Learning to Predict the Mobility of Users in Mobile mmWave Networks

Liu, Xiaolan; Yu, Jiadong; Qi, Haoran; Yang, Jun; Rong, Wenge; Zhang, Xiu Yin; Gao, Yue

doi:10.1109/mwc.001.1900241

Cited by 26 publications

(14 citation statements)

References 5 publications

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“…To address power-efficient beam design, the authors of [28] proposed a feasible point search method and then developed a hybrid analog-digital mapping algorithm. To lay the foundation for beam tracking, the authors of [29] used machine learning to track user mobility, in which a deep neural network is learned and then used to predict the direction of user movement.…”

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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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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“…In this paper, our primary objectives are the resource allocation of small cells and the reposition of drones via mobile users' mobility prediction. In the literature, [14]- [17] discussed the prediction model for mobile users. In [14], the use of support vector machines (SVMs) was proposed to predict the mobile users' movements over short time scales in highly dynamic ultra-dense small cell networks with frequently switching users.…”

Section: Related Workmentioning

confidence: 99%

“…Simulation results showed that the proposed scheme could accurately track the variation of the angles. In [17], machine learning techniques are utilized to learn mobile users' mobility and predict their moving directions. By tracking users' trajectories, the authors brought out the beam tracking methods based on users' mobility.…”

Section: Related Workmentioning

confidence: 99%

Proactive Power Control and Position Deployment for Drone Small Cells: Joint Supervised and Unsupervised Learning

Cheng¹,

Shih

Chang

2021

IEEE Access

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Since unmanned aerial vehicles (UAV) are easily deployed, highly mobile, and hover capability, they are utilized for many commercial applications. In particular, small cells mounted on UAVs, also known as drone small cells (DSC), may provide temporary relief or ancillary programs for the wireless network. In this paper, we design a prediction-based proactive drone management (P 2 DM) framework to reduce network interference and improve energy efficiency in the multiple DSCs scenario. The P 2 DM framework can be divided into offline and online phases. In the offline phase, supervised learning is used to build a highly accurate mobility prediction model according to the historical data. The prediction model is launched in the online phase to predict the user position only using a small sample set. The system proactively determines whether a DSC should be awake or asleep at the next timeslot due to the predicted user positions. Since DSC has more longer awake time in the deep sleeping mode, it is previously awoken to avoid data propagation delay. To further overcome the difficulty of obtaining the key performance indicator data (i.e., labeled data) in the online phase, an unsupervised learning technique is employed for DSC repositioning and power control to improve energy efficiency. Our simulation results show that the P 2 DM framework can demonstrate the advantage in terms of execution time and energy efficiency compared to the existing method based on genetic algorithm (i.e., a heuristic algorithm).INDEX TERMS Data driven, supervised learning, unsupervised learning, mobility prediction, and Drone small cells.

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“…In order to reduce the induction period and facilitate the radical propagation, numerous metal (Au, Pd, Cu, Co et al) or metal free (g-C 3 N 4 , N-hydroxyphthalimide, carbon materials et al) based catalysts have been developed. [15,17,[20][21][22][23][24][25][26][27][28][29][30][31][32][33] However, these metal or metal-free catalysts are expensive, complexly prepared or harmful to the environment.…”

Section: Introductionmentioning

confidence: 99%

“…or metal free (g‐C 3 N 4 , N‐hydroxyphthalimide, carbon materials et al.) based catalysts have been developed [15,17,20–33] . However, these metal or metal‐free catalysts are expensive, complexly prepared or harmful to the environment.…”

Section: Introductionmentioning

confidence: 99%

Radical Propagation Facilitating Aerobic Oxidation of Substituted Aromatics Promoted by Tert‐Butyl Hydroperoxide

et al. 2021

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The selective oxidation of aromatics to synthesize valuedadded products is of great significance for chemical industry. Molecule oxygen as oxidant is the priority for this process, however, low efficiency was usually obtained. In this work, a new strategy for aromatics oxidation using O 2 and Tert-butyl hydroperoxide (TBHP) as dual oxidants without catalyst was proposed. Typical radical process was observed when Toluene was used as the substrate. As high as 47.1 % conversion and 87.6 % selectivity of benzol acid were obtained, while a negligible or low activity were observed under O 2 or TBHP oxidative systems. Detailed investigation shows that the distribution of products was almost the same between TBHP + N 2 and TBHP + O 2 processes, in which the very close active energies were obtained (46.0 and 46.3 kJ/mol). These results indicated that TBHP + N 2 or TBHP + O 2 involved reactions underwent the same reaction process. The mechanism study demonstrated that the decomposition of TBHP could produce abundance of t-BuO * and * OH radicals, which significantly enhanced the αH abstraction from toluene and formed alkyl radicals. The alkyl radicals then react with O 2 to produce peroxides, which started the radical propagation cycle. The overall rate would be enhanced subsequently. Furthermore, an extension application of other aromatic substrates was investigated and displayed a great promising future to produce aromatic products.

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Learning to Predict the Mobility of Users in Mobile mmWave Networks

Cited by 26 publications

References 5 publications

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

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

Proactive Power Control and Position Deployment for Drone Small Cells: Joint Supervised and Unsupervised Learning

Radical Propagation Facilitating Aerobic Oxidation of Substituted Aromatics Promoted by Tert‐Butyl Hydroperoxide

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