Optimizing Rural Wireless Networks Using Machine Learning: Improving Connectivity in Remote Areas
DOI:
https://doi.org/10.62697/rmiie.v4i2.181Keywords:
Rural wireless networks, machine learning, network optimization, rural connectivityAbstract
The optimization of rural wireless networks through machine learning represents a fundamental challenge in reducing the digital divide in remote areas, this research aims to analyze machine learning-based optimization strategies for rural wireless networks through a systematic review of recent scientific literature, the methodology is based on a qualitative approach with a descriptive-exploratory design, analyzing academic publications indexed in IEEE Xplore, Scopus, and Web of Science during 2019-2024, the findings reveal that federated learning algorithms, neural networks, and reinforcement learning have demonstrated significant effectiveness in dynamic resource management, traffic pattern prediction, and energy optimization in rural environments, however, challenges persist in terms of data availability, computational capabilities, and adaptability to dynamic environments. It is concluded that the integration of machine learning with emerging technologies such as 5G, IoT, and edge computing is establishing the foundations for more robust connectivity ecosystems in rural areas, although further research is required in developing algorithms specifically adapted to the characteristic limitations of these environments, considering crucial aspects such as energy efficiency, scalability, sustainability, and the ability to respond to adverse environmental conditions typical of rural areas.
Downloads
References
Almurshed, O., Patros, P., Huang, V., Mayo, M., Ooi, M., & Chard, R. (2022). Adaptive Edge-Cloud Environments for Rural AI. 2022 IEEE International Conference on Services Computing (SCC), 74-83. https://www.doi.org/10.1109/scc55611.2022.00023
Chaabane, F., Réjichi, S., Salem, H. B., Elmabrouk, H., & Tupin, F. (2024). Strategic Planning of Rural Telecommunication Infrastructure: A Multi-Source Data Fusion and Optimization Model. Spatial Inf. Sci., 48(1), 73–78. https://www.doi.org/10.5194/isprs-archives-xlviii-1-2024-73-2024
Chandra-Gangwar, R., & Singh, R. (2023). Machine Learning Algorithms from Wireless Sensor Network’s Perspective. En Wireless Sensor Network’s Perspective. IntechOpen. https://doi.org/http://dx.doi.org/10.5772/intechopen.111417
Chaoub, A., Giordani, M., Lall, B., Bhatia, V., Kliks, A., & Mendes, L. (2022). 6G for Bridging the Digital Divide: Wireless Connectivity to Remote Areas. IEEE Wireless Communications, 29(1), 160-168. https://www.doi.org/10.1109/mwc.001.2100137
Dreifuerst, R. M., Daulton, S., Qian, Y., Varkey, P., Balandat, M., & Kasturia, S. (2021). Optimizing Coverage and Capacity in Cellular Networks using Machine Learning. 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 8138-8142. https://doi.org/10.1109/ICASSP39728.2021.9414155
Kulin, M., Kazaz, T., De-Poorter, E., & Moerman, I. (2021). A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer. Electronics, 10(3). /https://doi.org/10.3390/electronics10030318
Kumar-Samanta, R., Sadhukhan, B., Samaddar, H., Sarkar, S., Koner, C., & Ghosh, M. (2022). Scope of machine learning applications for addressing the challenges in next‐generation wireless networks. CAAI Transactions on Intelligence Technology, 7(3), 395 - 418. https://doi.org/10.1049/cit2.12114
Liu, D., Sun, C., Yang, C., & Hanzo, L. (2020). Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning. IEEE Network, 34(4), 270-277. https://doi.org/10.1109/MNET.001.1900517
Lü, M. (2024). Application of machine learning algorithms in resource allocation for wireless communications. Applied and Computational Engineering, 37-42. https://www.doi.org/10.54254/2755-2721/102/20240966
Mikola, V., Andrii, P., Andrii, O., & Diana, N. (2023). Optimization of intelligent telecommunication networks. Vìsnik Hmelʹnicʹkogo nacìonalʹnogo unìversitetu, 1, 33-41. https://doi.org/10.31891/2307-5732-2023-317-1-33-41
Naser, M. Z., & Alavi, A. H. (2021). Error Metrics and Performance Fitness Indicators for Artificial Intelligence and Machine Learning in Engineering and Sciences. Architecture, Structures and Construction, 3, 499–517. https://doi.org/10.1007/s44150-021-00015-8
