A Knowledge-Driven Meta-Learning Method for CSI Feedback

TL;DR

This paper introduces a knowledge-driven meta-learning approach for CSI feedback in massive MIMO systems, enabling rapid adaptation with minimal data and reduced training time by leveraging intrinsic channel knowledge.

Contribution

It proposes a meta-learning method that uses intrinsic CSI knowledge for faster, data-efficient adaptation in CSI feedback, improving deployment practicality.

Findings

Achieves faster convergence in new scenarios

Reduces need for large training datasets

Improves feedback performance

Abstract

Accurate and effective channel state information (CSI) feedback is a key technology for massive multiple-input and multiple-output (MIMO) systems. Recently, deep learning (DL) has been introduced to enhance CSI feedback in massive MIMO application, where the massive collected training data and lengthy training time are costly and impractical for realistic deployment. In this paper, a knowledge-driven meta-learning solution for CSI feedback is proposed, where the DL model initialized by the meta model obtained from meta training phase is able to achieve rapid convergence when facing a new scenario during the target retraining phase. Specifically, instead of training with massive data collected from various scenarios, the meta task environment is constructed based on the intrinsic knowledge of spatial-frequency characteristics of CSI for meta training. Moreover, the target task dataset is also augmented by exploiting the knowledge of statistical characteristics of channel, so that the DL model initialized by meta training can rapidly fit into a new target scenario with higher performance using only a few actually collected data in the target retraining phase. The method greatly reduces the demand for the number of actual collected data, as well as the cost of training time for realistic deployment. Simulation results demonstrate the superiority of the proposed approach from the perspective of feedback performance and convergence speed.