In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted integrated communication and localization network in emergency scenarios where a single UAV is deployed as both an airborne base station (BS) and anchor node to assist ground BSs in communication and localization services. We formulate an optimization problem to maximize the sum communication rate of all users under localization accuracy constraints by jointly optimizing the 3D position of the UAV, and communication bandwidth and power allocation of the UAV and ground BSs. To address the intractable localization accuracy constraints, we introduce a new performance metric and geometrically characterize the UAV feasible deployment region in which the localization accuracy constraints are satisfied. Accordingly, we combine Gibbs sampling (GS) and block coordinate descent (BCD) techniques to tackle the non-convex joint optimization problem. Numerical results show that the proposed method attains almost identical rate performance as the meta-heuristic benchmark method while reducing the CPU time by 89.3%.

翻译：本文研究了一种面向应急场景的无人机（UAV）辅助通感一体化网络，其中单架无人机同时作为空中基站（BS）与锚节点，协助地面基站提供通信与定位服务。我们构建了一个优化问题，旨在通过联合优化无人机的三维位置、通信带宽及无人机与地面基站的功率分配，在满足定位精度约束的前提下最大化所有用户的总通信速率。为处理棘手的定位精度约束，我们引入了一种新的性能度量指标，并几何刻画了满足定位精度约束的无人机可行部署区域。据此，我们结合吉布斯采样（GS）与块坐标下降（BCD）技术，攻克了这一非凸联合优化问题。数值结果表明，所提方法在取得与元启发式基准方法几乎完全相同的速率性能的同时，可将CPU计算时间降低89.3%。