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https://github.com/armanruet/q-crahn
Q-Learning Based Cognitive Radio Network Clustering
https://github.com/armanruet/q-crahn
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Q-Learning Based Cognitive Radio Network Clustering
- Host: GitHub
- URL: https://github.com/armanruet/q-crahn
- Owner: armanruet
- Created: 2024-11-03T22:17:24.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-03T23:32:13.000Z (2 months ago)
- Last Synced: 2024-11-17T08:49:56.857Z (2 months ago)
- Language: Python
- Size: 8.79 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Q-Learning Based Cognitive Radio Network Simulator
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](https://www.dropbox.com/scl/fi/vo5x3hons46fywgq0ieih/08932525.pdf?rlkey=g52l6mb038100vug5x826hsih&e=1&dl=0)
This repository imitates a simple version of the Q-learning based clustering algorithm for Cognitive Radio Ad Hoc Networks (CRAHN) as described in the paper "Q-Learning Based Multi-Objective Clustering Algorithm for Cognitive Radio Ad Hoc Networks" (IEEE Access, 2019).
## 📖 Overview
### System Architecture
```mermaid
flowchart TB
subgraph "Network Layer"
N1[Node Discovery]
N2[Channel Management]
N3[Topology Control]
end
subgraph "Learning Layer"
L1[Q-Learning Module]
L2[Channel Quality Assessment]
L3[Reward Calculation]
end
subgraph "Clustering Layer"
C1[Cluster Head Selection]
C2[Member Assignment]
C3[Gateway Selection]
end
N1 --> L1
N2 --> L2
L1 --> L3
L2 --> L3
L3 --> C1
C1 --> C2
C2 --> C3
N3 --> C1
```
### Use Case Scenarios
```mermaid
graph TB
subgraph "Primary Users"
PU1[Channel Occupancy]
PU2[Channel Release]
end
subgraph "Secondary Users"
SU1[Spectrum Sensing]
SU2[Channel Selection]
SU3[Cluster Formation]
SU4[Data Transmission]
end
PU1 --> SU1
PU2 --> SU1
SU1 --> SU2
SU2 --> SU3
SU3 --> SU4
```
### Node State Machine
```mermaid
stateDiagram-v2
[*] --> Initialization
Initialization --> ChannelSensing: Start
ChannelSensing --> QValueUpdate: Channel Quality
QValueUpdate --> ClusterFormation: Q-Values Ready
ClusterFormation --> MemberNode: Join Request
ClusterFormation --> ClusterHead: Selection
ClusterHead --> GatewayNode: Inter-cluster Link
MemberNode --> DataTransmission
GatewayNode --> DataTransmission
DataTransmission --> ChannelSensing: Periodic Update
```
### Q-Learning Process
```mermaid
sequenceDiagram
participant SU as Secondary User
participant ENV as Environment
participant CH as Cluster Head
SU->>ENV: Sense Channel
ENV->>SU: Channel State
SU->>SU: Calculate Reward
SU->>SU: Update Q-Value
SU->>CH: Share Q-Values
CH->>CH: Evaluate Fitness
CH->>SU: Cluster Decision
```
### Clustering Algorithm Flow
```mermaid
graph TD
A[Start] --> B[Initialize Nodes]
B --> C[Update Q-Values]
C --> D[Calculate Fitness]
D --> E{Is CH Candidate?}
E -->|Yes| F[Become CH]
E -->|No| G[Find Best CH]
F --> H[Select CADC]
G --> I[Join Cluster]
H --> J[Assign Members]
I --> K[Normal Operation]
J --> K
```
Cognitive Radio Networks (CRN) allow secondary users (SUs) to opportunistically access licensed spectrum bands when primary users (PUs) are inactive. This implementation focuses on:
- Q-learning based channel quality evaluation
- Multi-objective cluster formation
- Dynamic spectrum allocation
- Energy-efficient network organization
## 🎯 Key Features
- **Q-Learning Based Channel Evaluation**: Dynamic channel quality assessment using reinforcement learning
- **Multi-Objective Clustering**:
- Residual energy optimization
- Channel quality maximization
- Network connectivity enhancement
- **Distributed Architecture**: No central control, fully autonomous nodes
- **Performance Metrics**:
- 30% improved network lifetime
- 35% better energy efficiency
- Enhanced spectrum utilization
## 🚀 Getting Started
1. Clone the repository:
```bash
git clone https://github.com/armanruet/Q-CRAHN.git
```
1. Install dependencies:
```bash
cd Q-CRAHN
pip install -r requirements.txt
```
### Quick Start
```python
from crahn.simulator import CRAHNSimulation
# Initialize simulation
sim = CRAHNSimulation(
area_size=100,
n_nodes=40,
n_pus=12,
n_channels=12
)
# Run simulation
metrics = sim.run_simulation(n_iterations=100)
# Visualize results
sim.visualize_network()
```
## 🗂️ Repository Structure
```
├── crahn/
│ ├── __init__.py
│ ├── simulator.py
│ ├── node.py
│ └── utils.py
├── requirements.txt
└── README.md
```
## 📝 Citation
If you use this code in your research, please cite:
```bibtex
@article{hossen2019qlearning,
title={Q-Learning Based Multi-Objective Clustering Algorithm for Cognitive Radio Ad Hoc Networks},
author={Hossen, Md Arman and Yoo, Sang-Jo},
journal={IEEE Access},
volume={7},
pages={181959--181971},
year={2019},
publisher={IEEE}
}
```
## 🔬 Experimental Results
### Performance Analysis Architecture
```mermaid
graph LR
subgraph "Metrics Collection"
M1[Energy Efficiency]
M2[Cluster Stability]
M3[Spectrum Utilization]
end
subgraph "Analysis Tools"
A1[Performance Monitor]
A2[Network Analyzer]
A3[Visualization Engine]
end
subgraph "Output"
O1[Network Stats]
O2[Performance Graphs]
O3[Topology Maps]
end
M1 & M2 & M3 --> A1
A1 --> A2
A2 --> A3
A3 --> O1 & O2 & O3
```
### Network Performance Metrics
```mermaid
gantt
title Network Performance Timeline
dateFormat X
axisFormat %L
section Energy
CH Energy :e1, 0, 100
Member Energy :e2, 0, 80
section Clustering
Formation :c1, 0, 20
Stabilization :c2, 20, 60
Operation :c3, 60, 100
section Spectrum
Sensing :s1, 0, 30
Allocation :s2, 30, 70
Optimization :s3, 70, 100
```
### Cluster Formation
The simulation demonstrates effective cluster formation with the following characteristics:
- **Cluster Size**: Adaptive based on network conditions
- **Energy Distribution**: Balanced load across clusters
- **Spectrum Utilization**: Efficient channel assignment
## 🛠️ Implementation Details
### Q-Learning Parameters
- Learning Rate (α): 0.1
- Discount Factor (γ): 0.9
- ε-greedy Exploration: 0.1
### Network Parameters
- Area Size: 100m × 100m
- Number of Nodes: 40
- Number of Channels: 12
- Primary Users: 12
- Transmission Range: 30m
## 🤝 Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 📞 Contact
For any queries or suggestions, please reach out to:
- Email: [email protected]
- LinkedIn: [armanruet](https://www.linkedin.com/in/armanruet/)
---
Made with ❤️ by [Arman](https://armanruet.github.io/)