DeepSig’s 2018 Data Set: 2018.01.OSC.0001_1024x2M.h5.tar.gz

DeepSig’s data sets are popular in the machine-learning modulation-recognition community, and in that community there are many claims that the deep neural networks are vastly outperforming any expertly hand-crafted tired old conventional method you care to name (none are usually named though). So I’ve been looking under the hood at these data sets to see what the machine learners think of as high-quality inputs that lead to disruptive upending of the sclerotic mod-rec establishment. In previous posts, I’ve looked at two of the most popular DeepSig data sets from 2016 (here and here). In this post, we’ll look at one more and I will then try to get back to the CSP posts.

Let’s take a look at one more DeepSig data set: 2018.01.OSC.0001_1024x2M.h5.tar.gz.

The data set is from 2018 and is associated with the O’Shea paper The Literature [R137]. It, like the other two I’ve analyzed (here and here), is currently available on the DeepSig website. The data set contains 24 different signals each of which is provided at each of 26 different SNR values. For each signal and SNR combination, there are 4096 instances, and each instance always has length 1024 samples. The data are stored in an HDF5 format, so I used normal HDF5 tools to extract the signals, such as h5dump. I’ll show you below how I learned all these attributes of the data set.

When you unzip and untar the archive, you get three files: LICENSE.TXT, classes.txt, and GOLD_XYZ_OSC.0001_1024.hdf5. The latter is where the data is, and classes.txt looks like this:

The data itself is contained in the GOLD*hdf5 file, which requires a bit of examination to understand, and to attempt to connect to the classes shown in classes.txt.

I used hdf5-file tools available under linux (Ubuntu and Fedora Core in my case) to discover the structure of the data. The first step is to use h5dump to query the file about the data sets it contains. This leads to the following terse output:

So far from the description on DeepSig’s website, and the data description in the associated paper (The Literature [R137]), we know that there are 24 signals, the intended SNR range is dB, and that each individual signal data-record will always be 1024 samples in length, presumably complex-valued samples.

But we don’t know exactly how many of each signal there are nor the signal parameters, such as symbol rate. And, crucially, we don’t yet know which signal type is associated with each data record. So let’s look at the three datasets /X, /Y, and /Z.

The Datasets /X, /Y, and /Z

When we attempt to dump the contents of dataset /X, we get an output with the following header:

The DATASPACE SIMPLE line indicates that the dataset /X is three-dimensional, having 2555904 records in the first dimension, 1024 in the second, and 2 in the third. So we can guess that there are 2555904 total signals, each has 1024 samples, and those samples are complex, so need two values per sample. That explains the three dimensions.

Looking at dataset /Y, we see

So the /Y dataset is two-dimensional, with 2555904 records in the first dimension and 24 in the second. As we can see from the first few records, the 24-element vector for each record is a binary vector with only one value equal to 1 and the rest equal to 0. So this must be the modulation-type indicator. Looking good! Mysterious, and requiring some sleuthing, but good.

Turning to dataset /Z, we see

So /Z is a single vector of length 2555904 and with starting values of -20. If you look at the bottom of the vector, the value is 30, so this is the SNR-parameter dataset. There are 26 distinct SNR parameters, ranging from -20 to +30 in steps of 2. The parameter is held constant for 4096 values, then moves on to the next value. Once it gets to +30, the next value is again -20. The period of this parameter is therefore 4096*26 = 106496. To make this concrete, here are some plots of the SNR parameter:

So it looks like the SNR parameter is held constant for 4096 data records, and the SNR parameter sequence repeats after 4096*26 = 106496 data records, indicating that the signal class is likely held constant for 106496 data records. Turning back to the signal-class indicator dataset /Y, we see that the binary indicator vector switches from a one in the first location to a one in the second location exactly after 106496 data records:

The signal-class indicator dataset /Y isn’t free of mysteries, but almost. There are a few places where the indicator as output by h5dump doesn’t quite make sense:

Presumably the signal-class indicator vector position is a map into the classes.txt set of strings I showed at the top of this post. So if the vector shows a 1 in the first position, then that data record would correspond to the 32PSK signal type. How could we verify that?

Analysis of Extracted Data Records

Let’s try to analyze some of the data records we can extract from the hdf5 file and see if they have characteristics that match the corresponding signal type as determined by the associated position of the 1 in the signal-class vector of dataset /Y.

