Name
Abstract | ||
|---|---|---|
We consider vector-quantized (VQ) transmission of compressed sensing (CS) measurements over noisy channels. Adopting mean-square error (MSE) criterion to measure the distortion between a sparse vector and its reconstruction, we derive channel-optimized quantization principles for encoding CS measurement vector and reconstructing sparse source vector. The resulting necessary optimal conditions are used to develop an algorithm for training channel-optimized vector quantization (COVQ) of CS measurements by taking the end-to-end distortion measure into account. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2013.6638541 | ICASSP |
Keywords | DocType | Volume |
compressed sensing,distortion measurement,interference (signal),quantisation (signal),channel-optimized quantization principles,channel-optimized vector quantization,channel-optimized vector quantizer design,compressed sensing measurements,end-to-end distortion measurement,mean-square error,sparse source vector reconstruction,vector-quantized transmission,Channel-optimized vector quantizer,channel,compressed sensing,mean-square error,sparsity | Journal | abs/1404.7648 |
ISSN | Citations | PageRank |
1520-6149 | 3 | 0.38 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amirpasha Shirazinia | 1 | 62 | 6.90 |
| Saikat Chatterjee | 2 | 24 | 5.32 |
| Mikael Skoglund | 3 | 1397 | 175.71 |