883 research outputs found
Air-Ground Integrated Mobile Edge Networks: Architecture, Challenges and Opportunities
The ever-increasing mobile data demands have posed significant challenges in
the current radio access networks, while the emerging computation-heavy
Internet of things (IoT) applications with varied requirements demand more
flexibility and resilience from the cloud/edge computing architecture. In this
article, to address the issues, we propose a novel air-ground integrated mobile
edge network (AGMEN), where UAVs are flexibly deployed and scheduled, and
assist the communication, caching, and computing of the edge network. In
specific, we present the detailed architecture of AGMEN, and investigate the
benefits and application scenarios of drone-cells, and UAV-assisted edge
caching and computing. Furthermore, the challenging issues in AGMEN are
discussed, and potential research directions are highlighted.Comment: Accepted by IEEE Communications Magazine. 5 figure
Cost-Effective Cache Deployment in Mobile Heterogeneous Networks
This paper investigates one of the fundamental issues in cache-enabled
heterogeneous networks (HetNets): how many cache instances should be deployed
at different base stations, in order to provide guaranteed service in a
cost-effective manner. Specifically, we consider two-tier HetNets with
hierarchical caching, where the most popular files are cached at small cell
base stations (SBSs) while the less popular ones are cached at macro base
stations (MBSs). For a given network cache deployment budget, the cache sizes
for MBSs and SBSs are optimized to maximize network capacity while satisfying
the file transmission rate requirements. As cache sizes of MBSs and SBSs affect
the traffic load distribution, inter-tier traffic steering is also employed for
load balancing. Based on stochastic geometry analysis, the optimal cache sizes
for MBSs and SBSs are obtained, which are threshold-based with respect to cache
budget in the networks constrained by SBS backhauls. Simulation results are
provided to evaluate the proposed schemes and demonstrate the applications in
cost-effective network deployment
The edge cloud: A holistic view of communication, computation and caching
The evolution of communication networks shows a clear shift of focus from
just improving the communications aspects to enabling new important services,
from Industry 4.0 to automated driving, virtual/augmented reality, Internet of
Things (IoT), and so on. This trend is evident in the roadmap planned for the
deployment of the fifth generation (5G) communication networks. This ambitious
goal requires a paradigm shift towards a vision that looks at communication,
computation and caching (3C) resources as three components of a single holistic
system. The further step is to bring these 3C resources closer to the mobile
user, at the edge of the network, to enable very low latency and high
reliability services. The scope of this chapter is to show that signal
processing techniques can play a key role in this new vision. In particular, we
motivate the joint optimization of 3C resources. Then we show how graph-based
representations can play a key role in building effective learning methods and
devising innovative resource allocation techniques.Comment: to appear in the book "Cooperative and Graph Signal Pocessing:
Principles and Applications", P. Djuric and C. Richard Eds., Academic Press,
Elsevier, 201
Five Disruptive Technology Directions for 5G
New research directions will lead to fundamental changes in the design of
future 5th generation (5G) cellular networks. This paper describes five
technologies that could lead to both architectural and component disruptive
design changes: device-centric architectures, millimeter Wave, Massive-MIMO,
smarter devices, and native support to machine-2-machine. The key ideas for
each technology are described, along with their potential impact on 5G and the
research challenges that remain
Cellular-Broadcast Service Convergence through Caching for CoMP Cloud RANs
Cellular and Broadcast services have been traditionally treated independently
due to the different market requirements, thus resulting in different business
models and orthogonal frequency allocations. However, with the advent of cheap
memory and smart caching, this traditional paradigm can converge into a single
system which can provide both services in an efficient manner. This paper
focuses on multimedia delivery through an integrated network, including both a
cellular (also known as unicast or broadband) and a broadcast last mile
operating over shared spectrum. The subscribers of the network are equipped
with a cache which can effectively create zero perceived latency for multimedia
delivery, assuming that the content has been proactively and intelligently
cached. The main objective of this work is to establish analytically the
optimal content popularity threshold, based on a intuitive cost function. In
other words, the aim is to derive which content should be broadcasted and which
content should be unicasted. To facilitate this, Cooperative Multi- Point
(CoMP) joint processing algorithms are employed for the uni and broad-cast PHY
transmissions. To practically implement this, the integrated network controller
is assumed to have access to traffic statistics in terms of content popularity.
