Real-time Path Loss Prediction Using Deep Learning for Smart Meter Communication System

Type: MA thesis

Status: running

Date: September 1, 2024 - February 28, 2025

Supervisors: Andreas Maier, Adithya Ramachandran, Siming Bayer

Path Loss and Deep Learning:

Path loss measures the reduction in signal power between a transmitter and receiver. It plays a crucial role in determining the coverage and reliability of communication networks, especially in smart meter wireless communication systems. Therefore, accurate path loss prediction is essential for designing efficient networks and ensuring reliable data transfer. Traditional methods, such as empirical models [1] and deterministic approaches [2], when predicting path loss, faces limitations in generalizing across environments or suffer from high computational complexity. In contrast, machine learning and deep learning-based methods [3][4][5][6] offer a promising balance between accuracy and computational efficiency.

Thesis Outline:

This thesis aims to advance path loss prediction in smart meter systems, focusing specifically on LPWAN [7] communication technologies. While prior research has made significant strides, advancements in technology provide opportunities to improve model accuracy, reduce complexity, and enhance versatility. The primary goal is to develop and implement a robust deep learning model for real-time path loss prediction in a fixed-area network using existing smart meter data. Consequently, the outcomes of this work will be valuable for optimizing network management and enhancing the reliability of communication in smart meter deployments.

This thesis for path loss prediction using deep learning will cover the following aspects:

  • Conducting a comprehensive literature review,
  • Developing and implementing a machine learning or deep learning model for path loss prediction in a fixed network smart meter scenario, using available meter data,
  • Thoroughly evaluating the model’s performance in terms of accuracy, robustness, and real-time prediction capabilities.

References:

  1. Singh, Yuvraj. (2012). Comparison of Okumura, Hata and COST-231 Models on the Basis of Path Loss and Signal Strength. International Journal of Computer Applications. 59. 37-41. 10.5120/9594-4216.
  2. M. Ayadi, “A UHF Path Loss Model Using Learning Machine for Heterogeneous Networks,” IEEE Transactions on Antennas and Propagation, 2017.
  3. Y. Zhang, “Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion,” Applied Sciences, 2019.
  4. D. Wu, “Application of artificial neural networks for path loss prediction in railway environments,” in In Proceedings of the 2010 5th International ICST Conference on Communications and Networking, Beijing, China, 2010.
  5. I. Popescu, “ANN prediction models for indoor environment,” in In Proceedings of the 2006 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, Montreal, QC, Canada, 2006.
  6. F. Schneider, “Lokalisierung Smarter Zähler – Masterarbeit im Studiengang Informatik, Technische Hochschule Nürnberg” Nürnberg, 2021.
  7. B. S. Chaudhari, LPWAN Technologies for IoT and M2M Applications, 2020.