This problem is from Billingsley's "Probability and measure" book.
Let $a_n \to a$, $b_n \to b$ and $\{X_n\}$,$X$ be a sequence of random variables such that $X_n \to^w X$ (weak convergence). Prove that $$a_nX_n + b_n \to^w aX+b$$ using characteristic functions.
I was able to reduce the problem into proving the following:
If $a_n \to 0$ and $X_n \to^w X$ then $a_nX_n \to^w 0$.
To begin with, we have $$|E[e^{ita_nX_n}] - 1| \leq E[|e^{ita_nX_n} - 1|] = 2E|\sin\left(\frac{ta_nX_n}{2}\right)|$$
The trouble I am having is that the sequence $X_n$ is in the way. If $X_n$ are integrable, then the proof is complete using $|\sin(x)| \leq |x|$. However since that is not given, I don't know how to proceed. Any help would be appreciated.
Edit: I just saw an answer here. However that used Skorokhod's theorem. Isn't there some simplification that could be done to RHS?