CASTLE over the Air -- Distributed Scheduling for Cellular Data Transmissions (demo)
CASTLE over the Air -- Distributed Scheduling for Cellular Data Transmissions (demo)
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CASTLE over the Air——蜂窝数据传输的分布式调度（演示）
DOI：
10.1145/3307334.3328576
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Ha, Sangtae
中科院分区：
文献类型：
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作者：
Dhawaskar Sathyanarayana, Sandesh;Lee, Jihoon;Lee, Jinsung;Im, Youngbin;Rahimzadeh, Parisa;Zhang, Xiaoxi;Hollingsworth, Max;Joe-Wong, Carlee;Grunwald, Dirk;Ha, Sangtae
本文提出了一种全分布式调度框架，称为CASTLE (Client-side Adaptive Scheduler That minimminimasload and Energy)，它可以共同优化蜂窝网络的频谱效率和智能设备的电池消耗。为此，我们将重点放在许多智能设备在同一基站中竞争蜂窝资源的场景上：随着时间的推移分散传输，以便只有少数设备同时传输，从而提高频谱效率和电池消耗。为此，我们在《城堡》中设计了两个新颖的特点。首先，我们明确考虑胞间干扰，以获得准确的胞负荷估计。根据我们的观察，我们利用RSRQ（参考信号接收质量）和SINR作为机器学习算法的特征来准确估计蜂窝负载。其次，我们提出了一种完全分布式的调度算法，该算法基于每个客户端的本地估计负载水平来协调客户端之间的传输。我们在每个设备上最小化电池消耗的公式导致了一个优化的基于后退的算法，适合实际环境。为了评估这些功能，我们设计了一个完整的LTE系统测试平台，包括移动设备、enodeb、EPC（演进分组核心）和应用服务器。综合实验结果表明，与现有的集中式调度算法和类似csma的分布式协议相比，CASTLE的负载估计准确率高达91%，并且在更少的电池消耗下实现了更高的频谱效率。此外，我们开发了一个轻量级SDK，可以加快CASTLE在智能设备中的部署，并在商用LTE网络中对其进行评估。
This paper presents a fully distributed scheduling framework called CASTLE (Client-side Adaptive Scheduler That minimizes Load and Energy), which jointly optimizes the spectral efficiency of cellular networks and battery consumption of smart devices. To do so, we focus on scenarios when many smart devices compete for cellular resources in the same base station: spreading out transmissions over time so that only a few devices transmit at once improves both spectral efficiency and battery consumption. To this end, we devise two novel features in CASTLE. First, we explicitly consider inter-cell interference for accurate cellular load estimation. Based on our observations, we exploit the RSRQ (Reference Signal Received Quality) and SINR as features in a machine learning algorithm to accurately estimate the cellular load. Second, we propose a fully distributed scheduling algorithm that coordinates transmissions between clients based on the locally estimated load level at each client. Our formulation for minimizing battery consumption at each device leads to an optimized backoff-based algorithm that fits practical environments. To evaluate these features, we prototype a complete LTE system testbed consisting of mobile devices, eNodeBs, EPC (Evolved Packet Core) and application servers. Our comprehensive experimental results show that CASTLE's load estimation is up to 91% accurate, and that CASTLE achieves higher spectral efficiency with less battery consumption, compared to existing centralized scheduling algorithms as well as a distributed CSMA-like protocol. Furthermore, we develop a light-weight SDK that can expedite the deployment of CASTLE into smart devices and evaluate it in a commercial LTE network.