# Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting

arXiv: https://arxiv.org/abs/2310.03762 · HTML: https://mlsanity.com/paper/2310.03762/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 45 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 20 of 20)

- [Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling](https://mlsanity.com/paper/2303.11103/) (2023)
- [Channel Charting: Locating Users within the Radio Environment using Channel State Information](https://mlsanity.com/paper/1807.05247/) (2018)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Triplet-Based Wireless Channel Charting: Architecture and Experiments](https://mlsanity.com/paper/2005.12242/) (2020)
- [Deep Convolutional Neural Networks for Massive MIMO Fingerprint-Based Positioning](https://mlsanity.com/paper/1708.06235/) (2017)
- [6G White Paper on Machine Learning in Wireless Communication Networks](https://mlsanity.com/paper/2004.13875/) (2020)
- [Angle-Delay Profile-Based and Timestamp-Aided Dissimilarity Metrics for Channel Charting](https://mlsanity.com/paper/2308.09539/) (2023)
- [Improving Channel Charting with Representation-Constrained Autoencoders](https://mlsanity.com/paper/1908.02878/) (2019)
- [Siamese Neural Networks for Wireless Positioning and Channel Charting](https://mlsanity.com/paper/1909.13355/) (2019)
- [DNN-based Localization from Channel Estimates: Feature Design and Experimental Results](https://mlsanity.com/paper/2004.00363/) (2020)
- [Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting](https://mlsanity.com/paper/2204.13996/) (2022)
- [Wireless Channel Charting: Theory, Practice, and Applications](https://mlsanity.com/paper/2304.08095/) (2023)
- [Efficient channel charting via phase-insensitive distance computation](https://mlsanity.com/paper/2104.13184/) (2021)
- [Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach](https://mlsanity.com/paper/2210.06294/) (2022)
- [CSI-based Outdoor Localization for Massive MIMO: Experiments with a Learning Approach](https://mlsanity.com/paper/1806.07447/) (2018)
- [Integration of Communication and Sensing in 6G: a Joint Industrial and Academic Perspective](https://mlsanity.com/paper/2106.13023/) (2021)
- [Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer](https://mlsanity.com/paper/2110.11279/) (2021)
- [Channel charting based beamforming](https://mlsanity.com/paper/2212.12340/) (2022)
- [Positioning in 5G networks](https://mlsanity.com/paper/2102.03361/) (2021)
- [Model-Driven End-to-End Learning for Integrated Sensing and Communication](https://mlsanity.com/paper/2212.10211/) (2022)

## Built on by (most cited first, 5 of 5)

- [Velocity-Based Channel Charting with Spatial Distribution Map Matching](https://mlsanity.com/paper/2311.08016/) (2023)
- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [Model-based Deep Learning for Beam Prediction based on a Channel Chart](https://mlsanity.com/paper/2312.02239/) (2023)
- [Model-based learning for multi-antenna multi-frequency location-to-channel mapping](https://mlsanity.com/paper/2407.07719/) (2024)
- [Model-based Implicit Neural Representation for sub-wavelength Radio Localization](https://mlsanity.com/paper/2506.06387/) (2025)

## Abstract (arXiv)

Channel charting (CC) consists in learning a mapping between the space of raw channel observations, made available from pilot-based channel estimation in multicarrier multiantenna system, and a low-dimensional space where close points correspond to channels of user equipments (UEs) close spatially. Among the different methods of learning this mapping, some rely on a distance measure between channel vectors. Such a distance should reliably reflect the local spatial neighborhoods of the UEs. The recently proposed phase-insensitive (PI) distance exhibits good properties in this regards, but suffers from ambiguities due to both its periodic and oscillatory aspects, making users far away from each other appear closer in some cases. In this paper, a thorough theoretical analysis of the said distance and its limitations is provided, giving insights on how they can be mitigated. Guidelines for designing systems capable of learning quality charts are consequently derived. Experimental validation is then conducted on synthetic and realistic data in different scenarios.