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.

在过去的十年中，对无线带宽的需求呈指数级增长，从而激发了对新型频谱接入技术的需求。在该项目中，使用分层测试和实施方法来生成和评估创新的频谱接入技术。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。该团队关注的具体领域有四个：（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度量;以及（iv）链路设计，包括波形选择、信道编码和信道估计。一个中心的概念取决于合作渠道的作用，定期和统一的信息交换水平的作用进行了研究，从没有，部分和全部知识的情况下，跨网络的渠道可用性。 通过使用机器学习和动态训练，并观察网络决策和性能，重点转移到设计鲁棒性，以应对各种无线电设备之间的非标准协议和算法导致的更高级别的延迟和异构性。项目范围包括以下三个方面。 首先，采用敏捷的研究方法，使用实现复杂性的层次结构来评估多个设计理念，通过仿真工具，软件定义的平台和FPGA硬件实现，并根据每个设计理念的测量成功来衡量所花费的时间。第二，协作信道的作用被广泛研究和评估的频谱效率和团队之间的协调，试图预测频谱可用性的所有用户在相关的频谱在一个同构的网络上下文中，并选择信道的可用性更新速率和大小，试图最大限度地提高这两个指标的性能。第三，这些发现为扩展到网络堆栈中具有高延迟和异构性的网络奠定了基础，其中跨协作通道的信息交换灵活性更为关键，迫使内置于节点中的动态训练来观察和加强基于机器的决策。