Delay Analysis and Optimization of Beam Scanning-Based User Discovery in Millimeter Wave Systems

Fan, Jiancun; Han, Liyuan; Luo, Xizhao; Huang, Jin

doi:10.1109/access.2020.2970500

Cited by 8 publications

(2 citation statements)

References 20 publications

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“…The authors in [11] gave an overview of current beam management approaches based on 5G standardization, including some of the major challenges and future trends for mmWave communications in current 5G New Radio (NR) standards. By analyzing the average search delay of two different mmWave network models, the authors in [12] found that the average number of searches is related to the number of search sectors. The authors in [13] designed a low-cost beamforming module-assisted hybrid architecture and proposed a fast beam training method.…”

Section: Related Workmentioning

confidence: 99%

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

Gui

Liu

2022

J Wireless Com Network

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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.

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Section: Related Workmentioning

confidence: 99%

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

Gui

Liu

2022

J Wireless Com Network

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“…The authors of [25] analyzed the average search delay of mmWave systems, where the average search times for discovering user equipment is taken as the number of search sectors in the beamspace. The authors of [26] proposed a deep learning-based beam selection, which exploits sub-6 GHz channel information to select a mmWave beam.…”

Section: Mmwave Beam Managementmentioning

confidence: 99%

Network Capacity Optimization for Cellular‐Assisted Vehicular Systems by Online Learning‐Based mmWave Beam Selection

Gui

Liu

Deng

et al. 2021

Wireless Communications and Mobile Computing

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Directional communication is helpful to improve the performance of millimeter Wave (mmWave) links. However, the dynamic nature of vehicular scenarios raises the complexity of directional mmWave vehicular communications. Also, a mmWave link is susceptible to blockages. Therefore, a mmWave vehicular communication system requires high environmental adaptability and context-awareness. Due to inadequate context information and insufficient beam settings in the existing related algorithm, it is difficult to pick out the set of beams with more reasonable widths and directions, which hinders the further promotion of network capacity in vehicular networks. Therefore, we propose an improved fast machine learning (IFML) algorithm to overcome this shortcoming. In order to improve network capacity while suppressing the additional beam search overhead, a partitioned search method is designed in the IFML. Also, in order to be robust to occasional fluctuations and timely adapt to significant changes in communication environments, the IFML adopts a flexible beam performance update approach based on adjustable weight coefficient. The simulation results show that the IFML significantly outperforms the existing related algorithm in terms of aggregate received data after a certain number of online learning time periods.

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Flexible-beamwidth beam scanning for low-latency cell discovery in mmWave systems

Fan

Luo

et al. 2020

Sci. China Inf. Sci.

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Delay Analysis and Optimization of Beam Scanning-Based User Discovery in Millimeter Wave Systems

Cited by 8 publications

References 20 publications

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

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

Network Capacity Optimization for Cellular‐Assisted Vehicular Systems by Online Learning‐Based mmWave Beam Selection

Flexible-beamwidth beam scanning for low-latency cell discovery in mmWave systems

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