EAGER: SC2: Load Prediction and Collision Coordination for Collaboration Channel

EAGER：SC2：协作通道的负载预测和碰撞协调

• 批准号: 1737732
• 负责人: Joseph Camp
• 金额: $ 10万
• 依托单位: Southern Methodist University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2018-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737732&HistoricalAwards=false
• 关键词: EAGER; SC2; Load; Prediction; Collision

The demand for wireless bandwidth has grown exponentially in the past decade, motivating the need for novel spectrum access techniques. In this project, a hierarchical testing and implementation approach is used to generate and evaluate innovative spectral access techniques. To do so, the team is equipped with a broad range of backgrounds from FPGA development and machine learning to 3GPP standardization and extensive in-field experimentation. There are four particular areas of focus for the team: (i) network management including control structure and topology discovery, (ii) network discovery including modulation recognition and network recognition, (iii) spectrum access including channel selection, access mechanism design, and decision metrics, and (iv) link design including waveform selection, channel coding, and channel estimation. A central concept hinges on the role of the collaboration channel, on which the role of periodic and uniform levels of information exchange is studied related to channel availability across networks from no, partial, and full knowledge scenarios. Using machine learning and on-the-fly training with observations of network decisions and resulting performance, the focus then shifts to designing for robustness with respect to greater levels of latency and heterogeneity that result from non-standard protocols and algorithms across a diverse set of radios.The scope of the project includes the following three aspects. First, an agile research approach is employed using a hierarchy of implementation complexity to evaluate multiple design ideas, progressing through simulation tools, software-defined platforms, and FPGA hardware implementations and weighting the time spent according to the measured success of each design idea. Second, the role of the collaboration channel is extensively studied and evaluated in terms of spectral efficiency and coordination across teams by attempting to predict spectral availability of all users across the relevant spectrum in a homogeneous network context and select the channel availability update rate and size to attempt to maximize performance of these two metrics. Third, these findings develop the basis for extension to networks with high levels of latency and heterogeneity across the network stack where flexibility of information exchange across the collaboration channel is far more critical, forcing on-the-fly training that are built into the nodes to observe and reinforce machine-based decision making.

过去十年，对无线带宽的需求呈指数级增长，激发了对新型频谱接入技术的需求。在该项目中，采用分层测试和实施方法来生成和评估创新的频谱访问技术。为此，该团队拥有从 FPGA 开发和机器学习到 3GPP 标准化和广泛的现场实验的广泛背景。该团队有四个特别关注的领域：(i) 网络管理，包括控制结构和拓扑发现；(ii) 网络发现，包括调制识别和网络识别；(iii) 频谱接入，包括信道选择、接入机制设计和决策(iv) 链路设计，包括波形选择、信道编码和信道估计。一个中心概念取决于协作通道的作用，在此基础上，从无知识、部分知识和完整知识场景中研究了与跨网络的通道可用性相关的周期性和统一水平的信息交换的作用。 使用机器学习和动态训练以及对网络决策和结果性能的观察，然后重点转向设计鲁棒性，以应对由跨不同集合的非标准协议和算法导致的更高水平的延迟和异构性。项目范围包括以下三个方面。 首先，采用敏捷研究方法，使用实现复杂性层次来评估多个设计想法，通过仿真工具、软件定义平台和 FPGA 硬件实现进行进展，并根据每个设计想法的成功衡量来衡量所花费的时间。其次，通过尝试预测同质网络环境中相关频谱上所有用户的频谱可用性，并选择信道可用性更新速率和大小来对协作信道的作用进行广泛的研究和评估，从而在频谱效率和跨团队协调方面发挥作用。尝试最大化这两个指标的性能。第三，这些发现为扩展到整个网络堆栈中具有高延迟和异构性的网络奠定了基础，其中跨协作渠道的信息交换的灵活性更为重要，迫使内置于节点中的即时训练观察并加强基于机器的决策。