CW AI Operator – Can a Machine Run a Morse Code Contest? How Ready Are We for This?

CW AI Operator – Can a Machine Run a Morse Code Contest? How Ready Are We for This?


https://ibb.co/ch2r46z6

Can an AI operate in a real CW contest? Could it listen, decode, respond, and log QSOs – all by itself?

The answer is: yes — under certain conditions.

This article explains how such a system could be built, what’s required technically and legally, and how an amateur radio operator could have an AI “operator” running CW from their station.

Objective
To create a fully automated AI-based CW contest station, which can:

• Operate under a valid amateur radio callsign
  
  
• Listen to real-time CW signals
  
  
• Detect CQ calls and decide when to respond
  
  
• Transmit replies autonomously
  
  
• Log QSOs accurately in Cabrillo or ADIF
  
  
• Participate in contests as a real-time operator

✅ Is It Legal?
Yes, as long as a licensed human operator is responsible for the station. The AI acts as an assistant or tool — much like FT8 software or SDR automation.

✔️ Allowed:

• Use of automation and AI logic
  
  
• CW transmission through keying interface
  
  
• Autonomous logging and decision-making

❌ Not Allowed:

• Transmission without a licensed operator overseeing the station
  
  
• Unsupervised autonomous RF operation representing a callsign

What You Need to Build It
1. RF Interface

• SDR (e.g. AirSpy, HackRF, Hermes Lite) or regular transceiver (ICOM, Elecraft)
  
  
• Audio IN/OUT or IQ stream access
  
  
• Keying interface (USB, serial, or GPIO)

2. CW Decoder / Encoder

• CW decoder software (morse2txt, fldigi) or AI-based decoder using FFT
  
  
• CW keyer (cwdaemon, rigctld, Arduino-based)
  
  
• Text-to-CW generator (for transmitting responses)

3. AI Decision Engine

• Determines when to call CQ or respond
  
  
• Generates context-aware CW replies (e.g., “DE SV1XXX 599 20 TU”)
  
  
• Follows contest rules (zones, dupes, exchanges)
  
  
• Tracks scoring and band strategy

4. Logging & Scoring

• Real-time logging to .cabrillo or .adif
  
  
• Match logs to contest rules and multipliers
  
  
• QSO parsing and time tracking

5. Computer / Controller

• Raspberry Pi 4, Intel NUC, or laptop
  
  
• Linux OS recommended (Debian/Ubuntu)
  
  
• Headless operation possible for remote control

How It Works – Signal Flow

1. Band monitoring via audio or SDR input
   
   
2. Detects CQ or signal patterns (“CQ TEST DE W1AW”)
   
   
3. Decides to respond or call CQ (based on contest logic)
   
   
4. Transmits CW response using keyer or SDR modulator
   
   
5. Listens for reply and decodes signal
   
   
6. Logs QSO and updates status
   
   
7. Repeats cycle

Development Phases
Phase 1: Offline Simulation

• CW WAV files for decoding practice
  
  
• AI responds with CW replies (text-only)
  
  
• Logging in .csv or .txt

Phase 2: Live Reception

• SDR-based real-time audio input
  
  
• Live decoding using AI or conventional decoders
  
  
• Reactive logic and strategy

Phase 3: Full Operation

• Transceiver or SDR interface for TX/RX
  
  
• Autonomous participation in contests
  
  
• Supervised operation with legal callsign

Suggested Software Tools
Tool Function
fldigi CW decoding/transmitting
cwdaemon Keying and PTT control
morse2txt Lightweight CW decoder
SoapySDR, GQRX SDR interface
Python + AI logic QSO detection & decisions
Custom logger Cabrillo/ADIF output

 

I do have to ask - outside of the personal satisfaction building something that works, why would you do this?

Ham radio is an intensely human proposition, for a hobby, a way to increase one's technical knowledge or just a way to 'meet' folks. None of which your robot CW machine will accomplish.

Anyway - good luck on your project.

 

Thank you for your comment! To clarify — this is not my personal project; it was a response generated during a conversation with ChatGPT. The topic was whether it is technically and practically feasible to automate a CW Operator using AI, as part of a project titled "CW AI Operator – Can a Machine Run a Morse Code Contest? How Ready Are We for This?"

I completely agree that the human aspect of ham radio is unique and cannot be replaced. However, I find it interesting to explore the technological boundaries—not to replace human contact but to better understand how far technology can go and how it might support or evolve our hobby.

