8,657 research outputs found
Joint Source-Channel Coding with Time-Varying Channel and Side-Information
Transmission of a Gaussian source over a time-varying Gaussian channel is
studied in the presence of time-varying correlated side information at the
receiver. A block fading model is considered for both the channel and the side
information, whose states are assumed to be known only at the receiver. The
optimality of separate source and channel coding in terms of average end-to-end
distortion is shown when the channel is static while the side information state
follows a discrete or a continuous and quasiconcave distribution. When both the
channel and side information states are time-varying, separate source and
channel coding is suboptimal in general. A partially informed encoder lower
bound is studied by providing the channel state information to the encoder.
Several achievable transmission schemes are proposed based on uncoded
transmission, separate source and channel coding, joint decoding as well as
hybrid digital-analog transmission. Uncoded transmission is shown to be optimal
for a class of continuous and quasiconcave side information state
distributions, while the channel gain may have an arbitrary distribution. To
the best of our knowledge, this is the first example in which the uncoded
transmission achieves the optimal performance thanks to the time-varying nature
of the states, while it is suboptimal in the static version of the same
problem. Then, the optimal \emph{distortion exponent}, that quantifies the
exponential decay rate of the expected distortion in the high SNR regime, is
characterized for Nakagami distributed channel and side information states, and
it is shown to be achieved by hybrid digital-analog and joint decoding schemes
in certain cases, illustrating the suboptimality of pure digital or analog
transmission in general.Comment: Submitted to IEEE Transactions on Information Theor
Integer-Forcing Source Coding
Integer-Forcing (IF) is a new framework, based on compute-and-forward, for
decoding multiple integer linear combinations from the output of a Gaussian
multiple-input multiple-output channel. This work applies the IF approach to
arrive at a new low-complexity scheme, IF source coding, for distributed lossy
compression of correlated Gaussian sources under a minimum mean squared error
distortion measure. All encoders use the same nested lattice codebook. Each
encoder quantizes its observation using the fine lattice as a quantizer and
reduces the result modulo the coarse lattice, which plays the role of binning.
Rather than directly recovering the individual quantized signals, the decoder
first recovers a full-rank set of judiciously chosen integer linear
combinations of the quantized signals, and then inverts it. In general, the
linear combinations have smaller average powers than the original signals. This
allows to increase the density of the coarse lattice, which in turn translates
to smaller compression rates. We also propose and analyze a one-shot version of
IF source coding, that is simple enough to potentially lead to a new design
principle for analog-to-digital converters that can exploit spatial
correlations between the sampled signals.Comment: Submitted to IEEE Transactions on Information Theor
Joint Wyner-Ziv/Dirty Paper coding by modulo-lattice modulation
The combination of source coding with decoder side-information (Wyner-Ziv
problem) and channel coding with encoder side-information (Gel'fand-Pinsker
problem) can be optimally solved using the separation principle. In this work
we show an alternative scheme for the quadratic-Gaussian case, which merges
source and channel coding. This scheme achieves the optimal performance by a
applying modulo-lattice modulation to the analog source. Thus it saves the
complexity of quantization and channel decoding, and remains with the task of
"shaping" only. Furthermore, for high signal-to-noise ratio (SNR), the scheme
approaches the optimal performance using an SNR-independent encoder, thus it is
robust to unknown SNR at the encoder.Comment: Submitted to IEEE Transactions on Information Theory. Presented in
part in ISIT-2006, Seattle. New version after revie
Piggybacking Codes for Network Coding: The High/Low SNR Regime
We propose a piggybacking scheme for network coding where strong source
inputs piggyback the weaker ones, a scheme necessary and sufficient to achieve
the cut-set upper bound at high/low-snr regime, a new asymptotically optimal
operational regime for the multihop Amplify and Forward (AF) networks
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