21,717 research outputs found
Empirical exploration of air traffic and human dynamics in terminal airspaces
Air traffic is widely known as a complex, task-critical techno-social system,
with numerous interactions between airspace, procedures, aircraft and air
traffic controllers. In order to develop and deploy high-level operational
concepts and automation systems scientifically and effectively, it is essential
to conduct an in-depth investigation on the intrinsic traffic-human dynamics
and characteristics, which is not widely seen in the literature. To fill this
gap, we propose a multi-layer network to model and analyze air traffic systems.
A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN)
encapsulate critical physical and operational characteristics; an Integrated
Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network
(ICCN) are formulated to represent air traffic flow transmissions and
intervention from air traffic controllers, respectively. Furthermore, a set of
analytical metrics including network variables, complex network attributes,
controllers' cognitive complexity, and chaotic metrics are introduced and
applied in a case study of Guangzhou terminal airspace. Empirical results show
the existence of fundamental diagram and macroscopic fundamental diagram at the
route, sector and terminal levels. Moreover, the dynamics and underlying
mechanisms of "ATCOs-flow" interactions are revealed and interpreted by
adaptive meta-cognition strategies based on network analysis of the ICCN.
Finally, at the system level, chaos is identified in conflict system and human
behavioral system when traffic switch to the semi-stable or congested phase.
This study offers analytical tools for understanding the complex human-flow
interactions at potentially a broad range of air traffic systems, and underpins
future developments and automation of intelligent air traffic management
systems.Comment: 30 pages, 28 figures, currently under revie
Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges
As a promising paradigm for fifth generation (5G) wireless communication
systems, cloud radio access networks (C-RANs) have been shown to reduce both
capital and operating expenditures, as well as to provide high spectral
efficiency (SE) and energy efficiency (EE). The fronthaul in such networks,
defined as the transmission link between a baseband unit (BBU) and a remote
radio head (RRH), requires high capacity, but is often constrained. This
article comprehensively surveys recent advances in fronthaul-constrained
C-RANs, including system architectures and key techniques. In particular, key
techniques for alleviating the impact of constrained fronthaul on SE/EE and
quality of service for users, including compression and quantization,
large-scale coordinated processing and clustering, and resource allocation
optimization, are discussed. Open issues in terms of software-defined
networking, network function virtualization, and partial centralization are
also identified.Comment: 5 Figures, accepted by IEEE Wireless Communications. arXiv admin
note: text overlap with arXiv:1407.3855 by other author
Telecommunications Network Planning and Maintenance
Telecommunications network operators are on a constant challenge to provide new services which require ubiquitous broadband access. In an attempt to do so, they are faced with many problems such as the network coverage or providing the guaranteed Quality of Service (QoS). Network planning is a multi-objective optimization problem which involves clustering the area of interest by minimizing a cost function which includes relevant parameters, such as installation cost, distance between user and base station, supported traffic, quality of received signal, etc. On the other hand, service assurance deals with the disorders that occur in hardware or software of the managed network. This paper presents a large number of multicriteria techniques that have been developed to deal with different kinds of problems regarding network planning and service assurance. The state of the art presented will help the reader to develop a broader understanding of the problems in the domain
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.
BackgroundSingle-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve.ResultsWe introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods.ConclusionsSlingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression
Caching Improvement Using Adaptive User Clustering
In this article we explore one of the most promising technologies for 5G
wireless networks using an underlay small cell network, namely proactive
caching. Using the increase in storage technologies and through studying the
users behavior, peak traffic can be reduced through proactive caching of the
content that is most probable to be requested. We propose a new method, in
which, instead of caching the most popular content, the users within the
network are clustered according to their content popularity and the caching is
done accordingly. We present also a method for estimating the number of
clusters within the network based on the Akaike information criterion. We
analytically derive a closed form expression of the hit probability and we
propose an optimization problem in which the small base stations association
with clusters is optimized
A Practical Cooperative Multicell MIMO-OFDMA Network Based on Rank Coordination
An important challenge of wireless networks is to boost the cell edge
performance and enable multi-stream transmissions to cell edge users.
Interference mitigation techniques relying on multiple antennas and
coordination among cells are nowadays heavily studied in the literature.
Typical strategies in OFDMA networks include coordinated scheduling,
beamforming and power control. In this paper, we propose a novel and practical
type of coordination for OFDMA downlink networks relying on multiple antennas
at the transmitter and the receiver. The transmission ranks, i.e.\ the number
of transmitted streams, and the user scheduling in all cells are jointly
optimized in order to maximize a network utility function accounting for
fairness among users. A distributed coordinated scheduler motivated by an
interference pricing mechanism and relying on a master-slave architecture is
introduced. The proposed scheme is operated based on the user report of a
recommended rank for the interfering cells accounting for the receiver
interference suppression capability. It incurs a very low feedback and backhaul
overhead and enables efficient link adaptation. It is moreover robust to
channel measurement errors and applicable to both open-loop and closed-loop
MIMO operations. A 20% cell edge performance gain over uncoordinated LTE-A
system is shown through system level simulations.Comment: IEEE Transactions or Wireless Communications, Accepted for
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