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的算法和参数。大多数现有的跨层优化解决方案依赖于启发式程序来解决这个问题。然而，为了获得无线用户的最佳效用，跨层优化应该严格地制定为顺序决策问题，该问题考虑到各个层自主地对其经历的动态进行预测的能力，并执行前瞻性的适应，同时坚持现有的分层网络架构。为了应对这一挑战，研究人员研究了一种新的跨层优化系统框架，该框架允许每一层对选定的传输策略进行自主和前瞻性决策（例如，协议参数和算法），同时通过最佳地确定应该在层之间交换什么必要的信息来合作地最大化无线用户的效用。（a）开发一种新的跨层优化框架，在该框架中，每一层基于其自身经验的动态和与其他层交换的信息来优化其自身的协议参数和算法，以协作地最大化无线用户？（B）设计用于跨层优化的分层在线学习算法，以允许每一层交互式地学习来自其它层的经验动态和其它必要信息，使得跨层策略可以协作地优化。这项研究为设计无线网络、系统和应用程序提供了一种全新的方式，设备通过随着时间的推移从与环境的交互中学习，不断发展并变得更加智能。