Accurate predictions of base stations' traffic load are essential to mobile cellular operators and their users as they support the efficient use of network resources and allow delivery of services that sustain smart cities and roads. Traditionally, cellular network time-series have been considered for this prediction task. More recently, exogenous factors such as points of interest and other environmental knowledge have been explored too. In contrast to incorporating external factors, we propose to learn the processes underlying cellular load generation by employing population dynamics data. In this study, we focus on smart roads and use road traffic measures to improve prediction accuracy. Comprehensive experiments demonstrate that by employing road flow and speed, in addition to cellular network metrics, base station load prediction errors can be substantially reduced, by as much as $56.5\%.$ The code, visualizations and extensive results are available on https://github.com/nvassileva/DataMatters.
Cellular traffic prediction is an indispensable part for intelligent telecommunication networks. Nevertheless, due to the frequent user mobility and complex network scheduling mechanisms, cellular traffic often inherits complicated spatial-temporal patterns, making the prediction incredibly challenging. Although recent advanced algorithms such as graph-based prediction approaches have been proposed, they frequently model spatial dependencies based on static or dynamic graphs and neglect the coexisting multiple spatial correlations induced by traffic generation. Meanwhile, some works lack the c…
Domain-wide recognized by their high value in human presence and activity studies, cellular network datasets (i.e., Charging Data Records, named CdRs), however, present accessibility, usability, and privacy issues, restricting their exploitation and research reproducibility.This paper tackles such challenges by modeling Cdrs that fulfill real-world data attributes. Our designed framework, named Zen follows a four-fold methodology related to (i) the LTSM-based modeling of users' traffic behavior, (ii) the realistic and flexible emulation of spatiotemporal mobility behavior, (iii) the structure …
It is crucial for the service provider to comprehend and forecast mobile traffic in large-scale cellular networks in order to govern and manage mechanisms for base station placement, load balancing, and network planning. The purpose of this article is to extract and simulate traffic patterns from more than 14,000 cells that have been installed in different metropolitan areas. To do this, we create, implement, and assess a method in which cells are first categorized by their point of interest and then clustered based on the temporal distribution of cells in each region. The proposed model has b…
ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network
大规模移动流量分析对于数字基础设施配置、公共交通、活动规划和其他领域变得至关重要。然而，监控全市范围的移动流量是一个复杂而昂贵的过程，依赖于专用探头。其中一些探测器的精度或覆盖范围有限，另一些每天收集数十GB的日志，这些日志独立提供有限的见解。提取细粒度的模式涉及昂贵的测量、存储和后处理的空间聚合。在本文中，我们提出了一种移动流量超分辨率技术，克服了这些问题，通过推断狭隘的本地化的流量消费从粗糙的测量。我们从图像处理中汲取灵感，设计了一个针对移动网络的深度学习架构，它结合了Zipper网络（ZipNet）和生成对抗神经网络（GAN）模型。这使得能够唯一地捕获在广泛覆盖区域上常规监测的交通量快照（“低分辨率”）与通常在密集计算之后获得的0.05 km $^2$水平上的相应消耗（“高分辨率”）之间的时空关系。我们进行的实验与现实世界的数据集表明，拟议的ZipNet（-GAN）推断流量消耗具有显着的准确性和高达100 $\times$更高的粒度相比，标准的探测，同时优于现有的数据插值技术。据我们所知，这是第一次将超分辨率概念应用于大规模移动交通分析，我们的解决方案是第一个从粗聚合物中推断细粒度城市交通模式的解决方案。
Institute for Informatics and Telematics, National Research Council（国家研究委员会信息与远程信息处理研究所）
The widespread diffusion of mobile phones is triggering an exponential growth of mobile data traffic that is likely to cause, in the near future, considerable traffic overload issues even in last-generation cellular networks. Offloading part of the traffic to other networks is considered a very promising approach and, in particular, in this paper, we consider offloading through opportunistic networks of users' devices. However, the performance of this solution strongly depends on the pattern of encounters between mobile nodes, which should therefore be taken into account when designing offload…
The dramatic growth in cellular traffic volume requires cellular network operators to develop strategies to carefully dimension and manage the available network resources. Forecasting traffic volumes is a fundamental building block for any proactive management strategy and is therefore of great interest in such a context. Differently from what found in the literature, where network traffic is generally predicted in the short-term, in this work we tackle the problem of forecasting busy hour traffic, i.e., the time series of observed daily maxima traffic volumes. We tackle specifically forecasti…
FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning
Orange Innovation（奥兰治创新公司）；Nantes University（南特大学）；École Centrale Nantes（南特中央理工学院）；IMT Atlantique（大西洋电信与技术学院）；CNRS（法国国家科学研究中心）；INRIA（法国国家信息与自动化研究所）；LS2N（南特数字科学实验室）；University of Luxembourg（卢森堡大学）；SnT（卢森堡大学安全、可靠性和信任中心）
To improve capacity and overcome some of the limitations of cellular wireless networks, drones with aerial base stations can be deployed to assist the terrestrial cellular wireless networks. The mobility of drones allows flexible network reconfiguration to adapt to dynamic traffic and channel conditions. However, this is achieved at the expense of more handovers since even a static user may experience a handover when the drones are mobile. In this letter, we provide an exact analysis of the handover rate and sojourn time (time between two subsequent handovers) for a network of drone base stati…
Multi-grained spatial-temporal feature complementarity for accurate online cellular traffic prediction
School of Information Science and Engineering, Southeast University（信息科学与工程学院，东南大学）；Purple Mountain Laboratories（紫金山实验室）；China Telecom Corporation Limited Technology Innovation Center（中国电信股份有限公司技术创新中心）
Cellular networks are ubiquitous entities that provide major means of communication all over the world. One major challenge in cellular networks is a dynamic change in the number of users and their usage of telecommunication service which results in overloading at certain base stations. One class of solution to deal with this overloading issue is the deployment of drones that can act as temporary base stations and offload the traffic from the overloaded base station. There are two main challenges in the development of this solution. Firstly, the drone is expected to be present around the base …
Fifth generation (5G) networks are expected to connect a huge number of Internet of Things (IoT) devices in many usage scenarios. The challenges of typical massive IoT applications with sporadic and short packet uplink transmissions are well studied, while not enough attention is given to the delivery of content of common interest, such as software/firmware updates and remote control, towards IoT devices in emerging point-to-multipoint scenarios. Moreover, the delivery of delay-sensitive IoT traffic is not sufficiently addressed in the literature. In this work we (i) identify the drawbacks of …
Global System for Mobile Communications (GSM) is a cellular network that is popular and has been growing in recent years. It was developed to solve fragmentation issues of the first cellular system, and it addresses digital modulation methods, level of the network structure, and services. It is fundamental for organizations to become learning organizations to keep up with the technology changes for network services to be at a competitive level. A simulation analysis using the NetSim tool in this paper is presented for comparing different cellular network codecs for GSM network performance. The…
Currently, tens of millions of devices around the world communicate with/ each other via cellular networks. In this paper, we study the stability of network content delivery protocols to the effects of network interference. To conduct the research, a tool was developed that allows testing of protocols, such as TCP, UDP, and QUIC. The analysis and comparison of the obtained test results were carried out. In the conclusion, the best protocols for content delivery were shown
Understanding and Partitioning Mobile Traffic using Internet Activity Records Data -- A Spatiotemporal Approach
University of Science and Technology Beijing（北京科技大学）；COMSATS University Islamabad（伊斯兰堡COMSATS大学）；University of Engineering and Technology Lahore（拉合尔工程技术大学）
Denoising Refinement Diffusion Models for Simultaneous Generation of Multi-scale Mobile Network Traffic
Department of Electronic Engineering, BNRist, Tsinghua University（电子工程系，北京理工大学，清华大学）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（网络与交换技术国家重点实验室，北京邮电大学）；China Mobile Communications Group Guangxi Co., Ltd.（中国移动通信集团广西有限公司）
This paper presents OpenCV2X, the first publicly available, open-source simulation model of the Third Generation Partnership Project (3GPP) Release 14 Cellular Vehicle to Everything (C-V2X) sidelink, which forms the basis for 5G NR Mode 2 under later releases. This model is fully compliant with the existing vehicular service and application layers, including messaging sets as defined by the automotive and standards communities providing a fully standardised, cross-layer communication model. Using this model, we show how the current sidelink scheduling mechanism performs poorly when scheduling …
