Baseband FM PoC

[drowe67]

Some experiments in using ML with baseband FM radios.

1. First pass training command line:

   python3 ./train_bbfm.py --cuda-visible-devices 0 --sequence-length 400 --batch-size 512 --epochs 100 --lr 0.003 --lr-decay-factor 0.0001 --plot_loss ~/Downloads/tts_speech_16k_speexdsp.f32 model_bbfm_01 --range_EbNo --range_EbNo_start 6 --plot_loss
   

2. Inference (runs encoder and decoder, and outputs symbols z_hat.f32):

   ./bbfm_inference.sh model_bbfm_01/checkpoints/checkpoint_epoch_100.pth wav/brian_g8sez.wav - --write_latent z_hat.f32
   

3. Stand alone decoder, outputs speech from z_hat.f32 to sound card:

   ./bbfm_rx.sh model_bbfm_01/checkpoints/checkpoint_epoch_100.pth z_hat.f32 -
   

4. Or save speech out to a wave file:

   ./bbfm_rx.sh model_bbfm_01/checkpoints/checkpoint_epoch_100.pth z_hat.f32 t.wav
   

5. Plot sequence of received symbols:

   octave:4> radae_plots; do_plots_bbfm('z_hat.f32')
   

[drowe67]

HF channel sim (two path Rayleigh) is pretty close to TIA-102.CAAA-E 1.6.33 Faded Channel Simulator. The measured level crossing rate (LCR) seems to meet req (f), for v=60 km/hr, f = 450 MHz, and P=1 when measured over a 10 second sample. We've used Rs=2000 symb/s here, so x-axis of plot is 1 second in time.

octave:39>  multipath_samples("lmr60",8000, 2000, 1, 10, "h_lmr60.f32")
Generating Doppler spreading samples...
fd = 25.000
path_delay_s = 2.0000e-04
Nsecplot = 1
Pav = 1.0366
P = 1
LCR_theory = 23.457
LCR_meas = 24.400

[drowe67]

Using the single carrier modem:

1. Create a file of BBFM symbols, 80 symbols every 40ms, plays expected output speech:

   ./bbfm_inference.sh model_bbfm_01/checkpoints/checkpoint_epoch_100.pth wav/brian_g8sez.wav - --write_latent z.f32
   

2. Sanity check of modem, BER test using digital, BPSK symbols, the symbols in z.f32 are replaced with BPSK symbols. t.int16 is a real valued Fs=9600Hz sample file, that could be played into a FM radio.

   cat z.f32 | python3 sc_tx.py --ber_test > t.int16
   cat t.int16 | python3 sc_rx.py --ber_test --plots > /dev/null
   

3. Send the BBFM symbols over the modem, and listen to results:

   cat z.f32 | python3 sc_tx.py > t.int16
   cat t.int16 | python3 sc_rx.py > z_hat.f32
   ./bbfm_rx.sh model_bbfm_01/checkpoints/checkpoint_epoch_100.pth z_hat.f32 -
   

4. Compare MSE of features passed through the system, first with z == z_hat, then with z passed through modem to get z_hat:

     python3 loss.py features_in.f32 features_out.f32
     loss: 0.033
     python3 loss.py features_in.f32 features_rx_out.f32
     loss: 0.035
   

This is a really good result, and likely inaudible. The feature*.f32 files are produced as intermediate outputs form the bbfm_inference.sh and bbfm_rx.sh scripts.