Dataset from: Context-Aware Lossless and Lossy Compression of Radio Frequency Signals

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Submitted by:
Aniol Marti
Last updated:
Tue, 03/28/2023 - 17:14
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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.


The dataset contains a ZIP file with AM signals from the Medium Wave Broadcast Band (526.5 - 1606.5 kHz) sampled at 15.625 kHz, another ZIP with APRS signals (144.8 MHz) also sampled at 15.625 kHz and 3 more ZIP files with signals captured with OPS-SAT at 433 MHz, 1575.42 MHz and 1602.56 MHz, sampled at 3 MHz.

Funding Agency: 
European Space Agency (ESA); Spanish Ministry of Science and Innovation; Generalitat de Catalunya (AGAUR); Universitat Politècnica de Catalunya; Banc de Santander
Grant Number: 
4000137290; PID2019-105717RB-C22 (RODIN), PID2021-122842OB-C21; 2021 SGR 1033; Fellowship FPI-UPC 2022