Given the rapid advancement of technology and AI itself, it is almost certain that projects like this will become a reality sooner rather than later.

Again, thanks for your thoughtful insights — it’s great to have meaningful dialogue on these topics. 73!

 

I don't use Morse Code copiers other than my head, but the problem I see is none that I have played with are capable of accurately copying Morse code except for brief periods. Add in QRM from a crowded contest band and acknowledging that the human brain seems much more capable of ignoring stations OTHER THAN the station one wants to copy, seems to me the greatest challenge is being able to accurately copy. Witness the current state of hams using "code copiers" to try and work a rare DX station, for instance. It is fraught with errors from simply copying a call wrong to not understanding "up 1" or "EU only."

I can automate FT8 right now to work stations 24/7 if I wanted to. If everyone used computer generated CW, it would help the ability for AI to copy but that's not likely to happen. Factor in that interesting "fist" with a swing and I think the current state of affairs would make it quite difficult, though not entirely impossible. *IF* every station was run by AI and computer generated CW, that would ease some of the problems, but what's the fun in that??

Always interesting to think about.

...............Bob

 

I completely agree, Bob — CW recognition under real contest conditions is probably the biggest challenge of the whole endeavor. That’s exactly where the more “experimental” part focuses: how can AI approach the capabilities of the human ear/brain?

Traditional decoding using FFT and threshold logic has clear limitations. But where it starts to get interesting is with AI-based pattern recognition (e.g., RNNs or transformers trained on real-world noisy CW), where the machine doesn’t just “look” at signals, but learns patterns of human style.

And of course — as you say — it doesn’t make sense to automate 100% if the magic is lost. The project isn’t meant to “replace” the operator, but to experiment with the limits of technology and learn something new.

Perhaps the goal isn’t to build the “perfect CW bot,” but to discover exactly where the boundary lies between human and machine listener.
Building an autonomous AI-based CW contest operator comes with several well-known challenges — but modern machine learning offers promising solutions. Many of the limitations of traditional CW decoders can be mitigated or even fully overcome with AI techniques.

1. Decoding in QRM / QRN / QRZ Conditions
✔️ Solvable with AI

Deep learning models (e.g., CNNs on spectrograms or transformer-based architectures) can recognize CW patterns even under heavy noise, far better than threshold-based decoders. Training with real-world, noisy data is crucial for robust performance.

2. Selective Attention to the Right Station
Partially solvable

The human brain’s ability to focus on one signal among many is hard to replicate. However, AI-based source separation and spectral attention filtering can help isolate the signal of interest, especially in SDR environments with multi-channel analysis.

3. Variability in "Fist" and Timing Irregularities
✔️ Solvable

Recurrent neural networks (RNNs), LSTMs, and transformers can model timing patterns and adapt to irregular sending styles. Unlike rigid decoders, AI systems can generalize and track different operator rhythms.

4. Wide Variation in Human Keying Styles
✔️ Handled with Adaptive Learning

AI systems can learn and adapt to diverse keying behaviors over time. Using online learning or real-time model adjustment, the decoder improves through experience, reducing error rates across varying operator habits.

Bottom Line: While the human ear still sets the gold standard, modern AI brings us closer than ever to replicating — and in some cases exceeding — human-level decoding performance in real CW operating conditions.

 

As long as your automating one side of the QSO, just automate both sides. If the code is machine generated, it should be easy to decode.

This could be way to get techs on HF with no effort. You'll need a webpage to keep scores tho. Without some form of competition, interest will fall off.

 

Contesting might end up like chess for a while-- no one can beat the computer. Nonetheless, chess is thriving, and so will contesting.

Those buying the barrel, the fish, the water, and the ammo to shoot fish in a barrel (forgive me Dave) will likely be shunned by the remaining humans that didn't buy all the gear/computer time, to win the plaque.

Indeed contesting rules might be changed to prohibit such assistance. Let the howling begin.

73 Tom W9YW

 

I find it interesting — and a bit puzzling — how some die-hard CW enthusiasts feel genuinely threatened by the idea of AI copying or sending Morse.
But is it really about replacing the human ear and hand? Or is it more about the fear that a machine might someday do it better?
This kind of project could actually make a fantastic university thesis — a way to explore the limits of machine learning in a domain that has always been deeply human.

 

Blatant Trolls noted.

It's not about "fear" of anything.

I get that many folks are all about piling up the bean-counts and "winning" and will do whatever it takes, even to the point of taking the amateur operator out of amateur radio. If that makes them feel good, then fine, go for it. Myself, I just don't get it.

