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

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，每个环境变化都会触发新的无线信道特性，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性。这种表征过程会导致网络上的高开销，大大降低了寻求优化的无线链路的整体性能。在这个项目中，不同的无线场景将被分类到一个有限的集合中，以识别无线信道类型并优化每个链路和网络范围的决策。该项目将大大减少每个环境需要进行的表征量，特别是当重新访问一个位置或当一个新的位置与以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，并创建一个信道类型的概念，该概念将被输入在线培训框架，以优化传输速率和频带选择。在第一种方法中，先前遇到的信道类型将被识别为立即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，将经由众包来推断先前未遇到的信道类型，对于这些信道类型存在不足的训练水平，作为用于即时训练的初始起点。有四个主要的智力推力的建议：（一）的几何关系，形成之间的链路级性能和环境因素的n维空间，有助于这种性能的特点，以分类和识别跨地理上不同的区域的信道类型。(ii)将利用具有蜂窝和基于WiFi的移动的电话用户的大数据集的众包方法，以实验方式隔离地理位置、土地使用和情境背景的作用，从而在地理空间上推断信道类型。(iii)将开发动态训练框架以利用这两个信道类型的概念，用于先前遇到的和良好训练的场景，用于最佳链路自适应、网络范围的决策和正在进行的训练，以及先前未遇到的或训练不良的场景，以使用推断的信道类型，决策和所得性能用于构建足够的训练。(iv)信道类型的知识将沿着当前频谱活动和需求一起被利用，以优化多频带、多跳网络中的频带分配。

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.