Dataset for the Machine-Learning Challenge [CSPB.ML.2018]

Update February 2023: I’ve posted a third challenge dataset here. It is CSPB.ML.2023 and features cochannel signals.

Update April 2022. I’ve also posted a second dataset here. This new dataset is similar to the original ML Challenge dataset except the random variable representing the carrier frequency offset has a slightly different distribution.

If you refer to either of the posted datasets in a published paper, please use the following designators, which I am also using in papers I’m attempting to publish:

Original ML Challenge Dataset: CSPB.ML.2018.

Shifted ML Challenge Dataset: CSPB.ML.2022.

Update September 2020. I made a mistake when I created the signal-parameter “truth” files signal_record.txt and signal_record_first_20000.txt. Like the DeepSig RML datasets that I analyzed on the CSP Blog here and here, the SNR parameter in the truth files did not match the actual SNR of the signals in the data files. I’ve updated the truth files and the links below. You can still use the original files for all other signal parameters, but the SNR parameter was in error.

Update July 2020. I originally posted signals in the posted dataset. I’ve now added another for a total of signals. The original signals are contained in Batches 1-5, the additional signals in Batches 6-28. I’ve placed these additional Batches at the end of the post to preserve the original post’s content.

Overview of Dataset

The signals are stored in five zip files, each containing individual signal files:

Batch 1

Batch 2

Batch 3

Batch 4

Batch 5

The zip files are each about 1 GB in size.

The modulation-type labels for the signals, such as “BPSK” or “MSK,” are contained in the text file:

signal_record_first_20000.txt

Each signal file is stored in a binary format involving interleaved real and imaginary parts, which I call ‘.tim’ files. You can read a .tim file into MATLAB using read_binary.m. Or use the code inside read_binary.m to write your own data-reader; the format is quite simple.

The Label and Parameter File

Let’s look at the format of the truth/label file. The first line of signal_record_first_20000.txt is

1 bpsk  11  -7.4433467080e-04  9.8977795076e-01  10  9  5.4532617590e+00  0.0

which comprises fields. All temporal and spectral parameters (times and frequencies) are normalized with respect to the sampling rate. In other words, the sampling rate can be taken to be unity in this dataset. These fields are described in the following list:

1. Signal index. In the case above this is `1′ and that means the file containing the signal is called signal_1.tim. In general, the th signal is contained in the file signal_n.tim. The Batch 1 zip file contains signal_1.tim through signal_4000.tim.
2. Signal type. A string indicating the modulation format of the signal in the file. For this dataset, I’ve only got eight modulation types: BPSK, QPSK, 8PSK, -DQPSK, 16QAM, 64QAM, 256QAM, and MSK. These are denoted by the strings bpsk, qpsk, 8psk, dqpsk, 16qam, 64qam, 256qam, and msk, respectively.
3. Base symbol period. In the example above (line one of the truth file), the base symbol period is .
4. Carrier offset. In this case, it is .
5. Excess bandwidth. The excess bandwidth parameter, or square-root raised-cosine roll-off parameter, applies to all of the signal types except MSK. Here it is . It can be any real number between and .
6. Upsample factor. The sixth field is an upsampling parameter U.
7. Downsample factor. The seventh field is a downsampling parameter D. The actual symbol rate of the signal in the file is computed from the base symbol period, upsample factor, and downsample factor: . So the BPSK signal in signal_1.tim has rate . If the downsample factor is zero in the truth-parameters file, no resampling was done to the signal.
8. Inband SNR (dB). The ratio of the signal power to the noise power within the signal’s bandwidth, taking into account the signal type and the excess bandwidth parameter.
9. Noise spectral density (dB). It is always dB. So the various SNRs are generated by varying the signal power.

To ensure that you have correctly downloaded and interpreted my data files, I’m going to provide some PSD plots and a couple of the actual sample values for a couple of the files.

signal_1.tim

The line from the truth file is:

1 bpsk  11  -7.4433467080e-04  9.8977795076e-01  10  9  5.4532617590e+00  0.0

The first ten samples of the file are:

-5.703014e-02   -6.163056e-01
-1.285231e-01   -6.318392e-01
6.664069e-01    -7.007506e-02
7.731103e-01    -1.164615e+00
3.502680e-01    -1.097872e+00
7.825349e-01    -3.721564e-01
1.094809e+00    -3.123962e-01
4.146149e-01    -5.890701e-01
1.444665e+00    7.358724e-01
-2.217039e-01   -1.305001e+00

An FSM-based PSD estimate for signal_1.tim is:

And the blindly estimated cycle frequencies (using the SSCA) are:

The previous plot corresponds to the numerical values:

Non-conjugate :

8.181762695e-02  7.480e-01  5.406e+00

Conjugate :

8.032470942e-02  7.800e-01  4.978e+00
-1.493096002e-03  8.576e-01  1.098e+01
-8.331298083e-02  7.090e-01  5.039e+00

signal_4000.tim

The line from the truth file is

4000 256qam  9  8.3914849139e-04  7.2367959637e-01  9  8  1.0566301192e+01  0.0

which means the symbol rate is given by . The carrier offset is and the excess bandwidth is . Because the signal type is 256QAM, it has a single (non-zero) non-conjugate cycle frequency of and no conjugate cycle frequencies. But the square of the signal has cycle frequencies related to the quadrupled carrier:

Final Thoughts

Is waveforms a large enough dataset? Maybe not. I have generated tens of thousands more, but will not post until there is a good reason to do so. And that, my friends, is up to you!

That’s about it. I think that gives you enough information to ensure that you’ve interpreted the data and the labels correctly. What remains is experimentation, machine-learning or otherwise I suppose. Please get back to me and the readers of the CSP Blog with any interesting results using the Comments section of this post or the Challenge post.

For my analysis of a commonly used machine-learning modulation-recognition dataset (RML), see the All BPSK Signals post. I also analyze two other datasets from the RML authors (DeepSig Inc.) here and here.

Additional Batches of Signals:

Batch 6

Batch 7

Batch 8

Batch 9

Batch 10

Batch 11

Batch 12

Batch 13

Batch 14

Batch 15

Batch 16

Batch 17

Batch 18

Batch 19

Batch 20

Batch 21

Batch 22

Batch 23

Batch 24

Batch 25

Batch 26

Batch 27

Batch 28

Signal parameters text file