Spatial modeling of the traffic density in cellular networks

Lee, Dong Hoon; Zhou, Sheng; Zhong, Xiaofeng; Niu, Zhisheng; Zhou, Xuan; Zhang, Honggang

doi:10.1109/mwc.2014.6757900

Cited by 188 publications

(109 citation statements)

References 9 publications

Supporting

Mentioning

105

Contrasting

Unclassified

Order By: Relevance

“…Cici et al [2] characterizes the relationship between people's application interests and mobility patterns based on a population of over 280, 000 users of a 3G mobile network. Lee et al [16] demonstrated that the spatial distribution of the traffic density can be approximated by the log-normal or Weibull distribution. However, mobile data traffic across a city-wide range with different time scale and variations contains complicated interaction between the space and time, which requires a deep and comprehensive understanding.…”

Section: Related Workmentioning

confidence: 99%

“…Despite of the aforementioned lack of knowledge, understanding the traffic patterns of cellular towers in the large scale urban environment is extremely valuable for Internet service providers (ISP), mobile users, and government managers of modern cites [6,16,21]. If we can identify and model the patterns of cellular towers, instead of using the same strategy to provide services, such as using the same load balancing and data pricing algorithms on each tower, an ISP can exploit the modeled traffic patterns and customize the strategies for individual cellular towers.…”

Section: Introductionmentioning

confidence: 99%

“…These factors, sometimes, compound with each other and further complicate our analysis. For example, significant traffic variation is observed at both fine-grained (hours) and coarse-grained (days) time scale, and across towers deployed in different locations [25,16]. By addressing these challenges, in this paper, we investigate how to extract and model the mobile traffic patterns of thousands of cellular towers in a large scale urban environment via credible dataset collected by one of the largest commercial mobile operators.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Wang

et al. 2017

IEEE/ACM Trans. Networking

213

105

View full text Add to dashboard Cite

Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency-domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis. Categories and Subject Descriptors Modeling techniquesPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. IMC '15, October 28-30, 2015, Tokyo, Japan.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Wang

et al. 2017

IEEE/ACM Trans. Networking

213

105

View full text Add to dashboard Cite

show abstract

“…It has been revealed that the correlation distance of the traffic density is less than 80 meters in urban areas [11], indicating that the RF energy harvesting process may follow similar spatial correlation. Two nodes that are more than 100 meters apart tend to have almost independent energy harvesting processes, and thus node collaboration can be performed to exploit the independent relationship between energy profiles, e.g., to achieve energy harvesting diversity gains.…”

Section: A Node Collaborationmentioning

confidence: 99%

Simultaneous Information and Energy Flow for IoT Relay Systems with Crowd Harvesting

et al. 2016

Self Cite

View full text Add to dashboard Cite

Abstract-It is expected that the number of wireless devices will grow rapidly over the next few years due to the growing proliferation of Internet-of-Things (IoT). In order to improve the energy efficiency of information transfer between small devices, we review state-of-the-art research in simultaneous wireless energy and information transfer, especially for relay based IoT systems. In particular, we analyze simultaneous informationand-energy transfer from the source node, and the design of time-switching and power-splitting operation modes, as well as the associated optimization algorithms. We also investigate the potential of crowd energy harvesting from transmission nodes that belong to multiple radio networks. The combination of source and crowd energy harvesting can greatly reduce the use of battery power and increase the availability and reliability for relaying. We provide insight into the fundamental limits of crowd energy harvesting reliability based on a case study using real city data. Furthermore, we examine the optimization of transmissions in crowd harvesting, especially with the use of node collaboration while guaranteeing Quality-of-Service (QoS).

show abstract

“…In general, traffic is an important input in different research areas and in different types of networks. While temporal and spatial traffic distributions are widely covered for wireless networks (e.g., [7,8]), this issue has been relatively unexplored for wired networks (particularly in the context of green core networking). This is first due to the fact that node mobility does not affect the amount and distribution of traffic exchanged among the nodes in core networks.…”

Section: Related Workmentioning

confidence: 99%

Spatial and Temporal Traffic Variation in Core Networks: Impact on Energy Saving and Devices Lifetime

et al. 2016

View full text Add to dashboard Cite

Abstract:We assess the impact of traffic variations on energy consumption and devices lifetime in a core network. Specifically, we first define a model to control the spatial as well as the temporal variations of traffic. We generate different sets of traffic matrices by adopting our model, which are then used as input to an energy-aware algorithm, with the aim of finding the set of Line Cards (LCs) in Sleep Mode (SM) for each traffic matrix. Given this output, we are able to compute different evaluation metrics, including: the total energy consumption, the normalized lifetime as a consequence of activation/deactivation of network devices, and the total network profitability (i.e., the monetary gain/loss for the operator). Our results show that the temporal variation of traffic affects the LCs energy consumption, but has a limited impact on their lifetime. Moreover, the spatial variation of traffic is no obstacle for energy saving. Eventually, the frequency of the power cycles, introduced by the energy-aware algorithm, is particularly important for the lifetime of LCs. Finally, we show that positive total profitability is achieved in most of the considered cases.

show abstract

Spatial modeling of the traffic density in cellular networks

Cited by 188 publications

References 9 publications

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Simultaneous Information and Energy Flow for IoT Relay Systems with Crowd Harvesting

Spatial and Temporal Traffic Variation in Core Networks: Impact on Energy Saving and Devices Lifetime

Contact Info

Product

Resources

About