Enabling FDD Massive MIMO through Deep Learning-based Channel Prediction

8 Jan 2019Maximilian ArnoldSebastian DörnerSebastian CammererSarah YanJakob HoydisStephan ten Brink

A major obstacle for widespread deployment of frequency division duplex (FDD)-based Massive multiple-input multiple-output (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 $f_\text{DL}$ by solely observing uplink (UL) CSI on a different, yet adjacent frequency band around $f_\text{UL}$; no more pilot/reporting overhead is needed than with a genuine time division duplex (TDD)-based system... (read more)

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