NeTS: Small: Channel Recognition for Optimized Links And Networks (CROLA)

NeTS：小型：优化链路和网络的信道识别 (CROLA)

• 批准号: 1526269
• 负责人: Joseph Camp
• 金额: $ 46万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526269&HistoricalAwards=false
• 关键词: NeTS; Small; Channel; Recognition; Optimized

Wirelessly-connected users encounter a vast array of environmental settings from factors including diverse terrain, buildings, vegetation, weather conditions, and velocities. Today, when traversing across these diverse conditions, each environmental change triggers a new wireless channel characterization so that links can have optimal performance for transmission rate and frequency band decisions, both of which depend on spatial and environmental characteristics. This characterization process can induce a high overhead on the network, greatly reducing the overall performance of the wireless links which were seeking to be optimized. In this project, diverse wireless scenarios will be classified into a finite set to recognize wireless channel types and optimize per-link and network-wide decisions. The project will significantly reduce the amount of characterization that needs to be performed per environment, especially when revisiting a location or when a new location shares many similarities as those previously visited.The researchers will use two different approaches to classify wireless channels and create a notion of channel type which will be fed into an online training framework to optimize transmission rate and frequency band selection. In the first approach, previously encountered channel types will be recognized for immediate link-level and network-wide decisions and the resulting performance from these decisions will be observed to improve future decisions. In the second approach, previously-unencountered channel types for which insufficient levels of training exist will be inferred via crowdsourcing as an initial starting point for on-the-fly training. There are four main intellectual thrusts to the proposal: (i) The geometric relationship that forms between the link-level performance and the n-dimensional space of environmental factors contributing to such performance will be characterized in order to classify and recognize channel types across geographically diverse regions. (ii) A crowdsourcing approach with a large data set of cellular and WiFi-based mobile phone users will be leveraged to experimentally isolate the roles of geolocation, land use, and situational context to geo-spatially infer channel types. (iii) An on-the-fly training framework will be developed to leverage these two notions of channel type for previously-encountered and well-trained scenarios for optimal link adaptation, network-wide decisions, and ongoing training, and previously-unencountered or poorly-trained scenarios to use inferred channel types with decisions and resulting performance used for constructing a sufficient training. (iv) This knowledge of channel type will be exploited along with the current spectral activity and demand to optimize the band assignment in multiband, multihop networks.

无线连接的用户会遇到各种各样的环境设置，包括不同的地形、建筑物、植被、天气条件和速度。如今，当穿越这些不同的条件时，每种环境变化都会触发新的无线信道特征，以便链路可以在传输速率和频带决策方面具有最佳性能，这两者都取决于空间和环境特征。这种表征过程可能会导致网络上产生较高的开销，从而大大降低了寻求优化的无线链路的整体性能。在该项目中，不同的无线场景将被分类为有限的集合，以识别无线信道类型并优化每个链路和网络范围的决策。该项目将显着减少每个环境需要执行的表征量，特别是在重新访问某个位置或新位置与之前访问过的位置有许多相似之处时。研究人员将使用两种不同的方法对无线信道进行分类并创建一个概念信道类型的信息将被输入到在线训练框架中，以优化传输速率和频段选择。在第一种方法中，将识别先前遇到的信道类型以进行直接链路级和网络范围的决策，并且将观察这些决策所产生的性能以改进未来的决策。在第二种方法中，将通过众包推断出以前未遇到过的、训练水平不足的渠道类型，作为即时训练的初始起点。该提案有四个主要的思想要点： (i) 将表征链路级性能与促成此类性能的环境因素的 n 维空间之间形成的几何关系，以便跨地理区域对信道类型进行分类和识别不同的地区。 (ii) 将利用包含蜂窝和基于 WiFi 的移动电话用户的大型数据集的众包方法，通过实验分离地理位置、土地使用和情境背景的作用，以推断地理空间信道类型。 (iii) 将开发一个动态培训框架，以利用这两种信道类型概念来处理以前遇到过的和训练有素的场景，以实现最佳链路适应、全网决策和持续培训，以及以前未遇到过的或训练有素的场景。训练不良的场景使用推断的通道类型以及用于构建足够训练的决策和结果性能。 (iv) 信道类型的知识将与当前的频谱活动和需求一起利用，以优化多频带、多跳网络中的频带分配。