Enabling FDD Massive MIMO through Deep Learning-based Channel Prediction

A major obstacle for widespread deployment of frequency division duplex (FDD)-based Massive multiple-input multipleoutput (MIMO)communications is the large signaling overhead for reporting full downlink (DL) channel state information (CSI) back to the basestation (BS), in order to enable closed-loop precoding. We completely remove this overhead by a deep-learning based channel extrapolation (or "prediction") approach and demonstrate that a neural network (NN) at the BS can infer the DL CSI centered around a frequency f UL; nomore pilot/reporting overhead is needed than with a genuine time division duplex (TDD)-based system. The rationale is that scatterers and the large-scale propagation environment are sufficiently similar to allow a NN to learn about the physical connections and constraints between two neighboring frequency bands, and thus provide a well-operating system even when classic extrapolation methods, like the Wiener filter (used as a baseline for comparison throughout) fails. We study its performance for various state-of-the-art Massive MIMO channel models, and, even more so, evaluate the scheme using actual Massive MIMO channel measurements, rendering it to be practically feasible at negligible loss in spectral efficiency when compared to a genuine TDD-based system. I. INTRODUCTION With a significant increase in area throughput, Massive multiple-input multiple-output (MIMO) antenna communication has become an enabling technology for the upcoming fifth generation (5G) wireless mobile communication systems [1], [2], [3], [4]. However, Massive MIMO systems described in current research literature commonly exploit channel reciprocity and hence rely on time division duplex (TDD)-based approaches [1], i.e., uplink (UL) and downlink (DL) channels share the same frequency band in orthogonal time intervals. Achieving such reciprocity in practice requires accurate hardware with costly calibration circuitry. To mitigate this issue, various alternatives to a full Massive MIMO system have been proposed such as the grid of beams [5] and codebook Massive MIMO [6].