  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%.

Signal Processing;Machine Learning