# Reinforcement Learning for AOLLA

**Repository Path**: owl-cj/Reinforcement_Learning_for_AOLLA

## Basic Information

- **Project Name**: Reinforcement Learning for AOLLA
- **Description**: Experimental validation of the paper, Reinforcement Learning for a Deployment-friendly Adaptive Outer Loop Link Adaptation
- **Primary Language**: Unknown
- **License**: MulanPSL-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-03-09
- **Last Updated**: 2023-03-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: Matlab

## README

# Reinforcement Learning for a Deployment-friendly Adaptive Outer Loop Link Adaptation

Experimental validation of the paper, Reinforcement Learning for a Deployment-friendly Adaptive Outer Loop Link Adaptation

This article gives a description of the complete process of deploying the four algorithms and experimental validation mentioned in the article, Reinforcement Learning for a Deployment-friendly Adaptive Outer Loop Link Adaptation, and instructions for using the source code. A complete log of the experimental data from the paper and the training model for reinforcement learning is provided in the attached file.

For details, please read the Experimental_description_of_Q_L_AOLLA.pdf. For developers who want to verify the algorithms in the paper, we strongly recommend to read carefully Section V, Experimental Validation on an SDR-based Hardware Platform, of the Experimental_description_of_Q_L_AOLLA.pdf.