I’ve developed another data set for use in assessing modulation-recognition algorithms (machine-learning-based or otherwise) that is more complex than the original sets I posted for the ML Challenge (CSPB.ML.2018 and CSPB.ML.2022). Half of the new dataset consists of one signal in noise and the other half consists of two signals in noise. In most cases the two signals overlap spectrally, which is a signal condition called cochannel interference.
We’ll call it CSPB.ML.2023.
Dataset Description
Let’s start by describing the data set in detail. Then I’ll present my current processing results as a performance target.
I created 60,000 IQ data files that contain a single signal in white Gaussian noise and 60,000 IQ data files that contain two signals in white Gaussian noise. For the single-signal files, the spectral density of the noise is always one (0 dB), and for the two-signal files, it is always two (3 dB). This latter fact arises because the way the two-signal files are created is by adding two of the single-signal files together. Since the noises are always independent file-to-file, the noises add on a power basis, and we get twice the noise spectral density. All data files have length 262,144 samples.
I’m not trying to make these datasets realistic in terms of some specific receiver noise floor. In fact, lots of realistic things are ignored. In my various machine-learner challenge datasets, the idea isn’t to solve the general modulation-recognition problem, it is to explore the strengths and weaknesses of the machine-learning optimization techniques because those strengths and weaknesses are not studied carefully by actual machine learners in favor of just pushing hype and false claims of superiority. (Sorry, but it’s true dudes.)
The dataset employs eight signals, like the original Challenge datasets, but here the excess bandwidth is fixed at 0.35 for the PSK and QAM signals, and is whatever it is for the MSK and GMSK signals. The eight signals are BPSK, QPSK, 8PSK, 16QAM, 64QAM, SQPSK, MSK, and GMSK. The randomized parameters are the symbol rate, carrier frequency offset, and SNR.
The symbol rates range from about 0.1 Hz to 0.6 Hz, the carrier frequency offsets range from -0.2 Hz to 0.2 Hz, and the signal powers range from 2 dB to 20 dB. The distributions of these parameters are not uniform.
The sampling rate is set equal to unity; we (I) don’t care about the physical setup. We are trying to see how well-trained neural networks perform on the dataset and how well they generalize–I’ll be posting a generalized (slightly) dataset eventually.
Internally at the CSP Blog, this dataset is called “PSK Mixtures,” and so the involved data files have names like ‘psk_mixture_1200.tim.’ And if you see the acronym ‘PM’ related to CSPB.ML.2023, it stands for PSK Mixtures. You can read these binary data files into MATLAB using read_binary.m. Even easier, perhaps, is to realize that the data-file format of these .tim files is nearly identical to the Ettus SDR binary float data-file format, with the exception that the first two records of a .tim file are two integers: a 1 or 2 to indicate real or complex data, and the number of samples in the data file.
The original motivation for this dataset comes from a project I was working on a couple years back. In that project, there was an RF situation where multiple cochannel signals could be received and we desired to successfully demodulate one of them in the presence of the other(s). That is, we had to perform signal separation. But to do that, we needed to assess the situation: How many signals are present, what are their parameters, and what are their types? So this is a bit closer to RF Scene Analysis than a typical machine-learning modulation-recognition paper usually gets (they just output labels). But it isn’t a made-up problem or situation, it really happens sometimes.
Single-Signal Truth File (Metadata)
The truth parameters for the single-signal files are contained in a simple text file called PM_single_truth_10000.txt. The first few entries are shown in Figure 1.
There is some redundancy in the file. The first three fields indicate the index of the data file. For Index_1, the corresponding data file in the dataset is psk_mixtures_1.tim. The number after Index_ in the truth files is always the index embedded in the data-file name. The fourth field is the symbol rate, the fifth is the carrier frequency offset, the sixth indicates the modulation variant, the seventh is the modulation type, and the last parameter is the signal power in dB.
Modulation-Type Parameter
The seventh field is the modulation-type parameter, which can be 1 for PSK, 2 for QAM, or 3 for staggered modulation (MSK, GMSK, SQPSK).
Modulation-Variant Parameter
This indicates the variant within the modulation type. For modulation-type 1 (PSK), the value of the modulation-variant parameter is the number of bits per symbol. The same is true for modulation-type 2 (QAM). For modulation-type 3 (staggered), if the variant is 1, the signal is SRRC SQPSK, if the variant is 2, the signal is MSK, and if it is 3, the signal is GMSK.
| Mod- Type | Mod- Variant | Signal |
| 1 | 1 | BPSK |
| 1 | 2 | QPSK (4QAM) |
| 1 | 3 | 8PSK |
| 2 | 2 | 4QAM (QPSK) |
| 2 | 4 | 16QAM |
| 2 | 6 | 64QAM |
| 3 | 1 | SQPSK |
| 3 | 2 | MSK |
| 3 | 3 | GMSK |
Two-Signal Truth File (Metadata)
The two-signal files are created by simply adding together each successive pair of single-signal files. The first two-signal file is for index 60001 (so it would be psk_mixtures_60001.tim) and is just the sum of the single-signal data files corresponding to Index_1 and Index_2 in Figure 1. The metadata for each of the involved signals is copied over to the corresponding line in the two-signal truth file PM_two_truth_10000.txt. See Figure 2 for a snippet of the full file.
