2,612 research outputs found
Cooperative Hierarchical Caching in 5G Cloud Radio Access Networks (C-RANs)
Over the last few years, Cloud Radio Access Network (C-RAN) has arisen as a
transformative architecture for 5G cellular networks that brings the
flexibility and agility of cloud computing to wireless communications. At the
same time, content caching in wireless networks has become an essential
solution to lower the content-access latency and backhaul traffic loading,
which translate into user Quality of Experience (QoE) improvement and network
cost reduction. In this article, a novel Cooperative Hierarchical Caching (CHC)
framework in C-RAN is introduced where contents are jointly cached at the
BaseBand Unit (BBU) and at the Radio Remote Heads (RRHs). Unlike in traditional
approaches, the cache at the BBU, cloud cache, presents a new layer in the
cache hierarchy, bridging the latency/capacity gap between the traditional
edge-based and core-based caching schemes. Trace-driven simulations reveal that
CHC yields up to 80% improvement in cache hit ratio, 21% decrease in average
content-access latency, and 20% reduction in backhaul traffic load compared to
the edge-only caching scheme with the same total cache capacity. Before closing
the article, several challenges and promising opportunities for deploying
content caching in C-RAN are highlighted towards a content-centric mobile
wireless network.Comment: to appear on IEEE Network, July 201
Cooperative Multi-Bitrate Video Caching and Transcoding in Multicarrier NOMA-Assisted Heterogeneous Virtualized MEC Networks
Cooperative video caching and transcoding in mobile edge computing (MEC)
networks is a new paradigm for future wireless networks, e.g., 5G and 5G
beyond, to reduce scarce and expensive backhaul resource usage by prefetching
video files within radio access networks (RANs). Integration of this technique
with other advent technologies, such as wireless network virtualization and
multicarrier non-orthogonal multiple access (MC-NOMA), provides more flexible
video delivery opportunities, which leads to enhancements both for the
network's revenue and for the end-users' service experience. In this regard, we
propose a two-phase RAF for a parallel cooperative joint multi-bitrate video
caching and transcoding in heterogeneous virtualized MEC networks. In the cache
placement phase, we propose novel proactive delivery-aware cache placement
strategies (DACPSs) by jointly allocating physical and radio resources based on
network stochastic information to exploit flexible delivery opportunities.
Then, for the delivery phase, we propose a delivery policy based on the user
requests and network channel conditions. The optimization problems
corresponding to both phases aim to maximize the total revenue of network
slices, i.e., virtual networks. Both problems are non-convex and suffer from
high-computational complexities. For each phase, we show how the problem can be
solved efficiently. We also propose a low-complexity RAF in which the
complexity of the delivery algorithm is significantly reduced. A Delivery-aware
cache refreshment strategy (DACRS) in the delivery phase is also proposed to
tackle the dynamically changes of network stochastic information. Extensive
numerical assessments demonstrate a performance improvement of up to 30% for
our proposed DACPSs and DACRS over traditional approaches.Comment: 53 pages, 24 figure
Fairness and Transmission-Aware Caching and Delivery Policies in OFDMA-Based HetNets
Recently, wireless edge caching has been emerged as a promising technology
for future wireless networks to cope with exponentially increasing demands for
high data rate and low latency multimedia services by proactively storing
contents at the network edge. Here, we aim to design efficient cache placement
and delivery strategies for an orthogonal frequency division multiple access
(OFDMA)-based cache-enabled heterogeneous cellular network (C-HetNet) which
operates in two separated phases: caching phase (CP) and delivery phase (DP).
