267 research outputs found
Power-Constrained Sparse Gaussian Linear Dimensionality Reduction over Noisy Channels
In this paper, we investigate power-constrained sensing matrix design in a
sparse Gaussian linear dimensionality reduction framework. Our study is carried
out in a single--terminal setup as well as in a multi--terminal setup
consisting of orthogonal or coherent multiple access channels (MAC). We adopt
the mean square error (MSE) performance criterion for sparse source
reconstruction in a system where source-to-sensor channel(s) and
sensor-to-decoder communication channel(s) are noisy. Our proposed sensing
matrix design procedure relies upon minimizing a lower-bound on the MSE in
single-- and multiple--terminal setups. We propose a three-stage sensing matrix
optimization scheme that combines semi-definite relaxation (SDR) programming, a
low-rank approximation problem and power-rescaling. Under certain conditions,
we derive closed-form solutions to the proposed optimization procedure. Through
numerical experiments, by applying practical sparse reconstruction algorithms,
we show the superiority of the proposed scheme by comparing it with other
relevant methods. This performance improvement is achieved at the price of
higher computational complexity. Hence, in order to address the complexity
burden, we present an equivalent stochastic optimization method to the problem
of interest that can be solved approximately, while still providing a superior
performance over the popular methods.Comment: Accepted for publication in IEEE Transactions on Signal Processing
(16 pages
Optimized Compressed Sensing Matrix Design for Noisy Communication Channels
We investigate a power-constrained sensing matrix design problem for a
compressed sensing framework. We adopt a mean square error (MSE) performance
criterion for sparse source reconstruction in a system where the
source-to-sensor channel and the sensor-to-decoder communication channel are
noisy. Our proposed sensing matrix design procedure relies upon minimizing a
lower-bound on the MSE. Under certain conditions, we derive closed-form
solutions to the optimization problem. Through numerical experiments, by
applying practical sparse reconstruction algorithms, we show the strength of
the proposed scheme by comparing it with other relevant methods. We discuss the
computational complexity of our design method, and develop an equivalent
stochastic optimization method to the problem of interest that can be solved
approximately with a significantly less computational burden. We illustrate
that the low-complexity method still outperforms the popular competing methods.Comment: Submitted to IEEE ICC 2015 (EXTENDED VERSION
Sensing Throughput Optimization in Fading Cognitive Multiple Access Channels With Energy Harvesting Secondary Transmitters
The paper investigates the problem of maximizing expected sum throughput in a
fading multiple access cognitive radio network when secondary user (SU)
transmitters have energy harvesting capability, and perform cooperative
spectrum sensing. We formulate the problem as maximization of sum-capacity of
the cognitive multiple access network over a finite time horizon subject to a
time averaged interference constraint at the primary user (PU) and almost sure
energy causality constraints at the SUs. The problem is a mixed integer
non-linear program with respect to two decision variables namely spectrum
access decision and spectrum sensing decision, and the continuous variables
sensing time and transmission power. In general, this problem is known to be NP
hard. For optimization over these two decision variables, we use an exhaustive
search policy when the length of the time horizon is small, and a heuristic
policy for longer horizons. For given values of the decision variables, the
problem simplifies into a joint optimization on SU \textit{transmission power}
and \textit{sensing time}, which is non-convex in nature. We solve the
resulting optimization problem as an alternating convex optimization problem
for both non-causal and causal channel state information and harvested energy
information patterns at the SU base station (SBS) or fusion center (FC). We
present an analytic solution for the non-causal scenario with infinite battery
capacity for a general finite horizon problem.We formulate the problem with
causal information and finite battery capacity as a stochastic control problem
and solve it using the technique of dynamic programming. Numerical results are
presented to illustrate the performance of the various algorithms
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
- …