A Slepian-Wolf based cross layer scheme suitable to sensor networks
MobiMedia '06 Proceedings of the 2nd international conference on Mobile multimedia communications
Low-complexity coding and source-optimized clustering for large-scale sensor networks
ACM Transactions on Sensor Networks (TOSN)
Compress-spread-forward with multiterminal source coding and complete complementary sequences
IEEE Transactions on Communications
Distributed quantization over noisy channels
IEEE Transactions on Communications
Distributed arithmetic coding for the Slepian-Wolf problem
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
Multiple-description coding by dithered delta-sigma quantization
IEEE Transactions on Information Theory
On practical design for joint distributed source and network coding
IEEE Transactions on Information Theory
Nonlinear coding and estimation for correlated data in wireless sensor networks
IEEE Transactions on Communications
Unequal error protection based on symmetric Slepian-Wolf coding in wireless sensor network
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
On rate-constrained estimation in unreliable sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Lattice codes for the Gaussian wiretap channel
IWCC'11 Proceedings of the Third international conference on Coding and cryptology
Distributed Video Coding: an overview of basics, research issues and solutions
International Journal of Ad Hoc and Ubiquitous Computing
| Hi-index | 754.96 |
In many multiterminal communication problems, constructions of good source codes involve finding distributed partitions (into bins) of a collection of quantizers associated with a group of source encoders. Further, computationally efficient procedures to index these bins are also required. In this work, we consider a constructive approach for distributed binning in an algebraic framework. Several application scenarios fall under the scope of this paper including the CEO problem, distributed source coding, and n-channel symmetric multiple description source coding with n2. Specifically, in this exposition we consider the case of two codebooks while focusing on the Gaussian CEO problem with mean squared error reconstruction and with two symmetric observations. This problem deals with distributed encoding of correlated noisy observations of a source into descriptions such that the joint decoder having access to them can reconstruct the source with a fidelity criterion. We employ generalized coset codes constructed in a group-theoretic setting for this approach, and analyze the performance in terms of distance properties and decoding algorithms.