4,928 research outputs found
A survey on MAC protocols for complex self-organizing cognitive radio networks
Complex self-organizing cognitive radio (CR) networks serve as a framework for accessing the spectrum allocation dynamically where the vacant channels can be used by CR nodes opportunistically. CR devices must be capable of exploiting spectrum opportunities and exchanging control information over a control channel. Moreover, CR nodes should intelligently coordinate their access between different cognitive radios to avoid collisions on the available spectrum channels and to vacate the channel for the licensed user in timely manner. Since inception of CR technology, several MAC protocols have been designed and developed. This paper surveys the state of the art on tools, technologies and taxonomy of complex self-organizing CR networks. A detailed analysis on CR MAC protocols form part of this paper. We group existing approaches for development of CR MAC protocols and classify them into different categories and provide performance analysis and comparison of different protocols. With our categorization, an easy and concise view of underlying models for development of a CR MAC protocol is provided
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
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
IEEE Access
Volume 3, 2015, Article number 7217798, Pages 1512-1530
Open Access
Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article)
Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc
a Department of Information Engineering, University of Padua, Padua, Italy
b Department of General Psychology, University of Padua, Padua, Italy
c IRCCS San Camillo Foundation, Venice-Lido, Italy
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Abstract
In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
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