11,820 research outputs found
Resource Allocation in Wireless Networks with RF Energy Harvesting and Transfer
Radio frequency (RF) energy harvesting and transfer techniques have recently
become alternative methods to power the next generation of wireless networks.
As this emerging technology enables proactive replenishment of wireless
devices, it is advantageous in supporting applications with quality-of-service
(QoS) requirement. This article focuses on the resource allocation issues in
wireless networks with RF energy harvesting capability, referred to as RF
energy harvesting networks (RF-EHNs). First, we present an overview of the
RF-EHNs, followed by a review of a variety of issues regarding resource
allocation. Then, we present a case study of designing in the receiver
operation policy, which is of paramount importance in the RF-EHNs. We focus on
QoS support and service differentiation, which have not been addressed by
previous literatures. Furthermore, we outline some open research directions.Comment: To appear in IEEE Networ
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Energy-Efficient Optimization for Wireless Information and Power Transfer in Large-Scale MIMO Systems Employing Energy Beamforming
In this letter, we consider a large-scale multiple-input multiple-output
(MIMO) system where the receiver should harvest energy from the transmitter by
wireless power transfer to support its wireless information transmission. The
energy beamforming in the large-scale MIMO system is utilized to address the
challenging problem of long-distance wireless power transfer. Furthermore,
considering the limitation of the power in such a system, this letter focuses
on the maximization of the energy efficiency of information transmission (bit
per Joule) while satisfying the quality-of-service (QoS) requirement, i.e.
delay constraint, by jointly optimizing transfer duration and transmit power.
By solving the optimization problem, we derive an energy-efficient resource
allocation scheme. Numerical results validate the effectiveness of the proposed
scheme.Comment: 4 pages, 3 figures. IEEE Wireless Communications Letters 201
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
Max-min Fair Wireless Energy Transfer for Secure Multiuser Communication Systems
This paper considers max-min fairness for wireless energy transfer in a
downlink multiuser communication system. Our resource allocation design
maximizes the minimum harvested energy among multiple multiple-antenna energy
harvesting receivers (potential eavesdroppers) while providing quality of
service (QoS) for secure communication to multiple single-antenna information
receivers. In particular, the algorithm design is formulated as a non-convex
optimization problem which takes into account a minimum required
signal-to-interference-plus-noise ratio (SINR) constraint at the information
receivers and a constraint on the maximum tolerable channel capacity achieved
by the energy harvesting receivers for a given transmit power budget. The
proposed problem formulation exploits the dual use of artificial noise
generation for facilitating efficient wireless energy transfer and secure
communication. A semidefinite programming (SDP) relaxation approach is
exploited to obtain a global optimal solution of the considered problem.
Simulation results demonstrate the significant performance gain in harvested
energy that is achieved by the proposed optimal scheme compared to two simple
baseline schemes.Comment: 5 pages, invited paper, IEEE Information Theory Workshop 2014,
Hobart, Tasmania, Australia, Nov. 201
- …