Massive MIMO for Future Wireless Communication Networks

频谱紧张是一种全球现象，受稀缺频谱资源的限制，无线网络无法跟上移动宽带使用的爆炸性增长，特别是在智能手机和平板电脑变得更加普遍和频繁使用的时候。每个新的机会都必须被最大限度地利用，以应对这种频谱短缺，并通过通过现有频谱推动更多数据来满足爆炸性的宽带使用需求。大规模多输入多输出(MIMO)是2010年才开发的一种先进天线技术，它提供了这样的机会。大规模MIMO支持稀疏基础设施网络，从而单个基站(BS)足够强大，可以通过高度定向的波束形成来消除小区间干扰，从而避免任何小区到小区协调的需要。初步的工作，特别是实验已经证明了大规模MIMO的可行性。然而，目前对海量MIMO的基本限制还缺乏深入的认识，在理想和非理想的实际条件下对海量MIMO的性能评估也存在很大差距。本项目的目的是为海量MIMO的基本极限建立一个统一的理论框架，并开发复杂的信号处理算法，以在现实环境中实现海量MIMO的概念。该项目的创新之处在于，将使用先进的数学工具，如随机矩阵理论和随机几何，来捕捉多用户无线信道的动态性质。复杂的信号处理方法，如博弈论算法和压缩感知，将被应用于大规模MIMO，以克服诸如频率选择性信道衰落和有限的信道反馈等实际限制。

The spectrum crunch is a global phenomenon, where wireless networks constrained by scarce spectrum resource cannot keep pace with the explosion in mobile broadband use, particularly at a time when smartphones and tablets are becoming even more prevalent and heavily used. Every new opportunity has to be maximally exploited to cope with this spectrum deficit and meet the demands of explosive broadband usage by pushing more data through existing spectrum. Massive multiple-input multiple-output (MIMO), an advanced antenna technology only developed in 2010 offers one such opportunity. Massive MIMO enables a sparse infrastructure network, whereby a single base station (BS) is powerful enough to eliminate inter-cell interference through highly directional beamforming, and hence avoid the need for any cell-to-cell coordination. Initial work, particularly the experiments in have demonstrated the feasibility of massive MIMO. However, there is still lack of insightful understanding of the fundamental limits of massive MIMO, and also there is a large gap in the performance evaluation of massive MIMO under ideal and non-ideal practical conditions. The aim of this project is to establish a unified theoretical framework for the fundamental limits of massive MIMO with various practical constraints, and develop sophisticated signal processing algorithms to realize the concept of massive MIMO in realistic environments. The novelty of this project lies in the fact that advanced mathematical tools, such as random matrix theory and stochastic geometry, will be used to capture the dynamic nature of multi-user wireless channels. Sophisticated signal processing methods, such as game theoretic algorithms and compressed sensing, will be applied to massive MIMO in order to combat the practical constraints, such as frequency selective channel fading and limited channel feedback.