5,008 research outputs found
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
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
Dynamic Spectrum Access: Signal Processing, Networking, and Regulatory Policy
In this article, we first provide a taxonomy of dynamic spectrum access. We
then focus on opportunistic spectrum access, the overlay approach under the
hierarchical access model of dynamic spectrum access. we aim to provide an
overview of challenges and recent developments in both technological and
regulatory aspects of opportunistic spectrum access.Comment: 20 pages, 7 figures, submitted to IEEE Signal Processing Magazin
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
On Green Multicasting over Cognitive Radio Fading Channels
In this paper, an underlay cognitive radio (CR) multicast network, consisting
of a cognitive base station (CBS) and multiple multicast groups of secondary
users (SUs), is considered. All SUs, belonging to a particular multicast group,
are served by the CBS using a common primary user (PU) channel. The goal is to
maximize the energy efficiency (EE) of the system, through dynamic adaptation
of target rate and transmit power for each multicast group, under the PUs'
individual interference constraints. The optimization problem formulated for
this is proved to be non quasi-concave with respect to the joint variation of
the CBS's transmit power and target rate. An efficient iterative algorithm for
EE maximization is proposed along with its complexity analysis. Simulation
results illustrate the performance gain of our proposed scheme.Comment: 5 pages, 4 figures, Submitted in IEEE Transactions on Vehicular
Technolog
Power Allocation for Cognitive Wireless Mesh Networks by Applying Multi-agent Q-learning Approach
As the scarce spectrum resource is becoming over-crowded, cognitive radios
(CRs) indicate great flexibility to improve the spectrum efficiency by
opportunistically accessing the authorized frequency bands. One of the critical
challenges for operating such radios in a network is how to efficiently
allocate transmission powers and frequency resource among the secondary users
(SUs) while satisfying the quality-of-service (QoS) constraints of the primary
users (PUs). In this paper, we focus on the non-cooperative power allocation
problem in cognitive wireless mesh networks (CogMesh) formed by a number of
clusters with the consideration of energy efficiency. Due to the SUs' selfish
and spontaneous properties, the problem is modeled as a stochastic learning
process. We first extend the single-agent Q-learning to a multi-user context,
and then propose a conjecture based multi-agent Qlearning algorithm to achieve
the optimal transmission strategies with only private and incomplete
information. An intelligent SU performs Q-function updates based on the
conjecture over the other SUs' stochastic behaviors. This learning algorithm
provably converges given certain restrictions that arise during learning
procedure. Simulation experiments are used to verify the performance of our
algorithm and demonstrate its effectiveness of improving the energy efficiency
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
Reconfigurable Wireless Networks
Driven by the advent of sophisticated and ubiquitous applications, and the
ever-growing need for information, wireless networks are without a doubt
steadily evolving into profoundly more complex and dynamic systems. The user
demands are progressively rampant, while application requirements continue to
expand in both range and diversity. Future wireless networks, therefore, must
be equipped with the ability to handle numerous, albeit challenging
requirements. Network reconfiguration, considered as a prominent network
paradigm, is envisioned to play a key role in leveraging future network
performance and considerably advancing current user experiences. This paper
presents a comprehensive overview of reconfigurable wireless networks and an
in-depth analysis of reconfiguration at all layers of the protocol stack. Such
networks characteristically possess the ability to reconfigure and adapt their
hardware and software components and architectures, thus enabling flexible
delivery of broad services, as well as sustaining robust operation under highly
dynamic conditions. The paper offers a unifying framework for research in
reconfigurable wireless networks. This should provide the reader with a
holistic view of concepts, methods, and strategies in reconfigurable wireless
networks. Focus is given to reconfigurable systems in relatively new and
emerging research areas such as cognitive radio networks, cross-layer
reconfiguration and software-defined networks. In addition, modern networks
have to be intelligent and capable of self-organization. Thus, this paper
discusses the concept of network intelligence as a means to enable
reconfiguration in highly complex and dynamic networks. Finally, the paper is
supported with several examples and case studies showing the tremendous impact
of reconfiguration on wireless networks.Comment: 28 pages, 26 figures; Submitted to the Proceedings of the IEEE (a
special issue on Reconfigurable Systems
Enhanced Multi-Parameter Cognitive Architecture for Future Wireless Communications
The very original concept of cognitive radio (CR) raised by Mitola targets at
all the environment parameters, including those in physical layer, MAC layer,
application layer as well as the information extracted from reasoning. Hence
the first CR is also referred to as "full cognitive radio". However, due to its
difficult implementation, FCC and Simon Haykin separately proposed a much more
simplified definition, in which CR mainly detects one single parameter, i.e.,
spectrum occupancy, and is also called as "spectrum sensing cognitive radio".
With the rapid development of wireless communication, the infrastructure of a
wireless system becomes much more complicated while the functionality at every
node is desired to be as intelligent as possible, say the self-organized
capability in the approaching 5G cellular networks. It is then interesting to
re-look into Mitola's definition and think whether one could, besides obtaining
the "on/off" status of the licensed user only, achieve more parameters in a
cognitive way. In this article, we propose a new cognitive architecture
targeting at multiple parameters in future cellular networks, which is a one
step further towards the "full cognition" compared to the most existing CR
research. The new architecture is elaborated in detailed stages, and three
representative examples are provided based on the recent research progress to
illustrate the feasibility as well as the validity of the proposed
architecture.Comment: 15 pages, 6 figures, IEEE Communications Magazin
Signal Processing and Optimal Resource Allocation for the Interference Channel
In this article, we examine several design and complexity aspects of the
optimal physical layer resource allocation problem for a generic interference
channel (IC). The latter is a natural model for multi-user communication
networks. In particular, we characterize the computational complexity, the
convexity as well as the duality of the optimal resource allocation problem.
Moreover, we summarize various existing algorithms for resource allocation and
discuss their complexity and performance tradeoff. We also mention various open
research problems throughout the article.Comment: To appear in E-Reference Signal Processing, R. Chellapa and S.
Theodoridis, Eds., Elsevier, 201
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