2,553 research outputs found
A Satisfactory Power Control for 5G Self-Organizing Networks
SmallCells are deployed in order to enhance the network performance by
bringing the network closer to the user. However, as the number of low power
nodes grows increasingly, the overall energy consumption of the SmallCells base
stations cannot be ignored. A relevant amount of energy could be saved through
several techniques, especially power control mechanisms. In this paper, we are
concerned with energy aware self organizing networks that guarantee a
satisfactory performance. We consider satisfaction equilibria, mainly the
efficient satisfaction equilibrium (ESE), to ensure a target quality of service
(QoS) and save energy. First, we identify conditions of existence and
uniqueness of ESE under a stationary channel assumption. We fully characterize
the ESE and prove that, whenever it exists, it is a solution of a linear
system. Moreover, we define satisfactory Pareto optimality and show that, at
the ESE, no player can increase its QoS without degrading the overall
performance. Under a fast fading channel assumption, as the robust satisfaction
equilibrium solution is very restrictive, we propose an alternative solution
namely the long term satisfaction equilibrium, and describe how to reach this
solution efficiently. Finally, in order to find satisfactory solution per all
users, we propose fully distributed strategic learning schemes based on
Banach-Picard, Mann and Bush Mosteller algorithms, and show through simulations
their qualitative properties. fully distributed strategic learning schemes
based on Banach Picard, Mann and Bush Mosteller algorithms, and show through
simulations their qualitative properties
Complex Systems Science meets 5G and IoT
We propose a new paradigm for telecommunications, and develop a framework
drawing on concepts from information (i.e., different metrics of complexity)
and computational (i.e., agent based modeling) theory, adapted from complex
system science. We proceed in a systematic fashion by dividing network
complexity understanding and analysis into different layers. Modelling layer
forms the foundation of the proposed framework, supporting analysis and tuning
layers. The modelling layer aims at capturing the significant attributes of
networks and the interactions that shape them, through the application of tools
such as agent-based modelling and graph theoretical abstractions, to derive new
metrics that holistically describe a network. The analysis phase completes the
core functionality of the framework by linking our new metrics to the overall
network performance. The tuning layer augments this core with algorithms that
aim at automatically guiding networks toward desired conditions. In order to
maximize the impact of our ideas, the proposed approach is rooted in relevant,
near-future architectures and use cases in 5G networks, i.e., Internet of
Things (IoT) and self-organizing cellular networks
A Game Theoretic Perspective on Self-organizing Optimization for Cognitive Small Cells
In this article, we investigate self-organizing optimization for cognitive
small cells (CSCs), which have the ability to sense the environment, learn from
historical information, make intelligent decisions, and adjust their
operational parameters. By exploring the inherent features, some fundamental
challenges for self-organizing optimization in CSCs are presented and
discussed. Specifically, the dense and random deployment of CSCs brings about
some new challenges in terms of scalability and adaptation; furthermore, the
uncertain, dynamic and incomplete information constraints also impose some new
challenges in terms of convergence and robustness. For providing better service
to the users and improving the resource utilization, four requirements for
self-organizing optimization in CSCs are presented and discussed. Following the
attractive fact that the decisions in game-theoretic models are exactly
coincident with those in self-organizing optimization, i.e., distributed and
autonomous, we establish a framework of game-theoretic solutions for
self-organizing optimization in CSCs, and propose some featured game models.
