认知无线电频谱感知误差和参数不确定性下的鲁棒资源分配算法研究

In recent years, resource allocation has become a hot research topic since it can realize resource sharing and satisfy quality of service (QoS) of different users in cognitive radios. However, the existing resource allocation algorithms are strongly depending on precise spectrum sensing and system parameters, without considering the dynamic characteristics and transmission stability of the system, and are not suitable for the cognitive systems with the characteristics of dynamic spectrum sensing and strong anti-interference performance. Therefore, in order to improve the transmission quality and robustness of communication system, this project will focus on robust resource allocation problems for cognitive radio systems under sensing errors and parameter uncertainties. The main works are: 1. Considering spectrum sensing errors and parameter perturbations, a resource allocation model is built for multiuser cognitive radio networks. Based on the established model, a robust resource allocation algorithm under hybrid overlay/underlay spectrum sharing is studied. 2. A robust distributed resource allocation algorithm is investigated to reduce information exchange and computation complexity meanwhile the effect from both user's mobility and parameter perturbation to system performance is analyzed. The uniqueness of solution is proved via variational inequalities theory. 3. To improve the effectiveness of energy, an energy-efficient robust resource allocation scheme is study in cognitive radio networks. This project has very important theoretical significance for promoting the practical application of cognitive radios and supplies the theory basis and technique support for the spectrum reframing of 5G technology and resource sharing of heterogeneous wireless networks, respectively.

资源分配能够实现认知无线电资源共享、满足不同用户QoS而成为近年来的研究热点。而现有资源分配算法都强烈依赖于精确的频谱感知和系统参数，没有考虑系统动态特性、传输稳定性等问题，不适用于动态频谱感知和抗干扰性强的认知系统。因此，为了提高通信系统的传输质量和鲁棒性，本课题将针对认知无线电系统存在感知误差和参数不确定下的鲁棒资源分配问题展开研究。主要工作：1.建立感知误差和参数扰动下的多用户认知无线电资源分配模型，研究混合Overlay/Underlay频谱共享方式下的鲁棒资源分配算法。2.为减少信息交换和计算复杂度，研究鲁棒分布式资源分配算法，理论分析用户活动和参数摄动对系统性能的影响，利用变分不等式理论研究稳定性问题。3.为提高能量效率，研究能量有效性的认知无线电鲁棒资源分配算法。本项目对推动认知无线电实际应用具有重要的理论意义，为5G技术频谱重整、异构无线网络资源共享提供理论基础和技术支持。

本项目以下垫式(underlay)认知异构无线网络为研究对象，从参数不确定性的角度出发，研究多用户异构无线网络鲁棒资源分配方法和性能分析，并在此基础上扩展到传输时间优化、干扰管理、基站选择、载波分配、能量收集系数优化等问题上，最后考虑了先进的非正交多址接入、无线信息与功率同传、无线能量收集等下一代通信网络的应用场景。具体来讲，建立了多层认知异构无线网络鲁棒资源分配传输模型，同时分析了信道增益、干扰功率等参数不确定性对系统资源分配问题的影响，研究了有界范数参数不确定性下鲁棒资源分配算法，保护主用户性能，避免产生中断事件；研究了高斯随机参数不确定性下的鲁棒资源分配算法，在保证满足一定中断概率的同时，提高频谱效率和传输质量；为了克服随机不确定性对所建立不确定模型的影响，研究了Model-free的鲁棒资源分配问题，提出了一种鲁棒min-max资源分配算法，克服了对不确定性参数模型的依赖。为今后进一步的相关研究工作奠定了基础。鲁棒资源分配也为在无线通信系统中的进一步应用展示了良好的前景。

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