Enabling FDD Massive MIMO through Deep Learning-based Channel Prediction

TL;DR

This paper introduces a deep learning-based method for predicting downlink CSI in FDD Massive MIMO systems using uplink CSI from adjacent frequencies, eliminating the need for extensive feedback and matching TDD performance.

Contribution

It proposes a neural network approach for channel extrapolation in FDD Massive MIMO, reducing signaling overhead and enabling practical deployment.

Findings

Neural network accurately predicts downlink CSI from uplink data.

The method outperforms traditional extrapolation techniques like Wiener filtering.

Practical tests show negligible spectral efficiency loss compared to TDD systems.

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.