Specifically, their basic models are presented, some examples are discussed and
future research directions are given.Comment: 8 Pages, 8 Figures, to appear in IEEE Communications Magazin
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
A Computation Offloading Incentive Mechanism with Delay and Cost Constraints under 5G Satellite-ground IoV architecture
The 5G Internet of Vehicles has become a new paradigm alongside the growing
popularity and variety of computation-intensive applications with high
requirements for computational resources and analysis capabilities. Existing
network architectures and resource management mechanisms may not sufficiently
guarantee satisfactory Quality of Experience and network efficiency, mainly
suffering from coverage limitation of Road Side Units, insufficient resources,
and unsatisfactory computational capabilities of onboard equipment, frequently
changing network topology, and ineffective resource management schemes. To meet
the demands of such applications, in this article, we first propose a novel
architecture by integrating the satellite network with 5G cloud-enabled
Internet of Vehicles to efficiently support seamless coverage and global
resource management. A incentive mechanism based joint optimization problem of
opportunistic computation offloading under delay and cost constraints is
established under the aforementioned framework, in which a vehicular user can
either significantly reduce the application completion time by offloading
workloads to several nearby vehicles through opportunistic vehicle-to-vehicle
channels while effectively controlling the cost or protect its own profit by
providing compensated computing service. As the optimization problem is
non-convex and NP-hard, simulated annealing based on the Markov Chain Monte
Carlo as well as the metropolis algorithm is applied to solve the optimization
problem, which can efficaciously obtain both high-quality and cost-effective
approximations of global optimal solutions. The effectiveness of the proposed
mechanism is corroborated through simulation results
Energy Efficient User Association and Power Allocation in Millimeter Wave Based Ultra Dense Networks with Energy Harvesting Base Stations
Millimeter wave (mmWave) communication technologies have recently emerged as
an attractive solution to meet the exponentially increasing demand on mobile
data traffic. Moreover, ultra dense networks (UDNs) combined with mmWave
technology are expected to increase both energy efficiency and spectral
efficiency. In this paper, user association and power allocation in mmWave
based UDNs is considered with attention to load balance constraints, energy
harvesting by base stations, user quality of service requirements, energy
efficiency, and cross-tier interference limits. The joint user association and
power optimization problem is modeled as a mixed-integer programming problem,
which is then transformed into a convex optimization problem by relaxing the
user association indicator and solved by Lagrangian dual decomposition. An
iterative gradient user association and power allocation algorithm is proposed
and shown to converge rapidly to an optimal point. The complexity of the
proposed algorithm is analyzed and the effectiveness of the proposed scheme
compared with existing methods is verified by simulations.Comment: to appear, IEEE Journal on Selected Areas in Communications, 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
Cellular Network Architectures for the Society in Motion
Due to rising mobility worldwide, a growing number of people utilizes
cellular network services while on the move. Persistent urbanization trends
raise the number of daily commuters, leading to a situation where
telecommunication requirements are mainly dictated by two categories of users:
1) Static users inside buildings, demanding instantaneous and virtually
bandwidth unlimited access to the Internet and Cloud services; 2) moving users
outside, expecting ubiquitous and seamless mobility even at high velocity.
While most work on future mobile communications is motivated by the first
category of users, we outline in this article a layered cellular network
architecture that has the potential to efficiently support both user groups
simultaneously. We deduce novel transceiver architectures and derive research
questions that need to be tackled to effectively maintain wireless connectivity
for the envisioned Society in Motion
Distributed Spectrum Access for Cognitive Small Cell Networks: A Robust Graphical Game Approach
This letter investigates the problem of distributed spectrum access for
cognitive small cell networks. Compared with existing work, two inherent
features are considered: i) the transmission of a cognitive small cell base
station only interferes with its neighbors due to the low power, i.e., the
interference is local, and ii) the channel state is time-varying due to fading.
We formulate the problem as a robust graphical game, and prove that it is an
ordinal potential game which has at least one pure strategy Nash equilibrium
(NE). Also, the lower throughput bound of NE solutions is analytically
obtained. To cope with the dynamic and incomplete information constraints, we
propose a distribute spectrum access algorithm to converge to some stable
results. Simulation results validate the effectiveness of the proposed
game-theoretic distributed learning solution in time-varying spectrum
environment.Comment: 7 pages, 5 figures, Submitted to IEEE Transactions on Vehicular
Technology as a correspondenc
Securing Heterogeneous IoT with Intelligent DDoS Attack Behavior Learning
The rapid increase of diverse Internet of things (IoT) services and devices
has raised numerous challenges in terms of connectivity, computation, and
security, which networks must face in order to provide satisfactory support.
This has led to networks evolving into heterogeneous IoT networking
infrastructures characterized by multiple access technologies and mobile edge
computing (MEC) capabilities. The heterogeneity of the networks, devices, and
services introduces serious vulnerabilities to security attacks, especially
distributed denial-of-service (DDoS) attacks, which exploit massive IoT devices
to exhaust both network and victim resources. As such, this study proposes
MECshield, a localized DDoS prevention framework leveraging MEC power to deploy
multiple smart filters at the edge of relevant attack-source/destination
networks. The cooperation among the smart filters is supervised by a central
controller. The central controller localizes each smart filter by feeding
appropriate training parameters into its self-organizing map (SOM) component,
based on the attacking behavior. The performance of the MECshield framework is
verified using three typical IoT traffic scenarios. The numerical results
reveal that MECshield outperforms existing solutions.Comment: This work has been submitted to the IEEE journal for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessibl
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