RADE V2 prototyping

[drowe67]

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
• Training for 200 Epochs reduced loss from 0.126 to 0.115, this is worth doing for any network

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: