Context-aware lossless and lossy compression of radio frequency signals


We propose an algorithm based on linear prediction that can perform both the lossless and near-lossless compression of RF signals. The proposed algorithm is coupled with two signal detection methods to determine the presence of relevant signals and apply varying levels of loss as needed. The first method uses spectrum sensing techniques, while the second one takes advantage of the error computed in each iteration of the Levinson–Durbin algorithm. These algorithms have been integrated as a new pre-processing stage into FAPEC, a data compressor first designed for space missions. We test the lossless algorithm using two different datasets. The first one was obtained from OPS-SAT, an ESA CubeSat, while the second one was obtained using a SDRplay RSPdx in Barcelona, Spain. The results show that our approach achieves compression ratios that are 23% better than gzip (on average) and very similar to those of FLAC, but at higher speeds. We also assess the performance of our signal detectors using the second dataset. We show that high ratios can be achieved thanks to the lossy compression of the segments without any relevant signal.This work was (partially) funded by the European Space Agency (ESA) Contract No. 4000137290, the Spanish Ministry of Science and Innovation projects PID2019-105717RB-C22 (RODIN) and PID2021-122842OB-C21, the ERDF (a way of making Europe) by the European Union, the Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia María de Maeztu) through grant CEX2019-000918-M, grant 2021 SGR 1033 by Generalitat de Catalunya (AGAUR), and fellowship FPI-UPC 2022 by Universitat Politècnica de Catalunya and Banc de Santander.Postprint (published version

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