1,725 research outputs found
On Two-Pair Two-Way Relay Channel with an Intermittently Available Relay
When multiple users share the same resource for physical layer cooperation
such as relay terminals in their vicinities, this shared resource may not be
always available for every user, and it is critical for transmitting terminals
to know whether other users have access to that common resource in order to
better utilize it. Failing to learn this critical piece of information may
cause severe issues in the design of such cooperative systems. In this paper,
we address this problem by investigating a two-pair two-way relay channel with
an intermittently available relay. In the model, each pair of users need to
exchange their messages within their own pair via the shared relay. The shared
relay, however, is only intermittently available for the users to access. The
accessing activities of different pairs of users are governed by independent
Bernoulli random processes. Our main contribution is the characterization of
the capacity region to within a bounded gap in a symmetric setting, for both
delayed and instantaneous state information at transmitters. An interesting
observation is that the bottleneck for information flow is the quality of state
information (delayed or instantaneous) available at the relay, not those at the
end users. To the best of our knowledge, our work is the first result regarding
how the shared intermittent relay should cooperate with multiple pairs of users
in such a two-way cooperative network.Comment: extended version of ISIT 2015 pape
Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding
We propose deep learning based communication methods for adaptive-bandwidth transmission of images over wireless channels. We consider the scenario in which images are transmitted progressively in layers over time or frequency, and such layers can be aggregated by receivers in order to increase the quality of their reconstructions. We investigate two scenarios, one in which the layers are sent sequentially, and incrementally contribute to the refinement of a reconstruction, and another in which the layers are independent and can be retrieved in any order. Those scenarios correspond to the well known problems of successive refinement and multiple descriptions, respectively, in the context of joint source-channel coding (JSCC). We propose DeepJSCC-l, an innovative solution that uses convolutional autoencoders, and present three architectures with different complexity trade-offs. To the best of our knowledge, this is the first practical multiple-description JSCC scheme developed and tested for practical information sources and channels. Numerical results show that DeepJSCC-l can learn to transmit the source progressively with negligible losses in the end-to-end performance compared with a single transmission. Moreover, DeepJSCC-l has comparable performance with state of the art digital progressive transmission schemes in the challenging low signal-to-noise ratio (SNR) and small bandwidth regimes, with the additional advantage of graceful degradation with channel SNR
Multiuser Successive Refinement and Multiple Description Coding
We consider the multiuser successive refinement (MSR) problem, where the
users are connected to a central server via links with different noiseless
capacities, and each user wishes to reconstruct in a successive-refinement
fashion. An achievable region is given for the two-user two-layer case and it
provides the complete rate-distortion region for the Gaussian source under the
MSE distortion measure. The key observation is that this problem includes the
multiple description (MD) problem (with two descriptions) as a subsystem, and
the techniques useful in the MD problem can be extended to this case. We show
that the coding scheme based on the universality of random binning is
sub-optimal, because multiple Gaussian side informations only at the decoders
do incur performance loss, in contrast to the case of single side information
at the decoder. We further show that unlike the single user case, when there
are multiple users, the loss of performance by a multistage coding approach can
be unbounded for the Gaussian source. The result suggests that in such a
setting, the benefit of using successive refinement is not likely to justify
the accompanying performance loss. The MSR problem is also related to the
source coding problem where each decoder has its individual side information,
while the encoder has the complete set of the side informations. The MSR
problem further includes several variations of the MD problem, for which the
specialization of the general result is investigated and the implication is
discussed.Comment: 10 pages, 5 figures. To appear in IEEE Transaction on Information
Theory. References updated and typos correcte
Source Coding Problems with Conditionally Less Noisy Side Information
A computable expression for the rate-distortion (RD) function proposed by
Heegard and Berger has eluded information theory for nearly three decades.
Heegard and Berger's single-letter achievability bound is well known to be
optimal for \emph{physically degraded} side information; however, it is not
known whether the bound is optimal for arbitrarily correlated side information
(general discrete memoryless sources). In this paper, we consider a new setup
in which the side information at one receiver is \emph{conditionally less
noisy} than the side information at the other. The new setup includes degraded
side information as a special case, and it is motivated by the literature on
degraded and less noisy broadcast channels. Our key contribution is a converse
proving the optimality of Heegard and Berger's achievability bound in a new
setting. The converse rests upon a certain \emph{single-letterization} lemma,
which we prove using an information theoretic telescoping identity {recently
presented by Kramer}. We also generalise the above ideas to two different
successive-refinement problems
Rate-Distortion Function for a Heegard-Berger Problem with Two Sources and Degraded Reconstruction sets
In this work, we investigate an instance of the Heegard-Berger problem with
two sources and arbitrarily correlated side information sequences at two
decoders, in which the reconstruction sets at the decoders are degraded.
Specifically, two sources are to be encoded in a manner that one of the two is
reproduced losslessly by both decoders, and the other is reproduced to within
some prescribed distortion level at one of the two decoders. We establish a
single-letter characterization of the rate-distortion function for this model.
The investigation of this result in some special cases also sheds light on the
utility of joint compression of the two sources. Furthermore, we also
generalize our result to the setting in which the source component that is to
be recovered by both users is reconstructed in a lossy fashion, under the
requirement that all terminals (i.e., the encoder and both decoders) can share
an exact copy of the compressed version of this source component, i.e., a
common encoder-decoders reconstruction constraint. For this model as well, we
establish a single-letter characterization of the associated rate-distortion
function.Comment: Submitted to IEEE Trans. on Information Theor
Efficient Downlink Channel Reconstruction for FDD Multi-Antenna Systems
In this paper, we propose an efficient downlink channel reconstruction scheme
for a frequency-division-duplex multi-antenna system by utilizing uplink
channel state information combined with limited feedback. Based on the spatial
reciprocity in a wireless channel, the downlink channel is reconstructed by
using frequency-independent parameters. We first estimate the gains, delays,
and angles during uplink sounding. The gains are then refined through downlink
training and sent back to the base station (BS). With limited overhead, the
refinement can substantially improve the accuracy of the downlink channel
reconstruction. The BS can then reconstruct the downlink channel with the
uplink-estimated delays and angles and the downlink-refined gains. We also
introduce and extend the Newtonized orthogonal matching pursuit (NOMP)
algorithm to detect the delays and gains in a multi-antenna multi-subcarrier
condition. The results of our analysis show that the extended NOMP algorithm
achieves high estimation accuracy. Simulations and over-the-air tests are
performed to assess the performance of the efficient downlink channel
reconstruction scheme. The results show that the reconstructed channel is close
to the practical channel and that the accuracy is enhanced when the number of
BS antennas increases, thereby highlighting that the promising application of
the proposed scheme in large-scale antenna array systems
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