how to deal with out of range values in dataset (RSSI values)
One option to deal with this issue, you could try to impute (change) the values that are out of range with a more reasonable value. There are several approaches you could take to do this:

Replacing the out-of-range values with the mean or median of the in-range values
Using linear interpolation to estimate the missing values based on the surrounding values

The choice will depend on the goal of your machine learning model and what you want to achieve.
I have dataset for indoor localization.the dataset contain columns for wireless access point about 520 column with the RSSI value for each one .the problem is each row of the dataset has values of one scan for a signals that can be captured by a device and the maximum number of wireless access point that can be captured about only 20 ( the signal can be from 0dbm which is when the device near the access point and minus 100dbm  when the device far from the access point but it can capture the signal) the rest of access points which are out of the range of the device scan they have been compensated with a default value of 100 positive.these value (100 dbm ) about 500 column in each row and have different columns when ever the location differ .the question is how to deal with them?