Computer Science > Networking and Internet Architecture
arXiv:2403.14516 (cs)
[Submitted on 21 Mar 2024]
Title:A Mathematical Introduction to Deep Reinforcement Learning for 5G/6G Applications
Authors:Farhad Rezazadeh
View a PDF of the paper titled A Mathematical Introduction to Deep Reinforcement Learning for 5G/6G Applications, by Farhad Rezazadeh
View PDFHTML (experimental)Abstract:Algorithmic innovation can unleash the potential of the beyond 5G (B5G)/6G communication systems. Artificial intelligence (AI)-driven zero-touch network slicing is envisaged as a promising cutting-edge technology to harness the full potential of heterogeneous 6G networks and enable the automation of demand-aware management and orchestration (MANO). The network slicing continues towards numerous slices with micro or macro services in 6G networks, and thereby, designing a robust, stable, and distributed learning mechanism is considered a necessity. In this regard, robust brain-inspired and dopamine-like learning methods, such as Actor-Critic approaches, can play a vital role. The tutorial begins with an introduction to network slicing, reinforcement learning (RL), and recent state-of-the-art (SoA) algorithms. Then, the paper elaborates on the combination of value-based and policy-based methods in the form of Actor-Critic techniques tailored to the needs of future wireless networks.
| Comments: | 6 pages, 2 figures. arXiv admin note: text overlap witharXiv:2211.03430 |
| Subjects: | Networking and Internet Architecture (cs.NI) |
| Cite as: | arXiv:2403.14516 [cs.NI] |
| (orarXiv:2403.14516v1 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2403.14516 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled A Mathematical Introduction to Deep Reinforcement Learning for 5G/6G Applications, by Farhad Rezazadeh
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