Code Repository

This repository contains the complete codebase for my Honours year project, based on research into cost-effective deployment and optimisation of next-generation (B5G/6G) reconfigurable RAN and optical X-haul networks. It includes implementations, datasets, and utilities for generating, analysing, and optimising network configurations.

Algorithms provided:

• Greedy Algorithm: Fast, heuristic-based solutions for initial network configurations.
• Genetic Algorithm: Evolutionary optimisation for exploring larger solution spaces.
• Local Search: Iterative improvement for refining solutions.
• CPLEX Models: Exact mathematical programming approaches, including shortest-path and full graph optimisation.

Helper scripts are included for preprocessing data, running batch experiments, and analysing results.

The project explores deployment strategies for next-generation (B5G/6G) reconfigurable RAN and optical X-haul networks, balancing cost, scalability, and performance.

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Associated Research Papers:

• Optical X-haul for Beyond 5G: Cost-effective Deployment Strategies
  Presented at the Optical Fiber Communication Conference (OFC) 2025.
  Focus: CPLEX shortest path model for efficient X-haul deployment.

• Cost Optimal Network Planning for Converged Optical X-haul in Beyond 5G Networks
  Published in the Journal of Optical Communications and Networking (JOCN).
  Focus: CPLEX full graph model with Steiner tree post-processing for optimal network design.

• Genetic Algorithm for Multi-layered Location-Allocation-Routing Problems in Network Planning
  Submitted to Operations Research (under review).
  Focus: Establishes the Steiner tree theorem and demonstrates the genetic algorithm as a scalable alternative to CPLEX for large datasets.

Deakin Mathematics Yearbook Report:

• A cost effective framework fo the design and deployment of B5G in rural Australia - published in the Deakin Mathematics Yearbook 2024.

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Repository Structure

The project is organised into modular components. Below is the folder layout, along with a description of each directory:

📂 Repository/

• 📁 code/ — Core source code

  • 📁 logs/ — Experiment logs
  • 📁 modules/ — Shared modules & utilities
    • 📁 algorithms/ — Algorithm-specific helpers
    • 📁 data-classes/ — Custom dataclasses for components
    • 📄 README.md — Guide to the modules and algorithms
  • 📁 results/ — Results from algorithm & CPLEX runs
  • 🐍 CPLEX_graph_model.py — CPLEX full graph model implementation
  • 🐍 CPLEX_shortest_path.py — CPLEX shortest-path model implementation
  • 🐍 genetic_algorithm.py — Genetic algorithm implementation
  • 📓 greedy_solution_generator.ipynb — Greedy algorithm implementation notebook
  • 🐍 local_search.py — Local search algorithm implementation
  • 📄 README.md — Guide to running algorithms

• 📁 dataset/ — Input datasets

  • 📁 full-dataset/ — Large-scale experiments
  • 📁 small-dataset/ — Debug & quick runs
  • 📄 README.md — Dataset format & usage

• 📁 figures/ — Diagrams, plots, and figures

• 📁 scripts/ — Processing & analysis tools

• 📄 LICENSE — Project license

• 📄 requirements.txt - Python requirements for the project

• 📄 README.md — Main repository documentation

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Algorithms

Greedy Algorithm

• Purpose: Provides a fast, heuristic-based solution for initial network configurations.
• Advantages: Lightweight, quick to implement, and useful as a baseline.
• Implementation: See code/greedy_solution_generator.ipynb.

Genetic Algorithm

• Purpose: Explores larger solution spaces through evolutionary optimisation.
• Advantages: Finds higher-quality solutions at the cost of runtime and computational resources.
• Implementation: See code/genetic_algorithm.py.
• Features:
  • Customisable crossover and mutation operators
  • Configurable selection strategies
  • Logging of population performance over time

Local Search

• Purpose: Provides an alternative to genetic algorithms for refining solutions via iterative improvement.
• Advantages: Efficient for fine-tuning and escaping local optima.
• Implementation: See code/local_search.py.

CPLEX Models (Exact Solution Methods)

• CPLEX Shortest Path: Uses mathematical programming to find exact optimal network paths.
  • Implementation: See code/CPLEX_shortest_path.py.
• CPLEX Graph Model: Provides full graph-based optimisation for deployment strategies, delivering exact solutions.
  • Implementation: See code/CPLEX_graph_model.py.

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Datasets

The repository includes two datasets designed on a region within rural Australia, each tailored for different testing scenarios:

• Full Dataset: Large-scale, complex data designed to fully test algorithms and evaluate scalability.

• Small Dataset: Simplified data for debugging, quick experiments, and verifying correctness.

See the README in dataset/ for details on dataset structure and usage.

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Scripts and Utilities

Located in the scripts/ directory, these tools support:

• Dataset Updates: Updating dataset files when changes are made.
• Visualisation: Viewing datasets on a map and plotting algorithm iteration results.

See the README in scripts/ for details on available scripts and their usage.

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Figures

The figures/ folder stores images, diagrams, and charts used for documentation within this README. This includes dataset visualisations.

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Results

Algorithm outputs are stored under heuristic-algorithm/results/, with logs in heuristic-algorithm/code/logs/.

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Meet the Lead Developer (kidding!)

Phoebe: Lead Developer

Phoebe: Chief Bug Finder

Contribution Rule: If you submit code, you must include a photo of your cat. No cat? No commit! (Phoebe enforces this strictly.)

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Getting Started

1. Clone the repository:

   git clone https://github.com/breezy-codes/B5G-network-planning-algorithms.git
   cd B5G-network-planning-algorithms

2. Install dependencies (Python 3.9+ recommended):

   pip install -r requirements.txt

3. Explore the project:
   Review the folder structure and documentation to understand available algorithms, datasets, and utilities.

4. Run experiments:

   • For the Greedy Algorithm, open and run the notebook:

     jupyter notebook code/greedy_solution_generator.ipynb

   • For the Genetic Algorithm, execute:

     python code/genetic_algorithm.py

   • For the Local Search, run:

     python code/local_search.py

   • For CPLEX models, execute either:

     python code/CPLEX_shortest_path.py

     or

     python code/CPLEX_graph_model.py

5. Inspect results:

   • Outputs will be saved in the results/ directory.
   • Logs can be found in code/logs/.
   • Use the provided scripts in scripts/ for further analysis or visualisation of results.

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Contribution Guidelines

• Fork the repository and create a feature branch.
• Add documentation for all new scripts or algorithms.
• Include experiment logs and a summary of results for reproducibility.
• Optional (but encouraged): Share a photo of your cat colleague.

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License

This project is released under the GPL-3.0 License. See the LICENSE file for details.