Hi, thanks again Achilleas for suggestions and help,

so far I’ve tried this for the convolutional 1,2 code.

this is the file for setting up the finite state machine

2 4 4

0 2

0 2

1 3

1 3

0 3

3 0

2 1

1 2

does it make sense?

and this is the code for connecting the encoder into the flow grpah

```
file_chunker=gr.packed_to_unpacked_bb(1,gr.GR_LSB_FIRST)
```

f=trellis.fsm("/root/MAIN/soft/gnuradio/gr-mystuff/finite_state_machine")

repacker=gr.packed_to_unpacked_bb(2,gr.GR_LSB_FIRST)

convolutional_encoder = trellis.encoder_bb(f,0)

self.connect(file_source_bytewise,file_chunker,convolutional_encoder,repacker,symbol_mapper)

this bringrs out a spectrum picture which is significantly different

than the one without the encoder… much more “squared”

am I going the right way?

what about the viterbi decoder?

I’ve got wrong data size errors when connecting

a bit by bit stream obtained from demodulated symbols to

trellis.metrics, this way in the Rx chain:

symbols_to_bytes=gr.constellation_decoder_cb(constellation,constellation_bytes)

print constellation_bytes

```
repacker_rx_1=gr.packed_to_unpacked_bb(1,gr.GR_LSB_FIRST)
K=1
metrics =
```

trellis.metrics_c(f.O(),3,constellation,trellis.TRELLIS_EUCLIDEAN)

viterbi_decoder = trellis.viterbi_b(f,K,0,-1)

self.connect(gain,series_to_parallel_2,direct_fft,parallel_to_series_2,symbols_to_bytes,repacker_rx_1,metrics,viterbi_decoder,file_repacker,file_sink)

many thanks

vincenzo