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Compressive Sensing Based Channel Estimator and LDPC Theory for OFDM using SDR

Abstract:

This work proposes the application of a channel estimator based on Compressive Sensing (CS) on a system that employs Orthogonal Frequency Division Multiplexing (OFDM), utilizing Software Defined Radio (SDR) devices. The application of the CS theory is given through the use of sparse reconstruction algorithms such as Orthogonal Matching Pursuit (OMP) and Compressive Sampling Matching Pursuit (CoSaMP), in order to take advantage of the sparse nature of the pilot subcarriers used in OFDM, optimizing the bandwidth of system. In addition, to improve the performance of these algorithms, the concept of sparse parity checking matrix is used, which is implemented in the deployment of low density parity check codes (LDPC) to obtain a sensing matrix that improves the isometric restriction property (IRP) belonging to the CS paradigm. The document shows the model implemented in the SDR equipment, analyzing the bit error rate and the number of pilot symbols used.