# Multi-Agent Context Learning Strategy for Interference-Aware Beam Allocation in mmWave Vehicular Communications

arXiv: https://arxiv.org/abs/2401.02323 · HTML: https://mlsanity.com/paper/2401.02323/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 4 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2012.07753/) (2020)
- [A Survey on Resource Allocation in Vehicular Networks](https://mlsanity.com/paper/1909.13587/) (2019)
- [Generalized Coordinated Multipoint Framework for 5G and Beyond](https://mlsanity.com/paper/2008.06343/) (2020)
- [Towards mmWave V2X in 5G and Beyond to Support Automated Driving](https://mlsanity.com/paper/2011.09590/) (2020)
- [Use Cases, Requirements, and Design Considerations for 5G V2X](https://mlsanity.com/paper/1712.01754/) (2017)
- [Caching Meets Millimeter Wave Communications for Enhanced Mobility Management in 5G Networks](https://mlsanity.com/paper/1701.05125/) (2017)
- [Beam Based Stochastic Model of the Coverage Probability in 5G Millimeter Wave Systems](https://mlsanity.com/paper/1704.07079/) (2017)
- [Coverage and Connectivity Analysis of Millimeter Wave Vehicular Networks](https://mlsanity.com/paper/1803.01136/) (2018)
- [Machine Learning-based Inter-Beam Inter-Cell Interference Mitigation in mmWave](https://mlsanity.com/paper/2103.05061/) (2021)

## Abstract (arXiv)

Millimeter wave (mmWave) has been recognized as one of key technologies for 5G and beyond networks due to its potential to enhance channel bandwidth and network capacity. The use of mmWave for various applications including vehicular communications has been extensively discussed. However, applying mmWave to vehicular communications faces challenges of high mobility nodes and narrow coverage along the mmWave beams. Due to high mobility in dense networks, overlapping beams can cause strong interference which leads to performance degradation. As a remedy, beam switching capability in mmWave can be utilized. Then, frequent beam switching and cell change become inevitable to manage interference, which increase computational and signalling complexity. In order to deal with the complexity in interference control, we develop a new strategy called Multi-Agent Context Learning (MACOL), which utilizes Contextual Bandit to manage interference while allocating mmWave beams to serve vehicles in the network. Our approach demonstrates that by leveraging knowledge of neighbouring beam status, the machine learning agent can identify and avoid potential interfering transmissions to other ongoing transmissions. Furthermore, we show that even under heavy traffic loads, our proposed MACOL strategy is able to maintain low interference levels at around 10%.