NeTS: Small: Control of Partially Observable Wireless Networks: Fundamental Limits, Optimal Algorithms and Practical Implementation

NeTS：小型：部分可观测无线网络的控制：基本限制、最优算法和实际实现

基本信息

批准号：
1262329
负责人：
Lei Ying
金额：
$ 33万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2012
资助国家：
美国
起止时间：
2012-08-16 至 2017-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1262329&HistoricalAwards=false
关键词：
NeTS Small Control Partially Observable

项目摘要

State dependent resource allocation is critical for improving the network efficiency. Over the past two decades, remarkable progress has been made on the design of wireless networks with maximum throughput and low latency by using state dependent resource allocation algorithms. However, most of these works assume the network is fully observable and the network state information is perfectly known. This assumption is becoming increasingly questionable because of the multi-carrier technology, which makes it extremely expensive to obtain the complete network state information, and transmission delays and measurement errors, which make it impossible to know the exact network state. With wireless networks become pervasive in our daily life, new theories and algorithms are needed for partially observable wireless networks.In this project, the PI models a partially observable wireless network as a partially observable Markov process, and then applies the framework of Markov decision processes (MDP). While important structure properties may be discovered using the MDP framework, finding optimal solutions in general is an NP-hard problem. To overcome this difficulty, this project uses drift-based competitive analysis and drift-based large-deviations analysis for quantifying fundamental limits and deriving optimal or near optimal algorithms. The project is expected to lead to breakthroughs in managing partially observable wireless networks. Fundamental limits of partially observable wireless networks, novel resource allocation algorithms with provable throughput and latency guarantees, and implementations on a real-world test bed will have a significant impact on the design and implementation of future wireless networks.

状态相关的资源分配对于提高网络效率至关重要。在过去的二十年中，通过使用状态相关的资源分配算法，在具有最大吞吐量和低延迟的无线网络设计方面取得了显着的进展。然而，这些工作大多数都假设网络是完全可观察的，并且网络状态信息是完全已知的。由于多载波技术的出现，这种假设变得越来越值得怀疑，这使得获取完整的网络状态信息变得极其昂贵，而传输延迟和测量误差又使得人们无法知道确切的网络状态。随着无线网络在我们日常生活中的普及，部分可观测无线网络需要新的理论和算法。在这个项目中，PI将部分可观测无线网络建模为部分可观测马尔可夫过程，然后应用马尔可夫决策过程的框架（MDP）。虽然使用 MDP 框架可以发现重要的结构属性，但寻找最佳解决方案通常是一个 NP 难题。为了克服这个困难，该项目使用基于漂移的竞争分析和基于漂移的大偏差分析来量化基本限制并推导最佳或接近最佳算法。该项目预计将在管理部分可观测无线网络方面取得突破。部分可观测无线网络的基本限制、具有可证明吞吐量和延迟保证的新颖资源分配算法以及在现实世界测试台上的实现将对未来无线网络的设计和实现产生重大影响。