The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions
Open accessAbstract
Driven by the demand to accommodate today's growing mobile traffic, 5G is designed to be a key enabler and a leading infrastructure provider in the information and communication technology industry by supporting a variety of forthcoming services with diverse requirements. Considering the ever-increasing complexity of the network, and the emergence of novel use cases such as autonomous cars, industrial automation, virtual reality, e-health, and several intelligent applications, machine learning (ML) is expected to be essential to assist in making the 5G vision conceivable. This paper focuses on the potential solutions for 5G from an ML-perspective. First, we establish the fundamental concepts of supervised, unsupervised, and reinforcement learning, taking a look at what has been done so far in the adoption of ML in the context of mobile and wireless communication, organizing the literature in terms of the types of learning. We then discuss the promising approaches for how ML can contribute to supporting each target 5G network requirement, emphasizing its specific use cases and evaluating the impact and limitations they have on the operation of the network. Lastly, this paper investigates the potential features of Beyond 5G (B5G), providing future research directions for how ML can contribute to realizing B5G. This article is intended to stimulate discussion on the role that ML can play to overcome the limitations for a wide deployment of autonomous 5G/B5G mobile and wireless communications.
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Cited by
Borges, A. F. S., Laurindo, F. J. B., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102225 · Cited by 760
Noor-A-Rahim, M., Liu, Z., Lee, H., Khyam, M. O., He, J., Pesch, D., Moessner, K., Saad, W., & Poor, H. V. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3173031 · Cited by 737
Self-citationMassive Access for 5G and Beyond
Chen, X., Ng, D. W. K., Yu, W., Larsson, E. G., Al-Dhahir, N., & Schober, R. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3019724 · Cited by 681
Edge Machine Learning for AI-Enabled IoT Devices: A Review
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The Role of Machine Learning in Cybersecurity
Apruzzese, G., Laskov, P., Montes de Oca, E., Mallouli, W., Brdalo Rapa, L., Grammatopoulos, A. V., & Di Franco, F. (2023) · Digital Threats: Research and Practice · doi:10.1145/3545574 · Cited by 279
Metadata
- Authors
- Morocho-Cayamcela, M. E., Lee, H., & Lim, W.
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 137184-137206
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2973449430
- Cited by
- 418
- References
- 23
- FWCI
- 17.89
- Type
- journal article
- Language
- en
- Open access
- gold
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- Morocho-Cayamcela, M. E., Lee, H., & Lim, W. (2019). Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions. IEEE Access. https://doi.org/10.1109/access.2019.2942390
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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