The high mobility of future high-speed trains (up to 500 km/h) poses great challenges on the design of dynamic beamforming (BF) algorithms for millimeter-wave (mmWave) communications in high-speed rail (HSR) networks. Thanks to the linear structure of HSR cellular networks, the track of the train is almost predictable. Thus, the fixed BF, i.e., highly directional antennas with fixed pointing directions, can be employed as a low-cost solution to replace dynamic BF. However, the performance gap between dynamic BF and fixed BF should be evaluated to justify the use of fixed BF in HSR mmWave communications. In this paper, empirical studies are conducted based on the raw data obtained from extensive measurement campaigns. Two classic environments in railway traffic are considered: the traditional train station and the HSR tunnel. Analyses of the measurement results, including the received signal strength, power delay profile, root-mean-squared delay spread, and channel non-stationarity are presented. Then, based on the measured data, we generalize the widely-used close-in (CI) free-space path loss (PL) model so that the generalized model can characterize the PL in the HSR tunnel with a higher accuracy. Finally, the performance gap between perfect dynamic BF and fixed BF is evaluated based on the generalized model and the measured data. Our results show that the average throughput of dynamic BF is only 4% higher than that of fixed BF in the HSR tunnel, but 21% higher in the train station when severe beam misalignment is present.

In this paper we describe a hardware-software co-design experiments platform for NAND flash based on Zynq. Our novel experimental platform utilizes the PL (Programmable Logic within Zynq) to achieve the timing control and bad block management of NAND flash, and to provide a high-speed parallel algorithm verification environment for users. Besides, it utilizes the PS (Processing System within the Zynq) to achieve the famous hybrid FAST FTL algorithm, so it can also provide a valuation baseline of FTL algorithms, and provide the running environment for compute-intensive data processing algorithms, like compression or error correction. Test results show that our experiment platform has a high speed and stable performance to manage the storage data, and can effectively evaluate the indicators of storage system based on NAND flash.