320 research outputs found
Sparse Regression Codes for Multi-terminal Source and Channel Coding
We study a new class of codes for Gaussian multi-terminal source and channel
coding. These codes are designed using the statistical framework of
high-dimensional linear regression and are called Sparse Superposition or
Sparse Regression codes. Codewords are linear combinations of subsets of
columns of a design matrix. These codes were recently introduced by Barron and
Joseph and shown to achieve the channel capacity of AWGN channels with
computationally feasible decoding. They have also recently been shown to
achieve the optimal rate-distortion function for Gaussian sources. In this
paper, we demonstrate how to implement random binning and superposition coding
using sparse regression codes. In particular, with minimum-distance
encoding/decoding it is shown that sparse regression codes attain the optimal
information-theoretic limits for a variety of multi-terminal source and channel
coding problems.Comment: 9 pages, appeared in the Proceedings of the 50th Annual Allerton
Conference on Communication, Control, and Computing - 201
Wyner-Ziv Coding over Broadcast Channels: Digital Schemes
This paper addresses lossy transmission of a common source over a broadcast
channel when there is correlated side information at the receivers, with
emphasis on the quadratic Gaussian and binary Hamming cases. A digital scheme
that combines ideas from the lossless version of the problem, i.e.,
Slepian-Wolf coding over broadcast channels, and dirty paper coding, is
presented and analyzed. This scheme uses layered coding where the common layer
information is intended for both receivers and the refinement information is
destined only for one receiver. For the quadratic Gaussian case, a quantity
characterizing the overall quality of each receiver is identified in terms of
channel and side information parameters. It is shown that it is more
advantageous to send the refinement information to the receiver with "better"
overall quality. In the case where all receivers have the same overall quality,
the presented scheme becomes optimal. Unlike its lossless counterpart, however,
the problem eludes a complete characterization
Multiaccess Channels with State Known to Some Encoders and Independent Messages
We consider a state-dependent multiaccess channel (MAC) with state
non-causally known to some encoders. We derive an inner bound for the capacity
region in the general discrete memoryless case and specialize to a binary
noiseless case. In the case of maximum entropy channel state, we obtain the
capacity region for binary noiseless MAC with one informed encoder by deriving
a non-trivial outer bound for this case. For a Gaussian state-dependent MAC
with one encoder being informed of the channel state, we present an inner bound
by applying a slightly generalized dirty paper coding (GDPC) at the informed
encoder that allows for partial state cancellation, and a trivial outer bound
by providing channel state to the decoder also. The uninformed encoders benefit
from the state cancellation in terms of achievable rates, however, appears that
GDPC cannot completely eliminate the effect of the channel state on the
achievable rate region, in contrast to the case of all encoders being informed.
In the case of infinite state variance, we analyze how the uninformed encoder
benefits from the informed encoder's actions using the inner bound and also
provide a non-trivial outer bound for this case which is better than the
trivial outer bound.Comment: Accepted to EURASIP Journal on Wireless Communication and Networking,
Feb. 200
Degraded Broadcast Diamond Channels with Non-Causal State Information at the Source
A state-dependent degraded broadcast diamond channel is studied where the
source-to-relays cut is modeled with two noiseless, finite-capacity digital
links with a degraded broadcasting structure, while the relays-to-destination
cut is a general multiple access channel controlled by a random state. It is
assumed that the source has non-causal channel state information and the relays
have no state information. Under this model, first, the capacity is
characterized for the case where the destination has state information, i.e.,
has access to the state sequence. It is demonstrated that in this case, a joint
message and state transmission scheme via binning is optimal. Next, the case
where the destination does not have state information, i.e., the case with
state information at the source only, is considered. For this scenario, lower
and upper bounds on the capacity are derived for the general discrete
memoryless model. Achievable rates are then computed for the case in which the
relays-to-destination cut is affected by an additive Gaussian state. Numerical
results are provided that illuminate the performance advantages that can be
accrued by leveraging non-causal state information at the source.Comment: Submitted to IEEE Transactions on Information Theory, Feb. 201
Wide spread spectrum watermarking with side information and interference cancellation
Nowadays, a popular method used for additive watermarking is wide spread
spectrum. It consists in adding a spread signal into the host document. This
signal is obtained by the sum of a set of carrier vectors, which are modulated
by the bits to be embedded. To extract these embedded bits, weighted
correlations between the watermarked document and the carriers are computed.
Unfortunately, even without any attack, the obtained set of bits can be
corrupted due to the interference with the host signal (host interference) and
also due to the interference with the others carriers (inter-symbols
interference (ISI) due to the non-orthogonality of the carriers). Some recent
watermarking algorithms deal with host interference using side informed
methods, but inter-symbols interference problem is still open. In this paper,
we deal with interference cancellation methods, and we propose to consider ISI
as side information and to integrate it into the host signal. This leads to a
great improvement of extraction performance in term of signal-to-noise ratio
and/or watermark robustness.Comment: 12 pages, 8 figure
Nested turbo codes for the costa problem
Driven by applications in data-hiding, MIMO broadcast channel coding, precoding for interference cancellation, and transmitter cooperation in wireless networks, Costa coding has lately become a very active research area. In this paper, we first offer code design guidelines in terms of source- channel coding for algebraic binning. We then address practical code design based on nested lattice codes and propose nested turbo codes using turbo-like trellis-coded quantization (TCQ) for source coding and turbo trellis-coded modulation (TTCM) for channel coding. Compared to TCQ, turbo-like TCQ offers structural similarity between the source and channel coding components, leading to more efficient nesting with TTCM and better source coding performance. Due to the difference in effective dimensionality between turbo-like TCQ and TTCM, there is a performance tradeoff between these two components when they are nested together, meaning that the performance of turbo-like TCQ worsens as the TTCM code becomes stronger and vice versa. Optimization of this performance tradeoff leads to our code design that outperforms existing TCQ/TCM and TCQ/TTCM constructions and exhibits a gap of 0.94, 1.42 and 2.65 dB to the Costa capacity at 2.0, 1.0, and 0.5 bits/sample, respectively
State Amplification
We consider the problem of transmitting data at rate R over a state dependent
channel p(y|x,s) with the state information available at the sender and at the
same time conveying the information about the channel state itself to the
receiver. The amount of state information that can be learned at the receiver
is captured by the mutual information I(S^n; Y^n) between the state sequence
S^n and the channel output Y^n. The optimal tradeoff is characterized between
the information transmission rate R and the state uncertainty reduction rate
\Delta, when the state information is either causally or noncausally available
at the sender. This result is closely related and in a sense dual to a recent
study by Merhav and Shamai, which solves the problem of masking the state
information from the receiver rather than conveying it.Comment: 9 pages, 4 figures, submitted to IEEE Trans. Inform. Theory, revise
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