Emphasis mine:
PART II: THE DISCRETE CHANNEL WITH NOISE
11. Representation of a Noisy Discrete Channel
We now consider the case where the signal is perturbed by noise during transmission or at one or the other of the terminals. This means the received signal is not necessarily the same as that sent out by the transmitter. Two cases may be distinguished. If a particular transmitted signal always produces the same received signal, i.e. the received signal is a definite function of the transmitted signal, then the effect may be called distortion. If this function has an inverse – no two transmitted signals producing the same received signal – distortion may be correct, at least in principle, by merely performing the inverse functional operation on the received signal.
The case of interest here is that in which the signal does not always undergo the same change in transmission. In this case we may assume the received signal E to be a function of the transmitted signal S, and a second variable, the noise N.
The noise is considered to be a chance variable just as the message was above. …
It could be thermal noise. And it could also be interference that adds to the signal.
It is a chance variable just like the message. The model is the same regardless of the content of the message or the noise.
The bottom line in this segment of the Shannon model is how to handle the situation where the received signal is not the same as the transmitted signal. That is where encoding, decoding, channel capacity, speed of transmission and redundancy come into play.
I indicated that there's a distinction between noise and interference, because it matters for anything more detailed than the most trivial model. Noise and interference are distinguishable, they have different chacteristics and effects. Any effective treatment of the received signal to eliminate errors depends on recognizing whether errors are due to noise, or interference.