A Deep-Reinforcement-Learning-Based Approach to Dynamic eMBB/URLLC Multiplexing in 5G NR

This article investigates the dynamic multiplexing of enhanced mobile broadband (eMBB) and ultrareliable and low latency communications (URLLC) on the same channel in 5G NR. Due to significant difference in transmission time scale, URLLC employs a preemptive puncturing technique to multiplex its tra...

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Veröffentlicht in:	IEEE internet of things journal 2020-07, Vol.7 (7), p.6439-6456
Hauptverfasser:	Huang, Yan, Li, Shaoran, Li, Chengzhang, Hou, Y. Thomas, Lou, Wenjing
Format:	Artikel
Sprache:	eng
Schlagworte:	3GPP Standards 5G NR Algorithms Approximation algorithms Broadband Computer Science Computer Science, Information Systems Decoding Deep learning deep reinforcement learning (DRL) Engineering Engineering, Electrical & Electronic enhanced mobile broadband (eMBB)/ultrareliable and low latency communication (URLLC) multiplexing Mobile computing Multiplexing Network latency Neural networks Optimization Piercing Preempting preemption puncturing resource allocation Resource management Resource scheduling Science & Technology Technology Telecommunications
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