Machine Learning-Based Payload Allocation in a Service Provided Area to Maximize the Efficiency of the Network

DOI

10.2991/978-94-6463-252-1_52How to use a DOI?

Keywords

XGBOOST; Machine Learning; Colab; Payload; Resource block; Allocation

Abstract

Resource block allocation in cellular networks plays a vital role in defining the efficient use of the spectrum and maximizing user density. The resource blocks, also referred to as “payload,” is a wagon carrying the actual user data, and this study uses machine learning to forecast the needed payload for cellular consumers. They are payload as a target feature from the simulated large dataset with different frequency bands of arbitrary service provider cell sites. XGBOOST Regression ML model is used to optimize the payload allocation to various cell sites. The complete design is implemented in Google Colaboratory (Colab). It is an open-source cloud platform.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - B. Suresh
AU  - K. L. V. Sai Prakash Sakuru
PY  - 2023
DA  - 2023/11/09
TI  - Machine Learning-Based Payload Allocation in a Service Provided Area to Maximize the Efficiency of the Network
BT  - Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
PB  - Atlantis Press
SP  - 494
EP  - 504
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-252-1_52
DO  - 10.2991/978-94-6463-252-1_52
ID  - Suresh2023
ER  -