An update on this block. I am now using a gr_sync_interpolator to build the
block but the performance is still the same. The flowgraph slows down and
hangs. I have to force stop it using the 'kill' command on the linux
terminal.
What should I do so that the flowgraph works smoothly like benchmark_tx.py
normally does with the other modulation schemes?
Thanks
On Mon, Nov 15, 2010 at 12:17 PM, John Andrews <gnu.f...@gmail.com> wrote:
> This is what I am doing in general_work
>
> 1. I read an item from the input stream.
> 2. Check if its 0x01 or 0x00.
> 3. If its 0x01 I output the contents of d_pn_array[0], d_n_pn times.
> (Basically I am spreading the input data by the ( pn_length * d_n_pn times
> ))
> 4. But if its 0x00 I output the contents of d_pn_array[1], d_n_pn times.
> (Basically I am spreading the input data by the ( pn_length * d_n_pn times
> ))
>
> The arrays d_pn_array[0] and d_pn_array[1] were initialised in the
> constructor.
>
> I only read the contents of the arrays and set the values to out[i]. This
> shouldn't take such a long time although I must say that the lengths of the
> arrays d_pn_array are 1023 and d_n_pn is 5 i.e. I output 1023*5 = 5115 items
> for each input item.
>
>
> int
> dsss_spreading_b::general_work(int noutput_items,gr_vector_int
> &ninput_items,gr_vector_const_void_star &input_items,gr_vector_void_star
> &output_items)
> {
> const unsigned char *in = (const unsigned char *)input_items[0];
> unsigned char *out = (unsigned char *)output_items[0];
> int data_items=noutput_items/(d_length_PN*d_n_pn);
> int nout=0;
> for(int i=0;i<data_items;i++){
> if(in[i]&0x01){
> for(int j=0;j<d_length_PN*d_n_pn;j++){
> out[nout]=d_pn_array1[j%d_length_PN];
> nout++;
> }
> }
> else{
> for(int j=0;j<d_length_PN*d_n_pn;j++){
> out[nout]=d_pn_array0[j%d_length_PN];
> nout++;
> }
> }
> }
>
> consume(0,data_items);
> return noutput_items;
>
> }
>
> On Mon, Nov 15, 2010 at 12:00 PM, Tom Rondeau <trondeau1...@gmail.com>wrote:
>
>> On Mon, Nov 15, 2010 at 11:43 AM, John Andrews <gnu.f...@gmail.com>
>> wrote:
>> > On another note I use 'gr_block' to build this custom block
>> >
>> > On Mon, Nov 15, 2010 at 11:37 AM, John Andrews <gnu.f...@gmail.com>
>> wrote:
>> >>
>> >> Hi,
>> >> I have a modified dbpsk.py in which I use a custom block after the
>> >> self.diffenc (differential encoder block). This custom C++ block
>> outputs
>> >> 1000 output_items of size 'gr_sizeof_char' for each input_item of size
>> >> 'gr_sizeof_char'. I then use benchmark_tx.py to test the functioning of
>> the
>> >> modified dbpsk.py and upon doing so the flowgraph slows down
>> incredibly.
>> >> What can I do to speed up the process?
>> >>
>> >> DiffEnc --> Custom Block --> Chunks2Symbols
>> >> ( n ouputs) --> (n * 1000 outputs) --> (n *1000 outputs)
>> >>
>> >> Thanks
>>
>> You're really going to have to provide a lot more information about
>> the block you've created. Posting the general_work function would be
>> useful.
>>
>> Tom
>>
>
>
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