Since guaranteeing fairness among mobile users (MUs) is not well investigated
in cache-assisted wireless networks, we first propose two delay-based fairness
schemes called proportional fairness (PF) and min-max fairness (MMF). The PF
scheme deals with minimizing the total weighted latency of MUs while MMF aims
at minimizing the maximum latency among them. In the CP, we propose a novel
proactive fairness and transmission-aware cache placement strategy (CPS)
corresponding to each target fairness scheme by exploiting the flexible
wireless access and backhaul transmission opportunities. Specifically, we
jointly perform the allocation of physical resources as storage and radio, and
user association to improve the flexibility of the CPSs. Moreover, In the DP of
each fairness scheme, an efficient delivery policy is proposed based on the
arrival requests of MUs, CSI, and caching status. Numerical assessments
demonstrate that our proposed CPSs outperform the total latency of MUs up to
27% compared to the conventional baseline popular CPSs.Comment: 42 pages, 13 figures, under review in IEEE Transactions on Mobile
Computing (TMC
Generalized Sparse and Low-Rank Optimization for Ultra-Dense Networks
Ultra-dense network (UDN) is a promising technology to further evolve
wireless networks and meet the diverse performance requirements of 5G networks.
With abundant access points, each with communication, computation and storage
resources, UDN brings unprecedented benefits, including significant improvement
in network spectral efficiency and energy efficiency, greatly reduced latency
to enable novel mobile applications, and the capability of providing massive
access for Internet of Things (IoT) devices. However, such great promises come
with formidable research challenges. To design and operate such complex
networks with various types of resources, efficient and innovative
methodologies will be needed. This motivates the recent introduction of highly
structured and generalizable models for network optimization. In this article,
we present some recently proposed large-scale sparse and low-rank frameworks
for optimizing UDNs, supported by various motivating applications. A special
attention is paid on algorithmic approaches to deal with nonconvex objective
functions and constraints, as well as computational scalability.Comment: This paper has been accepted by IEEE Communication Magazine, Special
Issue on Heterogeneous Ultra Dense Network
Joint Caching and Resource Allocation in D2D-Assisted Wireless HetNet
5G networks are required to provide very fast and reliable communications
while dealing with the increase of users traffic. In Heterogeneous Networks
(HetNets) assisted with Device-to-Device (D2D) communication, traffic can be
offloaded to Small Base Stations or to users to improve the network's
successful data delivery rate. In this paper, we aim at maximizing the average
number of files that are successfully delivered to users, by jointly optimizing
caching placement and channel allocation in cache-enabled D2D-assisted HetNets.
At first, an analytical upper-bound on the average content delivery delay is
derived. Then, the joint optimization problem is formulated. The non-convexity
of the problem is alleviated, and the optimal solution is determined. Due to
the high time complexity of the obtained solution, a low-complex sub-optimal
approach is proposed. Numerical results illustrate the efficacy of the proposed
solutions and compare them to conventional approaches. Finally, by
investigating the impact of key parameters, e.g. power, caching capacity, QoS
requirements, etc., guidelines to design these networks are obtained.Comment: 24 pages, 5 figures, submitted to IEEE Transactions on Wireless
Communications (12-Feb-2019
Network Issues in Virtual Machine Migration
Software Defined Networking (SDN) is based basically on three features:
centralization of the control plane, programmability of network functions and
traffic engineering. The network function migration poses interesting problems
that we try to expose and solve in this paper. Content Distribution Network
virtualization is presented as use case.Comment: 6 pages, 8 figure
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
Along with the rapid developments in communication technologies and the surge
in the use of mobile devices, a brand-new computation paradigm, Edge Computing,
is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications
are thriving with the breakthroughs in deep learning and the many improvements
in hardware architectures. Billions of data bytes, generated at the network
edge, put massive demands on data processing and structural optimization. Thus,
there exists a strong demand to integrate Edge Computing and AI, which gives
birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI
for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial
Intelligence on Edge). The former focuses on providing more optimal solutions
to key problems in Edge Computing with the help of popular and effective AI
technologies while the latter studies how to carry out the entire process of
building AI models, i.e., model training and inference, on the edge. This paper
provides insights into this new inter-disciplinary field from a broader
