7,656 research outputs found
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
Energy Efficiency in Cache Enabled Small Cell Networks With Adaptive User Clustering
Using a network of cache enabled small cells, traffic during peak hours can
be reduced considerably through proactively fetching the content that is most
probable to be requested. In this paper, we aim at exploring the impact of
proactive caching on an important metric for future generation networks,
namely, energy efficiency (EE). We argue that, exploiting the correlation in
user content popularity profiles in addition to the spatial repartitions of
users with comparable request patterns, can result in considerably improving
the achievable energy efficiency of the network. In this paper, the problem of
optimizing EE is decoupled into two related subproblems. The first one
addresses the issue of content popularity modeling. While most existing works
assume similar popularity profiles for all users in the network, we consider an
alternative caching framework in which, users are clustered according to their
content popularity profiles. In order to showcase the utility of the proposed
clustering scheme, we use a statistical model selection criterion, namely
Akaike information criterion (AIC). Using stochastic geometry, we derive a
closed-form expression of the achievable EE and we find the optimal active
small cell density vector that maximizes it. The second subproblem investigates
the impact of exploiting the spatial repartitions of users with comparable
request patterns. After considering a snapshot of the network, we formulate a
combinatorial optimization problem that enables to optimize content placement
such that the used transmission power is minimized. Numerical results show that
the clustering scheme enable to considerably improve the cache hit probability
and consequently the EE compared with an unclustered approach. Simulations also
show that the small base station allocation algorithm results in improving the
energy efficiency and hit probability.Comment: 30 pages, 5 figures, submitted to Transactions on Wireless
Communications (15-Dec-2016
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
Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks with Mobile Users
In this paper, the problem of proactive caching is studied for cloud radio
access networks (CRANs). In the studied model, the baseband units (BBUs) can
predict the content request distribution and mobility pattern of each user,
determine which content to cache at remote radio heads and BBUs. This problem
is formulated as an optimization problem which jointly incorporates backhaul
and fronthaul loads and content caching. To solve this problem, an algorithm
that combines the machine learning framework of echo state networks with
sublinear algorithms is proposed. Using echo state networks (ESNs), the BBUs
can predict each user's content request distribution and mobility pattern while
having only limited information on the network's and user's state. In order to
predict each user's periodic mobility pattern with minimal complexity, the
memory capacity of the corresponding ESN is derived for a periodic input. This
memory capacity is shown to be able to record the maximum amount of user
information for the proposed ESN model. Then, a sublinear algorithm is proposed
to determine which content to cache while using limited content request
distribution samples. Simulation results using real data from Youku and the
Beijing University of Posts and Telecommunications show that the proposed
approach yields significant gains, in terms of sum effective capacity, that
reach up to 27.8% and 30.7%, respectively, compared to random caching with
clustering and random caching without clustering algorithm.Comment: Accepted in the IEEE Transactions on Wireless Communication
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
Proactive Video Chunks Caching and Processing for Latency and Cost Minimization in Edge Networks
Recently, the growing demand for rich multimedia content such as Video on
Demand (VoD) has made the data transmission from content delivery networks
(CDN) to end-users quite challenging. Edge networks have been proposed as an
extension to CDN networks to alleviate this excessive data transfer through
caching and to delegate the computation tasks to edge servers. To maximize the
caching efficiency in the edge networks, different Mobile Edge Computing (MEC)
servers assist each others to effectively select which content to store and the
appropriate computation tasks to process. In this paper, we adopt a
collaborative caching and transcoding model for VoD in MEC networks. However,
unlike other models in the literature, different chunks of the same video are
not fetched and cached in the same MEC server. Instead, neighboring servers
will collaborate to store and transcode different video chunks and consequently
optimize the limited resources usage. Since we are dealing with chunks caching
and processing, we propose to maximize the edge efficiency by studying the
viewers watching pattern and designing a probabilistic model where chunks
popularities are evaluated. Based on this model, popularity-aware policies,
namely Proactive caching policy (PcP) and Cache replacement Policy (CrP), are
introduced to cache only highest probably requested chunks. In addition to PcP
and CrP, an online algorithm (PCCP) is proposed to schedule the collaborative
caching and processing. The evaluation results prove that our model and
policies give better performance than approaches using conventional replacement
policies. This improvement reaches up to 50% in some cases.Comment: Submitted to International Conference on Wireless Communications and
Networking (WCNC) 201
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
Resource Management in Multi-Access Edge Computing (MEC)
This PhD thesis investigates the effective ways of managing the resources of a Multi-Access Edge Computing Platform (MEC) in 5th Generation Mobile Communication (5G) networks.
