RADE V2 prototyping

[drowe67]

Bandwidth/PAPR

Exploring ideas to improve 99% power bandwidth (spectral mask) from RADE V1. Just prototyping with "mixed rate" training and inference, i.e. no pilots or CP, genie phase.

• Worked out how to put a BPF in training loop (conv1d with training disabled)
• Take away that phase only (PAPR 0dB) works quite well
• clip-BPF x 3 produces reasonable 99% power BW, 0dB PAPR, good loss
• Doc ML EQ training and inference in README.md when we get to final V2 version. Just collect notes here in comments until then

Training:

python3 train.py --cuda-visible-devices 0 --sequence-length 400 --batch-size 512 --epochs 200 --lr 0.003 --lr-decay-factor 0.0001 ~/Downloads/tts_speech_16k_speexdsp.f32 250117_test --bottleneck 3 --h_file h_nc20_train_mpp.f32 --range_EbNo --plot_loss --auxdata --txbpf
Epoch 200 Loss 0.116

Testing:

./inference.sh 250117_test/checkpoints/checkpoint_epoch_200.pth wav/brian_g8sez.wav - --bottleneck 3 --auxdata --write_tx tx_bpf.f32 --write_latent z.f32 --txbpf
          Eb/No   C/No     SNR3k  Rb'    Eq     PAPR
Target..: 100.00  133.01   98.24  2000
Measured: 102.89          101.12       1243.47  0.00
loss: 0.121 BER: 0.000

octave:154> radae_plots; do_plots('z.f32','tx_bpf.f32')
bandwidth (Hz): 1255.813953 power/total_power: 0.990037

Red lines mark 99% power bandwidth:

ML EQ

Classical DSP:

python3 ml_eq.py --eq dsp --notrain --EbNodB 4 --phase_offset

MSE loss function:

python3 ml_eq.py --EbNodB 4 --phase_offset --lr 0.001 --epochs 100

Phase loss function:

python3 ml_eq.py --EbNodB 4 --phase_offset --lr 0.001 --epochs 100 --loss_phase

[drowe67]

Frame 2 EQ examples

1. Ideal (perfect EQ)

   python3 ml_eq.py --frame 2 --notrain --eq bypass --EbNodB 4
   <snip>
   EbNodB:  4.00 n_bits: 240000 n_errors: 3027 BER: 0.013
   

2. Classical DSP lin:

   python3 ml_eq.py --eq dsp --notrain --EbNodB 4 --phase_offset --frame 2
   <snip>
   EbNodB:  4.00 n_bits: 240000 n_errors: 3921 BER: 0.016
   

3. ML EQ (using MSE loss function):

   python3 ml_eq.py --frame 2 --lr 0.1 --epochs 100 --EbNodB 4 --phase_offset --n_syms 1000000 --batch_size 128
   <snip>
   EbNodB:  4.00 n_bits: 24000000 n_errors: 437933 BER: 0.018
   

[drowe67]

ML waveform training

1. Generate 10 hour complex h file:

   Fs=8000; Rs=50; Nc=20; multipath_samples('mpp', Fs, Rs, Nc, 10*60*60, 'h_nc20_train_mpp.c64',"",1);
   

2. Training:

   python3 train.py --cuda-visible-devices 0 --sequence-length 400 --batch-size 512 --epochs 200 --lr 0.003 --lr-decay-factor 0.0001 ~/Downloads/tts_speech_16k_speexdsp.f32 250204_test --bottleneck 3 --h_file h_nc20_train_mpp.c64 --h_complex --range_EbNo --plot_loss --auxdata