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

EURASIP Journal on Wireless Communications and Networking

Jinsong Gui¹  Yao Liu¹ 

[1] School of Computer Science and Engineering, Central South University, 410083, Changsha, China;

关键词: Energy efficiency;    Vehicular networks;    Fast machine learning;    Millimeter wave;    Beam selection;   

DOI  :  10.1186/s13638-021-02080-5

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY