Project Planning

This month I worked on another round of project planning, preparing updated project plan and effort estimate documents to cover the next 18 months of our ARDC funded grant work. A lot has changed since the start of the project, so we put some thought into how to use the remaining project funds effectively. Compared to our original grant application this represents a pivot away from classical DSP like Codec 2 towards modern machine learning approaches like RADE. Thanks Mooneer and Peter for helping out with this planning, and the ARDC for approving our new plan.

Baseband FM ASR Curves

To support the BBFM work I finished up the analog FM simulation, which we use as a “control” to compare with the RADE waveform we are developing. This was driving me a bit crazy – there are no actual standard for the audio “shaping” (filtering, compression etc) in analog FM – it’s all manufacturer specific and very subjective – so I had to draw the line somewhere. Thanks Tibor Bece (an expert in this area) for tips.

The next step was to perform some automatic speech recognition tests, comparing our simulation of analog FM to the BBFM version of RADE. The general idea of the ASR tests is described here, where we compared HF RADE to SSB. A low word error rate (WER) is the goal. So green is RADE, blue analog FM. Solid is AWGN, dotted is a fading channel (a vehicle moving at 60 km/hr). You can compare the difference in dB by drawing a horizontal line at a given WER (e.g. 5%).

These curves indicate some impressive results. Compared to analog FM, RADE seems to shrug off multipath fading. It approaches the WER of the baseline FARGAN vocoder at high SNRs. Here is a sample of the simulated analog FM with fading at a Rx level of -110dBm, followed by RADE/BBFM operating over the same channel:

So I’m getting “too good to be true” feelings again. Now these results are all from simulation. To back them up Tibor is working hard on building up a test jig to introduce the same fading channel to real radio signals, using a pair of over the shelf FM radios and a RF mixer than can be driven by the same fading simulation of a vehicle traveling at 60 km/hr. This will enable him to “bench test” over the cable (OTC) to simulate fading and demonstrate the system using real radio hardware.

To support these tests he has spent some time dealing with frame synchronization – just like in my HF work we find that RADE works so well at low SNRs it stresses some of other sub-systems like frame synchronization (we’re all used to the vocoder falling over first).

ML Fine Timing

Getting back to the HF work – “fine timing” is the last module of code we need to start initial testing of RADE V2 over the air. This month I put quite a lot of work into the design of a suitable hybrid DSP/ML network and figuring out how to train for multipath channels. Once again – I have built on the fine work of Jean-Marc and his co-authors (primary author K. Subramani ) who developed a neural pitch estimator. Looking at the DSP involved, we reasoned that a network designed for pitch estimation is also suitable for fine timing estimation. I’ve obtained some initial results indicating this approach works down to 0 dB on fading HF channels. At this stage it’s unclear if it outperforms classical DSP fine timing approaches (I still need to write the appropriate test scripts).

The plot below is a histogram of fine timing errors from the ML estimator for a fading HF channel. The ideal result is 0, an actual OFDM symbol is 160 samples long (+/-80 on the x- axis). The timing error shown in the histogram is quite usable for an OFDM waveform.

RADE V2 Smoke Test

OK, so now it’s time for a “smoke test” of RADE V2. Lets put it on the air and see if it blows up, or works OK. There are many new concepts in this waveform so I need to see if they are “for real” as quickly as possible. So I’m working up a bare bones receiver that can be tested over the air – similar to where we were with RADE V1 about 12 months ago. This will allow us to sends and receive stored files of speech and RADE V2 samples using a batch processing approach, in order to sanity check the various sub-systems. The receiver is working OK when I test it with my channel simulation tools. Next month I plan to try it over real HF channels.