Random Access Scheme for Machine Type Communication Networks Using Reinforcement Learning Approach
Author:
Affiliation:
1. University of Cape Town,Dept. Electrical Engineering,Cape Town,South Africa
Funder
University of Cape Town
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10293181/10293212/10293515.pdf?arnumber=10293515
Reference19 articles.
1. Dynamic massive access for machine type communication in lte/lte-a;orim;Proceedings of the 2018 Southern Africa Telecommunication Networks and Applications Conference (SATNAC-2018,2018
2. D2D Assisted Q-Learning Random Access for NOMA-Based MTC Networks
3. Analysis and modelling of power consumption-aware priority-based scheduling for m2m data aggregation over long-term-evolution networks;aiqahtani;LET Communications,2017
4. 3GPP Release 15 Early Data Transmission
5. Priority‐based learning automata in Q‐learning random access scheme for cellular M2M communications
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