Edge Computing for Low Latency 5G Applications Using Q-Learning Algorithm

Aastha A Neeralgi, Anuj Baddi, Ishwari Naik, Madivalesh Demakkanavar, Sandeep Kulkarni, Vijayalakshmi M

2025

Abstract

Next-generation technologies like industrial automa tion, augmented reality, and driverless cars depend on edge computing for low-latency 5G applications. In real-time appli cations, achieving ultra-low latency is essential to guarantee un interrupted communication, reduce delays, and improve user ex perience. Computational tasks are brought closer to end users by utilizing edge computing, which greatly cuts down on delays and enhances system responsiveness. The focus here is on lowering latency, avoiding needless handovers, and guaranteeing reliable connections by integrating Q-learning with edge computing to enhance handover decisions in 5G networks. The state space for the Q-learning algorithm is made simpler by incorporating crucial characteristics like latency and Signal-to-Noise Ratio (SNR) through preprocessing methods like normalization and discretization.Effective and flexible decision-making is ensured via a well-balanced exploration exploitation approach and a well tuned reward system. In comparison to Random Forest model we trained, experimental results demonstrate an impressive 7.8% reduction in latency. This framework opens the door for developments in next-generation network technologies by offering a scalable, effective technique to handle major issues in latency sensitive 5G applications.

Paper Citation

in Harvard Style

Neeralgi A., Baddi A., Naik I., Demakkanavar M., Kulkarni S. and M V. (2025). Edge Computing for Low Latency 5G Applications Using Q-Learning Algorithm. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 733-740. DOI: 10.5220/0013643400004664

in Bibtex Style

@conference{incoft25,
author={Aastha Neeralgi and Anuj Baddi and Ishwari Naik and Madivalesh Demakkanavar and Sandeep Kulkarni and Vijayalakshmi M},
title={Edge Computing for Low Latency 5G Applications Using Q-Learning Algorithm},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={733-740},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013643400004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Edge Computing for Low Latency 5G Applications Using Q-Learning Algorithm
SN - 978-989-758-763-4
AU - Neeralgi A.
AU - Baddi A.
AU - Naik I.
AU - Demakkanavar M.
AU - Kulkarni S.
AU - M V.
PY - 2025
SP - 733
EP - 740
DO - 10.5220/0013643400004664
PB - SciTePress