MILP-based Optimal Handover Decisions: A Benchmark for Mobility Management Algorithms [1]

Description

MILP benchmark for UE RRC handover optimization. Provides a fully linearized (pure MILP) formulation of handover state machines (N310/N311/T310/RLF), enabling reproducible optimization and benchmarking against simulation traces.

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Installation

To install the handover-optim-milp package, follow these steps:

1. Clone the repository:

   git clone https://github.com/kit-cel/handover-optim-milp

2. Navigate to the project directory:

   cd handover-optim-milp

3. Install the package:

   python -m pip install .

   i.e., to install it in editable mode/develop mode:

   python -m pip install -e .

You are now ready to use the handover-optim-milp framework for your projects.

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Getting Started

Get the Dataset

Download the corresponding dataset at https://ieee-dataport.org/ and place it in the handover-optim-milp directory.

Note regarding the availability of the dataset: Please note that due to the size of the dataset and the individual results, it is not possible to make the data available in this repository. Upon acceptance and/or publication of the associated paper, the relevant datasets and detailed optimization results (per-UE results) will be published on IEEE Dataport to provide access via a persistent link (DOI).

Run the MILP Optimization and the RRC Reference Simulation

1. Run the optimization:

   python -m ho_optim_milp.run run_optimization --ep-idx=0 --ue-idx=0

   where --ep-idx specifies the episode (0-5) of the dataset and --ue-idx defines the UE trajectory that should be used (0-99).
2. Run the RRC reference simulation:

   python -m ho_optim_milp.run run_reference --ep-idx=0

   where --ep-idx specifies the episode (0-5) of the dataset. The reference simulation is automatically performed for all UEs in the dataset.

Results

You can plot the results stored in the dataset and reproduce the figures in [1] using the included plotting functionality.

1. Plot the rate-outage Pareto fronts of the optimization and the reference.:

   python -m ho_optim_milp.run plot_pareto_fronts

2. Plot the trade-off between the mean achieved rate and the relative connected time versus the Lagrangian multiplier lambda.:

   python -m ho_optim_milp.run plot_tradeoff

Citation [1]

If you use the handover-optim-milp framework in your work, please cite our paper:

@article{11554293,
  author={Voigt, Johannes and Rost, Peter M.},
  journal={IEEE Communications Letters}, 
  title={{MILP-Based Optimal Handover Decisions: A Benchmark for Mobility Management Algorithms}},
  year={2026},
  volume={30},
  number={},
  pages={2193-2197},
  keywords={Optimization;Radio access networks;Regional area networks;Timing;Cells (biology);Modeling;Handover;Joining processes;3GPP;Interrupters;Handover;mixed-integer linear programming;mobility management;mobile network optimization},
  doi={10.1109/LCOMM.2026.3701321}}

License

This project is licensed under the MIT License. See the LICENSE file for details.