K-bit Hamming compressed sensing

Publication Type:
Conference Proceeding
Citation:
IEEE International Symposium on Information Theory - Proceedings, 2013, pp. 679 - 683
Issue Date:
2013-12-19
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2013003398OK.pdf604.31 kB
Adobe PDF
We consider recovering d-level quantization of a signal from k-level quantization of linear measurements. This problem has great potential in practical systems, but has not been fully addressed in compressed sensing (CS). We tackle it by proposing k-bit Hamming compressed sensing (HCS). It reduces the decoding to a series of hypothesis tests of the bin where the signal lies in. Each test equals to an independent nearest neighbor search for a histogram estimated from quantized measurements. This method is based on that the distribution of the ratio between two random projections is defined by their intersection angle. Compared to CS and 1-bit CS, k-bit HCS leads to lower cost in both hardware and computation. It admits a trade-off between recovery/measurement resolution and measurement amount and thus is more flexible than 1-bit HCS. A rigorous analysis shows its error bound. Extensive empirical study further justifies its appealing accuracy, robustness and efficiency. © 2013 IEEE.
Please use this identifier to cite or link to this item: