EE 512 — Network Optimization for Communication Networks

This repository contains hands-on Python (PuLP) implementations of core optimization models used in communication networks, prepared for the graduate-level course EE 512 – Optimization for Communication Networks at TED University.

The material bridges mathematical formulations and solver-based implementations, with clear visualizations to support both learning and teaching.

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📘 Course Context

• Course: EE 512 – Optimization for Communication Networks
• Level: Graduate (MSc / PhD)
• Department: Electrical and Electronics Engineering
• Institution: TED University
• Course link: https://www.tedu.edu.tr/ee-512

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📂 Contents

1) Fundamental Network Optimization Models (LP / MIP)

Implemented using Linear Programming (LP) and Mixed-Integer Programming (MIP):

• Simple LP and MIP Examples
• Shortest Path (Min-Cost Flow formulation)
• Minimum Spanning Tree (MILP – MTZ / rooted arborescence style)
• Maximum Flow
• Minimum s–t Cut

2) Wireless Network Optimization Models (Energy / Lifetime / Throughput / Delay)

Energy-aware and performance-aware routing models on multi-ring wireless topologies:

• Minimum Total Energy Routing
• Min–Max (Bottleneck) Energy Routing
• Maximum Network Lifetime Optimization
• Maximum Throughput (Common-Rate) Routing
• Minimum Hop-Count (Min-Delay) Routing

Each model includes:

• Mathematical formulation (commented at the top of the code)
• PuLP implementation
• Solver output inspection
• Network visualizations and per-node energy plots

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🛠 Tools & Libraries

• Python 3
• PuLP (LP/MIP modeling)
• NetworkX (graph construction)
• Matplotlib (visualization)

All models run with the default CBC solver bundled with PuLP (if available in your environment).

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▶️ How to Run

Option A — Jupyter / Colab

1. Open the notebook under notebooks/.
2. Run cells from top to bottom.

Option B — Local Environment

pip install pulp networkx matplotlib

🎯 Learning Objectives

By studying these notebooks, students will be able to:

• Translate communication-network problems into LP/MIP models.
• Use flow-based modeling for routing, energy, and lifetime.
• Compare objectives: energy vs bottleneck vs throughput vs delay.
• Interpret solver outputs and constraint behavior.
• Visualize routing decisions and resource usage.

📌 Notes for Students

• The code is intentionally readable (teaching-first), not hyper-optimized.
• Variable names and constraints closely follow slide notation.
• Try modifying:
  • energy budgets E_0,
  • bandwidth limits B_ij,
  • topology parameters (radii, rotations, Rmax),
  • objective functions

to observe trade-offs.

👤 Author

Hüseyin Uğur Yıldız,
Associate Professor, Electrical and Electronics Engineering,
TED University

📜 License

This repository is intended for educational and academic use.
You may reuse and adapt the material for teaching or research with attribution.