6,896 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
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
Heterogeneous Cloud Radio Access Networks: A New Perspective for Enhancing Spectral and Energy Efficiencies
To mitigate the severe inter-tier interference and enhance limited
cooperative gains resulting from the constrained and non-ideal transmissions
between adjacent base stations in heterogeneous networks (HetNets),
heterogeneous cloud radio access networks (H-CRANs) are proposed as
cost-efficient potential solutions through incorporating the cloud computing
into HetNets. In this article, state-of-the-art research achievements and
challenges on H-CRANs are surveyed. In particular, we discuss issues of system
architectures, spectral and energy efficiency performances, and promising key
techniques. A great emphasis is given towards promising key techniques in
H-CRANs to improve both spectral and energy efficiencies, including cloud
computing based coordinated multi-point transmission and reception, large-scale
cooperative multiple antenna, cloud computing based cooperative radio resource
management, and cloud computing based self-organizing network in the cloud
converging scenarios. The major challenges and open issues in terms of
theoretical performance with stochastic geometry, fronthaul constrained
resource allocation, and standard development that may block the promotion of
H-CRANs are discussed as well.Comment: 20 pages, 6 figures, to be published in IEEE Wireless Communication
Scalable Application- and User-aware Resource Allocation in Enterprise Networks Using End-host Pacing
Scalable user- and application-aware resource allocation for heterogeneous
applications sharing an enterprise network is still an unresolved problem. The
main challenges are: (i) How to define user- and application-aware shares of
resources? (ii) How to determine an allocation of shares of network resources
to applications? (iii) How to allocate the shares per application in
heterogeneous networks at scale? In this paper we propose solutions to the
three challenges and introduce a system design for enterprise deployment.
Defining the necessary resource shares per application is hard, as the intended
use case and user's preferences influence the resource demand. Utility
functions based on user experience enable a mapping of network resources in
terms of throughput and latency budget to a common user-level utility scale. A
multi-objective MILP is formulated to solve the throughput- and delay-aware
embedding of each utility function for a max-min fairness criteria. The
allocation of resources in traditional networks with policing and scheduling
cannot distinguish large numbers of classes. We propose a resource allocation
system design for enterprise networks based on Software-Defined Networking
principles to achieve delay-constrained routing in the network and application
pacing at the end-hosts. The system design is evaluated against best effort
networks with applications competing for the throughput of a constrained link.
The competing applications belong to the five application classes web browsing,
file download, remote terminal work, video streaming, and Voice-over-IP. The
results show that the proposed methodology improves the minimum and total
utility, minimizes packet loss and queuing delay at bottlenecks, establishes
fairness in terms of utility between applications, and achieves predictable
application performance at high link utilization.Comment: Accepted for publication in ACM Transactions on Modeling and
Performance Evaluation of Computing Systems (TOMPECS
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
Survey of Important Issues in UAV Communication Networks
Unmanned Aerial Vehicles (UAVs) have enormous potential in the public and
civil domains. These are particularly useful in applications where human lives
would otherwise be endangered. Multi-UAV systems can collaboratively complete
missions more efficiently and economically as compared to single UAV systems.
However, there are many issues to be resolved before effective use of UAVs can
be made to provide stable and reliable context-specific networks. Much of the
work carried out in the areas of Mobile Ad Hoc Networks (MANETs), and Vehicular
Ad Hoc Networks (VANETs) does not address the unique characteristics of the UAV
networks. UAV networks may vary from slow dynamic to dynamic; have intermittent
links and fluid topology. While it is believed that ad hoc mesh network would
be most suitable for UAV networks yet the architecture of multi-UAV networks
has been an understudied area. Software Defined Networking (SDN) could
facilitate flexible deployment and management of new services and help reduce
cost, increase security and availability in networks. Routing demands of UAV
networks go beyond the needs of MANETS and VANETS. Protocols are required that
would adapt to high mobility, dynamic topology, intermittent links, power
constraints and changing link quality. UAVs may fail and the network may get
partitioned making delay and disruption tolerance an important design
consideration. Limited life of the node and dynamicity of the network leads to
the requirement of seamless handovers where researchers are looking at the work
done in the areas of MANETs and VANETs, but the jury is still out. As energy
supply on UAVs is limited, protocols in various layers should contribute
towards greening of the network. This article surveys the work done towards all
of these outstanding issues, relating to this new class of networks, so as to
spur further research in these areas.Comment: arXiv admin note: substantial text overlap with arXiv:1304.3904 by
other author
