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Millimeter wave (mmWave) has been recognized as one of key technologies for5G and beyond networks due to its potential to enhance channel bandwidth andnetwork capacity. The use of mmWave for various applications includingvehicular communications has been extensively discussed. However, applyingmmWave to vehicular communications faces challenges of high mobility nodes andnarrow coverage along the mmWave beams. Due to high mobility in dense networks,overlapping beams can cause strong interference which leads to performancedegradation. As a remedy, beam switching capability in mmWave can be utilized.Then, frequent beam switching and cell change become inevitable to manageinterference, which increase computational and signalling complexity. In orderto deal with the complexity in interference control, we develop a new strategycalled Multi-Agent Context Learning (MACOL), which utilizes Contextual Banditto manage interference while allocating mmWave beams to serve vehicles in thenetwork. Our approach demonstrates that by leveraging knowledge of neighbouringbeam status, the machine learning agent can identify and avoid potentialinterfering transmissions to other ongoing transmissions. Furthermore, we showthat even under heavy traffic loads, our proposed MACOL strategy is able tomaintain low interference levels at around 10%.

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