Simulation results are provided to assess the gain in terms of total spectral
efficiency. A conventional system, where the two networks operate
independently, is used as benchmark.Comment: Submitted to IEEE PIMRC 201
Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks
The next generation wireless networks (i.e. 5G and beyond), which would be
extremely dynamic and complex due to the ultra-dense deployment of
heterogeneous networks (HetNets), poses many critical challenges for network
planning, operation, management and troubleshooting. At the same time,
generation and consumption of wireless data are becoming increasingly
distributed with ongoing paradigm shift from people-centric to machine-oriented
communications, making the operation of future wireless networks even more
complex. In mitigating the complexity of future network operation, new
approaches of intelligently utilizing distributed computational resources with
improved context-awareness becomes extremely important. In this regard, the
emerging fog (edge) computing architecture aiming to distribute computing,
storage, control, communication, and networking functions closer to end users,
have a great potential for enabling efficient operation of future wireless
networks. These promising architectures make the adoption of artificial
intelligence (AI) principles which incorporate learning, reasoning and
decision-making mechanism, as natural choices for designing a tightly
integrated network. Towards this end, this article provides a comprehensive
survey on the utilization of AI integrating machine learning, data analytics
and natural language processing (NLP) techniques for enhancing the efficiency
of wireless network operation. In particular, we provide comprehensive
discussion on the utilization of these techniques for efficient data
acquisition, knowledge discovery, network planning, operation and management of
the next generation wireless networks. A brief case study utilizing the AI
techniques for this network has also been provided.Comment: ITU Special Issue N.1 The impact of Artificial Intelligence (AI) on
communication networks and services, (To appear
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
As a key technique for enabling artificial intelligence, machine learning
(ML) is capable of solving complex problems without explicit programming.
Motivated by its successful applications to many practical tasks like image
recognition, both industry and the research community have advocated the
applications of ML in wireless communication. This paper comprehensively
surveys the recent advances of the applications of ML in wireless
communication, which are classified as: resource management in the MAC layer,
networking and mobility management in the network layer, and localization in
the application layer. The applications in resource management further include
power control, spectrum management, backhaul management, cache management,
beamformer design and computation resource management, while ML based
networking focuses on the applications in clustering, base station switching
control, user association and routing. Moreover, literatures in each aspect is
organized according to the adopted ML techniques. In addition, several
conditions for applying ML to wireless communication are identified to help
readers decide whether to use ML and which kind of ML techniques to use, and
traditional approaches are also summarized together with their performance
comparison with ML based approaches, based on which the motivations of surveyed
literatures to adopt ML are clarified. Given the extensiveness of the research
area, challenges and unresolved issues are presented to facilitate future
studies, where ML based network slicing, infrastructure update to support ML
based paradigms, open data sets and platforms for researchers, theoretical
guidance for ML implementation and so on are discussed.Comment: 34 pages,8 figure
Joint Caching, Routing, and Channel Assignment for Collaborative Small-Cell Cellular Networks
We consider joint caching, routing, and channel assignment for video delivery
over coordinated small-cell cellular systems of the future Internet. We
formulate the problem of maximizing the throughput of the system as a linear
program in which the number of variables is very large. To address channel
interference, our formulation incorporates the conflict graph that arises when
wireless links interfere with each other due to simultaneous transmission. We
utilize the column generation method to solve the problem by breaking it into a
restricted master subproblem that involves a select subset of variables and a
collection of pricing subproblems that select the new variable to be introduced
into the restricted master problem, if that leads to a better objective
function value. To control the complexity of the column generation optimization
further, due to the exponential number of independent sets that arise from the
conflict graph, we introduce an approximation algorithm that computes a
solution that is within to optimality, at much lower complexity. Our
framework demonstrates considerable gains in average transmission rate at which
the video data can be delivered to the users, over the state-of-the-art
Femtocaching system, of up to 46%. These operational gains in system
performance map to analogous gains in video application quality, thereby
enhancing the user experience considerably
An Edge Computing Empowered Radio Access Network With UAV-Mounted FSO Fronthaul and Backhaul: Key Challenges and Approaches
One promising approach to address the supply-demand mismatch between the
terrestrial infrastructure and the temporary and/or unexpected traffic demands
is to leverage the unmanned aerial vehicle (UAV) technologies. Motivated by the
recent advancement of UAV technologies and retromodulator based free space
optical communication, we propose a novel edge-computing empowered radio access
network architecture where the fronthaul and backhaul links are mounted on the
UAVs for rapid event response and flexible deployment. The implementation of
hardware and networking technologies for the proposed architecture are
investigated. Due to the limited payload and endurance as well as the high
mobility of UAVs, research challenges related to the communication resource
management and recent research progress are reported.Comment: This work is accepted by IEEE Wireless Communications Magazin
Wearable Communications in 5G: Challenges and Enabling Technologies
As wearable devices become more ingrained in our daily lives, traditional
communication networks primarily designed for human being-oriented applications
are facing tremendous challenges. The upcoming 5G wireless system aims to
support unprecedented high capacity, low latency, and massive connectivity. In
this article, we evaluate key challenges in wearable communications. A
cloud/edge communication architecture that integrates the cloud radio access
network, software defined network, device to device communications, and
cloud/edge technologies is presented. Computation offloading enabled by this
multi-layer communications architecture can offload computation-excessive and
latency-stringent applications to nearby devices through device to device
communications or to nearby edge nodes through cellular or other wireless
technologies. Critical issues faced by wearable communications such as short
battery life, limited computing capability, and stringent latency can be
greatly alleviated by this cloud/edge architecture. Together with the presented
architecture, current transmission and networking technologies, including
non-orthogonal multiple access, mobile edge computing, and energy harvesting,
can greatly enhance the performance of wearable communication in terms of
spectral efficiency, energy efficiency, latency, and connectivity.Comment: This work has been accepted by IEEE Vehicular Technology Magazin
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