NEDG: A New Systematic Framework for Cross-layer Optimization

NEDG：跨层优化的新系统框架

• 批准号: 0831549
• 负责人: Mihaela van der Schaar
• 金额: $ 30.3万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0831549&HistoricalAwards=false
• 关键词: NEDG; New; Systematic; Framework; Cross

Cross-layer design problems in wireless networks are usually very complex, since they require simultaneously optimizing a large amount of algorithms and parameters. Most existing solutions for cross-layer optimization rely on heuristic procedures to solve this problem. However, to obtain an optimal utility for the wireless user, cross-layer optimization should be formulated rigorously as a sequential decision problem that takes into account the capability of the various layers to autonomously make forecasts about their experienced dynamics, and perform foresighted adaptation, while adhering to the existing layered network architecture. To address this challenge, the investigators study a new, systematic framework for cross-layer optimization that allows each layer to make autonomous and foresighted decisions on the selected transmission strategies (e.g. protocol parameters and algorithms), while cooperatively maximizing the utility of the wireless user by optimally determining what necessary information should be exchanged among layers.This research involves two main thrusts: (a) Develop a novel cross-layer optimization framework with message exchange among layers in which each layer optimizes its own protocol parameters and algorithms based on its own experienced dynamics and the information exchanged with other layers, in order to cooperatively maximize the wireless user?s utility in a foresighted manner while adhering to the layered network architecture; (b) Design layered on-line learning algorithms for the cross-layer optimization to allow each layer to interactively learn the experienced dynamics and other necessary information from other layers, such that the cross-layer strategies can be optimized cooperatively. This research leads to a fundamentally new way for designing wireless networks, systems and applications, where devices evolve and become smarter, by learning from their interactions with the environment over time.

无线网络中的跨层设计问题通常非常复杂，因为它们需要同时优化大量算法和参数。用于跨层优化的大多数现有解决方案都取决于启发式程序来解决此问题。但是，为了获得无线用户的最佳实用性，应严格配方跨层优化，作为顺序决策问题，考虑到各种层的能力自主对其经验丰富的动态进行自主进行预测，并执行预识适应性，同时粘附在现有的层网络架构上。为了应对这一挑战，研究人员研究了一个新的，系统的跨层优化框架，使每一层都可以对所选传输策略（例如协议参数和算法）做出自主和远见的决策（例如协议参数和算法），同时，通过确定哪些必要信息的范围涉及两种范围的无线用户的效用。在各个层之间的消息交换中，每个层都会根据自己的经验丰富的动态以及与其他图层交换的信息优化其自己的协议参数和算法，以便以预见的方式合作地最大化无线用户的实用性，同时粘贴到分层网络体系结构上； （b）设计分层的在线学习算法，用于跨层优化，以允许每一层从其他层中进行交互学习经验丰富的动态和其他必要的信息，从而可以合作地优化跨层策略。这项研究为设计无线网络，系统和应用程序提供了一种新的新方法，在该方法中，设备随着时间的推移与环境的互动中学习，设备会发展并变得更聪明。