6G RLA Simulator 📶

A modern, enterprise-grade simulation platform for resource allocation in 6G networks.

This project is a full-stack scientific simulator designed to compute and visualize Blocking Probability ($P_b$) in next-generation telecommunication systems (6G). It implements a highly optimized Reduced Load Approximation (RLA) algorithm based on the Kaufman-Roberts Formula (KRF).

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🎓 Academic Context & Authorship

• Thesis: Simulation of Resource Allocation in 6G Networks
• Author: Parissis Dimitrios (Computer Science Graduate)
• Supervisor: Professor Ioannis Vardakas
• Institution: University of Western Macedonia, Department of Informatics

Note: The theoretical analysis and initial Java prototype were developed under academic supervision.
The full-stack architecture, modern web simulator (React, Flask, MongoDB), and UI/UX design were independently designed and implemented by the author.

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📸 Dashboard Preview

6G-Video-GitHub.mp4

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✨ Key Features

• 📊 Multi-Rate Loss System Engine
  Computes end-to-end blocking probabilities across complex topologies (Fronthaul, Servers, Antennas) supporting multiple service classes simultaneously.

• ⚡ Dynamic Noise Injection
  Simulates real-world interference using configurable Gaussian noise parameters integrated directly into the capacity model.

• 🛡️ High-Load Stability (Damping Factor)
  Applies a damping factor (0.5) to the Erlang Fixed Point algorithm, ensuring convergence and eliminating oscillations under extreme loads (>1000 Erlang).

• 💾 O(1) Smart Caching (MongoDB)
  Identical simulations are cached. Re-running the same scenario instantly returns results without recomputation.

• 📈 Visualization & Export
  Interactive charts (Recharts) with instant CSV export (EU format), compatible with MATLAB and Excel.

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🏗️ Tech Stack & Architecture

This project follows a Monorepo / Feature-Driven Architecture, cleanly separating the UI from the computational engine.

🔧 Backend (Service-Oriented API)

• Framework: Python / Flask
• Database: MongoDB (PyMongo)
• Math Engine: NumPy (vectorized operations)

backend/
├── app/
│   ├── api/          # REST API routes
│   ├── core/         # Mathematical engine (RLA, KRF, BPC)
│   ├── db/           # MongoDB access layer
│   └── services/     # Business logic & orchestration
├── requirements.txt
└── .env              # Environment configuration

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🎨 Frontend (Feature-Driven UI)

• Framework: React 19 + Vite
• Styling: Bootstrap 5 + Custom CSS Variables (Dark/Light Mode)
• Charts: Recharts

frontend/
├── src/
│   ├── components/   # Reusable UI components
│   ├── features/     # Domain-specific modules
│   ├── hooks/        # State management hooks
│   ├── pages/        # Main dashboard views
│   └── services/     # API communication (Axios)
└── package.json

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🚀 Installation & Setup

Prerequisites

• Node.js 18+
• Python 3.10+
• MongoDB running locally on port 27017

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1️⃣ Backend Setup

# Navigate to backend
cd backend

# Create & activate virtual environment
python -m venv venv
source venv/bin/activate        # Windows: .\venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Create .env file and add:
# MONGO_URI=mongodb://localhost:27017/
# MONGO_DB_NAME=Reduced_Load_Approximation_Kaufman_Roberts

# Navigate to backend
cd backend

# Start Development Server
python -m main

Backend runs at: http://localhost:5000

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2️⃣ Frontend Setup

Open a new terminal:

# Navigate to frontend
cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

Frontend runs at: http://localhost:5173

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📌 Notes

• Designed for 6G network research and simulation
• Optimized for high computational loads
• Built with emphasis on performance, accuracy, and usability

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Built with precision for 6G Telecommunication Research