NeTS: Small: Spectrum Sensing and Resource Allocation for Cognitive Radio Networks

在过去的几十年里，由于无线行业的指数级增长和无线设备的快速扩散，无线电频谱已经成为一种越来越昂贵和稀缺的资源。具有讽刺意味的是，频谱使用研究表明，由于目前的静态分配政策，将频谱划分为不同的许可频段，许多可用频谱未得到充分利用。该项目的主要目标是开发一种基于新兴认知无线电技术的具有成本效益的解决方案，以解决频谱短缺问题，该技术有可能允许未授权用户回收已授权频段中的未使用频谱。认知无线电能够检测未使用或空闲的频谱，并根据所谓的频谱洞动态调整其发射和接收活动。一群未经许可的用户配备了认知无线电，可以形成一个网络，并通过这些频谱洞相互通信。本研究的重点是开发有效和准确的认知无线电方法，以识别频谱漏洞并分配该频谱，使未授权用户之间的通信不会对频谱带的授权用户造成有害干扰。该工作的主要挑战在于如何在认知无线电网络中进行频谱感知和资源分配，以最大限度地提高其容量，同时管理认知无线电带来的额外干扰和开销。如果项目成功，研究成果将对提高未来无线网络的容量和性能产生重大影响。这项研究有望推动认知无线电领域的发展，并有助于其在商业应用中的采用。本研究成果将适用于未来的基础设施无线网络，以及救灾等紧急情况下的无线通信。将开发一个实验平台来原型化并评估所提出的动态频谱接入方法在实际无线环境中的有效性。学生，包括一些来自代表性不足群体的学生，将通过使用认知无线电测试平台获得实践经验。将开发新的课程材料，向研究生水平的学生教授认知无线电技术的基础知识。本研究的技术思路以频谱感知与资源分配相结合为核心，考虑到频谱空洞的时间、空间和频率三个主要维度。将开发频谱洞的多维表征，其中包含二元马尔可夫链来模拟时间动力学，空间频谱传感背景下的功率控制，以及宽带状态下频谱洞的频域搜索。本文将开发一种基于简单同构的认知无线电网络模型，用于在认知无线电节点之间分配频谱资源，以及频谱感知所需的通信和计算资源，从网络角度优化性能。认知无线网络的干扰建模和管理将与资源分配相结合。该项目将在拟议的动态频谱接入框架内研究通信和计算资源之间的权衡。该项目的一个重要组成部分是基于开放软件无线电平台的认知无线电网络测试平台的开发，该平台将用于在现实无线场景中原型化和评估所提出的算法和协议的性能。

Radio spectrum has become an increasingly expensive and scarce resource due to the exponential growth of the wireless industry and the rapid proliferation of wireless devices over the past couple of decades. Ironically, studies of spectrum usage have shown that much of the available spectrum is highly underutilized, due to the current policy of static allocation, which partitions the spectrum into various licensed bands. The main goal of this project is to develop a cost-effective solution to the spectrum scarcity issue based on emerging cognitive radio technology, which has the potential to allow unlicensed users to reclaim unused spectrum in the licensed bands. A cognitive radio is capable of detecting unused or idle spectrum and dynamically tuning its transmission and reception activities to the so-called spectrum holes. A group of unlicensed users equipped with cognitive radios can form a network and communicate with each other via such spectrum holes. This research focuses on developing efficient and accurate methods for cognitive radios to identify spectrum holes and allocate this spectrum to enable communications among unlicensed users without causing harmful interference to the licensed users of a spectrum band. A major challenge of this work lies in how to perform spectrum sensing and resource allocation in a cognitive radio network to maximize its capacity while managing the additional interference and overhead incurred by the cognitive radios. If the project is successful, the research results should have a significant impact on increasing the capacity and performance of future wireless networks. The research is expected to advance the field of cognitive radio and contribute towards its adoption in commercial applications. The results of this research will be applicable to future infrastructured wireless networks, as well as wireless communications in emergency scenarios such as disaster relief. An experimental platform will be developed to prototype and evaluate the effectiveness of the proposed dynamic spectrum access approach in practical wireless environments. Students, including some from underrepresented groups, will gain practical experience from working with the cognitive radio testbed. New course materials will be developed to teach the basics of cognitive radio technology to students at the graduate level. The technical approach of this research centers on a joint consideration of spectrum sensing and resource allocation, taking into account the three main dimensions of spectrum holes, i.e., time, space, and frequency. A multidimensional characterization of spectrum holes will be developed, which incorporates a bivariate Markov chain to model temporal dynamics, power control in the context of spatial spectrum sensing, and frequency-domain search for spectrum holes in the wideband regime. A model of a cognitive radio network based on simplicial homology will be developed to allocate spectrum resources among cognitive radio nodes, as well as the communication and computational resources required for spectrum sensing, to optimize performance from a networking perspective. Interference modeling and management for the cognitive radio network will be addressed in conjunction with resource allocation. The project will study tradeoffs among communications and computational resources within the proposed framework for dynamic spectrum access. An important component of the project is the development of a cognitive radio network testbed based on an open software radio platform, which will be used to prototype and evaluate the performance of the proposed algorithms and protocols in realistic wireless scenarios.