Dataset from: 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.

本研究提出了一种基于线性预测的算法，该算法能够实现射频信号的无损及近似无损压缩。所提出的算法与两种信号检测方法相结合，以确定相关信号的存在并依据需求应用不同的损失级别。第一种方法采用频谱感知技术，而第二种方法则利用Levinson-Durbin算法每次迭代中计算出的误差。这些算法已被整合为FAPEC数据压缩器的一个新的预处理阶段，FAPEC最初是为太空任务设计的。我们使用两个不同的数据集对无损算法进行了测试。第一个数据集来自OPS-SAT，一个ESA立方星，而第二个数据集则是在西班牙巴塞罗那使用SDRplay RSPdx获得的。结果表明，我们的方法实现了比gzip（平均而言）高出23%的压缩比率，且速度更快，与FLAC的压缩比率非常接近。我们还使用第二个数据集评估了信号检测器的性能。我们展示了通过无损压缩段，在不包含任何相关信号的情况下，可以实现高比率。