8,292 research outputs found
Enabling RAN Slicing Through Carrier Aggregation in mmWave Cellular Networks
The ever increasing number of connected devices and of new and heterogeneous
mobile use cases implies that 5G cellular systems will face demanding technical
challenges. For example, Ultra-Reliable Low-Latency Communication (URLLC) and
enhanced Mobile Broadband (eMBB) scenarios present orthogonal Quality of
Service (QoS) requirements that 5G aims to satisfy with a unified Radio Access
Network (RAN) design. Network slicing and mmWave communications have been
identified as possible enablers for 5G. They provide, respectively, the
necessary scalability and flexibility to adapt the network to each specific use
case environment, and low latency and multi-gigabit-per-second wireless links,
which tap into a vast, currently unused portion of the spectrum. The
optimization and integration of these technologies is still an open research
challenge, which requires innovations at different layers of the protocol
stack. This paper proposes to combine them in a RAN slicing framework for
mmWaves, based on carrier aggregation. Notably, we introduce MilliSlice, a
cross-carrier scheduling policy that exploits the diversity of the carriers and
maximizes their utilization, thus simultaneously guaranteeing high throughput
for the eMBB slices and low latency and high reliability for the URLLC flows.Comment: 8 pages, 8 figures. Proc. of the 18th Mediterranean Communication and
Computer Networking Conference (MedComNet 2020), Arona, Italy, 202
Thoracic Disease Identification and Localization with Limited Supervision
Accurate identification and localization of abnormalities from radiology
images play an integral part in clinical diagnosis and treatment planning.
Building a highly accurate prediction model for these tasks usually requires a
large number of images manually annotated with labels and finding sites of
abnormalities. In reality, however, such annotated data are expensive to
acquire, especially the ones with location annotations. We need methods that
can work well with only a small amount of location annotations. To address this
challenge, we present a unified approach that simultaneously performs disease
identification and localization through the same underlying model for all
images. We demonstrate that our approach can effectively leverage both class
information as well as limited location annotation, and significantly
outperforms the comparative reference baseline in both classification and
localization tasks.Comment: Conference on Computer Vision and Pattern Recognition 2018 (CVPR
2018). V1: CVPR submission; V2: +supplementary; V3: CVPR camera-ready; V4:
correction, update reference baseline results according to their latest post;
V5: minor correction; V6: Identification results using NIH data splits and
various image model
Addressing the Challenges in Federating Edge Resources
This book chapter considers how Edge deployments can be brought to bear in a
global context by federating them across multiple geographic regions to create
a global Edge-based fabric that decentralizes data center computation. This is
currently impractical, not only because of technical challenges, but is also
shrouded by social, legal and geopolitical issues. In this chapter, we discuss
two key challenges - networking and management in federating Edge deployments.
Additionally, we consider resource and modeling challenges that will need to be
addressed for a federated Edge.Comment: Book Chapter accepted to the Fog and Edge Computing: Principles and
Paradigms; Editors Buyya, Sriram
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