|Subject:||Re: [Discuss-gnuradio] gr-cdma|
|Date:||Wed, 28 Jan 2015 23:24:17 -0500|
Sounds good. Can you please run your "txrx" simulation or separate "tx" "rx" simulation and screenshot me the output because I feel like what I am seeing is wrong. I want to be sure the output of the fft is correct. On the receiver side I am getting the message debug that tells me the packet number and packet length as well as if it's detecting a failed packet.On Tuesday, January 27, 2015 12:11 PM, Achilleas Anastasopoulos <address@hidden> wrote:
Frank,you can perform simulations and plot BER vs SNR using either of the two apps provided (ie, either the txrx or the separate tx and rx apps that communicate through FIFO). The file vs FIFO is irrelevant here: the FIFOs are just used for "emulating"the communication between the two different tx and rx applications.
In both cases you can either dump the rx results to a file and compare with the "known" tx transmission, or you can add BER GRC blocks...All the above are not gr-cdma related, so i am sure you can find plenty of examples of how this is done in this list and in the gnuradio examples.AchilleasOn Mon, Jan 26, 2015 at 10:01 PM, Frank Pinto <address@hidden> wrote:Sounds good! I figured this already just wanted to be sure. And will I be able to plot the BER vs. Eb/NO by using the writing and reading to fifo or simply reading and writing to a file and importing the data to matlab for the BER vs Eb/NO plot?On Friday, January 23, 2015 5:59 PM, Frank Pinto <address@hidden> wrote:
Sounds good! I figured this already just wanted to be sure. And will I be able to plot the BER vs. Eb/NO by using the writing and reading to fifo or simply reading and writing to a file and importing the data to matlab for the BER vs Eb/NO plot?
Sent from Yahoo Mail for iPhoneAt Jan 23, 2015, 5:53:58 PM, Achilleas Anastasopoulos wrote:The beautiful thing about open source is that all the detail are there for you to see!Looking at the cdma_parameters.py file,you can see:
pulse_training = numpy.array((1,1,1,1,-1,1,1,-1))+0j
pulse_data =numpy.array((-1,1,-1,1,-1,-1,-1,-1))+0jso we are using 8 chips per symbol with two orthogonal codes for training and data.You can change them and put anything you like (they better be orthogonal AND each should have goodautocorrelation properties-- or at least the training code should)best,
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