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

We propose an algorithm based on linear prediction to perform lossless (or near-lossless) compression of RF signals. In addition, we present two methods to detect the presence of relevant signals and apply different loss levels based on the former detection. The first method uses spectrum sensing techniques and the second one takes advantage of the error computed in each iteration of the Levinson-Durbin algorithm. These algorithms are integrated as a new pre-processing stage into FAPEC, a data compressor originally designed for space missions. Here, we test the lossless algorithm with two different datasets: the first one was obtained with OPS-SAT, an ESA CubeSat, and the second one was obtained with a SDRplay RSPdx in Barcelona, Spain. We show that our approach achieves good compression ratios at high-speeds and we compare it with other lossless compressors. Lastly, we assess the performance of the detectors with the second dataset and we show the high compression ratios achieved thanks to the lossy compression of the segments without a relevant signal.