5,957 research outputs found
Joint Source-Channel Codes for MIMO Block Fading Channels
We consider transmission of a continuous amplitude source over an L-block
Rayleigh fading MIMO channel when the channel state
information is only available at the receiver. Since the channel is not
ergodic, Shannon's source-channel separation theorem becomes obsolete and the
optimal performance requires a joint source -channel approach. Our goal is to
minimize the expected end-to-end distortion, particularly in the high SNR
regime. The figure of merit is the distortion exponent, defined as the
exponential decay rate of the expected distortion with increasing SNR. We
provide an upper bound and lower bounds for the distortion exponent with
respect to the bandwidth ratio among the channel and source bandwidths. For the
lower bounds, we analyze three different strategies based on layered source
coding concatenated with progressive, superposition or hybrid digital/analog
transmission. In each case, by adjusting the system parameters we optimize the
distortion exponent as a function of the bandwidth ratio. We prove that the
distortion exponent upper bound can be achieved when the channel has only one
degree of freedom, that is L=1, and . When we have more
degrees of freedom, our achievable distortion exponents meet the upper bound
for only certain ranges of the bandwidth ratio. We demonstrate that our
results, which were derived for a complex Gaussian source, can be extended to
more general source distributions as well.Comment: 36 pages, 11 figure
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
Source Broadcasting to the Masses: Separation has a Bounded Loss
This work discusses the source broadcasting problem, i.e. transmitting a
source to many receivers via a broadcast channel. The optimal rate-distortion
region for this problem is unknown. The separation approach divides the problem
into two complementary problems: source successive refinement and broadcast
channel transmission. We provide bounds on the loss incorporated by applying
time-sharing and separation in source broadcasting. If the broadcast channel is
degraded, it turns out that separation-based time-sharing achieves at least a
factor of the joint source-channel optimal rate, and this factor has a positive
limit even if the number of receivers increases to infinity. For the AWGN
broadcast channel a better bound is introduced, implying that all achievable
joint source-channel schemes have a rate within one bit of the separation-based
achievable rate region for two receivers, or within bits for
receivers
On the Design of a Novel Joint Network-Channel Coding Scheme for the Multiple Access Relay Channel
This paper proposes a novel joint non-binary network-channel code for the
Time-Division Decode-and-Forward Multiple Access Relay Channel (TD-DF-MARC),
where the relay linearly combines -- over a non-binary finite field -- the
coded sequences from the source nodes. A method based on an EXIT chart analysis
is derived for selecting the best coefficients of the linear combination.
Moreover, it is shown that for different setups of the system, different
coefficients should be chosen in order to improve the performance. This
conclusion contrasts with previous works where a random selection was
considered. Monte Carlo simulations show that the proposed scheme outperforms,
in terms of its gap to the outage probabilities, the previously published joint
network-channel coding approaches. Besides, this gain is achieved by using very
short-length codewords, which makes the scheme particularly attractive for
low-latency applications.Comment: 28 pages, 9 figures; Submitted to IEEE Journal on Selected Areas in
Communications - Special Issue on Theories and Methods for Advanced Wireless
Relays, 201
Source-Channel Diversity for Parallel Channels
We consider transmitting a source across a pair of independent, non-ergodic
channels with random states (e.g., slow fading channels) so as to minimize the
average distortion. The general problem is unsolved. Hence, we focus on
comparing two commonly used source and channel encoding systems which
correspond to exploiting diversity either at the physical layer through
parallel channel coding or at the application layer through multiple
description source coding.
For on-off channel models, source coding diversity offers better performance.
For channels with a continuous range of reception quality, we show the reverse
is true. Specifically, we introduce a new figure of merit called the distortion
exponent which measures how fast the average distortion decays with SNR. For
continuous-state models such as additive white Gaussian noise channels with
multiplicative Rayleigh fading, optimal channel coding diversity at the
physical layer is more efficient than source coding diversity at the
application layer in that the former achieves a better distortion exponent.
Finally, we consider a third decoding architecture: multiple description
encoding with a joint source-channel decoding. We show that this architecture
achieves the same distortion exponent as systems with optimal channel coding
diversity for continuous-state channels, and maintains the the advantages of
multiple description systems for on-off channels. Thus, the multiple
description system with joint decoding achieves the best performance, from
among the three architectures considered, on both continuous-state and on-off
channels.Comment: 48 pages, 14 figure
Asymptotic Estimates in Information Theory with Non-Vanishing Error Probabilities
This monograph presents a unified treatment of single- and multi-user
problems in Shannon's information theory where we depart from the requirement
that the error probability decays asymptotically in the blocklength. Instead,
the error probabilities for various problems are bounded above by a
non-vanishing constant and the spotlight is shone on achievable coding rates as
functions of the growing blocklengths. This represents the study of asymptotic
estimates with non-vanishing error probabilities.
In Part I, after reviewing the fundamentals of information theory, we discuss
Strassen's seminal result for binary hypothesis testing where the type-I error
probability is non-vanishing and the rate of decay of the type-II error
probability with growing number of independent observations is characterized.
In Part II, we use this basic hypothesis testing result to develop second- and
sometimes, even third-order asymptotic expansions for point-to-point
communication. Finally in Part III, we consider network information theory
problems for which the second-order asymptotics are known. These problems
include some classes of channels with random state, the multiple-encoder
distributed lossless source coding (Slepian-Wolf) problem and special cases of
the Gaussian interference and multiple-access channels. Finally, we discuss
avenues for further research.Comment: Further comments welcom
Reduced-Dimension Linear Transform Coding of Correlated Signals in Networks
A model, called the linear transform network (LTN), is proposed to analyze
the compression and estimation of correlated signals transmitted over directed
acyclic graphs (DAGs). An LTN is a DAG network with multiple source and
receiver nodes. Source nodes transmit subspace projections of random correlated
signals by applying reduced-dimension linear transforms. The subspace
projections are linearly processed by multiple relays and routed to intended
receivers. Each receiver applies a linear estimator to approximate a subset of
the sources with minimum mean squared error (MSE) distortion. The model is
extended to include noisy networks with power constraints on transmitters. A
key task is to compute all local compression matrices and linear estimators in
the network to minimize end-to-end distortion. The non-convex problem is solved
iteratively within an optimization framework using constrained quadratic
programs (QPs). The proposed algorithm recovers as special cases the regular
and distributed Karhunen-Loeve transforms (KLTs). Cut-set lower bounds on the
distortion region of multi-source, multi-receiver networks are given for linear
coding based on convex relaxations. Cut-set lower bounds are also given for any
coding strategy based on information theory. The distortion region and
compression-estimation tradeoffs are illustrated for different communication
demands (e.g. multiple unicast), and graph structures.Comment: 33 pages, 7 figures, To appear in IEEE Transactions on Signal
Processin
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