Resilient Channel Charting Under Varying Radio Link Availability

TL;DR

This paper introduces AdaPos, a resilient channel charting architecture that effectively handles variable antenna configurations and outages, improving RF-based localization robustness without multiple models.

Contribution

AdaPos is a novel channel charting model that natively manages variable channel inputs using convolutional and transformer components, reducing training complexity.

Findings

Maintains state-of-the-art accuracy with missing antennas.

Replaces multiple configuration-specific models with one unified model.

Provides resilience to antenna failures and outages.

Abstract

Channel charting (CC) has become a key technology for RF-based localization, enabling unsupervised radio fingerprinting, even in non line of sight scenarios, with a minimum of reference position labels. However, most CC models assume fixed-size inputs, such as a constant number of antennas or channel measurements. In practical systems, antennas may fail, signals may be blocked, or antenna sets may change during handovers, making fixed-input architectures fragile. Existing radio-fingerprinting approaches address this by training separate models for each antenna configuration, but the resulting training effort scales prohibitively with the array size. We propose Adaptive Positioning (AdaPos), a CC architecture that natively handles variable numbers of channel measurements. AdaPos combines convolutional feature extraction with a transformer-based encoder using learnable antenna identifiers and self-attention to fuse arbitrary subsets of CSI inputs. Experiments on two public real-world datasets (SISO and MIMO) show that AdaPos maintains state-of-the-art accuracy under missing-antenna conditions and replaces roughly 57 configuration-specific models with a single unified model. With AdaPos and our novel training strategies, we provide resilience to both individual antenna failures and full-array outages.