The CSP Blog is rolling along here in 2023!
March 2023 broke a record for pageviews in a calendar month with over 7,000 as of this writing early in the day on March 31.
Let’s note some other milestones and introduce a poll.
Continue reading “Blog Notes and Reader Poll”Danger Will Robinson! Non-technical post approaching!
When I was a wee engineer, I’d sometimes clash with other engineers that sneered at technical approaches that didn’t set up a linear-algebraic optimization problem as the first step. Never mind that I’ve been relentlessly focused on single-sensor problems, rather than array-processing problems, and so the naturalness of the linear-algebraic mathematical setting was debatable–however there were still ways to fashion matrices and compute those lovely eigenvalues. The real issue wasn’t the dimensionality of the data model, it was that I didn’t have a handy crank I could turn and pop out a provably optimal solution to the posed problem. Therefore I could be safely ignored. And if nobody could actually write down an optimization problem for, say, general radio-frequency scene analysis, then that problem just wasn’t worth pursuing.
Those critical engineers worship at the altar of optimality. Time for another rant.
Continue reading “The Altar of Optimality”Sometimes MATLAB’s resample.m gives results that can be trouble for subsequent CSP.
Previous SPTK Post: Echo Detection Next SPTK Post: TBD
In this brief Signal Processing Toolkit note, I warn you about relying on resample.m to increase the sampling rate of your data. It works fine a lot of the time, but when the signal has significant energy near the band edges, it does not.
Continue reading “SPTK Addendum: Problems with resampling using MATLAB’s resample.m”By the pricking of my thumbs, something wicked this way comes …
I attended a conference on dynamic spectrum access in 2017 and participated in a session on automatic modulation recognition. The session was connected to a live competition within the conference where participants would attempt to apply their modulation-recognition system to signals transmitted in the conference center by the conference organizers. Like a grand modulation-recognition challenge but confined to the temporal, spectral, and spatial constraints imposed by the short-duration conference.
What I didn’t know going in was the level of frustration on the part of the machine-learner organizers regarding the seemingly inability of signal-processing and machine-learning researchers to solve the radio-frequency scene analysis problem once and for all. The basic attitude was ‘if the image-processors can have the AlexNet image-recognition solution, and thereby abandon their decades-long attempt at developing serious mathematics-based image-processing theory and practice, why haven’t we solved the RFSA problem yet?’
Continue reading “Is Radio-Frequency Scene Analysis a Wicked Problem?”CSP can be used to separate cochannel contemporaneous signals. The involved signal-processing structure is linear but periodically time-varying.
In most of the posts on the CSP Blog we’ve applied the theory and tools of CSP to parameter estimation of one sort or another: cycle-frequency estimation, time-delay estimation, synchronization-parameter estimation, and of course estimation of the spectral correlation, spectral coherence, cyclic cumulant, and cyclic polyspectral functions.
In this post, we’ll switch gears a bit and look at the problem of waveform estimation. This comes up in two situations for me: single-sensor processing and array (multi-sensor) processing. At some point, I’ll write a post on array processing for waveform estimation (using, say, the SCORE algorithm The Literature [R102]), but here we restrict our attention to the case of waveform estimation using only a single sensor (a single antenna connected to a single receiver). We just have one observed sampled waveform to work with. There are also waveform estimation methods that are multi-sensor but not typically referred to as array processing, such as the blind source separation problem in acoustic scene analysis, which is often solved by principal component analysis (PCA), independent component analysis (ICA), and their variants.
The signal model consists of the noisy sum of two or more modulated waveforms that overlap in both time and frequency. If the signals do not overlap in time, then we can separate them by time gating, and if they do not overlap in frequency, we can separate them using linear time-invariant systems (filters).
Relevant FRESH filtering publications include My Papers [45, 46] and The Literature [R6].
Continue reading “Frequency Shift (FRESH) Filtering for Single-Sensor Cochannel Signal Separation”The next step in dataset complexity at the CSP Blog: cochannel signals.
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.
Continue reading “PSK/QAM Cochannel Data Set for Modulation Recognition Researchers [CSPB.ML.2023]”Am I out of a job?
Update January 31, 2023: I’ve added numbers in square brackets next to the worst of the wrong things. I’ll document the errors at the bottom of the post.
Of course I have to see what ChatGPT has to say about CSP. Including definitions, which I don’t expect it to get too wrong, and code for estimators, which I expect it to get very wrong.
Let’s take a look.
Continue reading “ChatGPT and CSP”Let’s apply some of our Signal Processing ToolKit tools to a problem in forensic signal processing!
Previous SPTK Post: The Sampling Theorem Next SPTK Post: Resampling in MATLAB
No, not that prisoner’s dilemma. The dilemma of a prisoner that claims, steadfastly, innocence. Even in the face of strong evidence and a fair jury trial.
