Exercise (Billingsley's Probability and Measure (3e), p.271, Exr.20.13): Given discrete measures, $\mu$ and $\nu$, consisting of masses $\alpha_n$ and $\beta_n$ for $n = 0, 1,2,\ldots$ Show that $\mu * \nu$ consists of a mass of $\sum_{k=0}^{n} \alpha_{k} \beta_{n-k}$ at $n, n=0,1,2, \ldots$
What I thought about doing first was pushing symbols together. First, since $\sum \alpha_n = 1 = \sum \beta_n$. Multiplying would result in $\sum_{k=0}^n \sum_{j=0}^n \alpha_j \beta_k = 1$. But I don't get the formula that I want. Especially not if I want to find the mass for an exact $n$.
The formula for the convolution of two (possibly arbitrary finite) measures (in Billingsley) is as follows:
$$(\mu * \nu) (H) = \int_{- \infty}^{+ \infty} \nu(H -x)\mu(dx), H \in \mathcal{R}^1,$$
where $\mathcal{R}^1$ is the $\sigma$-field generated by intervals in $\mathbb{R}$. Blindly pushing ahead with the current problem, we see that
$$\int_{- \infty}^{+ \infty} \nu(H -x)\mu(dx) = \sum \nu(H-x) \mu(x)$$
How do I manipulate $H$ and my equation here? Since after $H$ is measured, it is going to be $0$ almost everywhere except for some points that correspond to the masses of our measure.
A technical explanation of what we want to do in this problem and sort of verbalizing exactly what we do would be extremely helpful. But suggestions are also helpful.