University of Oulu

An AI-based solution for wireless channel interference prediction and wireless remote control

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Author: Padilla Olivo, Christian1
Organizations: 1University of Oulu, Faculty of Information Technology and Electrical Engineering, Communications Engineering
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.5 MB)
Pages: 48
Persistent link: http://urn.fi/URN:NBN:fi:oulu-202305051545
Language: English
Published: Oulu : C. Padilla Olivo, 2023
Publish Date: 2023-05-08
Thesis type: Master's thesis (tech)
Tutor: Samarakoon, Sumudu
Ali, Samad
Reviewer: Samarakoon, Sumudu
Ali, Samad
Description:

Abstract

Most control systems rely on wired connectivity between controllers and plants due to their need for fast and reliable real-time control. Yet the demand for mobility, scalability, low operational and maintenance costs call for wireless networked control system designs. Naturally, over-the-air communication is susceptible to interference and fading and therefore, enabling low latency and high reliability is crucial for wireless control scenarios. In this view, the work of this thesis aims to enhance reliability of the wireless communication and to optimize the energy consumption while maintaining low latency and the stability of the controller-plant system. To achieve this goal, two core abstractions have been used, a neural wireless channel interference predictor and a neural predictive controller. This neural predictor design is motivated by the capability of machine learning in assimilating underlying patterns and dynamics of systems using the observed data. The system model is composed of a controller-plant scheme on which the controller transmits control signals wirelessly. The neural wireless predictor and the neural controller predict wireless channel interference and plant states, respectively. This information is used to optimize energy consumption and prevent communication outages while controlling the plant. This thesis presents the development of the neural wireless predictor, the neural controller and a neural plant. Interaction and functionality of these elements are demonstrated using a Simulink simulation. Results of simulation illustrate the effectiveness of neural networks in both control and wireless domain. The proposed solution yields about 17% reduction in energy consumption compared to state-of-the-art designs by minimizing the impact of interference in the control links while ensuring plant stability.

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Copyright information: © Christian Padilla Olivo, 2023. Except otherwise noted, the reuse of this document is authorised under a Creative Commons Attribution 4.0 International (CC-BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). This means that reuse is allowed provided appropriate credit is given and any changes are indicated. For any use or reproduction of elements that are not owned by the author(s), permission may need to be directly from the respective right holders.
  https://creativecommons.org/licenses/by/4.0/