133,666 research outputs found
Multi-Class Source-Channel Coding
This paper studies an almost-lossless
source-channel coding scheme in which source messages
are assigned to different classes and encoded with a channel
code that depends on the class index. The code performance
is analyzed by means of random-coding error exponents and
validated by simulation of a low-complexity implementation
using existing source and channel codes. While each class
code can be seen as a concatenation of a source code and a
channel code, the overall performance improves on that of
separate source-channel coding and approaches that of joint
source-channel coding when the number of classes increase
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
The Finite Field Multi-Way Relay Channel with Correlated Sources: Beyond Three Users
The multi-way relay channel (MWRC) models cooperative communication networks
in which many users exchange messages via a relay. In this paper, we consider
the finite field MWRC with correlated messages. The problem is to find all
achievable rates, defined as the number of channel uses required per reliable
exchange of message tuple. For the case of three users, we have previously
established that for a special class of source distributions, the set of all
achievable rates can be found [Ong et al., ISIT 2010]. The class is specified
by an almost balanced conditional mutual information (ABCMI) condition. In this
paper, we first generalize the ABCMI condition to the case of more than three
users. We then show that if the sources satisfy the ABCMI condition, then the
set of all achievable rates is found and can be attained using a separate
source-channel coding architecture.Comment: Author's final version (to be presented at ISIT 2012
Multi-Class Cost-Constrained Random Coding for Correlated Sources over the Multiple-Access Channel.
This paper studies a generalized version of multi-class cost-constrained random-coding ensemble with multiple auxiliary costs for the transmission of N correlated sources over an N-user multiple-access channel. For each user, the set of messages is partitioned into classes and codebooks are generated according to a distribution depending on the class index of the source message and under the constraint that the codewords satisfy a set of cost functions. Proper choices of the cost functions recover different coding schemes including message-dependent and message-independent versions of independent and identically distributed, independent conditionally distributed, constant-composition and conditional constant composition ensembles. The transmissibility region of the scheme is related to the Cover-El Gamal-Salehi region. A related family of correlated-source Gallager source exponent functions is also studied. The achievable exponents are compared for correlated and independent sources, both numerically and analytically
Semantic Multi-Resolution Communications
Deep learning based joint source-channel coding (JSCC) has demonstrated
significant advancements in data reconstruction compared to separate
source-channel coding (SSCC). This superiority arises from the suboptimality of
SSCC when dealing with finite block-length data. Moreover, SSCC falls short in
reconstructing data in a multi-user and/or multi-resolution fashion, as it only
tries to satisfy the worst channel and/or the highest quality data. To overcome
these limitations, we propose a novel deep learning multi-resolution JSCC
framework inspired by the concept of multi-task learning (MTL). This proposed
framework excels at encoding data for different resolutions through
hierarchical layers and effectively decodes it by leveraging both current and
past layers of encoded data. Moreover, this framework holds great potential for
semantic communication, where the objective extends beyond data reconstruction
to preserving specific semantic attributes throughout the communication
process. These semantic features could be crucial elements such as class
labels, essential for classification tasks, or other key attributes that
require preservation. Within this framework, each level of encoded data can be
carefully designed to retain specific data semantics. As a result, the
precision of a semantic classifier can be progressively enhanced across
successive layers, emphasizing the preservation of targeted semantics
throughout the encoding and decoding stages. We conduct experiments on MNIST
and CIFAR10 dataset. The experiment with both datasets illustrates that our
proposed method is capable of surpassing the SSCC method in reconstructing data
with different resolutions, enabling the extraction of semantic features with
heightened confidence in successive layers. This capability is particularly
advantageous for prioritizing and preserving more crucial semantic features
within the datasets
Efficient Transmission in Multi-user Relay Networks with Node Clustering and Network Coding
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordThis paper investigates the communication problem in a class of multi-user dual-hop networks in which multiple source terminals desire to distribute their independent messages to multiple destinations through the assistance of multiple relay terminals. We consider an efficient transmission strategy that combines network coding and non-orthogonal transmission techniques to balance the achievability of spatial diversity and channel utilization. Specifically, in addition to applying a class of finite-field network codes in the relays, we divide the sources and the relays into clusters such that terminals within each cluster can access the same channel resource. The achievable error performance under Nakagami-m fading environment is derived and is shown to significantly outperform the conventional transmission methods.National Natural Science Foundation of ChinaEuropean Union Horizon 202
Side information aware source and channel coding in wireless networks
Signals in communication networks exhibit significant correlation, which can stem from the physical nature of the underlying sources, or can be created within the system. Current layered network architectures, in which, based on Shannon’s separation theorem, data is compressed and transmitted over independent bit-pipes, are in general not able to exploit such correlation efficiently. Moreover, this strictly layered architecture was developed for wired networks and ignore the broadcast and highly dynamic nature of the wireless medium, creating a bottleneck in the wireless network design. Technologies that exploit correlated information and go beyond the layered network architecture can become a key feature of future wireless networks, as information theory promises significant gains. In this thesis, we study from an information theoretic perspective, three distinct, yet fundamental, problems involving the availability of correlated information in wireless networks and develop novel communication techniques to exploit it efficiently. We first look at two joint source-channel coding problems involving the lossy transmission of Gaussian sources in a multi-terminal and a time-varying setting in which correlated side information is present in the network. In these two problems, the optimality of Shannon’s separation breaks down and separate source and channel coding is shown to perform poorly compared to the proposed joint source-channel coding designs, which are shown to achieve the optimal performance in some setups. Then, we characterize the capacity of a class of orthogonal relay channels in the presence of channel side information at the destination, and show that joint decoding and compression of the received signal at the relay is required to optimally exploit the available side information. Our results in these three different scenarios emphasize the benefits of exploiting correlated side information at the destination when designing a communication system, even though the nature of the side information and the performance measure in the three scenarios are quite different.Open Acces
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