多天线认知无线电网络中基于动态频谱检测的最优化资源分配算法和理论性能研究

Cognitive Radio has been proposed as the means to promote the efficient use of the spectrum by exploiting the existence of spectrum holes, which is considered as one of the most important technique in the 5G wireless communications. Incorporation of MIMO techniques into CRNs can improve the channel capacity by sending independent data streams simultaneously over different antennas. Due to the challenges associated to power allocation and spectrum optimization, all the existing works on MIMO CRNs do not consider the joint optimization over the sensing information. In our project, we consider the optimization problem in MIMO CRNs where the overall objective is to maximize the total throughput of each CR by jointly optimizing both the detection operation, transmit antenna radiation directions and transmit power over all channels. The optimization problem is a non-convex NP hard problem, which can be analyzed as a strategic non-cooperative game. Our project focuses on proposing efficient algorithms for spectrum map, simplifying the optimization of the non-convex problem, defining new concept of Nash equilibrium, giving the distributed algorithms. The efficient and the performance of the algorithm is verified by theoretical analysis and simulation.

通过利用频谱空洞大幅度提高频谱利用率的认知无线电技术，被广泛认为是第五代移动通信的关键技术之一。MIMO技术运用在认知无线电网络中可以大幅度提高信道容量，增加频谱检测准确率。然而由于在MIMO认知网络中基于频谱检测结果的功率优化方案的复杂性，现有的MIMO认知网络对这方面的研究还是处于起步阶段。本项目主要研究MIMO认知无线电网络资源优化问题，总体目标是基于频谱检测结果，优化发射天线的传输方向和发射功率，最大化每个认知用户的吞吐量。该优化问题是一个非凸NP难问题，拟采用非合作博弈论进行分析。本项目重点是提出高效频谱图算法，简化非凸优化模型，确定新纳什均衡的概念，提出分布式算法，并给出理论推导和仿真验证。

通过利用频谱空洞大幅度提高频谱利用率的认知无线电技术，被广泛认为是第五代移动通信的关键技术之一。另一方面，LET-U, 即非授权频谱上的LTE, 作为一种新兴的非授权频谱接入技术，为当前受限于有限的授权频谱资源的移动通信系统开辟了全新的频谱机会利用机制。我们结合LTE-U和CR 两种技术，构建并分析认知小蜂窝网络，它可以检测授权和未授权频段上的频谱空穴，实现授权和非授权频段资源的合理分配，从而最大化整个网络的传输速率，提高整体网络用户满意度。进一步，我们针对高密集小蜂窝的干扰问题，通过联合干扰协调与成本控制来最大化吞吐量,采用协作多点的方法来消除热区内的干扰。此外该方案中，小蜂窝基站会根据动态的用户业务量需求调整小蜂窝基站状态，建立浅眠，深睡，唤醒等多个状态的切换，有效提升整体网络的能效。最后，我们讨论了基于全双工和半双功的双频用户数据分流方案，为未来第五代移动通信网络的性能提升和技术演进提供理论和技术支撑。