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

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 properties with respect to the key parameters of CR (including the sensing power, idling power and traffic delay-sensitivity, etc), which hence provide a complete analytical framework for our transmission control mechanism. Second, we further investigate the low-complexity transmission control policy for CR's energy-saving. The low-complexity policy, which is based on the theory of certainty equivalent control from optimal control theory, avoids the heavy computational burden in iterative-calculations for deriving the optimal policy and facilitates its practical usage in real scenarios. Third, we consider a CR network servicing a group of Secondary Users (SUs) and propose a joint channel-and-rate allocation policy for CR network to achieve energy-efficiency. Besides investigating the optimal joint control policy, we further study the low-complexity and distributed control policies for practical usage. The low-complexity and distributed policies will gain the following advantages: i) decomposition of the global Q-function into each individual SU's Q-function, thus reducing the high computational complexity in iterative-calculations for deriving the global Q-function and facilitating the distributed implementation in practice, and ii) light communication overheads between SUs and CR base-station, thus relieving the heavy burden on message-exchange between them. Our proposed low-complexity and distributed control policies will contribute significantly to the implementation of energy-efficient CR networks. We believe that our proposal will shield light on achieving both energy-efficiency and spectrum efficiency for future wireless networks.

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。

本项目完成设计了一套高效认知无线网络能效优化无线资源管理方案，该方案在保证认知网络用户通信质量前提下实现认知网络节能与无线资源优化使用。本项目首先从无线资源优化管理与传输调度优化角度出发，针对认知无线网络的动态感知与机会式使用的工作特性和与之相应的动态能耗特性，提出一套无线资源优化分配与传输调度方案。该方案在保证用户传输质量需求的前提下成功实现包括认知用户能耗优化。其次，针对多用户无线认知网络，本项目在最优方案基础上利用等效控制原理、多网络协作优化等方法，设计了低复杂度、分布式资源管理策略从而在保障用户服务质量前提下有效提升网络资源使用效益并降低能耗。特别的，在本项目中我们综合应用了动态随机优化理论、协作机制、博弈论等重要方法，设计了高效、实用且具有很好可扩展性的动态协作无线资源优化管理方法，具有非常好的方法论意义，可被广泛应用于包括蜂窝无线网络数据分流管理、智能电网需求调度管理等重要相关研究领域。. 本项目研究成果主要体现在高水平学术研究成果，项目共发表SCI索引国际期刊论文9篇，其中包括IEEE Journal on Selected Areas in Communications 2篇、IEEE Transactions on Wireless Communications 1篇、中国计算机协会推荐A类期刊IEEE Transactions on Mobile Computing1篇和IEEE Transactions on Parallel and Distributed Systems 2篇，以及IEEE Communications Letters 2篇。发表EI索引领域内重要国际会议11篇。此外，我们获得相关授权发明专利4项。通过参与本项目培养硕士研究生8人次，其中获得研究生国家奖学金5人次，其中1人次获得校级优秀硕士论文荣誉称号。通过本项目培养，项目负责人入选2016年浙江省杰出青年自然科学基金项目资助，并入选国家留学基金委2015年度国家公派访问学者项目资助，访学单位为加拿大滑铁卢大学沈学民教授研究组。