Happy World Radio Day

Today is World Radio Day, so I thought I’d post something about analysing amateur radio data using R.

The radio bit

The ionosphere is a series of layers of the atmosphere, at heights between 50 and 1000 km, that are ionised by solar radiation. One of the things amateur radio operators do is experiment with how to use the ionosphere to bend the path of their radio waves and see where signals end up.

Given how the ionosphere is formed, it is dependent on how much radiation the sun is flinging at it. This is partly-driven by what bit of earth is currently pointing at the sun, so time of day is an important factor. There is an 11-year cycle of solar activity which has an enormous influence on ionisation. We’re currently on our way to a solar maximum next year, meaning that the ionosphere is particularly good at bending radio waves. The radio frequency used also influences how high a radio wave can travel before it is refracted by the ionosphere. Some frequencies pass straight through into space whereas others are easily absorbed; the trick is to choose the right frequency for season and time of day so that the wave is bent back to earth.

Digital modes of transmission are increasingly popular. They sound like robotic beeps and are produced and decoded by free software produced in the amateur radio community. These modes are used to explore different ways to encode information so that even if parts of a message are lost in the noise en route, there are ways to digitally reconstruct it at the receiving side.

One digital approach is called WSPR (pronounced “whisper”), which stands for “Weak Signal Propagation Reporter”. This is specifically designed for low power transmission, and transmitters and receivers are automatically controlled by computer. One challenge is how far you can get a signal with an absurdly low power transmitter and amateur antenna. All signal reports around the world are automatically logged on a web-based database, so it’s possible to analyse how far signals have travelled and what factors affect this.

The R bit

I had a play with WSPR data last year to see if I could find a way to visualise the impact of time of day and radio frequency on how far a signal travels. See my record of attempts, including many that turned out to be useless.

My favourite is below, showing reports of signals sent from an area around London. The colour of data points indicates how far a signal has travelled. The x-axis is date and time and y-axis is signal strength. One of the striking effects is how transmission over short distances is unaffected by time of day since the waves travel by line of sight (look for the horizontal lines). For longer distances, different parts of the world fade in and fade out as the earth spins and the sun’s effect on the ionosphere waxes and wanes. The three different graphs show results for three different frequencies.