Sparsity Adaptive Compressed Sensing and Reconstruction Architecture Based on Reed-Solomon Codes

Abstract

A novel sparsity adaptive compressed sensing (CS) scheme based on Reed-Solomon (RS) codes is proposed to reduce the amount of sampled data, which can adjust the sampling rate adaptively to achieve data sampling and reconstruct the original data accurately by any decoding algorithm on the finite field. Moreover, the proposed sparsity adaptive reconstruction architecture can greatly improve the data reconstruction efficiency of the proposed CS scheme. The simulation results show that the sparsity adaptive scheme can reduce 83.6% of the sampled data. The results illustrate that the proposed architecture needs about 95k gates and operates at 500 MHz to achieve the throughout of 4.0 Gb/s, which can greatly improve the efficiency of the communication system. Meanwhile, it is at least 21.4% more area-efficient compared with previously reported fixed sampling rate architecture based on RS codes.