In this Signal Processing ToolKit cul-de-sac of a post, we’ll look into a signal-processing adventure involving a digital sting recording and a claim of evidence tampering. We’ll be able to use some of our SPTK tools to investigate a real-world data record that might, just might, have been tampered with. (But most probably not!)
Continue reading “SPTK: Echo Detection and the Prisoner’s Dilemma”How can we train a neural network to make use of both IQ data samples and CSP features in the context of weak-signal detection?
I’ve been working with some colleagues at Northeastern University (NEU) in Boston, MA, on ways to combine CSP with machine learning. The work I’m doing with Old Dominion University is focused on basic modulation recognition using neural networks and, in particular, the generalization (dataset-shift) problem that is pervasive in deep learning with convolution neural networks. In contrast, the NEU work is focused on specific signal detection and classification problems and looks at how to use multiple disparate data types as inputs to neural-networks; inputs such as complex-valued samples (IQ data) as well as carefully selected components of spectral correlation and spectral coherence surfaces.
My NEU colleagues and I will be publishing a rather lengthy conference paper on a new multi-input-data neural-network approach called ICARUS at InfoCom 2023 this May (My Papers [53]). You can get a copy of the pre-publication version here or on arxiv.org.
Continue reading “ICARUS: More on Attempts to Merge IQ Data with Extracted-Feature Data in Machine Learning”The CSP Blog took a big step forward in 2022, with 66,700 67,965 page views and counting, which is 10,000 12,000 more than last year’s (record) number of about 56,000. Thanks to all my readers!
The CSP Blog recently received a comment from a signal processor that needed a small amount of debugging help with their python spectral correlation estimator code.
The code uses a form of the time-smoothing method and aims to compute and plot the spectral correlation estimate as well as the corresponding coherence estimate. What is cool about this code is that it is clear, well-organized, on github, and is written using Jupyter Notebook. Moreover, there is a Google Colab function so that anyone can run the code from a chrome browser and see the results, even a python newbie like me. Tres moderne.
Continue reading “CSP Community Spotlight: A Publicly Available python-Based SCF Estimator”It’s too close to home, and it’s too near the bone …
Park the car at the side of the road
“That Joke Isn’t Funny Anymore” by The Smiths
You should know
Time’s tide will smother you…
And I will too
I applaud the intent behind the paper in this post’s title, which is The Literature [R183], apparently accepted in 2022 for publication in IEEE Access, a peer-reviewed journal. That intent is to list all the found ways in which researchers preprocess radio-frequency data (complex sampled data) prior to applying some sort of modulation classification (recognition) algorithm or system.
The problem is that this attempt at gathering up all of the ‘representations’ gets a lot of the math wrong, and so has a high potential to confuse rather than illuminate.
There’s only one thing to do: correct the record.
Continue reading “Correcting the Record: Comments On “Wireless Signal Representation Techniques for Automatic Modulation Classification,” by X. Liu et al”Neural networks with CSP-feature inputs DO generalize in the modulation-recognition problem setting.
In some recently published papers (My Papers [50,51]), my ODU colleagues and I showed that convolutional neural networks and capsule networks do not generalize well when their inputs are complex-valued data samples, commonly referred to as simply IQ samples, or as raw IQ samples by machine learners.(Unclear why the adjective ‘raw’ is often used as it adds nothing to the meaning. If I just say Hey, pass me those IQ samples, would ya?, do you think maybe he means the processed ones? How about raw-I-mean–seriously-man–I-did-not-touch-those-numbers-OK? IQ samples? All-natural vegan unprocessed no-GMO organic IQ samples? Uncooked IQ samples?) Moreover, the capsule networks typically outperform the convolutional networks.
In a new paper (MILCOM 2022: My Papers [52]; arxiv.org version), my colleagues and I continue this line of research by including cyclic cumulants as the inputs to convolutional and capsule networks. We find that capsule networks outperform convolutional networks and that convolutional networks trained on cyclic cumulants outperform convolutional networks trained on IQ samples. We also find that both convolutional and capsule networks trained on cyclic cumulants generalize perfectly well between datasets that have different (disjoint) probability density functions governing their carrier frequency offset parameters.
That is, convolutional networks do better recognition with cyclic cumulants and generalize very well with cyclic cumulants.
So why don’t neural networks ever ‘learn’ cyclic cumulants with IQ data at the input?
The majority of the software and analysis work is performed by the first author, John Snoap, with an assist on capsule networks by James Latshaw. I created the datasets we used (available here on the CSP Blog [see below]) and helped with the blind parameter estimation. Professor Popescu guided us all and contributed substantially to the writing.
Continue reading “Neural Networks for Modulation Recognition: IQ-Input Networks Do Not Generalize, but Cyclic-Cumulant-Input Networks Generalize Very Well”And now for something completely different …
Let’s take an excursion outside of “Understanding and Using the Statistics of Communication Signals” by looking at a naturally occurring signal: the human genome.
Continue reading “Desultory CSP: The Human-Genome Edition”“That was excellently observed,” say I, when I read a passage in an author, where his opinion agrees with mine. When we differ, there I pronounce him to be mistaken.
– Jonathan Swift
Update November 2022: Added Professor Dave and Michael Woudenberg. (How could I have forgotten Dave in the first post??)
***
A big part of the CSP Blog in the past couple years has been a critical analysis of relevant engineering literature. By ‘relevant’ I mean relevant to CSP and its main applications of presence detection, modulation recognition, parameter estimation, source separation, and array processing. So I’ve produced many ‘Comments On …’ posts lately, and this tends to solidify my reputation as a critic rather than as a creative engineer. However, the CSP and SPTK posts on the CSP Blog still vastly outnumber the ‘Comments On …’ posts. We’ll see what balance the future brings.
But if you like to see critical reviews of current science, technology, and engineering work, there are others out there doing it much better than me and doing it for much more important topics, such as artificial intelligence, particle physics, cosmology, and social media.
So here in this post I want to introduce you, should you care to view the rest of the post, to other critics that you might enjoy, and might use as a balance against the typically credulous mainstream media and those pundits, bloggers, and YouTubers that do more promoting than analyzing.
Continue reading “Critic and Skeptic Roundup”Another brick in the wall, another drop in the bucket, another windmill on the horizon …
Let’s talk more about The Cult. No, I don’t mean She Sells Sanctuary, for which I do have considerable nostalgic fondness. I mean the Cult(ure) of Machine Learning in RF communications and signal processing. Or perhaps it is more of an epistemic bubble where there are The Things That Must Be Said and The Unmentionables in every paper and a style of research that is strictly adhered to but that, sadly, produces mostly error and promotes mostly hype. So we have shibboleths, taboos, and norms to deal with inside the bubble.
Time to get on my high horse. She’s a good horse named Ravager and she needs some exercise. So I’m going to strap on my claymore, mount Ravager, and go for a ride. Or am I merely tilting at windmills?
Let’s take a close look at another paper on machine learning for modulation recognition. It uses, uncritically, the DeepSig RML 2016 datasets. And the world and the world, the world drags me down…
Continue reading “Epistemic Bubbles: Comments on “Modulation Recognition Using Signal Enhancement and Multi-Stage Attention Mechanism” by Lin, Zeng, and Gong.”Introducing swag for the best CSP-Blog commenters.
Update January 2023: You can find the list of winners on this page.
The comments that CSP Blog readers have made over the past six years are arguably the most helpful part of the Blog for do-it-yourself CSP practitioners. In those comments, my many errors have been revealed, which then has permitted me to attempt post corrections. Many unclear aspects of a post have been clarified after pondering a reader’s comment. At least one comment has been elevated to a post of its own.
The readership of the CSP Blog has been steadily growing since its inception in 2015, but the ratio of page views to comments remains huge–the vast majority of readers do not comment. This is understandable and perfectly acceptable. I rarely comment on any of the science and engineering blogs that I frequent. Nevertheless, I would like to encourage more commenting and also reward it.
Continue reading “‘Comment of the Month’ on the CSP Blog”Starts as a personal gripe, but ends with weird stuff from the literature.
During my poking around on arxiv.org the other day (Grrrrr…), I came across some postings by O’Shea et al I’d not seen before, including The Literature [R176]: “Wideband Signal Localization and Spectral Segmentation.”
Huh, I thought, they are probably trying to train a neural network to do automatic spectral segmentation that is superior to my published algorithm (My Papers [32]). Yeah, no. I mean yes to a machine, no to nods to me. Let’s take a look.
Continue reading “What is the Minimum Effort Required to Find ‘Related Work?’: Comments on Some Spectrum-Sensing Literature by N. West [R176] and T. Yucek [R178]”May 2022 saw 6026 page views at the CSP Blog, a new monthly record!
Thanks so much to all my readers, new and old, signal processors and machine learners, commenters and lurkers.
My next non-ranty post is on frequency-shift (FRESH) filtering. I will go over cyclic Wiener filtering (The Literature [R6]), which is optimal FRESH filtering, and then describe some interesting puzzles and problems with CW filtering, which may form the seeds of some solid signal-processing research projects of the academic sort.
Black-box thinking is degrading our ability to connect effects to causes.
I’m learning, slowly because I’m stubborn and (I know it is hard to believe) optimistic, that there is no bottom. Signal processing and communications theory and practice are being steadily degraded in the world’s best (and worst of course) peer-reviewed journals.
I saw the accepted paper in the post title (The Literature [R177]) and thought this could be better than most of the machine-learning modulation-recognition papers I’ve reviewed. It takes a little more effort to properly understand and generate direct-sequence spread-spectrum (DSSS) signals, and the authors will likely focus on the practical case where the inband SNR is low. Plus there are lots of connections to CSP. But no. Let’s take a look.
Continue reading “Elegy for a Dying Field: Comments on “Detection of Direct Sequence Spread Spectrum Signals Based on Deep Learning,” by F. Wei et al”