Abstract: We consider an LTE downlink scheduling system where a base station allocates resource blocks (RBs) to users running delay-sensitive applications.

Abstract—We consider an LTE downlink scheduling system where a base station allocates resource blocks (RBs) to users running delay-sensitive applications.

Aug 1, 2020 · In this paper, we incorporate deep reinforcement learning (DRL) into the design of cellular packet scheduling. A delay-aware cell traffic ...

Besides, the DDPG algorithm was exploited to perform wireless routing in [30] to learn a scheduling policy that minimizes the end-to-end packet delay. ... ...

The LSTM approach surpassed the feed-forward neural networks (FNN) method to enhance the system's downlink transmission performance. Ref. [27] presented ...

In this paper, we incorporate deep reinforcement learning (DRL) into the design of cellular packet scheduling. A delay-aware cell traffic scheduling algorithm ...

A Deep Reinforcement Learning algorithm, namely Delay Minimizing Deep Q-Learning (DMDQ), that combines Long Short-term Memory with Q-learning is proposed ...

This thesis explores the use of reinforcement learning to handle downlink schedul- ing in LTE networks. We design and train, using reinforcement learning, a ...

Dec 7, 2020 · In this paper, we incorporate deep reinforcement learning (DRL) into the design of cellular packet scheduling. A delay-aware cell traffic ...

May 10, 2023 · It also shows that the DRL-based solution has better delay performance while the MAB-based solution has a faster training process. Index Terms— ...