. In this paper, we study a UAVs-aided self-organized device-to-device (D2D) network. Relay deployment, channel allocation and relay assignment are jointly optimized, aiming to maximize the capacity of the relay network. On account of the coupled relationship between the three optimization variables, an alternating optimization approach is proposed to solve this problem. The original problem is divided into two sub-problems. The first one is that of optimizing the channel allocation and relay assignment with fixed relay deployment. Considering without central controller, a reinforcement learning algorithm is proposed to solve this sub-problem. The second sub-problem is that of optimizing the relay deployment with fixed channel allocation and relay assignment. Assuming no knowledge of channel model and exact positions of the communication nodes, an online learning algorithm based on real-time capacity is proposed to solve this sub-problem. By solving the two sub-problems alternately and iteratively, the original problem is finally solved. Simulation results show that the UAVs-aided D2D network can achieve a high capacity via the joint optimization of relay deployment, channel allocation, and relay assignment.

中文翻译：

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无人机辅助D2D网络的中继部署，信道分配和中继分配的联合优化

无人飞行器（UAV）可以在空中部署，以提供很高的视线（LoS）传输概率，因此，无人机为无线通信系统带来了很多收益。在本文中，我们研究了无人机辅助的自组织设备到设备（D2D）网络。中继部署，信道分配和中继分配被共同优化，旨在最大化中继网络的容量。考虑到三个优化变量之间的耦合关系，提出了一种交替优化方法来解决该问题。原始问题分为两个子问题。第一个是通过固定的中继部署来优化信道分配和中继分配。考虑到没有中央控制器，提出了一种强化学习算法来解决该子问题。第二个子问题是使用固定的信道分配和中继分配来优化中继部署。假设不了解信道模型和通信节点的确切位置，提出了一种基于实时容量的在线学习算法来解决该子问题。通过交替迭代地解决两个子问题，最终解决了原始问题。仿真结果表明，无人机联合D2D网络可以通过中继部署，信道分配和中继分配的联合优化实现高容量。通过交替迭代地解决两个子问题，最终解决了原始问题。仿真结果表明，通过联合优化中继部署，信道分配和中继分配，无人机辅助的D2D网络可以实现高容量。通过交替迭代地解决两个子问题，最终解决了原始问题。仿真结果表明，通过联合优化中继部署，信道分配和中继分配，无人机辅助的D2D网络可以实现高容量。