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.