Robust and Energy Efficient Signal Processing for Massive MIMO Communication

近年来，电信行业对第五代(5G)通信网络的关注越来越多。5G技术可能会以网络智能为特色，为用户在各种复杂环境中提供非常快速但无缝的不同无线电空中接口接入。与此同时，绿色通信正在全球范围内引起高度关注，其中能效成为未来5G网络的主要设计目标之一。由于蜂窝网络对更高传输速率、链路可靠性、无处不在的互联网接入等更好服务的需求日益增长，大规模多输入多输出(MIMO)或大规模MIMO被认为是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成为一项成熟的技术，但大规模MIMO相对较新。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。其主要局限性之一是其性能在很大程度上依赖于信道状态信息(CSI)，而CSI无论是通过测量还是通过信道估计都很难获得，特别是在部署的天线数量非常多的情况下。另一个限制是移动终端的电池功率有限，这严重影响了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，应该更多地致力于智能无线网络的发展，这种网络可以根据信道状况、链路可靠性和系统总功率来优化和自适应地分配可用资源。*所提出的研究解决了下一代大规模MIMO通信中复杂的信号处理问题。该项目专注于为多用户大规模MIMO系统开发健壮和节能的信号处理技术。本研究的主要目标是在真实的信道条件下最大化海量MIMO网络的传输可靠性、频谱利用率和能量效率。即将开发的新技术包括：(1)大规模MIMO系统的稳健信道估计，目标是获得不完美的CSI；(2)基于实用CSI估计的大规模天线基站的能量高效波束形成设计；(3)用于大规模天线系统的低复杂度近最佳接收机的设计；(4)大规模MIMO系统性能研究的分析方法的发展；以及(5)集成所提出的信号处理算法的大规模MIMO系统验证的软件平台的设计。拟议的项目还包括将在康科迪亚大学为研究生和博士后研究员提供的重要培训内容。**

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. **