Documentation and Promotion

This month I spent quite a bit of time on the final edits of the HF RADE paper, as a conference submission deadline is approaching. If we are successful in publication, it will promote RADE technology in professional circles. It’s also been useful for me to better understand what we have built with RADE V1 and as a springboard for future work.

I also worked with Kanda-san, JH0PCF, on supporting a RADE presentation he and Yuichi-san JH0VEQ are preparing for the JARL Tokyo Branch and Saitama Branch Joint “Radio Science Seminar” on June 15, 2025 at the University of Tokyo.

Baseband FM (BBFM)

The BBFM project is machine learning (ML) applied to high quality and robust speech over land mobile radio (LMR) on VHF/UHF bands.

Last month I reported training the BBFM model for fading channels that model a vehicle traveling at 60 km/hr. This month I tested the same BBFM model over a range of simulated vehicle speeds. Here is a plot of the model “loss” versus received signal level R in dBm for three different vehicle speed. Lower loss represents less distortion in the decoded speech. The blue AWGN curve is the loss with no fading (e.g. a stationary vehicle).

As expected, in all cases the loss decreases as the signal strength R increases. Despite being trained at 60 km/hr, the model works well at 30 or even 120 km/hr. This is a good result as ML models are prone to failure when tested outside the range of data used for training.

For those experienced with HF RADE, it has a typical “loss” over 0.1 over similar datasets, so the speech quality is somewhat better for BBFM, in the same way that analog FM on VHF/UHF often sounds better than SSB on HF.

To support testing I also developed a streaming decoder for BBFM so it can decode speech in real time on a frame by frame basis. The next step is to evaluate the performance compared to analog FM for the same channels. More on that next month.

RADE V2 and MLEQ

I’ve been exploring various algorithms for getting the RADE V2 (MLEQ) prototype waveform on the air. The fine frequency offset performance is now acceptable – it can now handle +/- 2 Hz which is enough to test short samples over the air with modern radios.

One challenge is achieving frame sync when there are no pilot or frame sync symbols to signal the start and end of the frame. In RADE V1 the frame is 5 symbols in time (120ms) by 30 carriers in frequency. In every 5th symbol we use a set of pilot (known) symbols to indicate the frame boundary.

I explored shortening the RADE V2 frame to just one symbol in time, removing the frame structure entirely. This is quite novel for a “digital” waveform, and more like SSB or FM where there is no inherent frame structure. The end user would experience very fast PTT back and forth operation and no perceptible delay. It also removes a bunch of complicated acquisition DSP and logic, which is a constant source of pain for developers and maintainers, and impacts the end user experience. Unfortunately my current attempts at short frames results in some drop in quality for HF channels (bad), even though it delivered super short algorithmic delay/latency of just 20ms (good).

In another experiment I tried adjusting the range of noise levels over which we train, and found the performance at high and low SNRs can be traded off. So we can get higher quality than RADE V1 at high SNRs at the expense of poorer low SNR performance. This might be acceptable given many SSB contacts happen at medium to high SNRs. How to get significantly improved performance at both high and low SNRs without mode switching is an ongoing question, but may be possible with improvements to the training algorithm.

While promising many benefits (high speech quality, no pilot symbol losses, 0dB PAPR, improved low SNR performance, low RF bandwidth, low latency, less classical DSP code) this work is challenging as it breaks a lot of new ground. There are many avenues to explore and I’m hitting a few dead ends. Such is R&D! My challenge is to find a path through this new ground to get to a practical over the air waveform.