Deep Learning for Massive MIMO: Channel Completion for TDD Downlink

In a realistic fifth generation (5G) massive multiple-input multiple-output (MIMO) system, hardware constraints often pose challenges towards network design that are not sufficiently considered in the literature. In this work, we consider a time division duplex (TDD) network where user equipments (UEs) are equipped with N > 1 antennas for receiving in the downlink (DL) but only with a single antenna for transmitting in the uplink (UL). Thus it is not possible to learn the complete downlink channel in a single timeslot from the uplink utilizing channel reciprocity. In this paper, we propose a novel solution based on deep learning with auxiliary input of the estimated single antenna channel in the uplink to accomplish the downlink channel completion for full rank transmission from the base station (BS). We use synthetic data for deep learning training and testing provided by the stochastic quasi-deterministic radio channel generator (QuaDRiGa). Evaluation results show that our work outperforms existing deep learning based algorithms and can provide highly effective recovered channels even with complex channel data and low compression ratio.