Re: Noise figure paradox

Joel Koltner wrote:
Hi Richard,

"Richard Clark" <kb7qhc@xxxxxxxxxxx> wrote in message news:8gjjs4tm5il6ulkk50isuj09d02v33u8r4@xxxxxxxxxx
In other posts related to deep space
probe's abilities to recover data from beneath the noise floor, much
less cell phones to operate in a sea of congestion, I encountered the
economic objection that such methods cost too much - expense of
bandwidth.

I don't think anyone stated they cost "too much," just that there is a cost in increased bandwidth, and bandwidth isn't free.

In general the spread spectrum processing gain is proportional to the bandwidth increase over what the original data stream would require without any spreading.


For very low level signals spread spectrum doesn't necessarily buy you much. If you use 10 times the BW, you have 10 times the noise, so your received SNR is worse by a factor of 10dB. But you get 10dB of processing gain when you despread, and your output SNR is the same as it was before.

Of course, you consumed some electrical power on both ends to spread and despread things. wideband amplifiers are less efficient than narrow band ones, as well. Saturated amplifiers are more efficient than non saturated amplifiers.

In general, the most efficient (considering power consumed on both ends) transmission is a very narrow band signal, where the bandwidth is just wide enough to contain the required data rate.

This drives you to things like BPSK, GMSK, and QPSK. Ideally, the signal spectrum would be a nice fat rectangular pulse.

In the deep space probe business, watts count at every step of the way.


You make a good point that the Shannon limit gives a good quantitative measure of how you go about trading off bandwidth for SNR (effectively power if your noise if fixed by, e.g., atmospheric noise coming into an antenna). Shannong doesn't give any hint as to how to achieve the limits specified, although I've read that with fancy digital modulation techniques and "turbo" error-correcting codes, one can come very close to the limit.

Actually, state of the art is probably Low Density Parity Check (LDPC) codes, as far as approaching the limit. They've become more practical because digital logic is becoming a lot cheaper (in a nanowatts per bit sense) to do the coding/decoding. They're also unencumbered by the patents for turbo codes.

http://en.wikipedia.org/wiki/Low-density_parity-check_code
.

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