[论文解读] Predictive Deployment of UAV Base Stations in Wireless Networks: Machine Learning Meets Contract Theory

Qianqian Zhang, Walid Saad+4 more|arXiv (Cornell University)|Nov 3, 2018

UAV Applications and Optimization参考文献 31被引用 23

一句话总结

本文提出了一种预测性无人机基站部署框架，结合加权期望最大化（WEM）算法进行流量需求预测，并利用契约理论确保地面基站与无人机运营商之间的信息真实共享。该方法将预测误差控制在10%以内，显著提升了下行链路容量、能效和收入，优于事件驱动方案。

ABSTRACT

In this paper, a novel framework is proposed to enable a predictive deployment of unmanned aerial vehicles (UAVs) as temporary base stations (BSs) to complement ground cellular systems in face of downlink traffic overload. First, a novel learning approach, based on the weighted expectation maximization (WEM) algorithm, is proposed to estimate the user distribution and the downlink traffic demand. Next, to guarantee a truthful information exchange between the BS and UAVs, using the framework of contract theory, an offload contract is developed, and the sufficient and necessary conditions for having a feasible contract are analytically derived. Subsequently, an optimization problem is formulated to deploy an optimal UAV onto the hotspot area in a way that the utility of the overloaded BS is maximized. Simulation results show that the proposed WEM approach yields a prediction error of around 10%. Compared with the expectation maximization and k-mean approaches, the WEM method shows a significant advantage on the prediction accuracy, as the traffic load in the cellular system becomes spatially uneven. Furthermore, compared with two event-driven deployment schemes based on the closest-distance and maximal-energy metrics, the proposed predictive approach enables UAV operators to provide efficient communication service for hotspot users in terms of the downlink capacity, energy consumption and service delay. Simulation results also show that the proposed method significantly improves the revenues of both the BS and UAV networks, compared with two baseline schemes.

研究动机与目标

• 为解决在蜂窝网络中发生流量拥塞时预测性无人机部署的挑战。
• 在信息不对称条件下，实现地面基站与无人机运营商之间的信息真实交换。
• 通过最优无人机部署，最大化拥塞地面基站的效用。
• 在下行链路容量、能效和业务时延方面提升网络性能。
• 通过激励相容机制，提升地面网络与无人机网络的收入。

提出的方法

• 提出加权期望最大化（WEM）算法，以比EM和k-means算法更准确地估计用户分布与下行链路流量需求。
• 设计一种卸载契约，利用契约理论确保激励相容性与无人机的诚实报告。
• 推导出可行契约的充分必要条件，确保个体理性与激励相容性。
• 建立优化问题，将无人机部署在热点区域，以最大化地面基站的效用。
• 将流量预测与基于契约的无人机选择相结合，平衡能效与服务质量。
• 通过数学建模证明，该契约机制可确保无人机基于其能力进行诚实报告。

实验结果

研究问题

• RQ1 如何利用机器学习在蜂窝网络中准确预测流量需求与用户分布？
• RQ2 何种契约设计可确保具有私有能力的无人机运营商提供真实信息报告？
• RQ3 与事件驱动方案相比，预测性无人机部署在哪些方面展现出更优的网络性能？
• RQ4 何种条件可保证地面基站与无人机之间激励相容契约的可行性？
• RQ5 所提出的框架如何提升无人机辅助蜂窝网络中的收入与服务质量？

主要发现

• WEM算法的预测误差约为10%，显著优于EM与k-means算法，尤其在空间分布不均的流量负载下表现更优。
• 与最近距离和最大能量事件驱动方案相比，所提出的预测性部署可降低服务时延并提升下行链路容量。
• 由于基于预测需求的优化无人机定位，能耗降低，从而提升了无人机续航能力。
• 基于契约机制的分析推导证明，无人机能够诚实报告其能力，确保激励相容性。
• 由于高效的资源分配与真实合作，地面基站与无人机网络的收入均显著高于基线方案。
• 理论分析证实，在推导出的条件下，该契约机制满足个体理性与激励相容性。