> ... the received signal strength was roughly 100 times weaker than the background noise.
Okay, this sniped my curiosity. Where can I read more about the math/physics that makes this possible?
Okay, this sniped my curiosity. Where can I read more about the math/physics that makes this possible?
If you have big enough samples, you can tell a normal distribution with mean 0.01 apart from one with mean 0, even if their standard deviation is both 1000.
Error correcting codes are in some sense a very cool and efficient way to get the equivalent of that bigger sample.
(You could also just send your signal many, many times. But simple repetition is a vastly inferior solution to the ingenious codes people came up with.)
Here's a quick wiki link for that topic:
Basically, error correction can supply “coding gain”. So in the path-loss equation, you get to add gain for error correction.
In the above case, if the signal is 100 times weaker than the noise, you need a code with 20db of coding gain to reach break-even, if that makes sense.
As to the physics there's also the fact that the phenomenon that creates a good transmission line between participants is in flux and you have to get your data through while the transmission line passes enough signal that the receiver can decode it.
This is a method of spreading the power of your transmission over a much wider part of the spectrum. Hence, this doesn't help much if your transmit power is limited. However, it does help if your transmit power per slice of bandwidth is limited.
This is used for a few methods. The first is spectrum-sharing (which requires orthogonal codes). The second is detection avoidance (lower power per bandwidth makes it much harder to recognize the signal unless you already know how to de-spread). The third method is to avoid 'point noise sources' from blocking your signal. E.g. a wifi burst of 1MHz might drown out your signal if you use 1Mhz of bandwidth. But if you use 10 MHz, it only blocks one tenth of your signal.