At the start of this month I did battle with the problem of SNR estimation on the RADE V1 signal. As I have mentioned previously, this had some challenges due to the lack of structure in the RADE constellation. After a few false starts I managed to get something viable running using the properties of the pilot symbols. The plot below shows the estimated against actual SNR for a range of channels. In the -5 to 10dB range (of most interest to us) it’s within 1dB for all but the MPP (fast fading) channel where the reported estimate reads a few dB lower than the actual (Note Es/No roughly the same as SNR for this example).

I’ve started work on RADE V2, where we hope to use lessons learned from RADE V1 to make some improvements and develop a “stable” waveform for general Ham use. This month I have made some progress in jointly optimising the PAPR and bandwidth of the RADE signals. For regulatory purposes, the bandwidth of signals like OFDM are often specified in terms of the “occupied bandwidth” (OBW) that contains 99% of the power. The figure below shows the spectrum of a 1000 symbols/s signal with a 1235 Hz 99% occupied bandwidth OBW in red.

Machine Learning Equalisation

Also for RADE V2, I have been prototyping ML based equalisation, and have obtained good results for some examples using the BER of QPSK symbols as a metric. The plot below shows the BER against Eb/No for the classical DSP (blue), and two candidate ML equalisers (red and green, distinguished by different loss functions). The channel had random phase offsets for every frame, which the equaliser had to correct. The three equalisers have more or less the same performance.

These results show the equalisation function can be performed ML networks, with equivalent performance to classical DSP.

Project Management

Quite a bit of admin this month, including time spent recruiting prospective new PLT members, updating budgets, and our annual report. Not as much fun as playing with machine learning, but necessary to keep the project running smoothly.

It was time to write our annual report for the ARDC who have kindly funded this project for the last two years. Writing this report underlined what a good year we had in 2024, some highlights:

• The development and Beta release of the Radio Autoencoder RADE V1 which is well on the way to meeting our goals of being competitive with SSB at high and low SNRs. Special thanks to Jean-Marc Valin for your mentoring and vision on this project!
• The BBFM project, paving the way for high quality speech on VHF/UHF land module radio (LMR) applications, in collaboration with Tibor Bece and George Karan.
• New data modes to support FreeDATA, in collaboration with Simon DJ2LS.
• The release of ezDV and continued maintenance of freedv-gui largely by Mooneer’s efforts.
• Peter Marks joining our Project Leadership Team. He’s already making a big impact – thanks Peter!