RADE Documentation
One of the aims of this project is to document our work to a professional standard. This month I spent some time working on two research papers, one on the HF RADE work, and one on the baseband FM (BBFM) work. I’m also working on a presentation of RADE technology aimed at Hams, that I will present at my local AREG club in March.
The RADE work has been moving pretty fast over the past 12 months, so I’ve found writing up the work beneficial to help me collect my thoughts and prepare for further development. It’s very new technology, and a lot of people are curious about how RADE technology works. This all means it takes some time and effort to explain. Another good reason to document this work is to get it out of my head and into a form that others can work with in future (another one of our grant aims).
We hope to publish the papers later this year. By writing the papers we also hope to promote the project and help communicate our work at a professional level to commercial companies who may be interested in integrating RADE technology into their products.
ML Equalisation
RADE is a mixture of classical DSP and ML signal processing. One interesting design choice is how to partition the design – which chunks of signal processing should use old school DSP, and which ML?
For RADE V1 we use ML to generate QAM symbols, but classical DSP to “equalise” these symbols at the receiver. You can think of equalisation as removing any phase and frequency offsets in the received signal – a bit like fine tuning a SSB receiver. In regular data modems equalisation stops the received modem constellation from rotating or spinning, so the correct bits can be demodulated.
For RADE V2 I am prototyping the use of ML for the equalisation. The curves below show the performance of various schemes I have tested so far, with RADE V1 (blue) as the control. The “loss” is a measure of distortion, lower is better. You can see the loss decrease as SNR increases, just as we would expect.
Now in practice RADE V1 actual requires about 3dB more SNR once we add the classical DSP equalisation so for a fair comparison it should be shifted 3dB to the right, making all of the curves within a few dB of each other. So the ML network is indeed performing the equalisation function, but too early to say if we have something that can outperform the classical DSP approach used in RADE V1.
The yellow curve is intriguing – its suggests that with the right network we can get better speech quality than RADE V1 at high SNRs.
More work required to work through the equalisation question and it’s very much R&D rather than Engineering, which makes the timeline for RADE V2 hard to predict. More next month…..