Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation

Zhouyou Gu
Zhouyou Gu
Simon Lumb
Simon Lumb
David McKechnie
David McKechnie
Todd Essery
Todd Essery
Cited by: 0|Bibtex|Views8
Other Links: arxiv.org

Abstract:

In this paper, we develop a knowledge-assisted deep reinforcement learning (DRL) algorithm to design wireless schedulers in the fifth-generation (5G) cellular networks with time-sensitive traffic. Since the scheduling policy is a deterministic mapping from channel and queue states to scheduling actions, it can be optimized by using deep...More

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