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.

Automated summary

We propose an algorithm based on linear prediction for lossless and near-lossless compression of RF signals. Two signal detection methods are used: spectrum sensing and error computation in the Levinson-Durbin algorithm. These algorithms are integrated into FAPEC, a data compressor for space missions. Testing on different datasets shows that our approach achieves better compression ratios than gzip and is comparable to FLAC, but with higher speeds. Performance of our signal detectors is also assessed, demonstrating high compression ratios through lossy compression of irrelevant signal segments.