The main characteristics of MEC include distributed nature, proximity to users, and high availability. Based on these key features, solutions have been proposed for effective resource
management. In this research, two aspects of resource management in MEC have been addressed. They are the computational resource and the caching resource which corresponds to the services provided by the MEC.
MEC is a new 5G enabling technology proposed to reduce latency by bringing cloud computing capability closer to end-user Internet of Things (IoT) and mobile devices. MEC would support latency-critical user applications such as driverless cars and e-health. These applications will depend on resources and services provided by the MEC. However, MEC has
limited computational and storage resources compared to the cloud. Therefore, it is important to ensure a reliable MEC network communication during resource provisioning by eradicating the chances of deadlock. Deadlock may occur due to a huge number of devices contending for a limited amount of resources if adequate measures are not put in place. It is
crucial to eradicate deadlock while scheduling and provisioning resources on MEC to achieve a highly reliable and readily available system to support latency-critical applications. In this research, a deadlock avoidance resource provisioning algorithm has been proposed for industrial IoT devices using MEC platforms to ensure higher reliability of network interactions. The proposed scheme incorporates Banker’s resource-request algorithm using Software Defined Networking (SDN) to reduce communication overhead. Simulation and experimental results have shown that system deadlock can be prevented by applying the proposed algorithm which ultimately leads to a more reliable network interaction between mobile stations and MEC platforms.
Additionally, this research explores the use of MEC as a caching platform as it is proclaimed as a key technology for reducing service processing delays in 5G networks. Caching on MEC decreases service latency and improve data content access by allowing direct content delivery through the edge without fetching data from the remote server. Caching on MEC is also deemed as an effective approach that guarantees more reachability due to proximity to endusers. In this regard, a novel hybrid content caching algorithm has been proposed for MEC platforms to increase their caching efficiency. The proposed algorithm is a unification of a modified Belady’s algorithm and a distributed cooperative caching algorithm to improve data access while reducing latency. A polynomial fit algorithm with Lagrange interpolation is employed to predict future request references for Belady’s algorithm. Experimental results show that the proposed algorithm obtains 4% more cache hits due to its selective caching approach when compared with case study algorithms. Results also show that the use of a cooperative algorithm can improve the total cache hits up to 80%.
Furthermore, this thesis has also explored another predictive caching scheme to further improve caching efficiency. The motivation was to investigate another predictive caching approach as an improvement to the formal. A Predictive Collaborative Replacement (PCR) caching framework has been proposed as a result which consists of three schemes. Each of the schemes addresses a particular problem. The proactive predictive scheme has been proposed to address the problem of continuous change in cache popularity trends. The collaborative scheme addresses the problem of cache redundancy in the collaborative space. Finally, the replacement scheme is a solution to evict cold cache blocks and increase hit ratio. Simulation experiment has shown that the replacement scheme achieves 3% more cache hits than existing replacement algorithms such as Least Recently Used, Multi Queue and Frequency-based replacement. PCR algorithm has been tested using a real dataset (MovieLens20M dataset) and compared with an existing contemporary predictive algorithm. Results show that PCR performs better with a 25% increase in hit ratio and a 10% CPU utilization overhead
A survey on data and transaction management in mobile databases
The popularity of the Mobile Database is increasing day by day as people need
information even on the move in the fast changing world. This database
technology permits employees using mobile devices to connect to their corporate
networks, hoard the needed data, work in the disconnected mode and reconnect to
the network to synchronize with the corporate database. In this scenario, the
data is being moved closer to the applications in order to improve the
performance and autonomy. This leads to many interesting problems in mobile
database research and Mobile Database has become a fertile land for many
researchers. In this paper a survey is presented on data and Transaction
management in Mobile Databases from the year 2000 onwards. The survey focuses
on the complete study on the various types of Architectures used in Mobile
databases and Mobile Transaction Models. It also addresses the data management
issues namely Replication and Caching strategies and the transaction management
functionalities such as Concurrency Control and Commit protocols,
Synchronization, Query Processing, Recovery and Security. It also provides
Research Directions in Mobile databases.Comment: 20 Pages; International Journal of Database Management Systems
(IJDMS) Vol.4, No.5, October 2012. arXiv admin note: text overlap with
arXiv:0908.0076, arXiv:1005.1747, arXiv:1108.6195 by other author
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
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