Robust and Energy Efficient Signal Processing for Massive MIMO Communication

最近，电信行业更加关注第五代（5G）通信网络。5G技术可能具有网络智能，为用户提供在各种复杂环境中快速而无缝地访问不同无线电空中接口的能力。与此同时，绿色通信在全球范围内备受关注，能效成为未来5G网络的主要设计目标之一。由于蜂窝网络对更高的传输速率、链路可靠性、无处不在的互联网接入等更好的服务需求不断增长，大规模多输入多输出（massive multiple-input multiple-output， MIMO）或大规模MIMO被认为是5G无线通信的使能和有前景的技术之一。虽然MIMO在过去十年中已经发展成为一种成熟的技术，但大规模MIMO是相对较新的技术。尽管大规模MIMO具有诸多优点，但其在实际移动环境中的应用仍面临一些困难。其主要限制之一是其性能很大程度上取决于信道状态信息（CSI），而CSI无论是通过测量还是信道估计都很难获得，特别是在部署的天线数量非常大的情况下。另一个限制是移动终端中有限的电池电量，严重影响无线传输性能和链路容量。为了实现宽带和节能的绿色通信，应该更多地致力于开发智能无线网络，该网络可以根据信道状况、链路可靠性和系统总体功率对可用资源进行优化和自适应分配。

Recently, telecommunication industries have paid more attention to the fifth-generation (5G) communication networks. The 5G technology will likely feature network intelligence to provide users with very fast yet seamless access to different radio air interfaces in various complicated environments. Meanwhile, green communication is drawing a great deal of attention worldwide, in which energy efficiency becomes one of the major design objectives in future 5G networks. Motivated by the increasing demands for better services in cellular networks, such as higher transmission rate, link reliability, ubiquitous access to internet etc, massive multiple-input multiple-output (MIMO) or large-scale MIMO is considered as one of the enabling and promising technologies for 5G wireless communication. Although MIMO has been developed as a mature technology over the last decade, massive MIMO is relatively new. Despite the merits of massive MIMO, its use in practical mobile environments faces some difficulties. One of its major limitations is that its performance depends largely on the channel state information (CSI), which is very difficult to obtain either through measurement or by channel estimation, especially when the number of antennas deployed is very large. Another restriction is the limited battery power in mobile terminals that affects severely the wireless transmission performance and link capacity. To realize broad-band and energy efficient green communications, more effort should be devoted to the development of intelligent wireless networks that can optimally and adaptively allocate the available resources based on channel condition, link reliability and total system power as a whole. The proposed research addresses complex signal processing issues in the next-generation massive MIMO communication. The project focuses on the development of robust and energy efficient signal processing techniques for multi-user massive MIMO systems. The main objective of this research is to maximize the transmission reliability, spectral usage and energy efficiency of massive MIMO networks under realistic channel conditions. The new techniques to be developed include (1) robust channel estimation of massive MIMO systems with an objective to acquire imperfect CSI; (2) energy efficient beamforming design for large-scale antenna base stations using practical CSI estimate; (3) design of low-complexity near-optimal receivers for large-scale antenna systems; (4) development of analytical methods for performance study of massive MIMO systems; and (5) design of software platform for validation of the massive MIMO system integrating the proposed signal processing algorithms. The proposed project also contains a significant training component for graduate students and post-doctoral research fellows that will take place at Concordia University.