Originally posted on Jan 21, 2015
I’m not usually a fan of noise, but there are exceptions
My provisional assumption is that noise does indeed have mass. I support that notion with the following hare-brained chain of reasoning: The subject of noise has a gravity-like pull that compels me to write about it more than anything else. Because gravity comes from mass, noise therefore must have mass.Voila!
My previous posts dealing with noise have all been about minimizing it. Averaging away its effects, estimating the errors it causes, predicting and then subtracting noise power, and so on. Sometimes I just complain about it or wax philosophical.
I even created a webcast titled “conquering noise” but, of course, that was a bit of a conceit. Noise is a fundamental natural phenomenon and it is never vanquished. Instead, I have mentioned that noise can be beneficial in some circumstances—and now it’s time to describe one.
A few years ago, a colleague was using Keysight’s Advanced Design System (ADS) software to create 10 MHz WiMAX MIMO signals that included impairments. He started by adding attenuation to one transmitter, but after finding little or no effect on modulation quality, he added a 2 MHz bandpass filter to one channel, as shown below.
Surely a filter that removed most of one channel would confound the demodulator. Comparing the spectra of the two channels, the effect is dramatic.
Spectrum of two simulated WiMAXX signals with 10 MHz bandwidth. The signal in the bottom trace has been modified by a 2 MHz bandpass filter.
All that filtering in one channel had no significant effect on modulation quality! The VSA software he was using—as an embedded element in the simulation—showed the filter in the spectrum and the channel frequency response, but in demodulation it caused no problem.
He emailed a recording of the signal and I duplicated his results using the VSA software on my PC. I then told him he could “fix” the problem by simply adding some noise to the signals.
This may seem like an odd way to solve the problem, but in this case the simulation didn’t match reality in the way it responded to drastic channel filtering. The mismatch was due to the fact that the simulated signals were noise-free, and the channel equalization in demodulation operations could therefore perfectly correct for filter impairments, no matter how large they were.
In many ways it’s the opposite of the adaptive equalization used in real-world situations with high noise levels, and I have previously cautioned you to be careful what you ask for. When there is no noise, you can correct signals as much as you want, without ill effects.
Of course, “no noise” is not the world we live in or design for, and as much as I hate to admit it, there are times when it’s beneficial to add some.
There are certainly other uses for noise. Those also have that peculiar massive attraction and I know I’ll write about it again soon.