7,118 research outputs found
On Green Energy Powered Cognitive Radio Networks
Green energy powered cognitive radio (CR) network is capable of liberating
the wireless access networks from spectral and energy constraints. The
limitation of the spectrum is alleviated by exploiting cognitive networking in
which wireless nodes sense and utilize the spare spectrum for data
communications, while dependence on the traditional unsustainable energy is
assuaged by adopting energy harvesting (EH) through which green energy can be
harnessed to power wireless networks. Green energy powered CR increases the
network availability and thus extends emerging network applications. Designing
green CR networks is challenging. It requires not only the optimization of
dynamic spectrum access but also the optimal utilization of green energy. This
paper surveys the energy efficient cognitive radio techniques and the
optimization of green energy powered wireless networks. Existing works on
energy aware spectrum sensing, management, and sharing are investigated in
detail. The state of the art of the energy efficient CR based wireless access
network is discussed in various aspects such as relay and cooperative radio and
small cells. Envisioning green energy as an important energy resource in the
future, network performance highly depends on the dynamics of the available
spectrum and green energy. As compared with the traditional energy source, the
arrival rate of green energy, which highly depends on the environment of the
energy harvesters, is rather random and intermittent. To optimize and adapt the
usage of green energy according to the opportunistic spectrum availability, we
discuss research challenges in designing cognitive radio networks which are
powered by energy harvesters
Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning
The ability to intelligently utilize resources to meet the need of growing
diversity in services and user behavior marks the future of wireless
communication systems. Intelligent wireless communications aims at enabling the
system to perceive and assess the available resources, to autonomously learn to
adapt to the perceived wireless environment, and to reconfigure its operating
mode to maximize the utility of the available resources. The perception
capability and reconfigurability are the essential features of cognitive radio
while modern machine learning techniques project great potential in system
adaptation. In this paper, we discuss the development of the cognitive radio
technology and machine learning techniques and emphasize their roles in
improving spectrum and energy utility of wireless communication systems. We
describe the state-of-the-art of relevant techniques, covering spectrum sensing
and access approaches and powerful machine learning algorithms that enable
spectrum- and energy-efficient communications in dynamic wireless environments.
We also present practical applications of these techniques and identify further
research challenges in cognitive radio and machine learning as applied to the
existing and future wireless communication systems
Network Lifetime Maximization for Cellular-Based M2M Networks
High energy efficiency is critical for enabling massive machine-type
communications (MTC) over cellular networks. This work is devoted to energy
consumption modeling, battery lifetime analysis, lifetime-aware scheduling and
transmit power control for massive MTC over cellular networks. We consider a
realistic energy consumption model for MTC and model network battery-lifetime.
Analytic expressions are derived to demonstrate the impact of scheduling on
both the individual and network battery lifetimes. The derived expressions are
subsequently employed in the uplink scheduling and transmit power control for
mixed-priority MTC traffic in order to maximize the network lifetime. Besides
the main solutions, low-complexity solutions with limited feedback requirement
are investigated, and the results are extended to existing LTE networks. Also,
the energy efficiency, spectral efficiency, and network lifetime tradeoffs in
resource provisioning and scheduling for MTC over cellular networks are
investigated. The simulation results show that the proposed solutions can
provide substantial network lifetime improvement and network maintenance cost
reductionComment: IEEE Access 201
All Technologies Work Together for Good: A Glance to Future Mobile Networks
The astounding capacity requirements of 5G have motivated researchers to
investigate the feasibility of many potential technologies, such as massive
multiple-input multiple-output, millimeter wave, full-duplex, non-orthogonal
multiple access, carrier aggregation, cognitive radio, and network
ultra-densification. The benefits and challenges of these technologies have
been thoroughly studied either individually or in a combination of two or
three. It is not clear, however, whether all potential technologies operating
together lead to fulfilling the requirements posed by 5G. This paper explores
the potential benefits and challenges when all technologies coexist in an
ultra-dense cellular environment. The sum rate of the network is investigated
with respect to the increase in the number of small-cells and results show the
capacity gains achieved by the coexistence.Comment: Accepted for publication in IEEE Wireless Communication, Special
Issue-5G mmWave Small Cell Networks: Architecture, Self-Organization and
Managemen
Cloud Computing - Architecture and Applications
In the era of Internet of Things and with the explosive worldwide growth of
electronic data volume, and associated need of processing, analysis, and
storage of such humongous volume of data, it has now become mandatory to
exploit the power of massively parallel architecture for fast computation.