Application of Compressive Sensing Techniques in Distributed Sensor Networks: A Survey
In this survey paper, our goal is to discuss recent advances of compressive
sensing (CS) based solutions in wireless sensor networks (WSNs) including the
main ongoing/recent research efforts, challenges and research trends in this
area. In WSNs, CS based techniques are well motivated by not only the sparsity
prior observed in different forms but also by the requirement of efficient
in-network processing in terms of transmit power and communication bandwidth
even with nonsparse signals. In order to apply CS in a variety of WSN
applications efficiently, there are several factors to be considered beyond the
standard CS framework. We start the discussion with a brief introduction to the
theory of CS and then describe the motivational factors behind the potential
use of CS in WSN applications. Then, we identify three main areas along which
the standard CS framework is extended so that CS can be efficiently applied to
solve a variety of problems specific to WSNs. In particular, we emphasize on
the significance of extending the CS framework to (i). take communication
constraints into account while designing projection matrices and reconstruction
algorithms for signal reconstruction in centralized as well in decentralized
settings, (ii) solve a variety of inference problems such as detection,
classification and parameter estimation, with compressed data without signal
reconstruction and (iii) take practical communication aspects such as
measurement quantization, physical layer secrecy constraints, and imperfect
channel conditions into account. Finally, open research issues and challenges
are discussed in order to provide perspectives for future research directions
Wireless Power Transfer and Data Collection in Wireless Sensor Networks
In a rechargeable wireless sensor network, the data packets are generated by
sensor nodes at a specific data rate, and transmitted to a base station.
Moreover, the base station transfers power to the nodes by using Wireless Power
Transfer (WPT) to extend their battery life. However, inadequately scheduling
WPT and data collection causes some of the nodes to drain their battery and
have their data buffer overflow, while the other nodes waste their harvested
energy, which is more than they need to transmit their packets. In this paper,
we investigate a novel optimal scheduling strategy, called EHMDP, aiming to
minimize data packet loss from a network of sensor nodes in terms of the nodes'
energy consumption and data queue state information. The scheduling problem is
first formulated by a centralized MDP model, assuming that the complete states
of each node are well known by the base station. This presents the upper bound
of the data that can be collected in a rechargeable wireless sensor network.
Next, we relax the assumption of the availability of full state information so
that the data transmission and WPT can be semi-decentralized. The simulation
results show that, in terms of network throughput and packet loss rate, the
proposed algorithm significantly improves the network performance.Comment: 30 pages, 8 figures, accepted to IEEE Transactions on Vehicular
Technolog
Dynamic Radio Resource Management for Random Network Coding: Power Control and CSMA Backoff Control
Resource allocation in wireless networks typically occurs at PHY/MAC layers,
while random network coding (RNC) is a network layer strategy. An interesting
question is how resource allocation mechanisms can be tuned to improve RNC
performance. By means of a differential equation framework which models RNC
throughput in terms of lower layer parameters, we propose a gradient based
approach that can dynamically allocate MAC and PHY layer resources with the
goal of maximizing the minimum network coding throughput among all the
destination nodes in a RNC multicast. We exemplify this general approach with
two resource allocation problems: (i) power control to improve network coding
throughput, and (ii) CSMA mean backoff delay control to improve network coding
throughput. We design both centralized algorithms and online algorithms for
power control and CSMA backoff control. Our evaluations, including numerically
solving the differential equations in the centralized algorithm and an
event-driven simulation for the online algorithm, show that such gradient based
dynamic resource allocation yields significant throughput improvement of the
destination nodes in RNC. Further, our numerical results reveal that network
coding aware power control can regain the broadcast advantage of wireless
transmissions to improve the throughput.Comment: 28 pages, 9 figures. Submitted to IEEE Transactions on Wireless
Communication
Duality and Stability Regions of Multi-rate Broadcast and Multiple Access Networks
We characterize stability regions of two-user fading Gaussian multiple access
(MAC) and broadcast (BC) networks with centralized scheduling. The data to be
transmitted to the users is encoded into codewords of fixed length. The rates
of the codewords used are restricted to a fixed set of finite cardinality. With
successive decoding and interference cancellation at the receivers, we find the
set of arrival rates that can be stabilized over the MAC and BC networks. In
MAC and BC networks with average power constraints, we observe that the duality
property that relates the MAC and BC information theoretic capacity regions
extend to their stability regions as well. In MAC and BC networks with peak
power constraints, the union of stability regions of dual MAC networks is found
to be strictly contained in the BC stability region.Comment: 12 pages, 11 figures, submitted to IEEE Trans. Information Theory for
revie
- …