I have no doubt that a machine could "beat" me in a bean-counting contest. A lot of (probably most) human operators beat me all the time, even without AI and machines, but that doesn't "threaten" me. It's just a fact of life.

Not all of us are all about "winning". We do things we enjoy and get some personal satisfaction from. I would prefer that the other side of my QSOs and contacts were real people not AIs, but if that's all that's left on the air someday, I guess I'll have to deal with it or find a different hobby.

 

There is a fraternity of CW operators who have a nearly-religious belief that code shall only be copied by a human by ear. Anything else is perceived as crashing the fraternity without having paid your dues by learning to copy code by ear.

That being said...it's a fascinating project. If I had the skills and the time, I'd be tempted to try something like it myself.

I can offer a couple of points that you might want to consider in addition to those you've laid out:

First, you have a hurdle to overcome in that the current generation of code readers are lacking in their accuracy, and they are a little slow to actually process and decode received code. Someone relying on those code readers to copy code cannot run competitively due to the latency destroying rate, and inaccuracy destroying your score. My very first CW contest, when I was seeking instant gratification, relied on use of a code reader. My log check report was miserable....but I had a blast and stuck with it. This is solvable, but you will inherently be building a better code reader along the way.

Second, consider including Morserunner (either the standalone version or the one built into N1MM) as part of the training process. It's a CW contest simulator that, properly configured, will simulate a variety of human behaviors (including LIDs, QRM, and calling stations having trouble copying your transmission), and make use of real contesters' calls. However, while it does a good job simulating a contest...be careful that you don't over-train the AI on MorseRunner.

Third, be aware that a key to winning single-operator contests these days is mastering the art of SO2R and of 2BSIQ. You may wish to add that to your project plan.

Fourth, you might consider whether to train the AI to make use of spotting information for real-time decision-making, or to make use of public logs to analyze operating strategies from different locations. Successful human contesters do this.

Finally, you might consider a side project of a RTTY or FT* contest bot. Decoding signals with these modes is MUCH easier, and you'd be able to train the decision-making side of the AI while you work through the challenges of copying code in a contest environment. (Conceivably, you could also extend this to an SSB contesting bot, although you'd add in the challenges of generating speech and processing received speech in contest conditions.)

 

This isn't about winning a contest – and certainly not about having an AI compete in the same category as human operators. If such a system ever becomes functional, it would naturally be in a separate category. It would be like trying to outsmart a chess engine – it's not the point.

The core of amateur radio has always been experimentation and exploring new possibilities. If we, as humans, hadn’t pursued innovation, we’d still be living in caves.

This is, for now, a hypothetical and exploratory project, aimed at research and curiosity – not about plaques or trophies.

Imagine a FLEX-style SDR scanning the entire band while an AI decides which station is most valuable as a multiplier. Or in a pileup of 50–100 stations, the AI picking the most strategic or distant contact. It’s not about replacing people, but about pushing the boundaries of what’s possible.

The question posed is a simple and sincere one: How ready are we for this?

And let’s not fool ourselves – this isn’t coming tomorrow. But it is coming. Might as well start talking about it.

 

It's not the code reader that's innaccurate. It's the humans sending the code.

Tune in ARRL's code practice sessions and watch how well your code reader can work with machine-sent code.

 

In a contest environment, most code will be machine-generated.

The primary challenge in a contest environment comes from separating code from QRM/QRN, low-and-variable signal-to-noise ratios, and processing multiple overlapping signals sent at differing speeds.

 

This would be a major issue doing it with a manageable latency.

The ear-brain filter is superior when dealing with real-time scenarios having overlapping signals both in the time domain and in the frequency domain.

Otherwise, the information content or "entropy" in contest exchanges is very small, so processing this would present no greater issues.

As there are no known restrictions about fully automated amateur operations such as script-driven FT8, at least not in Europe, it would be "legal".

Such a system would however remove the last traces of "operator proficiency" from amateur radio.

 

What joy can possibly be had by having a CW contact with a robot? I will sign off and spin the dial if I ever learn I've contatced a CW robot. Yeah, it's a neat project, but it's a really bad idea. We are removing the human side of Amateur Radio, little by little. That's a big mistake. I'm also convinced CW is good for the brain; CW makes you sharp! Doing CW with a machine is just plain lazy. FT8 robots are OK, though; an interesting study of propagation.

I'll go back outside, and yell at some more clouds.....l