Elements of information theory
Elements of information theory
Convex Optimization
Optimal Rate and Power Allocation for Layered Transmission with Superposition Coding
DCC '07 Proceedings of the 2007 Data Compression Conference
Systematic lossy source/channel coding
IEEE Transactions on Information Theory
Hybrid digital-analog (HDA) joint source-channel codes for broadcasting and robust communications
IEEE Transactions on Information Theory
To code, or not to code: lossy source-channel communication revisited
IEEE Transactions on Information Theory
A broadcast approach for a single-user slowly fading MIMO channel
IEEE Transactions on Information Theory
Source-channel diversity for parallel channels
IEEE Transactions on Information Theory
Broadcasting over uncertain channels with decoding delay constraints
IEEE Transactions on Information Theory
Distortion Bounds for Broadcasting With Bandwidth Expansion
IEEE Transactions on Information Theory
On the Distortion SNR Exponent of Hybrid Digital–Analog Space–Time Coding
IEEE Transactions on Information Theory
Joint Source–Channel Codes for MIMO Block-Fading Channels
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Variable-rate channel capacity
IEEE Transactions on Information Theory
Generalizing capacity: new definitions and capacity theorems for composite channels
IEEE Transactions on Information Theory
Hi-index | 754.96 |
A transmitter without channel state information 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 number of channel uses per source symbol tends to zero, the power distribution that minimizes expected distortion converges to the one that maximizes expected capacity.