Joint reconstruction algorithms for one-bit distributed compressed sensing

Distributed compressed sensing (DCS), exploiting the correlation among multiple signals, enjoys the advantage of reduced number of measurements. This paper considers a type of joint sparsity model in DCS, where each signal contains a common component and an innovation component. In order to reduce the transmission cost, the measurements are derived as the sign information of the compressed samples by using one-bit quantization. We study such CS operation, and propose two joint reconstruction algorithms by iteratively deriving the sign information of each component. Simulation results show that the proposed algorithms can recover the signals efficiently.

[1]  Tian Yun,et al.  1-bit cooperative compressed spectrum sensing , 2012 .

[2]  Richard G. Baraniuk,et al.  Distributed Compressed Sensing Dror , 2005 .

[3]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[4]  Laurent Jacques,et al.  Dequantizing Compressed Sensing: When Oversampling and Non-Gaussian Constraints Combine , 2009, IEEE Transactions on Information Theory.

[5]  Richard G. Baraniuk,et al.  Compressive Sensing , 2008, Computer Vision, A Reference Guide.

[6]  Xiaoying Gan,et al.  Joint Compressive Sensing in Wideband Cognitive Networks , 2010, 2010 IEEE Wireless Communication and Networking Conference.

[7]  Wotao Yin,et al.  Trust, But Verify: Fast and Accurate Signal Recovery From 1-Bit Compressive Measurements , 2011, IEEE Transactions on Signal Processing.

[8]  Fumiyuki Adachi,et al.  Improved adaptive sparse channel estimation based on the least mean square algorithm , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[9]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[10]  Chinmay Hegde,et al.  Texas Hold 'Em algorithms for distributed compressive sensing , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  Chunyan Feng,et al.  Sparsity Order Estimation and its Application in Compressive Spectrum Sensing for Cognitive Radios , 2012, IEEE Transactions on Wireless Communications.

[12]  Yun Tian,et al.  A Distributed Compressed Sensing Scheme Based on One-Bit Quantization , 2014, 2014 IEEE 79th Vehicular Technology Conference (VTC Spring).

[13]  Laurent Jacques,et al.  Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors , 2011, IEEE Transactions on Information Theory.

[14]  Jiaru Lin,et al.  A joint recovery algorithm for distributed compressed sensing , 2012, Trans. Emerg. Telecommun. Technol..

[15]  Lin Cai,et al.  Scalable Video Coding with Compressive Sensing for Wireless Videocast , 2011, 2011 IEEE International Conference on Communications (ICC).

[16]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.