Unmanned aerial vehicles (UAVs) have brought a lot of flexibility in the network deployment. However, UAVs suffer from the high mobility and instability. To improve the capacity and reliability of the UAV networks, millimeter-wave (mmWave) and reconfigurable intelligent surfaces (RISs) can be used in the system. In this paper, we consider an RIS-assisted mmWave UAV wireless cellular network, where UAVs serve several users with the help of multiple RISs. We jointly optimize the deployment, user scheduling, beamforming vector, and RIS phases to maximize the sum-rate, with the constraints of the minimum rate, the UAV movement, the analog beamforming, and the RIS phases. To solve this complex problem, we use an iterative method, in which when we optimize one variable, we fix the other three variables. When optimizing the deployment, we find the optimal position for the UAV by a sphere search. Then, we formulate a mixed-integer non-linear problem (MINLP) to find the best scheduling. A spatial branch-and-bound (sBnB) method is used to solve the MINLP. When Optimizing the beamforming vector and the RIS phases, we propose an iterative algorithm that relies on the equivalence between the maximization of the sum-rate and the minimization of the summation of weighted mean-square errors (sum-WMMSE). The majority-minimization method is used to deal with the constant-modulus constraints for the analog beamforming and RIS phases. The proposed joint optimization offers significant advantages over the system without beamforming and RIS phase optimization and the system without deployment optimization. In addition, the RIS can compensate for the loss of throughput due to the blockage, especially in low flight altitudes.
翻译:无人驾驶航空飞行器(UAVs)在网络部署方面带来了许多灵活性。然而,无人驾驶航空飞行器(UAVs)在网络部署方面却存在高度流动性和不稳定性。为了提高UAV网络的能力和可靠性,系统可以使用毫米波(mmWave)和可重新配置智能表面(RISS)。在本文中,我们认为一个RIS-协助毫米Wave UAV无线蜂窝网络,在多个RISS的帮助下,UAVs为几个用户服务。我们共同优化部署、用户调度、信号调整矢量和RIS阶段,以尽量扩大总和率,同时限制最小速率、UAV运动、模拟成形和RIS阶段。为了解决这一复杂问题,我们使用迭代方法,当我们优化一个变量时,我们会修正另外三个变量。在优化部署时,我们通过一个域搜索找到UAVAV的最佳位置。然后,我们设计一个混合的不线性内更新的系统(MILP) 来找到最佳的列表。一个空间流流流流和平面系统之间的交易, 正在提议一个平流流式系统,我们用来提出一个平流流式系统。