8,561 research outputs found
Distortion Metrics of Composite Channels with Receiver Side Information
We consider transmission of stationary ergodic sources over non-ergodic composite channels with channel state information at the receiver (CSIR). Previously we introduced alternative capacity definitions to Shannon capacity, including outage and expected capacity. These generalized definitions relax the constraint of Shannon capacity that all transmitted information must be decoded at the receiver. In this work alternative end- to-end distortion metrics such as outage and expected distortion are introduced to relax the constraint that a single distortion level has to be maintained for all channel states. Through the example of transmission of a Gaussian source over a slow-fading Gaussian channel, we illustrate that the end-to-end distortion metrics dictate whether the source and channel coding can be separated for a communication system. We also show that the source and channel need to exchange information through an appropriate interface to facilitate separate encoding and decoding
Minimum Expected Distortion in Gaussian Layered Broadcast Coding with Successive Refinement
A transmitter without channel state information (CSI) wishes to send a
delay-limited Gaussian source over a slowly fading channel. The source is coded
in superimposed layers, with each layer successively refining the description
in the previous one. The receiver decodes the layers that are supported by the
channel realization and reconstructs the source up to a distortion. In the
limit of a continuum of infinite layers, the optimal power distribution that
minimizes the expected distortion is given by the solution to a set of linear
differential equations in terms of the density of the fading distribution. In
the optimal power distribution, as SNR increases, the allocation over the
higher layers remains unchanged; rather the extra power is allocated towards
the lower layers. On the other hand, as the bandwidth ratio b (channel uses per
source symbol) tends to zero, the power distribution that minimizes expected
distortion converges to the power distribution that maximizes expected
capacity. While expected distortion can be improved by acquiring CSI at the
transmitter (CSIT) or by increasing diversity from the realization of
independent fading paths, at high SNR the performance benefit from diversity
exceeds that from CSIT, especially when b is large.Comment: To appear in the proceedings of the 2007 IEEE International Symposium
on Information Theory, Nice, France, June 24-29, 200
Distortion Minimization in Gaussian Layered Broadcast Coding with Successive Refinement
A transmitter without channel state information (CSI) wishes to send a
delay-limited Gaussian source over a slowly fading channel. The source is coded
in superimposed layers, with each layer successively refining the description
in the previous one. The receiver decodes the layers that are supported by the
channel realization and reconstructs the source up to a distortion. The
expected distortion is minimized by optimally allocating the transmit power
among the source layers. For two source layers, the allocation is optimal when
power is first assigned to the higher layer up to a power ceiling that depends
only on the channel fading distribution; all remaining power, if any, is
allocated to the lower layer. For convex distortion cost functions with convex
constraints, the minimization is formulated as a convex optimization problem.
In the limit of a continuum of infinite layers, the minimum expected distortion
is given by the solution to a set of linear differential equations in terms of
the density of the fading distribution. As the bandwidth ratio b (channel uses
per source symbol) tends to zero, the power distribution that minimizes
expected distortion converges to the one that maximizes expected capacity.
While expected distortion can be improved by acquiring CSI at the transmitter
(CSIT) or by increasing diversity from the realization of independent fading
paths, at high SNR the performance benefit from diversity exceeds that from
CSIT, especially when b is large.Comment: Accepted for publication in IEEE Transactions on Information Theor
Energy-Efficient Joint Estimation in Sensor Networks: Analog vs. Digital
Sensor networks in which energy is a limited resource so that energy
consumption must be minimized for the intended application are considered. In
this context, an energy-efficient method for the joint estimation of an unknown
analog source under a given distortion constraint is proposed. The approach is
purely analog, in which each sensor simply amplifies and forwards the
noise-corrupted analog bservation to the fusion center for joint estimation.
The total transmission power across all the sensor nodes is minimized while
satisfying a distortion requirement on the joint estimate. The energy
efficiency of this analog approach is compared with previously proposed digital
approaches with and without coding. It is shown in our simulation that the
analog approach is more energy-efficient than the digital system without
coding, and in some cases outperforms the digital system with optimal coding.Comment: To appear in Proceedings of the 2005 IEEE International Conference on
Acoustics, Speech and Signal Processing, Philadelphia, PA, March 19 - 23,
200
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