论文标题
最大速率部署优化了靠背限制的机器人空中6G小细胞
Max-min Rate Deployment Optimization for Backhaul-limited Robotic Aerial 6G Small Cells
论文作者
论文摘要
为了克服有限的名义机载基站（ABS）的有限的机上电池问题，我们正在探索机器人机载基站（RABS）的使用，其能量中性抓地力最终效果可以在高大的城市地面上自主栖息。具体而言，本文研究了一个由可移动的Rabs作为一个小单元的辅助网络（HETNET），它通过有限容量的无线次数链路连接到宏基站（MBS），可以将其视为另一个主要挑战。为了利用RABS的移动性可以带来的潜在收益，通过共同优化RABS部署，用户协会和子载波分配，所有用户的最低利率是最大化的。该问题最初被提出为二进制多项式优化（BPO）问题。在将其重新定义为一种非凸的四次限制性二次编程（QCQP）之后，我们提出了一种基于半决赛的启发式方法（SDR），以在多项式时间内捕获高质量的解决方案。数值结果表明，将RABS部署为小细胞可以最多提高最低数据速率95.43％，平均为33.97％，而开发的SDR启发式算法显着优于线性松弛（LR）基线方法。
To overcome the limited on-board battery issue of nominal airborne base stations (ABSs), we are exploring the use of robotic airborne base station (RABS) with energy neutral grasping end-effectors that are able to autonomously perch at tall urban landforms. Specifically, this paper studies a heterogeneous network (HetNet) assisted by a movable RABS as a small cell which connects to a macro base station (MBS) through a limited-capacity wireless backhaul link, which can be deemed as another major challenge. To exploit the potential gains that the mobility of RABS can bring in the system, the minimum rate among all users is maximized by jointly optimizing the RABS deployment, user association and subcarrier allocation. This problem is initially formulated as a binary polynomial optimization (BPO) problem. After reformulating it as a nonconvex quadratically constrained quadratic programming (QCQP), we propose a semidefinite relaxation (SDR) based heuristic method to capture a high-quality solution in polynomial time. Numerical results reveal that deploying a RABS as the small cell can improve the minimum data rate by 95.43% at most and 33.97% on average, and the developed SDR heuristic algorithm significantly outperforms the linear relaxation (LR) baseline method.
