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The capacity of a multiple-input-multiple-output (MIMO) channel with N transmit and receive antennas for highspeed railways (HSRs) is analyzed based on the 3-D modeling of the line of sight (LOS). The MIMO system utilizes a uniform linear antenna array. Instead of increasing the number of antennas or simply changing the parameters of the antenna array, such as separation and geometry, the capacity gain can be obtained by adjusting the weights of multiantenna array groups, because there are few scatterers in strong LOS environments. On the other hand, it is hard to obtain the array gain of MIMO beamforming for HSRs because of drastic changes in the receiving angle when the train travels across E-UTRAN Node B. Without changing the antenna design of Long-Term Evolution systems, this paper proposes a multiple-group multiple-antenna (MGMA) scheme that makes the columns of such a MIMO channel orthogonal by adjusting the weights among MGMA arrays, and the stable capacity gain can be obtained. The value of weights depends on the practical network topologies of the railway wireless communication system. However, the reasonable scope of group number N is less than 6. In selecting N , one important consideration is the tradeoff between practical benefit and cost of implementation.Index Terms-High-speed railway (HSR) viaducts, multipleinput-multiple-output (MIMO) channel capacity, multiple group multiple antenna (MGMA).

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The integration of sub-6 GHz and millimeter wave (mmWave) bands has a great potential to enable both reliable coverage and high data rate in future vehicular networks. Nevertheless, during mmWave vehicle-to-infrastructure (V2I) handovers, the coverage blindness of directional beams makes it a significant challenge to discover target mmWave remote radio units (mmW-RRUs) whose active beams may radiate somewhere that the handover vehicles are not in. Besides, fast and soft handovers are also urgently needed in vehicular networks. Based on these observations, to solve the target discovery problem, we utilize channel state information (CSI) of sub-6 GHz bands and Kernel-based machine learning (ML) algorithms to predict vehicles' positions and then use them to pre-activate target mmW-RRUs. Considering that the regular movement of vehicles on almost linearly paved roads with finite corner turns will generate some regularity in handovers, to accelerate handovers, we propose to use historical handover data and K-nearest neighbor (KNN) ML algorithms to predict handover decisions without involving time-consuming target selection and beam training processes. To achieve soft handovers, we propose to