(@KanaBaba)
Sounds promising! Do you have any references like journals, technical report or thesis for this algorithm?
Well, even though I will start working with few common IEEE standards, I want to eventually implement practical algorithms for generic pilot location detection/estimation...
I have captured OFDM I-Q data at IF. I have down-converted to it to baseband and then simply performed autocorrleation. Based on autocorrelation-based techniques...
Is this true for other standard as well, like dvbt-t and wimax? the signal i was dealing with is not wi-fi, but other ieee standard like dvb-t and wimax.
Thanks for your feedback. This is hugely helpful. You have mentioned about OFDM symbol repeating indefinitely as this leads to cheap implementations in hardware...
[cp & symbol] aligns with [cp & symbol] at 0 lag where we an see a huge peak. The other peak is where cp aligns with symbol end. That's where we are going...
Here is the output of autocorr:| || | ||_____________|____|_____________|_|_ |_________|_|_|_... 0 ...
well, built-in correlation function with same signal can perform auto-correlation, right? Like xcorr(x,x)
The noisy received signal (rx) is completely unknown. The whole objective is to auto-correlate to "estimate" the OFDM useful symbol time (Tu). It is producing...
You take time sample and use python or matlab built-in function to perform autocorrelation. It's autocorrelation, so it was correlated with itself.
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