Enabling FDD Massive MIMO through Deep Learning-based Channel Prediction

Abstract

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. 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.

Extended version of the conference paper submitted to SPAWC2019

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• Blind Coherent Preamble Detection via Neural Networks
• Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling
• Realistic Channel Models Pre-training
• Deep Learning Based FDD Non-Stationary Massive MIMO Downlink Channel Reconstruction
• Downlink Extrapolation for FDD Multiple Antenna Systems Through Neural Network Using Extracted Uplink Path Gains
• Deep Learning for CSI Feedback Based on Superimposed Coding
• HyperRNN: Deep Learning-Aided Downlink CSI Acquisition via Partial Channel Reciprocity for FDD Massive MIMO