Cloud computing provides a cheap source of such computing framework for large
volume of data for real-time applications. It is, therefore, not surprising to
see that cloud computing has become a buzzword in the computing fraternity over
the last decade. This book presents some critical applications in cloud
frameworks along with some innovation design of algorithms and architecture for
deployment in cloud environment. It is a valuable source of knowledge for
researchers, engineers, practitioners, and graduate and doctoral students
working in the field of cloud computing. It will also be useful for faculty
members of graduate schools and universities.Comment: Edited Volume published by Intech Publishers, Croatia, June 2017. 138
pages. ISBN 978-953-51-3244-8, Print ISBN 978-953-51-3243-1. Link:
https://www.intechopen.com/books/cloud-computing-architecture-and-application
GATE: Greening At The Edge
Dramatic data traffic growth, especially wireless data, is driving a
significant surge in energy consumption in the last mile access of the
telecommunications infrastructure. The growing energy consumption not only
escalates the operators' operational expenditures (OPEX) but also leads to a
significant rise of carbon footprints. Therefore, enhancing the energy
efficiency of broadband access networks is becoming a necessity to bolster
social, environmental, and economic sustainability. This article provides an
overview on the design and optimization of energy efficient broadband access
networks, analyzes the energy efficient design of passive optical networks,
discusses the enabling technologies for next generation broadband wireless
access networks, and elicits the emerging technologies for enhancing the energy
efficiency of the last mile access of the network infrastructure.Comment: 7 Pages, 12 Figures, Submitted to IEEE Wireless Communication
Green Cellular Networks: A Survey, Some Research Issues and Challenges
Energy efficiency in cellular networks is a growing concern for cellular
operators to not only maintain profitability, but also to reduce the overall
environment effects. This emerging trend of achieving energy efficiency in
cellular networks is motivating the standardization authorities and network
operators to continuously explore future technologies in order to bring
improvements in the entire network infrastructure. In this article, we present
a brief survey of methods to improve the power efficiency of cellular networks,
explore some research issues and challenges and suggest some techniques to
enable an energy efficient or "green" cellular network. Since base stations
consume a maximum portion of the total energy used in a cellular system, we
will first provide a comprehensive survey on techniques to obtain energy
savings in base stations. Next, we discuss how heterogeneous network deployment
based on micro, pico and femto-cells can be used to achieve this goal. Since
cognitive radio and cooperative relaying are undisputed future technologies in
this regard, we propose a research vision to make these technologies more
energy efficient. Lastly, we explore some broader perspectives in realizing a
"green" cellular network technologyComment: 16 pages, 5 figures, 2 table
Wireless Internet over Heterogeneous Wireless Networks
One of the two keywords for the next generation wireless communications is seamless. Being involved in the essential e-Japan Plan promoted by the Japanese Government, the MIRAI (Multimedia Integrated network by Radio Access Innovation) project is responsible for the research and development on the seamless integration of various wireless access systems for practical use by the year 2005. A heterogeneous network architecture including a common tool, a common platform, and a common access is proposed in this paper. Concretely, software-defined-radio technologies are used to develop a multi-service user terminal to be used for access to different wireless networks. The common platform for various wireless networks is based on a wireless supporting IPv6 network. A basic access network, separated from other wireless access networks, is used as a means for wireless system discovery, signaling and paging. A proof-of-concept experimental demonstration system is available from March 200
Energy-Efficient Resource Allocation in OFDMA Systems with Hybrid Energy Harvesting Base Station
We study resource allocation algorithm design for energy-efficient
communication in an OFDMA downlink network with hybrid energy harvesting base
station. Specifically, an energy harvester and a constant energy source driven
by a non-renewable resource are used for supplying the energy required for
system operation. We first consider a deterministic offline system setting. In
particular, assuming availability of non-causal knowledge about energy arrivals
and channel gains, an offline resource allocation problem is formulated as a
non-convex optimization problem taking into account the circuit energy
consumption, a finite energy storage capacity, and a minimum required data
rate. We transform this non-convex optimization problem into a convex
optimization problem by applying time-sharing and fractional programming which
results in an efficient asymptotically optimal offline iterative resource
allocation algorithm. In each iteration, the transformed problem is solved by
using Lagrange dual decomposition. The obtained resource allocation policy
maximizes the weighted energy efficiency of data transmission. Subsequently, we
focus on online algorithm design. A stochastic dynamic programming approach is
employed to obtain the optimal online resource allocation algorithm which
requires a prohibitively high complexity. To strike a balance between system
performance and computational complexity, we propose a low complexity
suboptimal online iterative algorithm which is motivated by the offline
optimization.Comment: 32 pages, 7 figures, and 1 table. Submitted for possible journal
publication in 201
Data and Spectrum Trading Policies in a Trusted Cognitive Dynamic Network
Future wireless networks will progressively displace service provisioning
towards the edge to accommodate increasing growth in traffic. This paradigm
shift calls for smart policies to efficiently share network resources and
ensure service delivery. In this paper, we consider a cognitive dynamic network
architecture (CDNA) where primary users (PUs) are rewarded for sharing their
connectivities and acting as access points for secondary users (SUs). CDNA
creates opportunities for capacity increase by network-wide harvesting of
unused data plans and spectrum from different operators. Different policies for
data and spectrum trading are presented based on centralized, hybrid and
distributed schemes involving primary operator (PO), secondary operator (SO)
and their respective end users. In these schemes, PO and SO progressively
delegate trading to their end users and adopt more flexible cooperation
agreements to reduce computational time and track available resources
dynamically. A novel matching-with-pricing algorithm is presented to enable
self-organized SU-PU associations, channel allocation and pricing for data and
spectrum with low computational complexity. Since connectivity is provided by
the actual users, the success of the underlying collaborative market relies on
the trustworthiness of the connections. A behavioral-based access control
mechanism is developed to incentivize/penalize honest/dishonest behavior and
create a trusted collaborative network. Numerical results show that the
computational time of the hybrid scheme is one order of magnitude faster than
the benchmark centralized scheme and that the matching algorithm reconfigures
the network up to three orders of magnitude faster than in the centralized
scheme.Comment: 15 pages, 12 figures. A version of this paper has been published in
IEEE/ACM Transactions on Networking, 201
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