Robust and Energy Efficient Signal Processing for Massive MIMO Communication

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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. **