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%.

翻译：毫米波（mmWave）因其增强信道带宽和网络容量的潜力，已被公认为5G及未来网络的关键技术之一。将毫米波应用于车载通信等各类场景已得到广泛探讨。然而，毫米波在车载通信中面临高移动性节点与窄波束覆盖范围的挑战。密集网络中，由于节点高速移动，重叠波束会引发强干扰，导致性能下降。对此，可借助毫米波的波束切换能力作为应对手段。但频繁的波束切换与小区变更将成为管理干扰的必然选择，这将增加计算与信令复杂度。为处理干扰控制的复杂性，我们提出一种名为多智能体上下文学习（MACOL）的新策略，该策略利用上下文赌博机在分配毫米波波束以服务网络车辆的同时管理干扰。实验表明，通过利用相邻波束状态信息，机器学习智能体能够识别并避免对其他正在进行的传输造成潜在干扰。此外，即使在高流量负载下，我们提出的MACOL策略仍能将干扰水平维持在约10%的低水平。