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

Machine-Learning Applications in Energy Efficiency: A Bibliometric Approach and Research Agenda

“…One of her studies on energy efficiency used convolution computing to increase signal speed and to improve processing energy efficiency [55]. Another interesting collaboration study from this group of authors was published by Y. Liu, who proposed using deep learning (DL) techniques to select microwaves so as to enhance energy efficiency for cellular-assisted vehicular networks [56].…”

“…One of her studies on energy efficiency used convolution computing to increase signal speed and to improve processing energy efficiency [55]. Another interesting collaboration study from this group of authors was published by Y. Liu, who proposed using deep learning (DL) techniques to select microwaves so as to enhance energy efficiency for cellular-assisted vehicular networks [56]. Another interesting study developed an energy efficient spectrum-sensing method for multiple-input multiple-output and orthogonal frequency division multiplexing (MIMO-OFDM) systems to take advantage of the spatial and temporal correlations of the environment.…”