Ojo, S., Sari, A., & Ojo, T. P. (2022). Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models. Open Journal of Applied Sciences, 12(6). https://doi.org/10.4236/ojapps.2022.126068
Pandey, S. (2024). Optimizing UAV-Assisted Relaying through Split Learning for Enhanced Distributed Inference in IoT-Based Ecological Monitoring. Acceleron Aerospace Journal, 3(3). https://doi.org/10.61359/11.2106-2446
Pirinen, P., Saarnisaari, H., Beek, J. v., Matinmikko-Blue, M., Nilsson, R., & Latva-aho, M. (2019). Wireless Connectivity for Remote and Arctic Areas – Food for Thought. 2019 16th International Symposium on Wireless Communication Systems (ISWCS), 43-47. http://dx.doi.org/10.1109/ISWCS.2019.8877348
Ramírez-Arroyo, A., López, M., Rodríguez, I., Bro-Damsgaard, S., & Mogensen, P. (2024). Multi-Connectivity Solutions for Rural Areas: Integrating Terrestrial 5G and Satellite Networks to Support Innovative IoT Use Cases. arXiv. https://www.doi.org/10.48550/arxiv.2411.06979
Ryo-Koblitz, A., Maggi, L., & Andrews, M. (2024). Reducing the Environmental Impact of Wireless Communication via Probabilistic Machine Learning. Proceedings of the AAAI Symposium Series, 2(1), 89-93. https://doi.org/10.1609/aaaiss.v2i1.27654
Shi, Y., Lian, L., Shi, Y., Wang, Z., Zhou, Y., Fu, L., Bai, L., Zhang, J., & Zhang, W. (2023). Machine Learning for Large-Scale Optimization in 6G Wireless Networks. arXiv.org. https://www.doi.org/10.48550/arXiv.2301.03377
Srivastava, M. (2023). Adapting Machine Learning Techniques for Low-Resource Settings in Developing Countries: A Multidisciplinary Approach. OSF PREPRINTS. https://www.doi.org/10.31219/osf.io/62rbp
Temim, S., Talbi, L., & Bensebaa, F. (2023). Analysis and Multiobjective Optimization of a Machine Learning Algorithm for Wireless Telecommunication. Telecom, 4(2), 219–235. https://doi.org/10.3390/telecom4020013
Wang, J., Jiang, C., Zhang, H., Ren, Y., Chen, K.-C., & Hanzo, L. (2020). Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks. IEEE Communications Surveys & Tutorials, 22(3), 1472-1514. https://doi.org/10.1109/COMST.2020.2965856
Zhang, S., Yin, B., Zhang, W., & Cheng, Y. (2022). Topology Aware Deep Learning for Wireless Network Optimization. IEEE Transactions on Wireless Communications, 21(11), 9791 - 9805. /https://www.doi.org/10.1109/twc.2022.3179352
Zhou, H., Erol-Kantarci, M., Liu, Y., & Poor, H. V. (2023). A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks. IEEE Communications Surveys & Tutorials, 26(2), 781-823. https://arxiv.org/abs/2303.14320
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Diana Carolina Decimavilla-Alarcón, Emilio Fernando Jama-Rodríguez
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors who publish in Revista Mexicana de Investigación e Intervención Educativa (RMIIE), of Universidad Pablo Latapí Sarre agree to the following terms:
1. Copyright
Authors retain unrestricted copyright to their work. Authors grant the journal the right of first publication. To this end, they assign the journal non-exclusive exploitation rights (reproduction, distribution, public communication, and transformation). Authors may enter into additional agreements for the non-exclusive distribution of the version of the work published in the journal, provided that acknowledgment of its initial publication in this journal is given.
© The authors.
2. License
The articles are published in the journal under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). The terms can be found at: https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
This license allows:
- Sharing: Copying and redistributing the material in any medium or format.
- Adapting: Remixing, transforming, and building upon the material.
Under the following terms:
- Attribution: You must give appropriate credit, provide a link to the license, and indicate if any changes were made. You may do this in any reasonable manner, but not in any way that suggests the licensor endorses or sponsors your use.
- NonCommercial: You may not use the material for commercial purposes.
- ShareAlike: If you remix, transform, or build upon the material, you must distribute your creation under the same license as the original work.
There are no additional restrictions. You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
