Topic：2-D localization of incoherently distributed sources in massive MIMO systems

Time：2014年11月14日（周五）上午10:30-11:30

Venue：信电大楼-215学术会议室

Speaker：杨少石博士，英国南安普顿大学

Biography：

杨少石，2006年本科毕业于北京邮电大学信息工程专业，2013年获得英国南安普顿大学电子与电气工程博士学位，随后在该校继续从事博士后研究。主要研究方向包括：多天线无线通信系统，绿色无线电，异构网络，跨层干扰管理，凸优化及其在无线通信网络中的应用等，已在IEEE Transactions系列权威期刊上发表近20篇论文。杨少石博士曾在Intel中国研究中心从事移动WiMAX的国际标准化工作，是剑桥大学牛顿数学研究院初级会员，并担任IEEE Journal on Selected Areas in Communications (JSAC), ICC, GLOBECOM, PIMRC等通信、网络、信号处理领域权威期刊和会议的技术程序委员会成员。

Abstract：

In this talk, a pair of approaches for two-dimensional (2-D) localization of incoherently distributed (ID) sources in massive multiple-input multiple-output (MIMO) systems will be presented.

The first approach relies on estimating signal parameters via rotational invariance technique (ESPRIT) and is proposed for large-scale uniform rectangular arrays (URAs). The traditional ESPRIT-based methods are valid only for one-dimensional (1-D) localization of the ID sources, where only the azimuth angular parameters need to be estimated. By contrast, in the proposed approach the signal subspace is constructed for estimating the nominal azimuth and elevation direction-of-arrivals (DOAs) and the angular spreads. Our analytical and numerical results show that the proposed estimator imposes significantly lower computational complexity than the conventional 2-D estimation approaches, and albeit its performance is comparable with that of the traditional 2-D estimators, it improves when massive MIMO systems are employed.

Furthermore, a generalized beamspace approach is proposed for large-scale uniform cylindrical array (UCyA), which is composed of multiple uniform circular arrays (UCAs). The received signal vectors in the element space are transformed into the beamspace by employing beamforming vectors. As a beneficial result, the total dimensions of the received signal vectors are significantly reduced. In addition, it is shown that the error introduced by the transformation decreases as the number of UCyA antennas increases. Our analytical and numerical results demonstrate that the proposed approach outperforms the existing approach in terms of both performance and complexity in massive MIMO systems.