Channel Charting-Based Channel Prediction on Real-World Distributed Massive MIMO CSI

TL;DR

This paper introduces a novel channel prediction method using channel charting, a self-supervised learning technique, to forecast future CSI in distributed massive MIMO systems with user mobility, validated on real-world data.

Contribution

It presents a new channel prediction approach based on channel charting that leverages spatial relationships in CSI for improved accuracy in real-world distributed massive MIMO scenarios.

Findings

Channel charting effectively predicts future CSI.

The proposed method outperforms Wiener predictor and outdated CSI.

Validation on real-world data demonstrates improved sum rate.

Abstract

Distributed massive MIMO is considered a key advancement for improving the performance of next-generation wireless telecommunication systems. However, its efficacy in scenarios involving user mobility is limited due to channel aging. To address this challenge, channel prediction techniques are investigated to forecast future channel state information (CSI) based on previous estimates. We propose a new channel prediction method based on channel charting, a self-supervised learning technique that reconstructs a physically meaningful latent representation of the radio environment using similarity relationships between CSI samples. The concept of inertia within a channel chart allows for predictive radio resource management tasks through the latent space. We demonstrate that channel charting can be used to predict future CSI by exploiting spatial relationships between known estimates that are embedded in the channel chart. Our method is validated on a real-world distributed massive MIMO dataset, and compared to a Wiener predictor and the outdated CSI in terms of achievable sum rate.