Dynamic Channel Charting: An LSTM-AE-based Approach

TL;DR

This paper introduces a novel LSTM-AE-based method for dynamic channel charting in 6G systems, capturing temporal dependencies in CSI data to improve stability and topological consistency over time.

Contribution

It presents the first integration of LSTM networks with autoencoders for dynamic channel charting, addressing the limitations of static models in complex, time-varying environments.

Findings

Outperforms traditional CC methods in stability and trajectory continuity.

Enhances long-term predictability of channel states.

Demonstrates effectiveness in real-world communication scenarios.

Abstract

With the development of the sixth-generation (6G) communication system, Channel State Information (CSI) plays a crucial role in improving network performance. Traditional Channel Charting (CC) methods map high-dimensional CSI data to low-dimensional spaces to help reveal the geometric structure of wireless channels. However, most existing CC methods focus on learning static geometric structures and ignore the dynamic nature of the channel over time, leading to instability and poor topological consistency of the channel charting in complex environments. To address this issue, this paper proposes a novel time-series channel charting approach based on the integration of Long Short-Term Memory (LSTM) networks and Auto encoders (AE) (LSTM-AE-CC). This method incorporates a temporal modeling mechanism into the traditional CC framework, capturing temporal dependencies in CSI using LSTM and learning continuous latent representations with AE. The proposed method ensures both geometric consistency of the channel and explicit modeling of the time-varying properties. Experimental results demonstrate that the proposed method outperforms traditional CC methods in various real-world communication scenarios, particularly in terms of channel charting stability, trajectory continuity, and long-term predictability.