Siamese Neural Networks for Wireless Positioning and Channel Charting

TL;DR

This paper introduces a Siamese neural network architecture that unifies supervised wireless positioning and unsupervised channel charting, enabling flexible semi-supervised learning with comparable or improved accuracy.

Contribution

It presents a novel unified Siamese network framework that handles both positioning and channel charting, including semi-supervised learning, which was not addressed before.

Findings

Siamese networks achieve comparable or better accuracy than existing methods.

The framework supports semi-supervised learning with limited labeled data.

Unified architecture simplifies implementation for multiple wireless localization tasks.

Abstract

Neural networks have been proposed recently for positioning and channel charting of user equipments (UEs) in wireless systems. Both of these approaches process channel state information (CSI) that is acquired at a multi-antenna base-station in order to learn a function that maps CSI to location information. CSI-based positioning using deep neural networks requires a dataset that contains both CSI and associated location information. Channel charting (CC) only requires CSI information to extract relative position information. Since CC builds on dimensionality reduction, it can be implemented using autoencoders. In this paper, we propose a unified architecture based on Siamese networks that can be used for supervised UE positioning and unsupervised channel charting. In addition, our framework enables semisupervised positioning, where only a small set of location information is available during training. We use simulations to demonstrate that Siamese networks achieve similar or better performance than existing positioning and CC approaches with a single, unified neural network architecture.