72 research outputs found
Distributed Remote Vector Gaussian Source Coding for Wireless Acoustic Sensor Networks
In this paper, we consider the problem of remote vector Gaussian source
coding for a wireless acoustic sensor network. Each node receives messages from
multiple nodes in the network and decodes these messages using its own
measurement of the sound field as side information. The node's measurement and
the estimates of the source resulting from decoding the received messages are
then jointly encoded and transmitted to a neighboring node in the network. We
show that for this distributed source coding scenario, one can encode a
so-called conditional sufficient statistic of the sources instead of jointly
encoding multiple sources. We focus on the case where node measurements are in
form of noisy linearly mixed combinations of the sources and the acoustic
channel mixing matrices are invertible. For this problem, we derive the
rate-distortion function for vector Gaussian sources and under covariance
distortion constraints.Comment: 10 pages, to be presented at the IEEE DCC'1
Learning from compressed observations
The problem of statistical learning is to construct a predictor of a random
variable as a function of a related random variable on the basis of an
i.i.d. training sample from the joint distribution of . Allowable
predictors are drawn from some specified class, and the goal is to approach
asymptotically the performance (expected loss) of the best predictor in the
class. We consider the setting in which one has perfect observation of the
-part of the sample, while the -part has to be communicated at some
finite bit rate. The encoding of the -values is allowed to depend on the
-values. Under suitable regularity conditions on the admissible predictors,
the underlying family of probability distributions and the loss function, we
give an information-theoretic characterization of achievable predictor
performance in terms of conditional distortion-rate functions. The ideas are
illustrated on the example of nonparametric regression in Gaussian noise.Comment: 6 pages; submitted to the 2007 IEEE Information Theory Workshop (ITW
2007
Transmit Signal and Bandwidth Optimization in Multiple-Antenna Relay Channels
Transmit signal and bandwidth optimization is considered in multiple-antenna
relay channels. Assuming all terminals have channel state information, the
cut-set capacity upper bound and decode-and-forward rate under full-duplex
relaying are evaluated by formulating them as convex optimization problems. For
half-duplex relays, bandwidth allocation and transmit signals are optimized
jointly. Moreover, achievable rates based on the compress-and-forward
transmission strategy are presented using rate-distortion and Wyner-Ziv
compression schemes. It is observed that when the relay is close to the source,
decode-and-forward is almost optimal, whereas compress-and-forward achieves
good performance when the relay is close to the destination.Comment: 16 pages, 10 figure
On rate-distortion with mixed types of side information
In this correspondence, we consider rate-distortion examples in the presence of side information. For a system with some side information known at both the encoder and decoder, and some known only at the decoder, we evaluate the rate distortion function for both Gaussian and binary sources. While the Gaussian example is a straightforward generalization of the corresponding result by Wyner, the binary example proves more difficult and is solved using a multidimensional optimization approach. Leveraging the insights gained from the binary example, we then solve the more complicated binary Heegard and Berger problem of decoding when side information may be present. The results demonstrate the existence of a new type of successive refinement in which the refinement information is decoded together with side information that is not available for the initial description
Achievable Rate Regions for Two-Way Relay Channel using Nested Lattice Coding
This paper studies Gaussian Two-Way Relay Channel where two communication
nodes exchange messages with each other via a relay. It is assumed that all
nodes operate in half duplex mode without any direct link between the
communication nodes. A compress-and-forward relaying strategy using nested
lattice codes is first proposed. Then, the proposed scheme is improved by
performing a layered coding : a common layer is decoded by both receivers and a
refinement layer is recovered only by the receiver which has the best channel
conditions. The achievable rates of the new scheme are characterized and are
shown to be higher than those provided by the decode-and-forward strategy in
some regions.Comment: 27 pages, 13 figures, Submitted to IEEE Transactions on Wireless
Communications (October 2013
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
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