https://github.com/aravinda-1402/link-quality-prediction-for-lte-and-wifi-networks
This is study primarily deals with classifying the future link qualities using deep learning models such as Long Short-Term Memory networks (LSTM) and Bidirectional Long Short-Term Memory networks (BLSTM).
https://github.com/aravinda-1402/link-quality-prediction-for-lte-and-wifi-networks
blstm deep-learning deep-neural-networks lstm wireless-sensor-networks
Last synced: about 1 month ago
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This is study primarily deals with classifying the future link qualities using deep learning models such as Long Short-Term Memory networks (LSTM) and Bidirectional Long Short-Term Memory networks (BLSTM).
- Host: GitHub
- URL: https://github.com/aravinda-1402/link-quality-prediction-for-lte-and-wifi-networks
- Owner: aravinda-1402
- Created: 2022-05-02T02:25:20.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2025-01-31T15:27:27.000Z (4 months ago)
- Last Synced: 2025-04-11T00:06:22.281Z (about 1 month ago)
- Topics: blstm, deep-learning, deep-neural-networks, lstm, wireless-sensor-networks
- Language: Jupyter Notebook
- Homepage:
- Size: 3.47 MB
- Stars: 3
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# SDN-Based Multipath Data Offloading Using Link Quality Prediction
This repository implements the **LSTM-based channel quality prediction** methodology described in my paper: [SDN-Based Multipath Data Offloading Scheme Using Link Quality Prediction for LTE and WiFi Networks](https://ieeexplore.ieee.org/document/10767139). The implementation focuses on predicting channel quality using machine learning models (LSTM and BLSTM) to aid in decision-making for data offloading in heterogeneous LTE-WiFi networks.
---
## Key Highlights
- **Link Quality Prediction:** Uses LSTM and BLSTM models for classifying channel quality into Good, Intermediate, and Bad categories.
- **Metrics Used:**
- Hardware-based: Received Signal Strength Indicator (RSSI).
- Software-based: Packet Delivery Ratio (PDR).
- **Application Context:** This prediction is intended to assist SDN-based traffic offloading decisions between LTE and WiFi.
---
## Motivation
The rapid growth of mobile data traffic demands efficient utilization of network resources. This project:
- Implements link quality prediction using deep learning to classify and forecast channel conditions.
- Enables smarter decision-making for multipath data offloading schemes in SDN-enabled LTE-WiFi networks.
---
## Methodology
### 1. **Link Quality Prediction**
- **Models Implemented:**
- Long Short-Term Memory (LSTM).
- Bidirectional LSTM (BLSTM).
- **Input Metrics:**
- RSSI (hardware-based).
- PDR (software-based).
- **Classes:**
- Good, Intermediate, Bad.
### 2. **Data Preprocessing**
- Dataset is derived from IoT-LAB, with features such as RSSI and PDR.
- Preprocessing includes:
- Removing outliers and redundancies.
- Standardizing features using `StandardScaler` from `scikit-learn`.
### 3. **Training and Prediction**
- LSTM and BLSTM models are trained on an 80-20 train-test split.
- Prediction performance is evaluated using metrics like accuracy and confusion matrices.
---
## Results
- **Prediction Accuracy:**
- LSTM: 99.73%.
- BLSTM: 99.94%.
- Combined use of RSSI and PDR outperforms individual metrics.
---
## Requirements
- Python 3.9+
- Libraries: `tensorflow`, `scikit-learn`, `matplotlib`
## Citation 📝
If you use the code in this repository, please cite the following paper:
```
@article{kamath2024sdn,
title={SDN-Based Multipath Data Offloading Scheme Using Link Quality Prediction for LTE and WiFi Networks},
author={Kamath, Santhosha and Raman, J. Aravinda and Kumar, Pankaj and Singh, Sanjay and Kumar, M. Sathish},
journal={IEEE Access},
volume={12},
pages={176554--176568},
year={2024},
publisher={IEEE}
}
```