Mobility modelling and trajectory prediction for cellular networks with mobile base stations

2003 ◽  
Author(s):  
Pubudu N. Pathirana ◽  
Andrey V. Savkin ◽  
Sanjay Jha
Keyword(s):  
Cellular Networks ◽  
Base Stations ◽  
Trajectory Prediction ◽  
Mobile Base

scholarly journals Intelligent Interference Management in UAV-Based HetNets

Telecom ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 472-488
Author(s):  
Simran Singh ◽  
Abhaykumar Kumbhar ◽  
İsmail Güvenç ◽  
Mihail L. Sichitiu
Keyword(s):  
Cellular Networks ◽  
Hot Spots ◽  
Network Performance ◽  
Interference Management ◽  
Base Stations ◽  
Computationally Efficient ◽  
Q Learning ◽  
Conventional Optimization ◽  
Coverage Holes ◽  
Mobile Base

Unmanned aerial vehicles (UAVs) can play a key role in meeting certain demands of cellular networks. UAVs can be used not only as user equipment (UE) in cellular networks but also as mobile base stations (BSs) wherein they can either augment conventional BSs by adapting their position to serve the changing traffic and connectivity demands or temporarily replace BSs that are damaged due to natural disasters. The flexibility of UAVs allows them to provide coverage to UEs in hot-spots, at cell-edges, in coverage holes, or regions with scarce cellular infrastructure. In this work, we study how UAV locations and other cellular parameters may be optimized in such scenarios to maximize the spectral efficiency (SE) of the network. We compare the performance of machine learning (ML) techniques with conventional optimization approaches. We found that, on an average, a double deep Q learning approach can achieve 93.46% of the optimal median SE and 95.83% of the optimal mean SE. A simple greedy approach, which tunes the parameters of each BS and UAV independently, performed very well in all the cases that we tested. These computationally efficient approaches can be utilized to enhance the network performance in existing cellular networks.

scholarly journals Machine Learning for the Dynamic Positioning of UAVs for Extended Connectivity

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4618
Author(s):  
Francisco Oliveira ◽  
Miguel Luís ◽  
Susana Sargento
Keyword(s):  
Machine Learning ◽  
Cellular Networks ◽  
Real Data ◽  
Emerging Technology ◽  
Machine Learning Algorithms ◽  
Base Stations ◽  
Aerial Vehicle ◽  
Positioning Algorithm ◽  
The Military ◽  
Better Than

Unmanned Aerial Vehicle (UAV) networks are an emerging technology, useful not only for the military, but also for public and civil purposes. Their versatility provides advantages in situations where an existing network cannot support all requirements of its users, either because of an exceptionally big number of users, or because of the failure of one or more ground base stations. Networks of UAVs can reinforce these cellular networks where needed, redirecting the traffic to available ground stations. Using machine learning algorithms to predict overloaded traffic areas, we propose a UAV positioning algorithm responsible for determining suitable positions for the UAVs, with the objective of a more balanced redistribution of traffic, to avoid saturated base stations and decrease the number of users without a connection. The tests performed with real data of user connections through base stations show that, in less restrictive network conditions, the algorithm to dynamically place the UAVs performs significantly better than in more restrictive conditions, reducing significantly the number of users without a connection. We also conclude that the accuracy of the prediction is a very important factor, not only in the reduction of users without a connection, but also on the number of UAVs deployed.

scholarly journals Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhuofan Liao ◽  
Jingsheng Peng ◽  
Bing Xiong ◽  
Jiawei Huang
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Cellular Networks ◽  
Physical Distance ◽  
Edge Computing ◽  
Base Stations ◽  
Mobile Edge Computing ◽  
Total Energy Consumption ◽  
Average Delay ◽  
Server Selection

