[Introduction] [Numerical Validation] [Simulation Code] [Simple Toy Example]

Introduction

In this repo, we provide the numarical computation of beam analysis we presented in our paper:

A. Kose, H. Lee, C. H. Foh, M. Shojafar, "Multi-Agent Context Learning Strategy for Interference-Aware Beam Allocation in mmWave Vehicular Communications," vol. 25, no. 7, pp. 7477-7493, July 2024, doi: 10.1109/TITS.2024.3351488. [arXiv]

Citation

@ARTICLE{10433881,
  author={Kose, Abdulkadir and Lee, Haeyoung and Foh, Chuan Heng and Shojafar, Mohammad},
  journal={IEEE Transactions on Intelligent Transportation Systems}, 
  title={Multi-Agent Context Learning Strategy for Interference-Aware Beam Allocation in mmWave Vehicular Communications}, 
  year={2024},
  volume={25},
  number={7},
  pages={7477-7493},
  keywords={Interference;Millimeter wave communication;Array signal processing;Switches;Vehicle-to-everything;Throughput;Receivers;Vehicular networks;mmWave;beam management;machine learning;multi-armed bandit},
  doi={10.1109/TITS.2024.3351488}}

Numerical Validation

We share our numerical computation unit test source code runnable online: Beam Analysis.ipynb on

Simulation Code

The user simulation code is also available in this repo [click here to see the code].

The simulation code requires platform to run. We plan to open the source of Pymosim Platform soon. Meanwhile, you can inspect the simulation code and understand the simulation setup.

Simple Toy Example

You can find a simple toy example implementing MACOL here. The toy example is self-contained and only requires pygame package to run.

The following is a comparison between greedy (top) and MACOL (bottom) approaches. As can be seen, MACOL reduces the aggressiveness of the transmissions, leading to less intensive interference and more successful transmissions.

cfoh/MACOL

Introduction

Citation

Numerical Validation

Simulation Code

Simple Toy Example