基于传输控制与服务质量保障的无线认知网络能效优化方案研究

项目名称： 基于传输控制与服务质量保障的无线认知网络能效优化方案研究

项目编号： No.61303235

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 吴远

作者单位： 浙江工业大学

项目金额： 26万元

中文摘要： 提高能量使用效率和频谱利用效率是下一代无线网络重要的目标,认知网络动态频谱感知与机会式使用是提高频谱利用效率的关键技术，本项目正是针对该关键技术的动态能耗特性提出有效实用的能耗优化管理方案，在保证通信质量前提下实现节能通信。不同于以往认知网络能耗研究关注频谱感知、接入策略, 项目从传输控制角度出发，从理论分析和实际应用层面提出基于服务质量保证的认知网络能效管理优化方案。项目首先针对认知网络机会式工作机制与能耗特性（包括静默能耗）提出一个最优传输控制策略，该策略在保证用户传输质量需求的前提下实现包括感知、静默与数据传输在内的总能耗最小化。在此基础上，为减小策略计算复杂度、实现分布式控制，项目进一步研究低复杂度、分布式控制策略的设计方案从而有效减小认知网络总能耗。低复杂度、分布式控制策略的提出将能够解决动态规划高计算复杂度和依赖于集中式控制的难点,为能效优化控制策略在认知网络中的使用铺平道路。

中文关键词： 无线网络资源管理；能效优化；服务质量；传输调度；认知无线网络

英文摘要： Both energy-efficiency and spectrum-efficiency are two important objectives for future wireless networks. It has been widely believed that dynamic spectrum sensing and opportunistic spectrum usage, i.e. the two key features of Cognitive Radio (CR), will be the enabling techniques for future wireless networks to achieve efficient utilization of spectrum resource and avoid spectrum congestions. Fitting the needs of these key techniques, our proposal systematically investigates the resource management mechanism for energy-efficiency by exploiting their dynamic characteristics in energy-consumption and traffic delivery. Specifically, different from existing works which focused on optimizing the sensing and access strategies, in this proposal, we exploit the transmission resource control to optimize CR's energy-efficiency and to guarantee its Quality of Service (QoS) at same time. We first adopt the shortest path Markov decision process to model this energy-efficiency optimization problem and investigate the optimal transmission control policy which can minimize the aggregate energy consumption in CR's spectrum sensing, compulsory idling and data transmission. We further analyze the performance of this optimal policy (including the average energy consumption and the average transmission delay) and its structural prop

英文关键词： Radio Resource Management for Wireless Networks；Energy Efficiency；Quality of Service；Transmission Scheduling；Cognitive Radio Networks