Resumen

YANZA-VERDUGO, Anthony; PUCHA-CABRERA, Christian  y  ORTEGA, Juan Inga. Compressive Sensing Based Channel Estimator and LDPC Theory for OFDM using SDR. Ingenius [online]. 2020, n.23, pp.74-85. ISSN 1390-860X.  https://doi.org/10.17163/ings.n23.2020.07.

This work proposes the application of a channel estimator based on Compressive Sensing (CS) over a system that uses Orthogonal Frequency Division Multiplexing (OFDM) using 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 sparse parity checking matrix concept 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 (RIP) belonging to the CS paradigm. The document shows the model implemented in the SDR equipment and analyze the bit error rate and the number of pilot symbols used.

Palabras clave : Channel Estimation; LDPC; OFDM; SDR; Compressive Sensing.

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