Delay laxity-based scheduling with double-deep Q-learning for time-critical applications

X Ren, J Ji, L Cai - 2022 IEEE 30th International Conference on …, 2022 - ieeexplore.ieee.org
In this paper, we propose a novel delay-aware selective admission and scheduling
algorithm for time-critical applications to guarantee the delay requirement of each packet in …

Downlink scheduler for delay guaranteed services using deep reinforcement learning

J Ji, X Ren, L Cai, K Zhu - IEEE Transactions on Mobile …, 2023 - ieeexplore.ieee.org
In this article, we propose a novel scheduling scheme to guarantee per-packet delay in
single-hop wireless networks for delay-critical applications. We consider several classes of …

Deep reinforcement learning for reducing latency in mission critical services

M Elsayed, M Erol-Kantarci - 2018 IEEE Global …, 2018 - ieeexplore.ieee.org
Next-generation wireless networks will be supporting mission critical services such as safety
related applications of connected autonomous vehicles, and real-time control of medical and …

Learning to Schedule Network Resources Throughput and Delay Optimally Using Q+-Learning

J Bae, J Lee, S Chong - IEEE/ACM Transactions on Networking, 2021 - ieeexplore.ieee.org
As network architecture becomes complex and the user requirement gets diverse, the role of
efficient network resource management becomes more important. However, existing …

Timely-throughput optimal scheduling for wireless flows with deep reinforcement learning

Q Wang, C He, K Jaffrès-Runser… - 2022 IEEE/ACM 30th …, 2022 - ieeexplore.ieee.org
This paper addresses the problem of scheduling real-time wireless flows under dynamic
network conditions and general traffic patterns. The objective is to maximize the fraction of …

[HTML][HTML] Timeslot scheduling with reinforcement learning using a double deep q-network

J Ryu, J Kwon, JD Ryoo, T Cheung, J Joung - Electronics, 2023 - mdpi.com
Adopting reinforcement learning in the network scheduling area is getting more attention
than ever because of its flexibility in adapting to the dynamic changes of network traffic and …

Delay-aware cellular traffic scheduling with deep reinforcement learning

T Zhang, S Shen, S Mao… - GLOBECOM 2020-2020 …, 2020 - ieeexplore.ieee.org
Radio access network (RAN) in 5G is expected to satisfy the stringent delay requirements of
a variety of applications. The packet scheduler plays an important role by allocating …

Effective multi-user delay-constrained scheduling with deep recurrent reinforcement learning

P Hu, L Pan, Y Chen, Z Fang, L Huang - Proceedings of the Twenty …, 2022 - dl.acm.org
Multi-user delay constrained scheduling is important in many real-world applications
including wireless communication, live streaming, and cloud computing. Yet, it poses a …

Deep reinforcement learning for delay-sensitive LTE downlink scheduling

N Sharma, S Zhang, SRS Venkata… - 2020 IEEE 31st …, 2020 - ieeexplore.ieee.org
We consider an LTE downlink scheduling system where a base station allocates resource
blocks (RBs) to users running delay-sensitive applications. We aim to find a scheduling …

Multi-User Delay-Constrained Scheduling With Deep Recurrent Reinforcement Learning

P Hu, Y Chen, L Pan, Z Fang, F Xiao… - … /ACM Transactions on …, 2024 - ieeexplore.ieee.org
Multi-user delay-constrained scheduling is a crucial challenge in various real-world
applications, such as wireless communication, live streaming, and cloud computing. The …