An Overview of Low-Rank Channel Estimation for Massive MIMO Systems
An Overview of Low-Rank Channel Estimation for Massive MIMO Systems
复制标题
DOI：
10.1109/access.2016.2623772
复制
发表时间：
2016-01-01
期刊：
影响因子：
3.9
通讯作者：
Jin, Shi
中科院分区：
文献类型：
--
作者：
Xie, Hongxiang;Gao, Feifei;Jin, Shi
海量多输入多输出技术具有频谱和能量效率高、空间分辨率高、收发信机设计简单等优点，是5G无线通信的一种很有前途的物理层技术。为了利用其潜在的优势，信道状态信息的获取是至关重要的，这不幸地面临着许多挑战，例如上行链路的导频污染、下行链路训练和反馈的开销以及计算复杂性。为了降低有效信道的维度，研究者们从不同的角度研究了信道环境的低阶(稀疏)特性。然后，本文对当前的低阶信道估计方法进行了概述，包括它们的基本假设、关键结果以及在解决上述棘手挑战方面的优缺点。对这些方法进行了比较，并对这些低阶方法的未来研究前景进行了展望。
Massive multiple-input multiple-output is a promising physical layer technology for 5G wireless communications due to its capability of high spectrum and energy efficiency, high spatial resolution, and simple transceiver design. To embrace its potential gains, the acquisition of channel state information is crucial, which unfortunately faces a number of challenges, such as the uplink pilot contamination, the overhead of downlink training and feedback, and the computational complexity. In order to reduce the effective channel dimensions, researchers have been investigating the low-rank (sparse) properties of channel environments from different viewpoints. This paper then provides a general overview of the current low-rank channel estimation approaches, including their basic assumptions, key results, as well as pros and cons on addressing the aforementioned tricky challenges. Comparisons among all these methods are provided for better understanding and some future research prospects for these low-rank approaches are also forecasted.