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.
Highlights
Connected vehicles and Cooperative Intelligent Transport System (C-ITS) systems will depend on Vehicle to Everything (V2X) communications to improve traffic safety, driving efficiency, and infotainment experience
The authors in [31] modeled the problem of beam selection as a contextual combinatorial Multi-Armed Bandit (MAB) problem with delayed feedback and Quality of Service (QoS) constraints and proposed an online learning algorithm that achieves a good balance between satisfying the performance guarantee of the system and maximizing the network capacity
3.1 Network architecture An integrated millimeter Wave (mmWave)/sub-6 GHz cellular network is considered in this paper, in which some mmWave Small Base Station (mmSBS) are overlapped in the coverage area of an Long Term Evolution (LTE) eNB
Summary
Connected vehicles and Cooperative Intelligent Transport System (C-ITS) systems will depend on Vehicle to Everything (V2X) communications to improve traffic safety, driving efficiency, and infotainment experience. The authors in [31] modeled the problem of beam selection as a contextual combinatorial MAB problem with delayed feedback and Quality of Service (QoS) constraints and proposed an online learning algorithm that achieves a good balance between satisfying the performance guarantee of the system and maximizing the network capacity. Since this prediction mechanism requires the view information of the source mmWave base station, it will cause greater system overhead. It is the main motivation for this paper to consider user multicast groups and power adjustment requirements for green and energy-saving communications
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