Channel Reconstruction for High-Rank User Equipment
Abstract: In a 5 Generation massive Multiple Input Multiple Output radio network, the Channel State Information is playing a central role in the algorithm design and system evaluation. However, Acquisition of Channel State Information consumes system resources (e.g. time, frequency) which in turn decrease the link utilization, i.e. fewer resources left for actual data transmission. This problem is more apparent in a scenario when User Equipment terminals have multi-antennas and it would be beneficial to obtain Channel State Information between Base Station and different User Equipment antennas e.g. for purpose of high rank (number of streams) transmission towards this User Equipment. Typically, in current industrial implementations, in order to not waste system resources, Channel State Information is obtained for only one of the User Equipment antennas which then limits the downlink transmission rank to 1. Hence, we purpose a method based on Deep learning technique. In this paper, multi-layer perception and convolutional neural network are implemented. Data are generated by MATLAB simulator using the parameters provided by Huawei Technologies Co., Ltd. Finally, the model proposed by this project provides the best performance compared to the baseline algorithms.
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