AbstractWith the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

scholarly journals Latency optimization for D2D-enabled parallel mobile edge computing in cellular networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yan Cai ◽  
Liang Ran ◽  
Jun Zhang ◽  
Hongbo Zhu
Keyword(s):  
Cellular Networks ◽  
Optimal Solution ◽  
Vital Role ◽  
Base Stations ◽  
Low Latency ◽  
Mobile Edge Computing ◽  
System Delay ◽  
Simulation Results ◽  
And Task ◽  
Parallel Communication

AbstractEdge offloading, including offloading to edge base stations (BS) via cellular links and to idle mobile users (MUs) via device-to-device (D2D) links, has played a vital role in achieving ultra-low latency characteristics in 5G wireless networks. This paper studies an offloading method of parallel communication and computation to minimize the delay in multi-user systems. Three different scenarios are explored, i.e., full offloading, partial offloading, and D2D-enabled partial offloading. In the full offloading scenario, we find a serving order for the MUs. Then, we jointly optimize the serving order and task segment in the partial offloading scenario. For the D2D-enabled partial offloading scenario, we decompose the problem into two subproblems and then find the sub-optimal solution based on the results of the two subproblems. Finally, the simulation results demonstrate that the offloading method of parallel communication and computing can significantly reduce the system delay, and the D2D-enabled partial offloading can further reduce the latency.

scholarly journals Evaluación de niveles de exposición electromagnética causados por las estaciones base de Telefonía Móvil Celular [Assessment for electromagnetic exposure level caused by cellular mobile transmitting antennas in base station]

2014 ◽  
Author(s):  
Walder de Jesús Canova García
Keyword(s):  
Electromagnetic Field ◽  
Base Station ◽  
Base Stations ◽  
Exposure Level ◽  
Field Exposure ◽  
Palabras Clave ◽  
Exposure Levels ◽  
Mobile Base Station ◽  
Cellular Mobile ◽  
Mobile Base

Resumen El creciente número de estaciones base de telefonía móvil celular alrededor de sectores residenciales o tránsito de personas, causa preocupación en la comunidad sobre si la radiación de campos electromagnéticos puedan causar riesgos en la salud. Internacionalmente existen estándares que establecen límites a las diversas fuentes de campos electromagnéticos para garantizar que se minimizan los riesgos en la salud. Cada país adopta dentro de su legislación algún estándar o recomendación y exige su cumplimiento a los operadores de estaciones de telecomunicaciones, por ejemplo en Colombia rige el decreto 195 de 2005. El artículo presenta una evaluación, basados en mediciones técnicas en el 2010, para obtener los niveles de exposición a campos electromagnéticos generados por las antenas instaladas en las estaciones base de telefonía móvil. Luego aparece el procedimiento general de mediciones, donde incluye el plan ejecutorial de mediciones, la configuración de la instrumentación y la caracterización de los lugares y puntos de medición. Por último, los resultados medidos en algunos lugares, donde las antenas de transmisión cumplían con la normativa adoptada en Colombia. Palabras Clave: Exposición a campos Electromagnéticos, Estaciones base de Telefonía móvil celular, Mediciones de banda angosta.   Abstract The growth of installations of transmitting antennas on base stations surrounding residential spaces or person traffic causes concerns in the community, about whether the radiation of electromagnetic fields of transmitting antennas in mobile base station can generate health risk. Over the world, there are standards that establish maximum levels permitted to different electromagnetic field sources to accomplish security ranges for health risks. Each country adopts in their legislation some international standard and requires to telecommunication operators stations for its compliance. In Colombian, the decree 195 of 2005 is still valid. This article shows an assessment, based on technical measurements developed in 2010, to acquire the electromagnetic field exposure levels generated by transmitting antennas installed on Mobile Base Station. This assessment includes the measurement system procedure: plan of measurement, instrumental configuration, and characterization of measurement places. Finally, here presents the measured results in some places, which exposure levels satisfied the adopted legislation in Colombia. Keywords: Electromagnetic Field Exposure, Mobile Base Stations, Narrowband Measurement.

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