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非常快速但无缝地访问不同无线电空中接口的功能。与此同时，绿色通信在全球范围内引起了极大的关注，其中能源效率成为未来5G网络的主要设计目标之一。受蜂窝网络中对更好服务的日益增长的需求（诸如更高的传输速率、链路可靠性、无处不在的互联网接入等）的推动，大规模多输入多输出（MIMO）或大规模MIMO被认为是用于5G无线通信的使能和有前途的技术之一。虽然MIMO在过去十年中已经发展为成熟的技术，但大规模MIMO相对较新。尽管大规模MIMO具有优点，但其在实际移动的环境中的使用面临一些困难。其主要局限性之一是其性能在很大程度上取决于信道状态信息（CSI），这是非常困难的，无论是通过测量或信道估计，特别是当部署的天线数量是非常大的。另一个限制是移动的终端中有限的电池功率，其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，应该致力于开发智能无线网络，该网络可以基于信道条件、链路可靠性和系统总功率整体优化和自适应地分配可用资源。该研究解决了下一代大规模MIMO通信中的复杂信号处理问题。该项目的重点是为多用户大规模MIMO系统开发鲁棒和节能的信号处理技术。本研究的主要目标是在实际信道条件下最大限度地提高大规模MIMO网络的传输可靠性、频谱利用率和能量效率。这些新技术包括：（1）大规模MIMO系统的鲁棒信道估计，以获得不理想的信道状态信息（CSI）;（2）大规模天线基站的能量有效波束形成设计;（3）大规模天线系统的低复杂度近最优接收机设计;（4）大规模MIMO系统性能研究的分析方法的发展;（5）大规模MIMO系统性能研究的新方法。（5）设计了用于验证大规模MIMO系统性能的软件平台。拟议的项目还包括将在康考迪亚大学为研究生和博士后研究员举办的重要培训。**

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. **