Enabling URLLC in 5G NR IIoT Networks: A Full-Stack End-to-End Analysis

Author:

Cuozzo Giampaolo1,Cavallero Sara1,Pase Francesco2,Giordani Marco2,Eichinger Joseph3,Buratti Chiara1,Verdone Roberto1,Zorzi Michele1

Affiliation:

1. WiLab and University of Bologna,Italy

2. WiLab and University of Padova,Italy

3. Huawei Technologies, Munich Research Center,Germany

Publisher

IEEE

Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Design of a Feasible Wireless MAC Communication Protocol via Multi-Agent Reinforcement Learning;2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN);2024-05-05

2. Age of Information Performance of Ultra Reliable Low Latency Service in 5G New Radio Networks;Lecture Notes in Computer Science;2024

3. Deep Reinforcement Learning for Uplink Scheduling in NOMA-URLLC Networks;IEEE Transactions on Machine Learning in Communications and Networking;2024

4. On Learning Generalized Wireless MAC Communication Protocols via a Feasible Multi-Agent Reinforcement Learning Framework;IEEE Transactions on Machine Learning in Communications and Networking;2024

5. A 5G multi-gNodeB simulator for ultra-reliable 0.5–100 GHz communication in indoor Industry 4.0 environments;Computer Networks;2023-12

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