Improved Grant-Free Access for URLLC via Multi-Tier-Driven Computing: Network-Load Learning, Prediction, and Resource Allocation
Author:
Affiliation:
1. School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an, China
2. Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
Funder
National Key Research and Development Program of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Networks and Communications
Link
http://xplorestaging.ieee.org/ielx7/49/10044995/10007809.pdf?arnumber=10007809
Reference47 articles.
1. System level analysis of K-repetition for uplink grant-free URLLC in 5G NR;jacobsen;Proc 25th Eur Wireless Conf Eur Wireless,2019
2. Risk-Aware Resource Allocation for URLLC: Challenges and Strategies with Machine Learning
3. Uplink Grant-Free Access Solutions for URLLC services in 5G New Radio
4. Traffic-aware overload control scheme in 5G ultra-dense M2M networks
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