Achieving High Spectrum Efficiency on High Speed Train for 5G New Radio and Beyond

Realizing high mobile data rates in high mobility scenarios has been an important yet challenging target for wireless communications. One typical scenario of such "double high" communications is the HST, which has been one of the most representative infrastructures in more and more countries, especially in China. However, the current SE in HST communications according to the measurements is far from satisfactory to meet the data rate demand generated by the intensive users on HSTs. To address this issue, potential technologies to improve the SE performance of HST communications under various CSI assumptions are presented in this article. Specifically, two open loop beamforming methods with the help of location information (i.e., the location based MIMO precoding and the location assisted MIMO precoder cycling) are first proposed, which are shown to be robust to HST channel variations. Then, we study closed loop beamforming strategies, where channel prediction is proposed to be the key to obtain the essential CSI (e.g., channel matrix or channel covariance) with a low overhead. As HSTs usually go through complicated environments, a big data aided adaptation mechanism between the investigated methods is further proposed. Numerical results demonstrate that the proposed approaches are capable of achieving over 10x SE performance than the current HST transmission strategies and worthy to be studied and discussed in the future 5G New Radio (NR) standardization. It is anticipated that communications with high data rates in high mobility scenarios will be well supported for HSTs in the 5G era.