Name
Abstract | ||
|---|---|---|
We present a joint source-channel multiple de- scription (JSC-MD) framework for resource-constrained network communications (e.g., sensor networks), in which one or many deprived encoders communicate a Markov source against bit errors and erasure errors to many heterogeneous decoders, some powerful and some deprived. To keep the encoder complexity at minimum, the source is coded into K descriptions by a simple multiple description quantizer (MDQ) with neither entropy nor channel coding. The code diversity of MDQ and the path diversity of the network are exploited by decoders to correct transmission errors and improve coding efficiency. A key des ign objective is resource scalability: powerful nodes in the network can perform JSC-MD distributed estimation/decoding under the criteria of maximum a posteriori probability (MAP) or minimum mean-square error (MMSE), while primitive nodes resort to simpler MD decoding, all working with the same MDQ code. The application of JSC-MD to distributed estimation of hidden Markov models in a sensor network is demonstrated. The proposed JSC-MD MAP estimator is an algorithm of the longest path in a weighted directed acyclic graph, while the JSC- MD MMSE decoder is an extension of the well-known forward- backward algorithm to multiple descriptions. Both algorithms simultaneously exploit the source memory, the redundancy of the fixed-rate MDQ, and the inter-description correlations . They outperform the existing hard-decision MDQ decoders by large margins (up to 8dB). For Gaussian Markov sources, the com- plexity of JSC-MD distributed MAP sequence estimation can be made as low as that of typical single description Viterbi-type algorithms. The new JSC-MD framework also enjoys an operational advantage over the existing MDQ decoders. It eliminates the need for multiple side decoders to handle different combinations of the received descriptions by unifying the treatments of all these possible cases. |
Year | Venue | Keywords |
|---|---|---|
2007 | Clinical Orthopaedics and Related Research | complexity.,joint source-channel coding,multiple descriptions,hidden markov model,distributed sequence es- timation,forward-backward algorithm,sensor networks,channel coding,forward backward algorithm,directed acyclic graph,sensor network,longest path,minimum mean square error |
Field | DocType | Volume |
Computer science,Markov chain,Theoretical computer science,Directed acyclic graph,Encoder,Maximum a posteriori estimation,Decoding methods,Hidden Markov model,Longest path problem,Scalability | Journal | abs/0708.3 |
Citations | PageRank | References |
0 | 0.34 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaolin Wu | 1 | 3672 | 286.80 |
| Xiaohan Wang | 2 | 162 | 13.81 |
| Zhe Wang | 3 | 34 | 13.41 |