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USRP X410 with fft_cp FPGA image

R
rubenthill@ymail.com
Tue, Nov 5, 2024 10:23 AM

Hello together,

hi build UHD version 4.6 on the fft_cp_preview2 branch, I don’t know if this is (already) supported. And With the YAML files there I build the FPGA Image (x410_x4_200_fft_ddc_duc_rfnoc_image_core.yml) just at it was an flashed it to the USRP. All of this just worked fine. I Also can Use the fft blocks with the cyclic prefix.

Just two questions came up to me while tinkering with the ofdm_loopback.py example script. As far as I understand using the -l flag I avoid using the analog frontend and with the -d flag set to for instance 50 I can set a digital delay. All this also works… Now the question if I use the analog frontend (not setting these flags) and attaching cables the delay I measure is way longer. I’ve seen there is a delay of 188 samples set between the Tx and the Rx in the example script, is this due to the FPGA setup? Is there a way to make the Rx and Tx in time-sync? I’ve seen a timed command is used there and the delays between Tx and Rx are constant, but constantly to big. I can correct for this time error, is the the following correction right 188 / tick_rate? Or is there more to compensate for?
I tried to set the same time_offset in the transmit_and_receive function but still seen the same delay, so I guess that is a delay which can’t be removed?

The other thing I have noticed is that if I set channels just to 0, away from the default which is 0,1 I don’t receive any samples. Seems like the flow graph hangs after sending, this happens regardless of using the loopback (-l) or not..

Other than that I realy like this feature in UHD and am looking forward to the official release, great work keep it up!

Hello together, hi build UHD version 4.6 on the fft_cp_preview2 branch, I don’t know if this is (already) supported. And With the YAML files there I build the FPGA Image (x410_x4_200_fft_ddc_duc_rfnoc_image_core.yml) just at it was an flashed it to the USRP. All of this just worked fine. I Also can Use the fft blocks with the cyclic prefix. Just two questions came up to me while tinkering with the ofdm_loopback.py example script. As far as I understand using the -l flag I avoid using the analog frontend and with the -d flag set to for instance 50 I can set a digital delay. All this also works… Now the question if I use the analog frontend (not setting these flags) and attaching cables the delay I measure is way longer. I’ve seen there is a delay of 188 samples set between the Tx and the Rx in the example script, is this due to the FPGA setup? Is there a way to make the Rx and Tx in time-sync? I’ve seen a timed command is used there and the delays between Tx and Rx are constant, but constantly to big. I can correct for this time error, is the the following correction right 188 / tick_rate? Or is there more to compensate for?\ I tried to set the same time_offset in the transmit_and_receive function but still seen the same delay, so I guess that is a delay which can’t be removed? The other thing I have noticed is that if I set channels just to 0, away from the default which is 0,1 I don’t receive any samples. Seems like the flow graph hangs after sending, this happens regardless of using the loopback (-l) or not.. \ \ Other than that I realy like this feature in UHD and am looking forward to the official release, great work keep it up!
R
rubenthill@ymail.com
Tue, Nov 5, 2024 10:28 AM

PS: maybe I should have checked for typos before sending the message. I hope it is understandable, if not I’ll send a corrected version. Sorry for the inconvenience.

PS: maybe I should have checked for typos before sending the message. I hope it is understandable, if not I’ll send a corrected version. Sorry for the inconvenience.
JH
joerg.hofrichter@emerson.com
Wed, Nov 6, 2024 10:26 AM

Hi Ruben,

thanks for your question to the new FFT+CP feature.

  1. The question regarding the delay: you can use the ofdm_loopback.py example (probably renamed to fft_loopback.py when officially released) to calibrate the exact actual delay of the complete processing chain, including RF. Don’t provide CP insertion/removal values (no —cp-list/-p flag) and find the delay (number of samples as —delay/-d flag) where you see a sharp peak in the diagrams shown in the second row (=amplitude of the received signal). Note that symbol #0 has minor transient effects but all other symbols should have a sharp peak. 188 samples is what we measured for the 200 MHz bitfile, which value do you determine?

  2. As you noticed, the pre-release version in branch fft_cp_preview2 has a limitation that only two (“0,1” / “2,3) or four channels (“0,1,2,3”) are supported.

Kind regards,
Jörg

Hi Ruben, thanks for your question to the new FFT+CP feature. 1. The question regarding the delay: you can use the ofdm_loopback.py example (probably renamed to fft_loopback.py when officially released) to calibrate the exact actual delay of the complete processing chain, including RF. Don’t provide CP insertion/removal values (no —cp-list/-p flag) and find the delay (number of samples as —delay/-d flag) where you see a sharp peak in the diagrams shown in the second row (=amplitude of the received signal). Note that symbol #0 has minor transient effects but all other symbols should have a sharp peak. 188 samples is what we measured for the 200 MHz bitfile, which value do you determine? 2. As you noticed, the pre-release version in branch fft_cp_preview2 has a limitation that only two (“0,1” / “2,3) or four channels (“0,1,2,3”) are supported. Kind regards,\ Jörg
R
rubenthill@ymail.com
Wed, Jan 8, 2025 4:54 PM

Hello Jörg,
Hello interested reader,

sorry I was quite busy end of the year and then on vacation so I couldn’t continue with my tests here. But today I tinkered a little with what you described under 1.
Unfortuantely, I wasn’t able to reproduce what you are saying. Are we working on the same version of the code (ofdm_loopback.py) ?

here is some Info.