To make concrete what kinds of signal scenarios we’re talking about in the PSK Mixtures dataset, I plotted the PSD for the first 100 single-signal files and also the first 100 two-signal files in Video 1. Of particular note is that sometimes the two signals completely overlap spectrally and sometimes they do not overlap at all.
CSP-Based Performance Example
I’ve applied my CSP-based modulation-recognition system (My Papers [25,26,28,43]) to a tenth of the dataset–I processed every tenth file of the 120,000 files. (Actually there are only 119,999 files because there are only 59,999 of the two-signal files.)
There are several ways to assess whether the signal-processing algorithm has detected and successfully processed the signal in the data file. The first is to determine whether the produced symbol rate matches the true one. The produced estimate must be within 
The single-signal results are shown in Figure 3. “Correct Const” means that the modulation-variant parameter estimate matches the truth, and “Correct Mod” means the modulation-type parameter estimate matches the truth. For the SNR to be correct requires it is within 2 dB of the truth.
The two-signal results are shown in Figure 4. Here the problem is much harder, but the performance monotonically improves with increasing processing block length. The achievable performance is not well understood. I made no attempt to limit the range of signal-to-interference (SIR) values experienced by either signal–I just add successive pairs of the single-signal data files together. So that means sometimes there will be a low SIR indeed, when one of the signals has power at the high end of the range (20 dB) and the other at the low end (2 dB).
It would be great to compare machine-learning and CSP processing on this (and all) datasets in a uniform way. In particular, compare the modulation-class label performance AND compare the parameter-estimation performance. That was, in fact, the driving motivation behind the original Challenge dataset, but nobody has yet provided me anything from a neural network except the modulation-class labels. In many real-world applications, though, the parameters that define the signal are also of high interest–not just the label.
The Currently Available Portion of the Dataset
I’ve put the first 10,000 of the 60,000 single-signal files on the CSP Blog in 10 batches of about 2 GB each. I’ve also put the first 10,000 two-signal files on the Blog. This chunking and partial-posting is necessary because WordPress.com limits an individual file to 2 GB, and also if I tried to upload all 120,000 files I’d run out of storage space. I’m at the maximum you can get from WordPress, and I asked specifically for more and said I’d pay, but no luck. Maybe 10,000 of each type of PSK mixture will be enough to get people started?
Comments, errors, compliments, and humor are welcome in the Comments section below. Especially errors!
Great post, Chad! I am currently in the process of figuring out what my class project(s) for school will be this semester; maybe I’ll propose an algorithm or model to challenge your results in this post 🙂 Do you plan to release a data file withholding the labels for people to submit their results to you? I can only imagine that is a heavy lift on your part.
It’s funny, I have a MATLAB function that reads in your other datasets but it also will create co-channel examples in the exact way that you mention; it will simply average two other examples in the dataset and knock the SNR labels down by 3 dB. I average rather than sum to keep the noise floor at 0 dB.
I suppose while I’m on that topic, I did notice that in your 2022 dataset, the examples where the base symbol period is 1 have no visible second-order cycle feature. It looks like you resampled with a very sharp filter, so I am thinking they are effectively duobinary signaling. If so, they still should have higher-order cycle features but I haven’t looked yet.
As for your comments on storage space, have you considered Google Drive or Microsoft OneDrive? They are something like $2/month for 1 TB of cloud storage. I use OneDrive for personal use and love it.
My plan is to generate a different, but related, dataset for the purpose of testing the accuracy and generalization capabilities of networks trained on CSPB.ML.2023. That dataset will be similar in structure, but the random variables involved in the signal models will be different, just as I did in CSPB.ML.2022. The performance of my CSP algorithm will be similar because it features high generalization, but who knows about the neural-network solutions.
I also posted only a small fraction of the total number of files associated with CSPB.ML.2023, so I could post some of the remaining and withhold labels. That might be a reasonable generalization test, because there is a lot more variability in the two-signal situation so that the range of scene instances already posted might not overlap all that much with the reserved instances…
Yes, good catch! The number of instances like that is tiny, but most of those instances will foil the algorithm I apply because there are no features for such signals. The BPSK signal, though, does still show the conjugate doubled-carrier feature. But, yeah, I shouldn’t have included the case of a symbol period of one.
I did look into Google Drive, but I must have messed up because the prices I saw were much higher–I’d better check again. Thanks much!
The prices for Google Drive are an order of magnitude higher: for 2$/month you only get 100GB. (I have a yearly plan for 21$ that gets me 100GB). Quick google told me, that if you go with OneDrive business, you can get 1TB for 5$/month, personal is a little more at 7$/month, but you also get all the other Office things, which might or might not be useful.
But specifically for hosting datasets for ML, maybe huggingface will serve your needs? I don’t think they have a limit for free tiers. https://discuss.huggingface.co/t/is-there-a-size-limit-for-dataset-hosting/14861/3