The main analysis is for a subset of data records for each signal type. I used h5dump to extract one example of each SNR condition for each signal type. Since I don’t really know the signal types in classes.txt conform to the signal-class indicator vector in the archive, I’m just going to refer to each type in terms of the offset into the hdf5 file. We know that each new signal type starts at a data-record offset of , so I’ll refer to the signals in terms of the offset . I use h5dump commands like this:

h5dump -d /X -k 1,1,1 -s $offset,0,0 -S $stride,1,1 -c $num_blocks,1024,2 GOLD_XYZ_OSC.0001_1024.hdf5

where offset is , stride is 4096, and num_blocks is 26. I do this for all 24 offsets (starting with 0). This produces 24 data records each with length 26*1024 = 26624 complex samples.

First let’s look at the modulus of the 24 signals:

Since the SNR parameter increases to the right, but the moduli decrease (generally), the SNRs are achieved by decreasing the noise power, thereby decreasing the total power.

The closest thing to an exactly constant-modulus signal is Offset 21, but Offset 22 is also close to constant compared to the other signals. Offsets 17 and 18 are strange and non-monotonic in the modulus behavior. But not much else is evident. Let’s turn to plots of the real and imaginary parts:

From the real and imaginary plots, we see that most of the Offsets produce approximately zero-mean sequences, but Offsets 0-2, 17, and 18 do not. Finally, let’s look at some power spectra. First I’ll show the PSDs for each signal, taking into account all 26624 samples and using the TSM for spectrum estimation:

The first three Offsets (0, 1, and 2) produce a signal with a QAM/PSK-like spectral shape and also an additive sine-wave component, like what you see for OOK signals. These are followed by 14 PSDs that look like garden-variety PSK/QAM PSDs for a signal with symbol rate of around 1/10. Those are followed by Offsets 17-21, which show signal types that are very nearly sinusoidal (Offsets 17, 18, 21) or periodic (Offsets 19 and 20). Finally, there are two more garden-variety PSK/QAM PSD shapes. Since these PSDs take into account the full range of SNRs (-20 to +30), I’ll show the PSD estimates just for the final subblock (SNR parameter of +30) to get a low-noise look at each signal:

If the mapping between the signal-class strings in classes.txt matched this data, we would expect to see an approximately constant modulus (applied channel effects can ruin the constancy of the modulus) for FM, which would be Offset 3. But the closest to constant modulus is Offset 21. Offset 22 is the next closest. So the mapping is in serious doubt.

If we return to The Literature [R137], and examine the proffered confusion matrices there, we see a different ordering of the signal classes:

This ordering is more consistent with the signal-class indicator vector in dataset /Y. The first signal is OOK, which should have a PSD with a typical QAM/PSK bump and an impulse midband, which it does in Figure 15. The final two signals in the confusion matrix are GMSK and OQPSK, which also have PSK/QAM PSDs and should not have impulses, and that is what we see in the final two PSDs (Offsets 22 and 23) in Figure 15.

Between 8ASK and AM-SSB-WC, there are 14 garden-variety types in the confusion matrix, which is consistent with Figure 15. Finally, the analog signals correspond to Offsets 17-21 in the confusion matrix and those Offsets correspond to the PSDs in Figure 15 that are the most non-PSK/QAM in appearance.

So the mapping provided by DeepSig in classes.txt is incorrect, but a correct one is possibly

To check that the rather severe subsampling of the data records for each modulation offset above didn’t miss anything significant, I extracted every tenth 1024-point subblock from the archive for Offset = 3. Here are the PSDs:

Discussion

The impulses in 4ASK and 8ASK (Offsets 1 and 2) are expected because the constellations were probably all non-negative numbers along the real-axis, which is a typical definition of ASK, so that checks.

The five analog waveforms don’t make sense to me. They are essentially impulsive in the frequency domain, and there doesn’t appear to be much difference between those “with carrier” and those with “suppressed carrier.” In particular, suppressed-carrier signals should not contain impulses in their spectra. It looks like the AM and FM signals are being driven by a sinusoidal message signal (non-random).

Otherwise, this is a much better data set than the other two DeepSig data sets I’ve analyzed (see All BPSK Signals and More on DeepSig’s RML Data Sets).

It still suffers from the “one BPSK signal” flaw, because it looks like the symbol rate and carrier offset never significantly change (see Table I in The Literature [R137]). (Compare this to my Challenge Data Set.)

The data set also suffers from the preoccupation with very short data records. This prevents verification and other kinds of analysis and comparison. If the data records were made longer, presumably a machine learner could still train and test using a subset of each data record (use the first samples of each data record with length ), so there isn’t any disadvantage in making longer records except the size of the archive increases.

As usual, let me know if I’ve erred or if you have a relevant comment in the Comments section below.