  1. the command I used initially with a delay of “0” according to what you said this shouldn’t result in a sharp peak, which I am seeing.

./ofdm_loopback.py --args "addr=192.168.10.2" -s 2048 -n 10 -y 0.8 -d 0 -c 0,1 --tx-gain 0 --rx-gain 0 --rate 245760000

using arguments:

--args addr=192.168.10.2

--fft-size 2048

--num-symbols 10

--amplitude 0.8

--cp-list

--channels 0,1

--loopback False

--delay 0

--tx-gain 0

--rx-gain 0

--rate 245760000.0

Using samples per packet of 1024

[INFO] [UHD] linux; GNU C++ version 11.4.0; Boost_107400; DPDK_21.11; UHD_4.6.0.fft_cp_preview_2-14-g4a94995c

[INFO] [MPMD] Initializing 1 device(s) in parallel with args: mgmt_addr=192.168.10.2,type=x4xx,product=x410,serial=33D8E1F,name=ni-x4xx-33D8E1F,fpga=X4_200,claimed=False,addr=192.168.10.2

from the output you can see on which Version of the code-base I am working (fft_cp_preview_2-14-g4a94995c). I changed the amplitude to 0.8 but that doesn’t really affect the result.

So my guess is that in the current version of ofdm_loopback.py the delay is already accounted for by lines 640 and 641:

(…)
tx_time=tx_time,
rx_time=tx_time + get_tx_rx_delay(radio, args),
(…)

where get_tx_rx_delay is a function (starting from line 373) which sets the delay as follows:

(only showing lines 388-395)

else:
# With RF loopback there's a fixed delay between TX and RX
if tick_rate in [245.76e6, 250.0e6]:
# Add 188 cycles for 200 MHz FPGA
loopback_delay = 188
else:
raise NotImplementedError()
return loopback_delay / tick_rate

Am I right with my assumption?
So to check for the delay I should set that offset back to 0 from 188 right?
But when I tried that, the number of Rx samples didn’t match the number of Tx samples and the code didn’t execute fully.

So is there a different code I should focus on ?
Furthermore i wrote a code which estimates the delay on the phase-difference between tx and rx. But these delays somehow don’t match the time I get by loopback_delay / tick_rate which would be 188/245.76MHz in my case…

Do you have any suggestions what I can additionally try to determine the delay introduced by the USRP. Or maybe hint an error I might be doing…

Kind regards and a happy new year,

Ruben

Hello Jörg, \ Hello interested reader, sorry I was quite busy end of the year and then on vacation so I couldn’t continue with my tests here. But today I tinkered a little with what you described under 1. \ Unfortuantely, I wasn’t able to reproduce what you are saying. Are we working on the same version of the code (ofdm_loopback.py) ? here is some Info. 1) the command I used initially with a delay of “0” according to what you said this shouldn’t result in a sharp peak, which I am seeing. `./ofdm_loopback.py --args "addr=192.168.10.2" -s 2048 -n 10 -y 0.8 -d 0 -c 0,1 --tx-gain 0 --rx-gain 0 --rate 245760000` `using arguments:` `--args addr=192.168.10.2` `--fft-size 2048` `--num-symbols 10` `--amplitude 0.8` `--cp-list ` `--channels 0,1` `--loopback False` `--delay 0` `--tx-gain 0` `--rx-gain 0` `--rate 245760000.0` `Using samples per packet of 1024` `[INFO] [UHD] linux; GNU C++ version 11.4.0; Boost_107400; DPDK_21.11; UHD_4.6.0.fft_cp_preview_2-14-g4a94995c` `[INFO] [MPMD] Initializing 1 device(s) in parallel with args: mgmt_addr=192.168.10.2,type=x4xx,product=x410,serial=33D8E1F,name=ni-x4xx-33D8E1F,fpga=X4_200,claimed=False,addr=192.168.10.2` from the output you can see on which Version of the code-base I am working (fft_cp_preview_2-14-g4a94995c). I changed the amplitude to 0.8 but that doesn’t really affect the result. 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So my guess is that in the current version of ofdm_loopback.py the delay is already accounted for by lines 640 and 641: `(…)`\ `tx_time=tx_time,`\ `rx_time=tx_time + get_tx_rx_delay(radio, args),`\ `(…)` where get_tx_rx_delay is a function (starting from line 373) which sets the delay as follows: (only showing lines 388-395) `else:`\ ` # With RF loopback there's a fixed delay between TX and RX`\ ` if tick_rate in [245.76e6, 250.0e6]:`\ ` # Add 188 cycles for 200 MHz FPGA`\ ` loopback_delay = 188`\ ` else:`\ ` raise NotImplementedError()`\ ` return loopback_delay / tick_rate` Am I right with my assumption? \ So to check for the delay I should set that offset back to 0 from 188 right? \ But when I tried that, the number of Rx samples didn’t match the number of Tx samples and the code didn’t execute fully. \ \ So is there a different code I should focus on ? \ Furthermore i wrote a code which estimates the delay on the phase-difference between tx and rx. But these delays somehow don’t match the time I get by `loopback_delay / tick_rate` which would be 188/245.76MHz in my case… \ \ Do you have any suggestions what I can additionally try to determine the delay introduced by the USRP. Or maybe hint an error I might be doing… Kind regards and a happy new year, Ruben