limited white noise, it's pretty much the same curve.
Ron
On 03/27/2018 02:36 AM, Müller, Marcus (CEL) wrote:
> Oooh, that's a nice plot!
> This is way better than I would have anticipated. Can I attribute that
> to awesome whitening properties of the code itself and following
> scramblers/interleavers?
>
> Marcus
> On Tue, 2018-03-27 at 02:31 -0700, Ron Economos wrote:
>> CCDF (Complementary Cumulative Distribution Function) is often used to show PAPR probability. Here's what the GNU Radio DVB-T2 transmitter looks like at 32K (27841 active) carriers with an without tone reservation PAPR reduction.
>>
>> Ron
>> On 03/27/2018 02:09 AM, Müller, Marcus (CEL) wrote:
>>> On Fri, 2018-03-23 at 14:21 -0700, Martin Braun wrote:
>>>> If you've
>>>> increased the number of carriers, PAPR also goes up (a bit).
>>> Yep, by the same factor as you increase the number of carriers (proof
>>> idea: time-symbol with worst PAPR is the discrete dirac over the vector
>>> of FFT length N. That has a PAPR of N/1 = N if freq domain samples were
>>> amplitude-limited to 1.)
>>>
>>> The probability to hit a PAPR that bad is, however, limited.
>>> Considering an M-PSK modulation on the N subcarriers. Then there's a
>>> total of M^N possible time-domain OFDM symbols, but only M·N of these
>>> are worst-PAPR, so
>>> P(worst PAPR for N carriers) = M·N/M^N = N / M^(N-1)
>>> assuming equally likely symbols. Since M^N pretty certainly grows
>>> faster than N, your likelihood of ending up in the "worst PAPR"
>>> scenario actually drops with N.
>>>
>>> The story looks a bit different if you're not interested in the worst-
>>> PAPR-symbol, but in all symbols that have a PAPR worse than some
>>> threshold, e.g. 20dB. Especially for LTE, there's a lot of
>>> simulative/monte carlo PAPR>threshold curves, as things like trading
>>> clipping for amplifier efficiency plays a very commercially relevant
>>> role there.
>>>
>>> Best regards,
>>>
>>> Marcus
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