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Seeing a colleague make this mistake (one I've had to fix multiple times in other's work too) makes me want to ask the community: would you like the awgn() function/blocks to give the option for creating a SNR at the bandwidth of the signal? Your typical flow is something like this:
- Create a signal, usually at some nominal upsampling factor (e.g., 4) such that it's now nicely over sampled, especially if you're using a RRC or similar pulse shaping filter.
- Potentially add a frequency offset (which might make the sample frequency even higher)
- Add AWGN channel model for a desired SNR
- Put this into your detector/receiver model
The problem is, when someone says, "I'm detecting XYZ at foo SNR," it should not magically improve as a function of the oversample. The problem isn't that awgn() generates white noise, that's what it's supposed to do and the typical receiver has noise across the entire band. The problem is that SNR is most properly defined as the signal power over the noise power spectral density times the signal's noise equivalent bandwidth. Now I looked and there's no handy function for computing NEBW for an input signal (there's just a function for assessing analysis windows). In practice it can get a bit tricky. The occupied bandwidth or HPBW are often close enough to the NEBW, we're usually not haggling over hundredths of a dB. So, in my not so humble opinion, the "measured" flag for awgn() should give an option for bandwidth matching or at least document the behavior better in the help page. All too often I'm seeing 3-6 (or worse) dB errors because people aren't taking the signal's bandwidth into account.
Unlike last year's contest, there are some new technologies this year that might offer some advantages. Namely generative AI's like ChatGPT, Bard, etc. Not to be excluded, MathWorks just launched the AI Chat Playground :)
The MATLAB AI Chat Playground is open to everyone!
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Note that the first week (Nov. 6th, 2023, ~ Nov. 12th, 2023) is for creating entries only. Voting does not begin until the second week.
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You are invited to join our 2023 community contest – MATLAB Flipbook Mini Hack! This year’s contest revolves around creating interesting animations using MATLAB.
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This contest runs for 4 weeks from Nov. 6th to Dec. 3rd.
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I know the latest version of MATLAB R2023b has this feature already, put it should be added to R2023a as well because of its simplicity and convenience.
Basically, I want to make a bar graph that lets me name each column in a basic bar graph:
y=[100 99 100 200 200 300 500 800 1000];
x=["0-4" "5-17" "18-29" "30-39" "40-49" "50-64" "65-74" "75-84" "85+"];
bar(x,y)
However, in R2023a, this isn't a feature. I think it should be added because it helps to present data and ideas more clearly and professionally, which is the purpose of a graph to begin with.
Would it be a good thing to have implicit expansion enabled for cat(), horzcat(), vertcat()? There are often situations where I would like to be able to do things like this:
x=[10;20;30;40];
y=[11;12;13;14];
z=cat(3, 0,1,2);
C=[x,y,z]
with the result,
C(:,:,1) =
10 11 0
20 12 0
30 13 0
40 14 0
C(:,:,2) =
10 11 1
20 12 1
30 13 1
40 14 1
C(:,:,3) =
10 11 2
20 12 2
30 13 2
40 14 2
Wait for Walter, the rest of us are mere users.
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