Enhancing energy efficiency for cellular-assisted vehicular networks by online learning-based mmWave beam selection

Abstract Millimeter Wave (mmWave) technology has been regarded as a feasible approach for future vehicular communications. Nevertheless, high path loss and penetration raise severe questions on mmWave These problems can be mitigated by directional communication, which is not easy to achieve in highly dynamic The existing works addressed the beam alignment problem designing online learning-based selection schemes, well adapted scenarios. However, this kind of work focuses network throughput rather than energy efficiency, ignores consideration consumption. Therefore, we propose an Energy efficiency-based FML (EFML) scheme compensate shortfall. In EFML, consumption reduced far possible under premise meeting basic data rate requirements vehicle users, users requesting same content close proximity organized into receiving group share beam. simulation results demonstrate that, compare with comparison method best proposed EFML improves efficiency 17–41% different