147 research outputs found
Optimal Virtualized Inter-Tenant Resource Sharing for Device-to-Device Communications in 5G Networks
Device-to-Device (D2D) communication is expected to enable a number of new
services and applications in future mobile networks and has attracted
significant research interest over the last few years. Remarkably, little
attention has been placed on the issue of D2D communication for users belonging
to different operators. In this paper, we focus on this aspect for D2D users
that belong to different tenants (virtual network operators), assuming
virtualized and programmable future 5G wireless networks. Under the assumption
of a cross-tenant orchestrator, we show that significant gains can be achieved
in terms of network performance by optimizing resource sharing from the
different tenants, i.e., slices of the substrate physical network topology. To
this end, a sum-rate optimization framework is proposed for optimal sharing of
the virtualized resources. Via a wide site of numerical investigations, we
prove the efficacy of the proposed solution and the achievable gains compared
to legacy approaches.Comment: 10 pages, 7 figure
Future RAN architecture: SD-RAN through a general-purpose processing platform
In this article, we identify and study the potential of an integrated deployment solution for energy-efficient cellular networks combining the strengths of two very active current research themes: 1) software-defined radio access networks (SD-RANs) and 2) decoupled signaling and data transmissions, or beyond cellular green generation (BCG2) architecture, for enhanced energy efficiency. While SD-RAN envisions a decoupled centralized control plane and data-forwarding plane for flexible control, the BCG2 architecture calls for decoupling coverage from the capacity and coverage provided through an always-on low-power signaling node for a larger geographical area; the capacity is catered by various on-demand data nodes for maximum energy efficiency. In this article, we show that a combined approach that brings both specifications together can not only achieve greater benefits but also facilitate faster realization of both technologies. We propose the idea and design of a signaling controller that acts as a signaling node to provide always-on coverage, consuming low power, and at the same time host the control plane functions for the SDRAN through a general-purpose processing platform. The phantom cell concept is also a similar idea where a normal macrocell provides interference control to densely deployed small cells, although our initial results show that the integrated architecture has a much greater potential for energy savings than phantom cells
A Survey of Deep Learning for Data Caching in Edge Network
The concept of edge caching provision in emerging 5G and beyond mobile
networks is a promising method to deal both with the traffic congestion problem
in the core network as well as reducing latency to access popular content. In
that respect end user demand for popular content can be satisfied by
proactively caching it at the network edge, i.e, at close proximity to the
users. In addition to model based caching schemes learning-based edge caching
optimizations has recently attracted significant attention and the aim
hereafter is to capture these recent advances for both model based and data
driven techniques in the area of proactive caching. This paper summarizes the
utilization of deep learning for data caching in edge network. We first outline
the typical research topics in content caching and formulate a taxonomy based
on network hierarchical structure. Then, a number of key types of deep learning
algorithms are presented, ranging from supervised learning to unsupervised
learning as well as reinforcement learning. Furthermore, a comparison of
state-of-the-art literature is provided from the aspects of caching topics and
deep learning methods. Finally, we discuss research challenges and future
directions of applying deep learning for cachin
Network Orchestration in Mobile Networks via a Synergy of Model-driven and AI-based Techniques
As data traffic volume continues to increase, caching of popular content at
strategic network locations closer to the end user can enhance not only user
experience but ease the utilization of highly congested links in the network. A
key challenge in the area of proactive caching is finding the optimal locations
to host the popular content items under various optimization criteria. These
problems are combinatorial in nature and therefore finding optimal and/or near
optimal decisions is computationally expensive. In this paper a framework is
proposed to reduce the computational complexity of the underlying integer
mathematical program by first predicting decision variables related to optimal
locations using a deep convolutional neural network (CNN). The CNN is trained
in an offline manner with optimal solutions and is then used to feed a much
smaller optimization problems which is amenable for real-time decision making.
Numerical investigations reveal that the proposed approach can provide in an
online manner high quality decision making; a feature which is crucially
important for real-world implementations.Comment: 6 pages, 3 figures, the conference accepted versio
Interference-Aware Decoupled Cell Association in Device-to-Device based 5G Networks
Cell association in cellular networks is an important aspect that impacts
network capacity and eventually quality of experience. The scope of this work
is to investigate the different and generalized cell association (CAS)
strategies for Device-to-Device (D2D) communications in a cellular network
infrastructure. To realize this, we optimize D2D-based cell association by
using the notion of uplink and downlink decoupling that was proven to offer
significant performance gains. We propose an integer linear programming (ILP)
optimization framework to achieve efficient D2D cell association that minimizes
the interference caused by D2D devices onto cellular communications in the
uplink as well as improve the D2D resource utilization efficiency. Simulation
results based on Vodafone's LTE field trial network in a dense urban scenario
highlight the performance gains and render this proposal a candidate design
approach for future 5G networks.Comment: 5 pages, 5 figures. Accepted in IEEE VTC spring 201
Caching as an Image Characterization Problem using Deep Convolutional Neural Networks
Caching of popular content closer to the mobile user can significantly
increase overall user experience as well as network efficiency by decongesting
backbone network segments in the case of congestion episodes. In order to find
the optimal caching locations, many conventional approaches rely on solving a
complex optimization problem that suffers from the curse of dimensionality,
which may fail to support online decision making. In this paper we propose a
framework to amalgamate model based optimization with data driven techniques by
transforming an optimization problem to a grayscale image and train a
convolutional neural network (CNN) to predict optimal caching location
policies. The rationale for the proposed modelling comes from CNN's superiority
to capture features in grayscale images reaching human level performance in
image recognition problems. The CNN is trained with optimal solutions and
numerical investigations reveal that the performance can increase by more than
400% compared to powerful randomized greedy algorithms. To this end, the
proposed technique seems as a promising way forward to the holy grail aspect in
resource orchestration which is providing high quality decision making in real
time.Comment: 7 pages, 5 figure
Aerial IRS with Robotic Anchoring Capabilities: A Novel Way for Adaptive Coverage Enhancement
It is widely accepted that integrating intelligent reflecting surfaces (IRSs)
with unmanned aerial vehicles (UAV) or drones can assist wireless networks in
improving network coverage and end user Quality of Service (QoS). However, the
critical constrain of drones is their very limited hovering/flying time. In
this paper we propose the concept of robotic aerial IRSs (RA-IRSs), which are
in essence drones that in addition to IRS embed an anchoring mechanism that
allows them to grasp in an energy neutral manner at tall urban landforms such
as lampposts. By doing so, RA-IRSs can completely eliminate the flying/hovering
energy consumption and can offer service for multiple hours or even days
(something not possible with UAV-mounted IRSs). Using that property we show how
RA-IRS can increase network performance by changing their anchoring location to
follow the spatio-temporal traffic demand. The proposed methodology, developed
through Integer Linear Programming (ILP) formulations offers a significant
Signal-to-Noise (SNR) gain in highly heterogeneous regions in terms of traffic
demand compared to fixed IRS; hence, addressing urban coverage discrepancies
effectively. Numerical simulations validate the superiority of RA-IRSs over
fixed terrestrial IRSs in terms of traffic serviceability, sustaining more than
2 times the traffic demand in areas experiencing high heterogeneity,
emphasizing their adaptability in improving coverage and QoS in complex urban
terrains
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