A Mathematical Introduction to Deep Reinforcement Learning for 5G/6G Applications

Farhad Rezazadeh

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arXiv · 2403.14516

A Mathematical Introduction to Deep Reinforcement Learning for 5G/6G Applications

Farhad Rezazadeh

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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.

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Farhad Rezazadeh. 2024-03-21. A Mathematical Introduction to Deep Reinforcement Learning for 5G/6G Applications. https://arxiv.org/abs/2403.14516

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