SparSig Sparse Signal Processing in Wireless Communication

Compressive Sensing Simulation Framework

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The Compressive Sensing Simulation Framework (CSSF) python package features several reconstruction algorithms to be used in compressive sensing signal reconstruction in addition to a general tool-box for evaluating the performance of reconstruction algorithms. The package has been developed as a part of a student project at Aalborg University (AAU) yielding the paper "SURPASSING THE THEORETICAL 1-NORM PHASE TRANSITION IN COMPRESSIVE SENSING BY TUNING THE SMOOTHED L0 ALGORITHM".


Reconstruction of an undersampled signal is at the root of compressive sensing: when is an algorithm capable of reconstructing the signal? what quality is achievable? and how much time does reconstruction require? We have considered the worst-case performance of the smoothed l0 norm reconstruction algorithm in a noiseless setup. Through an empirical tuning of its parameters, we have improved the phase transition (capabilities) of the algorithm for fixed quality and required time. In this paper, we present simulation results that show a phase transition surpassing that of the theoretical l1 approach: the proposed modified algorithm obtains 1-norm phase transition with greatly reduced required computation time.

Paper published at International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013.

The link listed under Download points to a repository which contains both the CSSF python package and the python scripts necessary to reproduce the results presented in the paper. For help see the README files.