Bayesian Optimisation-Assisted Neural Network Training Technique for Radio Localisation

TL;DR

This paper introduces a Bayesian optimisation-based method for tuning neural network hyperparameters and selecting training features to improve indoor radio localisation accuracy and efficiency for IoT applications.

Contribution

It presents a novel automated hyperparameter tuning and feature selection technique using Bayesian optimisation for neural networks in radio localisation.

Findings

Enhanced localisation accuracy with automated tuning.

Reduced manual intervention in model training.

Improved training efficiency and adaptability.

Abstract

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.