Cognitive Radio Resource Scheduling using Multi agent QLearning for LTE

TL;DR

This paper introduces two novel LTE downlink scheduling algorithms based on multi-agent Q-learning, aiming to optimize spectrum utilization and fairness among primary and secondary users.

Contribution

The paper presents two new reinforcement learning-based scheduling algorithms for LTE, incorporating collaborative and competitive strategies for primary and secondary users.

Findings

Both algorithms achieved approximately 90% spectrum utilization.

The algorithms ensured fair spectrum sharing among users.

Implementation was validated through Matlab simulations.

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.