On sparse sensing of coded signals at Sub-Landau sampling rates

Sparse sampling is examined jointly with encoding the transmitted signal. It is shown that with coding the Landau condition may be relaxed and the sampling rate can be lower than the effective bandwidth. Equivalently, the number of measurements can be smaller than the support size. Tight bounds on information rates and on support detection performance are derived for the Gaussian sparsely sampled channel using the context of state dependent channels. Support detection results are verified by a simulation.

[1]  Yoram Bresler,et al.  Subspace Methods for Joint Sparse Recovery , 2010, IEEE Transactions on Information Theory.

[2]  Aaron D. Wyner,et al.  Channels with Side Information at the Transmitter , 1993 .

[3]  Young-Han Kim,et al.  State Amplification , 2008, IEEE Transactions on Information Theory.

[4]  Y. Bresler Spectrum-blind sampling and compressive sensing for continuous-index signals , 2008, 2008 Information Theory and Applications Workshop.

[5]  H. Landau Necessary density conditions for sampling and interpolation of certain entire functions , 1967 .

[6]  Neri Merhav,et al.  Channel Coding in the Presence of Side Information , 2008, Found. Trends Commun. Inf. Theory.

[7]  Urbashi Mitra,et al.  Causal State Communication , 2012, IEEE Transactions on Information Theory.

[8]  Galen Reeves,et al.  The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing , 2010, IEEE Transactions on Information Theory.

[9]  Urbashi Mitra,et al.  Joint Transmission and State Estimation: A Constrained Channel Coding Approach , 2011, IEEE Transactions on Information Theory.

[10]  Dongning Guo,et al.  Capacity of Gaussian Channels With Duty Cycle and Power Constraints , 2012, IEEE Transactions on Information Theory.

[11]  Andrew R. Barron,et al.  High-rate sparse superposition codes with iteratively optimal estimates , 2012, 2012 IEEE International Symposium on Information Theory Proceedings.

[12]  Shlomo Shamai,et al.  Support recovery with sparsely sampled free random matrices , 2011, ISIT.