We don’t know what paper said or did what.
The paper where I found the model says it is convex.
This is not sufficient. Please do not use CVX until you have thoroughly proven that the model is a convex optimization problem.
It does happen, sometimes, that authors are incorrect. But more commonly, they are imprecise. That is: some models that are claimed to be convex are convex geometrically, but they are not in a standard convex optimization form. For a simple, but contrived example, consider the constraint ⌈x⌉≥1 is not a valid constraint in a convex optimization setting, even though it describes the same interval as the linear inequality x>0 . (I’ve not seen this particular example in practice, but I have seen others; e.g., beamforming papers.)
If you are having difficulty proving that a model you found in a paper is convex, please contact the authors to see if you can get clarification. In fact, if they say they’ve solved the problem with CVX themselves, ask them for the code! But please do not try to use CVX until you’ve done so. And please do not ask for help with this here at this forum.