perspective. It discusses the core concepts and the research road-map, which
should provide the necessary background for potential future research
initiatives in Edge Intelligence.Comment: 13 pages, 3 figure
Toward Green Media Delivery: Location-Aware Opportunities and Approaches
Mobile media has undoubtedly become the predominant source of traffic in
wireless networks. The result is not only congestion and poor
Quality-of-Experience, but also an unprecedented energy drain at both the
network and user devices. In order to sustain this continued growth, novel
disruptive paradigms of media delivery are urgently needed. We envision that
two key contemporary advancements can be leveraged to develop greener media
delivery platforms: 1) the proliferation of navigation hardware and software in
mobile devices has created an era of location-awareness, where both the current
and future user locations can be predicted; and 2) the rise of context-aware
network architectures and self-organizing functionalities is enabling context
signaling and in-network adaptation. With these developments in mind, this
article investigates the opportunities of exploiting location-awareness to
enable green end-to-end media delivery. In particular, we discuss and propose
approaches for location-based adaptive video quality planning, in-network
caching, content prefetching, and long-term radio resource management. To
provide insights on the energy savings, we then present a cross-layer framework
that jointly optimizes resource allocation and multi-user video quality using
location predictions. Finally, we highlight some of the future research
directions for location-aware media delivery in the conclusion
On Content-centric Wireless Delivery Networks
The flux of social media and the convenience of mobile connectivity has
created a mobile data phenomenon that is expected to overwhelm the mobile
cellular networks in the foreseeable future. Despite the advent of 4G/LTE, the
growth rate of wireless data has far exceeded the capacity increase of the
mobile networks. A fundamentally new design paradigm is required to tackle the
ever-growing wireless data challenge.
In this article, we investigate the problem of massive content delivery over
wireless networks and present a systematic view on content-centric network
design and its underlying challenges. Towards this end, we first review some of
the recent advancements in Information Centric Networking (ICN) which provides
the basis on how media contents can be labeled, distributed, and placed across
the networks. We then formulate the content delivery task into a content rate
maximization problem over a share wireless channel, which, contrasting the
conventional wisdom that attempts to increase the bit-rate of a unicast system,
maximizes the content delivery capability with a fixed amount of wireless
resources. This conceptually simple change enables us to exploit the "content
diversity" and the "network diversity" by leveraging the abundant computation
sources (through application-layer encoding, pushing and caching, etc.) within
the existing wireless networks. A network architecture that enables wireless
network crowdsourcing for content delivery is then described, followed by an
exemplary campus wireless network that encompasses the above concepts.Comment: 20 pages, 7 figures,accepted by IEEE Wireless
Communications,Sept.201
When Machine Learning Meets Big Data: A Wireless Communication Perspective
We have witnessed an exponential growth in commercial data services, which
has lead to the 'big data era'. Machine learning, as one of the most promising
artificial intelligence tools of analyzing the deluge of data, has been invoked
in many research areas both in academia and industry. The aim of this article
is twin-fold. Firstly, we briefly review big data analysis and machine
learning, along with their potential applications in next-generation wireless
networks. The second goal is to invoke big data analysis to predict the
requirements of mobile users and to exploit it for improving the performance of
"social network-aware wireless". More particularly, a unified big data aided
machine learning framework is proposed, which consists of feature extraction,
data modeling and prediction/online refinement. The main benefits of the
proposed framework are that by relying on big data which reflects both the
spectral and other challenging requirements of the users, we can refine the
motivation, problem formulations and methodology of powerful machine learning
algorithms in the context of wireless networks. In order to characterize the
efficiency of the proposed framework, a pair of intelligent practical
applications are provided as case studies: 1) To predict the positioning of
drone-mounted areal base stations (BSs) according to the specific tele-traffic
requirements by gleaning valuable data from social networks. 2) To predict the
content caching requirements of BSs according to the users' preferences by
mining data from social networks. Finally, open research opportunities are
identified for motivating future investigations.Comment: This article has been accepted by IEEE Vehicular Technology Magazin
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