# Bayesian Optimisation-Assisted Neural Network Training Technique for Radio Localisation

arXiv: https://arxiv.org/abs/2203.04032 · HTML: https://mlsanity.com/paper/2203.04032/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Cost-aware Bayesian Optimization](https://mlsanity.com/paper/2003.10870/) (2020)
- [Deep Transfer Learning for WiFi Localization](https://mlsanity.com/paper/2103.05123/) (2021)
- [Wireless Localisation in WiFi using Novel Deep Architectures](https://mlsanity.com/paper/2010.08658/) (2020)
- [Standing on the Shoulders of Giants: AI-driven Calibration of Localisation Technologies](https://mlsanity.com/paper/1905.13118/) (2019)

## Abstract (arXiv)

Radio signal-based (indoor) localisation technique is important for IoT applications such as smart factory and warehouse. Through machine learning, especially neural networks methods, more accurate mapping from signal features to target positions can be achieved. However, different radio protocols, such as WiFi, Bluetooth, etc., have different features in the transmitted signals that can be exploited for localisation purposes. Also, neural networks methods often rely on carefully configured models and extensive training processes to obtain satisfactory performance in individual localisation scenarios. The above poses a major challenge in the process of determining neural network model structure, or hyperparameters, as well as the selection of training features from the available data. This paper proposes a neural network model hyperparameter tuning and training method based on Bayesian optimisation. Adaptive selection of model hyperparameters and training features can be realised with minimal need for manual model training design. With the proposed technique, the training process is optimised in a more automatic and efficient way, enhancing the applicability of neural networks in localisation.