Version 1
: Received: 18 June 2022 / Approved: 21 June 2022 / Online: 21 June 2022 (03:57:22 CEST)
How to cite:
Sirhan, N.; Martinez-Ramon, M. Cognitive Radio Resource Scheduling using Multi agent QLearning for LTE. Preprints2022, 2022060279. https://doi.org/10.20944/preprints202206.0279.v1
Sirhan, N.; Martinez-Ramon, M. Cognitive Radio Resource Scheduling using Multi agent QLearning for LTE. Preprints 2022, 2022060279. https://doi.org/10.20944/preprints202206.0279.v1
Sirhan, N.; Martinez-Ramon, M. Cognitive Radio Resource Scheduling using Multi agent QLearning for LTE. Preprints2022, 2022060279. https://doi.org/10.20944/preprints202206.0279.v1
APA Style
Sirhan, N., & Martinez-Ramon, M. (2022). Cognitive Radio Resource Scheduling using Multi agent QLearning for LTE. Preprints. https://doi.org/10.20944/preprints202206.0279.v1
Chicago/Turabian Style
Sirhan, N. and Manel Martinez-Ramon. 2022 "Cognitive Radio Resource Scheduling using Multi agent QLearning for LTE" Preprints. https://doi.org/10.20944/preprints202206.0279.v1
Abstract
In this paper, we propose, implement, and test two novel downlink LTE scheduling algorithms. The implementation and testing of these algorithms were in Matlab, and they are based on the use of Reinforcement Learning, more specifically, the Qlearning technique for scheduling two types of users. The first algorithm is called a Collaborative scheduling algorithm, and the second algorithm is called a Competitive scheduling algorithm. The first type of the scheduled users is the Primary Users, and they are the licensed subscribers that pay for their service. The second type of the scheduled users is the Secondary Users, and they could be unlicensed subscribers that dont pay for their service, device to device communications, or sensors. Each user whether it is a primary or secondary is considered as an agent. In the Collaborative scheduling algorithm, the primary user agents will collaborate in order to make a joint scheduling decision about allocating the resource blocks to each one of them, then the secondary user agents will compete among themselves to use the remaining resource blocks. In the Competitive scheduling algorithm, the primary user agents will compete among themselves over the available resources, then the secondary user agents will compete among themselves over the remaining resources. Experimental results show that both scheduling algorithms converged to almost ninety percent utilization of the spectrum, and provided fair shares of the spectrum among users.
Keywords
Long Term Evolution; Radio Resource Management; Packet Scheduling; Cognitive Radio; Multi agent Qlearning; Matlab
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.