The Science of Amateur Radio

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Thông tin tác giả Onno Benschop and Onno (VK6FLAB) được phát hiện bởi Player FM và cộng đồng của chúng tôi - bản quyền thuộc sở hữu của nhà sản xuất (publisher), không thuộc về Player FM, và audio được phát trực tiếp từ máy chủ của họ. Bạn chỉ cần nhấn nút Theo dõi (Subscribe) để nhận thông tin cập nhật từ Player FM, hoặc dán URL feed vào các ứng dụng podcast khác.
Foundations of Amateur Radio

The amateur radio community is as varied as humanity across the globe. It represents an endless supply of ideas and experiments that continue to attract people looking for something new and exiting.

On the face of it, our hobby is about radio and electronics, about propagation and antennas, about modes and contacts, but if you limit your outlook to those topics you'll miss out on a vast expanse of opportunity that is only just beginning to emerge.

Until quite recently, computing in amateur radio was essentially limited to logging and contest scoring. It has evolved to include digital modes like PSK31 and the advent of smaller, faster and cheaper computers in the home has brought the possibility of processing unimaginable amounts of data leading to modes like WSPR and FT8.

In the past I've spoken about how amateur radio means different things to different people. Making contact using a digital internet enabled repeater is sacrileges to one and manna from heaven to another. Between those two extremes there is room to move and explore. Similarly where one uses valves, another expects an integrated circuit. One wants low power, the other wants every Watt they can lay their hands on. Contesting versus rag chewing, nets vs contacts, SSB vs. CW, FT8 vs. RTTY. Each of these attracts a different part of the community with different outcomes and expectations. For some it's about antenna building, others going portable, climbing a mountain, or setting up in a park.

Those are all traditional amateur activities, but the choice and opportunity don't end there.

The longer I play with computers the more I see a convergence in the world, a coming together of technologies and techniques. I've talked about some of this before when in 1994 I produced a competition broadcast promotion for the radio station I was working at, using just a computer in the era of reel-to-reel tape and razor blades. My station manager couldn't quite put his finger on what was different, but with hindsight it represented a landslide change in how radio stations have operated since. Mind you, I'm not saying that I was the first, just the first in that particular radio station.

In many ways computing is an abstract effort. When asked, I like to express it as designing something intangible in an imaginary world using an made up language and getting paid real money to make it happen, well, numbers in my bank account at least.

Within that context, amateur radio is slowly beginning to reap the rewards that come from the exponential growth in home computing power. While the majority of humanity might use the vast amount of CPU cycles to scroll through cat videos online, that access to processing power allows us to do other things as well.

For example, right now I'm playing with the dataset that represents all the WSPR spots since March of 2008. As of now there are around four billion rows of contacts, containing data points like a time-stamp, the transmitter, the receiver, the signal strength, location, direction, and more.

As part of that investigation I went looking for documents containing the words "RStudio" and "maidenhead", so I could consider creating a map in my statistical tool that allowed me to represent my dataset. In making that search I discovered a thesis by a mathematician who was using the reverse beacon network in an attempt to predict which station could hear which transmitter at what time.

In reading the thesis, which I opened because I was looking for an example on how to convert a maidenhead locator into geo-spacial data types in R, a popular statistics platform, I discovered that the author didn't appear to have much, if any, amateur knowledge or experience, but they approached their task, attempting to predict as a mathematician what we in our community call propagation, based on a public dataset, downloaded straight from the reverse beacon network, created by amateurs like you and I.

This interaction between science and the amateur community isn't new. Sometimes it's driven by science, other times it's driven by amateur radio. There's a team exploring the ionospheric prediction models that we've used for decades, popularly referred to as VOACAP or Voice of America Coverage Analysis Program, based on multiple evolutions of empirical models of the ionosphere that were first developed in the 1960's, headed by both a scientist and an amateur, Chris KL3WX.

With the advent of WSPR and the associated data collection some experiments have started to compare the reality of propagation as logged by WSPR to the predicted propagation as modelled by VOACAP. One such experiment happened in 2018 where Chris and his team at HAARP, the High-Frequency Active Auroral Research Program, set out to make transmissions at specific times and frequencies, using the amateur community logging of WSPR spots to compare their transmissions to the predictions.

Interestingly they did not match. Just think about that for a moment. The tool we love and use all across our community, VOACAP, doesn't match the reality of propagation.

My own playing with WSPR data is driven by the very same thing that I use to be a better contester, a burning curiosity in all things. My VOACAP prediction experience has been poor to date. Setting up my own WSPR beacon is the first step in attempting to discover what my actual propagation looks like, but in doing so, it's also a possible contribution to the wider challenges of predicting propagation based on a dataset with four billion spots. One such approach might be to create an ionospheric prediction map based on actual data and compare that to the models as well as the published space weather maps and combining these efforts into a machine learning project which might give us the next generation of ionospheric prediction tools, but only time will tell.

No doubt I will have to learn more about statistics and machine learning than I expect, but then, that's half the fun.

So, next time you think of amateur radio as being limited to valves, transistors, soldering, antennas and rag chewing on HF, consider that there might be other aspects to this hobby that you have not yet considered.

What other research are you aware of that relates to amateur radio?

I'm Onno VK6FLAB

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