密集异构无线环境下极化资源感知与利用研究

Dense heterogeneous network is regarded as the main form of future mobile networks since its irreplaceable advantages in driving improvement of system capacity. However, the dense deployment of heterogeneous low-power nodes and frequency reuse result in complicated interference environment. This project innovatively proposes the exploration of polarization resources in cognitive radio to realize the coexistence of various access technologies and multilayer dense nodes, which is a promising approach to avoid co-channel interference and dramatically improve system capacity. Firstly, this project studies on polarization spectrum sensing and recognition theories and technologies for mixture signals and high-dimensional data to obtain polarization resources. Secondly, in order to realize the interference-free coexistence of dense heterogeneous primary networks and cognitive networks, this project correspondingly studies spectrum sharing theories and technologies including polarization spectrum sharing and polarization-other domain collaborative spectrum sharing, and polarization interference suppression theories and technologies. In the end, project studies on the modeling and theoretical upper bound estimation of spectrum efficiency in dense heterogeneous network. This research utilizes polarization in dense heterogeneous network which targets at small area coverage, and effectively avoids severe impact from depolarization effect of wireless communication channel on polarization resources. This research strives to open up a new way to solve challenge derived from complicated interference caused by frequency reuse in dense heterogeneous network.

作为未来无线移动通信主要存在形式的密集异构网络虽然对于提高系统容量具有不可替代的优势，但由于异构低功率节点的密集部署和频率复用，使得网络的干扰环境变得尤为复杂。课题开创性地提出引入极化维度这一新的资源采用认知无线电技术实现多种无线接入技术、多覆盖层次密集节点之间的共存，有效避免复杂干扰带来频谱效率的性能损伤，使得提升系统整体容量的目标真正得以实现。课题针对密集异构环境下极化资源的获取问题，研究新型的混叠信号高维数据的极化感知与识别理论与方法；针对密集异构授权网络与认知网络以及认知网络间无干扰共存问题，研究基于极化及极化-它域协同的频谱共享和干扰抑制理论与方法。同时，研究该密集异构网络的频谱效率模型，评估频谱效率提升的理论上界。课题巧妙地避开了极化受无线信道去极化效应影响较大的难题，将其应用于覆盖范围较小的密集异构网络中，为解决密集异构环境下的频率复用带来的复杂干扰难题开辟了一条新的途径。

项目引入极化维度这一新的资源解决密集异构环境下频率复用带来的复杂干扰问题。项目围绕如何实现极化资源感知与利用展开，从新型的混叠信号高维数据的极化感知与识别理论与方法、基于极化及极化-它域协同的授权网络、认知网络频谱共享理论与方法和密集多源干扰下的极化干扰抑制方法等方面进行研究。研究成果体现在发表高水平学术论文35篇，其中在IEEE Communications Surveys & Tutorials、IEEE Transactions on Vehicular Technology等通信领域顶级期刊发表SCI检索论文8篇，EI检索论文26篇，申请发明专利12项并获得1项授权，培养博士和硕士研究生20人等。.项目针对感知随机信号的高维特性和混叠特性，建立高维数据极化频谱感知模型，提出新型的高维数据极化频谱感知和信号识别算法；针对密集异构授权网络与认知网络以及认知网络间无干扰共存问题，建立极化及极化—它域频谱共享模型，提出一系列极化及极化—它域协同的频谱共享算法；针对密集多源干扰，提出了基于极化及极化-空域协同的干扰抑制方法；搭建了基于USRP2+GNU Radio的实验验证平台，实测性能评估表明相比于传统的密集异构网络频谱共享，项目所提新方法使得系统频谱效率提升了约30%，验证了密集异构网络中充分挖掘极化资源并与多域资源结合带来的性能优势。.项目的研究巧妙地避开了极化受无线信道去极化效应影响较大的难题，研究成果可应用于覆盖范围较小的密集异构网络中，同时对于未来数据业务海量化、网络异构化、用户密集化发展的无线移动通信网络实现系统容量的提升具有指导意义。

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