Learning the CSI Recovery in FDD Systems

TL;DR

This paper introduces a machine learning method using convolutional neural networks to recover full channel state information in FDD systems, trained solely on uplink data, eliminating the need for downlink training samples.

Contribution

It presents a novel CNN-based approach for CSI recovery in FDD systems that does not require downlink training data, validated through transfer learning analysis.

Findings

The proposed method accurately reconstructs downlink CSI from uplink data.

Transfer learning assumption is validated using maximum mean discrepancy analysis.

The technique outperforms traditional methods in CSI recovery accuracy.

Abstract

We propose an innovative machine learning-based technique to address the problem of channel acquisition at the base station in frequency division duplex systems. In this context, the base station reconstructs the full channel state information in the downlink frequency range based on limited downlink channel state information feedback from the mobile terminal. The channel state information recovery is based on a convolutional neural network which is trained exclusively on collected channel state samples acquired in the uplink frequency domain. No acquisition of training samples in the downlink frequency range is required at all. Finally, after a detailed presentation and analysis of the proposed technique and its performance, the "transfer learning'' assumption of the convolutional neural network that is central to the proposed approach is validated with an analysis based on the maximum mean discrepancy metric.