Continuously training one model with different dimensions each time I'm trying to solve a problem where I need to train one model with N dimensions and again train on top of that model with M dimensions. How can I achieve it?

To give you guys some context, I have 1 bluetooth beacon talking to 5 bluetooth readers that give out some value. Based on the value given out by the reader, I'm predicting the location of the beacon. My training data has 5 dimensions because there are values from 5 readers.

Now I have another scenario where I have 10 readers instead of 5. How do I train on top of my first model with 10 readers this time? Is it even possible?

The number of readers varies depending on the size of the room. So there's no way I can tell that there will always be a fixed number of readers (that is, fixed number of dimensions).

How do I go about solving this problem? Any help is appreciated!
You could try to summarise the data before handling it to the model:

If you have 5 readers, you could gather the information of "mean", "max", "min", "standard deviation" of them, this will work on 10, 15, any N of readers. Unless there is something which stops you from calculating mean, max, etc.