215 research outputs found
Spatio-temporal analysis and prediction of cellular traffic in metropolis
ISSN:1536-1233ISSN:1558-066
Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment
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.Comment: To appear at IMC 201
Fine-grained Spatio-Temporal Distribution Prediction of Mobile Content Delivery in 5G Ultra-Dense Networks
The 5G networks have extensively promoted the growth of mobile users and
novel applications, and with the skyrocketing user requests for a large amount
of popular content, the consequent content delivery services (CDSs) have been
bringing a heavy load to mobile service providers. As a key mission in
intelligent networks management, understanding and predicting the distribution
of CDSs benefits many tasks of modern network services such as resource
provisioning and proactive content caching for content delivery networks.
However, the revolutions in novel ubiquitous network architectures led by
ultra-dense networks (UDNs) make the task extremely challenging. Specifically,
conventional methods face the challenges of insufficient spatio precision,
lacking generalizability, and complex multi-feature dependencies of user
requests, making their effectiveness unreliable in CDSs prediction under 5G
UDNs. In this paper, we propose to adopt a series of encoding and sampling
methods to model CDSs of known and unknown areas at a tailored fine-grained
level. Moreover, we design a spatio-temporal-social multi-feature extraction
framework for CDSs hotspots prediction, in which a novel edge-enhanced graph
convolution block is proposed to encode dynamic CDSs networks based on the
social relationships and the spatio features. Besides, we introduce the
Long-Short Term Memory (LSTM) to further capture the temporal dependency.
Extensive performance evaluations with real-world measurement data collected in
two mobile content applications demonstrate the effectiveness of our proposed
solution, which can improve the prediction area under the curve (AUC) by 40.5%
compared to the state-of-the-art proposals at a spatio granularity of 76m, with
up to 80% of the unknown areas
Time and Location Aware Mobile Data Pricing
Mobile users' correlated mobility and data consumption patterns often lead to
severe cellular network congestion in peak hours and hot spots. This paper
presents an optimal design of time and location aware mobile data pricing,
which incentivizes users to smooth traffic and reduce network congestion. We
derive the optimal pricing scheme through analyzing a two-stage decision
process, where the operator determines the time and location aware prices by
minimizing his total cost in Stage I, and each mobile user schedules his mobile
traffic by maximizing his payoff (i.e., utility minus payment) in Stage II. We
formulate the two-stage decision problem as a bilevel optimization problem, and
propose a derivative-free algorithm to solve the problem for any increasing
concave user utility functions. We further develop low complexity algorithms
for the commonly used logarithmic and linear utility functions. The optimal
pricing scheme ensures a win-win situation for the operator and users.
Simulations show that the operator can reduce the cost by up to 97.52% in the
logarithmic utility case and 98.70% in the linear utility case, and users can
increase their payoff by up to 79.69% and 106.10% for the two types of
utilities, respectively, comparing with a time and location independent pricing
benchmark. Our study suggests that the operator should provide price discounts
at less crowded time slots and locations, and the discounts need to be
significant when the operator's cost of provisioning excessive traffic is high
or users' willingness to delay traffic is low.Comment: This manuscript serves as the online technical report of the article
accepted by IEEE Transactions on Mobile Computin
On the Fundamentals of Stochastic Spatial Modeling and Analysis of Wireless Networks and its Impact to Channel Losses
With the rapid evolution of wireless networking, it becomes vital to ensure transmission reliability, enhanced connectivity, and efficient resource utilization. One possible pathway for gaining insight into these critical requirements would be to explore the spatial geometry of the network. However, tractably characterizing the actual position of nodes for large wireless networks (LWNs) is technically unfeasible. Thus, stochastical spatial modeling is commonly considered for emulating the random pattern of mobile users. As a result, the concept of random geometry is gaining attention in the field of cellular systems in order to analytically extract hidden features and properties useful for assessing the performance of networks. Meanwhile, the large-scale fading between interacting nodes is the most fundamental element in radio communications, responsible for weakening the propagation, and thus worsening the service quality. Given the importance of channel losses in general, and the inevitability of random networks in real-life situations, it was then natural to merge these two paradigms together in order to obtain an improved stochastical model for the large-scale fading. Therefore, in exact closed-form notation, we generically derived the large-scale fading distributions between a reference base-station and an arbitrary node for uni-cellular (UCN), multi-cellular (MCN), and Gaussian random network models. In fact, we for the first time provided explicit formulations that considered at once: the lattice profile, the users’ random geometry, the spatial intensity, the effect of the far-field phenomenon, the path-loss behavior, and the stochastic impact of channel scatters. Overall, the results can be useful for analyzing and designing LWNs through the evaluation of performance indicators. Moreover, we conceptualized a straightforward and flexible approach for random spatial inhomogeneity by proposing the area-specific deployment (ASD) principle, which takes into account the clustering tendency of users. In fact, the ASD method has the advantage of achieving a more realistic deployment based on limited planning inputs, while still preserving the stochastic character of users’ position. We then applied this inhomogeneous technique to different circumstances, and thus developed three spatial-level network simulator algorithms for: controlled/uncontrolled UCN, and MCN deployments
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