A Mathematical Introduction to Deep Reinforcement Learning for 5G/6G Applications
<|reference_start|>A Mathematical Introduction to Deep Reinforcement Learning for 5G/6G Applications: 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.<|reference_end|>
@article{rezazadeh2024a,
    title={A Mathematical Introduction to Deep Reinforcement Learning for 5G/6G
  Applications},
    author={Farhad Rezazadeh},
    journal={arXiv preprint arXiv:2403.14516},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14516},
    primaryClass={cs.NI}
}