scholarly journals THE METHOD FOR INTELLIGENT FREQUENCY CHANNEL BORROWING IN CELLULAR MOBILE NETWORK BASED THE FLC-NN INTEGRATED SYSTEM

2010 ◽  
Vol 13 (2) ◽  
pp. 81-92
Author(s):  
Dao Manh Ha ◽  
Quynh Xuan Nguyen
Keyword(s):  
Cellular Networks ◽  
Fault Tolerant ◽  
Mobile Network ◽  
Arrival Rate ◽  
Integrated System ◽  
Base Stations ◽  
Communication Overhead ◽  
Learning Abilities ◽  
Control Center ◽  
Channel Borrowing

In a cellular network, the channel borrowing/locking problem is of NP-hard type. Many heuristic methods are proposed for its solution. In this network, the call-arrival rate, the call duration and the communication overhead between the base stations and the control center are vague and uncertain. Therefore, in this paper, we propose a new efficient dynamic-channel borrowing for load balancing in distributed cellular networks based on the intelligent controllers based the integrated system for GA- FL-NN technologies is presented to maximize the number of served calls in distributed wireless cellular networks. The proposed scheme exhibits better learning abilities, optimization abilities, robustness, and fault-tolerant capability thus yielding a better performance than other algorithms. The results demonstrate that our algorithm has lower new call blocking rate, lower handoff dropping rate, less update overhead, and shorter channel acquisition delay.

Game Theoretic Infrastructure Sharing in Wireless Cellular Networks

2015 ◽  
pp. 368-398
Author(s):  
Alexandra Bousia ◽  
Elli Kartsakli ◽  
Angelos Antonopoulos ◽  
Luis Alonso ◽  
Christos Verikoukis
Keyword(s):  
Energy Consumption ◽  
Cellular Networks ◽  
Cost Savings ◽  
Mobile Network ◽  
Cost Effective ◽  
Base Stations ◽  
Traffic Demand ◽  
Game Theoretic ◽  
Mobile Network Operators ◽  
Low Traffic

The emerging traffic demand has fueled the rapid densification of cellular networks. The increased number of Base Stations (BSs) leads to augmented energy consumption and expenditures for the Mobile Network Operators (MNOs), especially during low traffic, when many of the BSs remain underutilized. Hence, the MNOs are encouraged to provide “green” and cost effective solutions for their networks. In this chapter, an innovative algorithm for infrastructure sharing in two-operator environments is proposed, based on BSs switching off during low traffic periods. Motivated by the conflicting interests of the operators, the problem is formulated in a game theoretic framework that enables the MNOs to act individually to estimate the switching off probabilities that reduce their financial cost. The authors analytically and experimentally estimate the potential energy and cost savings that can be accomplished. The obtained results show a significant reduction in both energy consumption and expenditures, thus giving the operators the necessary incentives for infrastructure sharing.

Mobile network synthesis strategy

2019 ◽  
pp. 98-101
Author(s):  
V. Lyandres
Keyword(s):  
Average Distance ◽  
Mobile Network ◽  
Base Station ◽  
Service Area ◽  
Base Stations ◽  
Frequency Assignment ◽  
Network Synthesis ◽  
Universal Method ◽  
Synthesis Strategy ◽  
Adaptive Vector Quantization

Introduction:Effective synthesis of а mobile communication network includes joint optimisation of two processes: placement of base stations and frequency assignment. In real environments, the well-known cellular concept fails due to some reasons, such as not homogeneous traffic and non-isotropic wave propagation in the service area.Purpose:Looking for the universal method of finding a network structure close to the optimal.Results:The proposed approach is based on the idea of adaptive vector quantization of the network service area. As a result, it is reduced to a 2D discrete map split into zones with approximately equal number of service requests. In each zone, the algorithm finds such coordinates of its base station that provide the shortest average distance to all subscribers. This method takes into account the shortage of the a priory information about the current traffic, ensures maximum coverage of the service area, and what is not less important, significantly simplifies the process of frequency assignment.

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.

Perceptive Mobile Network: Cellular Networks With Radio Vision via Joint Communication and Radar Sensing

2020 ◽  
pp. 0-0
Author(s):  
Andrew Zhang ◽  
Md. Lushanur Rahman ◽  
Xiaojing Huang ◽  
Yingjie Jay Guo ◽  
Shanzhi Chen ◽  
...  
Keyword(s):  
Cellular Networks ◽  
Mobile Network ◽  
Radar Sensing ◽  
Radio Vision

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 Autonomous UAV Simulator for Research and Development Applied to 5G Networks

2021 ◽  
Author(s):  
Carlos Eduardo Dias Vinagre Neto ◽  
Ailton Pinto de Oliveira ◽  
Felipe Henrique Bastos e Bastos ◽  
Emerson Oliveira Junior ◽  
Aldebaro Klautau
Keyword(s):  
Performance Indicators ◽  
Communication Channel ◽  
Mobile Network ◽  
Network Capacity ◽  
Base Stations ◽  
5G Networks ◽  
Simulation Tools ◽  
Channel Evolution ◽  
Product Delivery ◽  
Intense Research

Unmanned aerial vehicles (UAVs) are being used in many applications,such as surveillance and product delivery. Currently, manyUAVs are controlled by WiFi or proprietary radio technologies.However, it is envisioned that 5G and beyond 5G (B5G) networkscan connect the UAVs and increase the overall security due to improvedcontrol by operators and governments. Soon, UAVs willalso be used as mobile radio base stations to extend reach or improvethe network capacity. All this motivates intense research on5G technologies for supporting UAV-based applications. However,there are currently few simulation tools for testing and investigatingtelecommunication systems that involve UAV solutions. Forinstance, modern 5G networks use multiple antennas that enablebeamforming. A realistic simulation, in this case, requires not onlysupport for beamforming but also for realistic UAV trajectories,which impact the communication channel evolution over time. Toevaluate scenarios with connected UAVs, this paper presents a toolthat simulates flights in a virtual environment, gathers informationabout the channels among UAVs and the mobile network, andcalculates performance indicators regarding the communicationsystem.

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