9,367 research outputs found
Optimization of Energy Harvesting MISO Communication System with Feedback
Optimization of a point-to-point (p2p) multipleinput single-output (MISO)
communication system is considered when both the transmitter (TX) and the
receiver (RX) have energy harvesting (EH) capabilities. The RX is interested in
feeding back the channel state information (CSI) to the TX to help improve the
transmission rate. The objective is to maximize the throughput by a deadline,
subject to the EH constraints at the TX and the RX. The throughput metric
considered is an upper bound on the ergodic rate of the MISO channel with
beamforming and limited feedback. Feedback bit allocation and transmission
policies that maximize the upper bound on the ergodic rate are obtained. Tools
from majorization theory are used to simplify the formulated optimization
problems. Optimal policies obtained for the modified problem outperform the
naive scheme in which no intelligent management of energy is performed.Comment: 11 page
Joint Wireless Information and Energy Transfer with Reduced Feedback in MIMO Interference Channels
To determine the transmission strategy for joint wireless information and
energy transfer (JWIET) in the MIMO interference channel (IFC), the information
access point (IAP) and energy access point (EAP) require the channel state
information (CSI) of their associated links to both the information-decoding
(ID) mobile stations (MSs) and energy-harvesting (EH) MSs (so-called local
CSI). In this paper, to reduce th e feedback overhead of MSs for the JWIET in
two-user MIMO IFC, we propose a Geodesic energy beamforming scheme that
requires partial CSI at the EAP. Furthermore, in the two-user MIMO IFC, it is
proved that the Geodesic energy beamforming is the optimal strategy. By adding
a rank-one constraint on the transmit signal covariance of IAP, we can further
reduce the feedback overhead to IAP by exploiting Geodesic information
beamforming. Under the rank-one constraint of IAP's transmit signal, we prove
that Geodesic information/energy beamforming approach is the optimal strategy
for JWIET in the two-user MIMO IFC. We also discuss the extension of the
proposed rank-one Geodesic information/energy beamforming strategies to general
K-user MIMO IFC. Finally, by analyzing the achievable rate-energy performance
statistically under imperfect partial CSIT, we propose an adaptive bit
allocation strategy for both EH MS and ID MS.Comment: accepted to IEEE Journal of Selected Areas in Communications (IEEE
JSAC), Special Issue on Wireless Communications Powered by Energy Harvesting
and Wireless Energy Transfe
Power-Optimal Feedback-Based Random Spectrum Access for an Energy Harvesting Cognitive User
In this paper, we study and analyze cognitive radio networks in which
secondary users (SUs) are equipped with Energy Harvesting (EH) capability. We
design a random spectrum sensing and access protocol for the SU that exploits
the primary link's feedback and requires less average sensing time. Unlike
previous works proposed earlier in literature, we do not assume perfect
feedback. Instead, we take into account the more practical possibilities of
overhearing unreliable feedback signals and accommodate spectrum sensing
errors. Moreover, we assume an interference-based channel model where the
receivers are equipped with multi-packet reception (MPR) capability.
Furthermore, we perform power allocation at the SU with the objective of
maximizing the secondary throughput under constraints that maintain certain
quality-of-service (QoS) measures for the primary user (PU)
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
Optimal Energy Allocation for Kalman Filtering over Packet Dropping Links with Imperfect Acknowledgments and Energy Harvesting Constraints
This paper presents a design methodology for optimal transmission energy
allocation at a sensor equipped with energy harvesting technology for remote
state estimation of linear stochastic dynamical systems. In this framework, the
sensor measurements as noisy versions of the system states are sent to the
receiver over a packet dropping communication channel. The packet dropout
probabilities of the channel depend on both the sensor's transmission energies
and time varying wireless fading channel gains. The sensor has access to an
energy harvesting source which is an everlasting but unreliable energy source
compared to conventional batteries with fixed energy storages. The receiver
performs optimal state estimation with random packet dropouts to minimize the
estimation error covariances based on received measurements. The receiver also
sends packet receipt acknowledgments to the sensor via an erroneous feedback
communication channel which is itself packet dropping.
The objective is to design optimal transmission energy allocation at the
energy harvesting sensor to minimize either a finite-time horizon sum or a long
term average (infinite-time horizon) of the trace of the expected estimation
error covariance of the receiver's Kalman filter. These problems are formulated
as Markov decision processes with imperfect state information. The optimal
transmission energy allocation policies are obtained by the use of dynamic
programming techniques. Using the concept of submodularity, the structure of
the optimal transmission energy policies are studied. Suboptimal solutions are
also discussed which are far less computationally intensive than optimal
solutions. Numerical simulation results are presented illustrating the
performance of the energy allocation algorithms.Comment: Submitted to IEEE Transactions on Automatic Control. arXiv admin
note: text overlap with arXiv:1402.663
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
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