[guided]The inequality
\begin{align*}
m\rho
\leq
C_R\alpha^{-1/p}\sqrt{pm}
\end{align*}
holds for every $p\geq 2$. We now choose $p$ so that the factor $\alpha^{-1/p}$ is bounded by an absolute constant.
First handle the endpoint $\alpha=1$. Then $A=G$, and the [Fourier transform](/page/Fourier%20Transform) of $\mathbb{1}_G$ is supported only at the trivial character. Indeed,
\begin{align*}
\widehat{\mathbb{1}_G}(\gamma)
=
\int_G \overline{\gamma(x)}\,d\mu_G(x),
\end{align*}
which equals $1$ for the trivial character and $0$ for non-trivial characters by orthogonality of characters. Thus the large spectrum is generated by the empty dissociated set, and the estimate is immediate.
Now suppose $0<\alpha<1$, and define
\begin{align*}
L:=\log(1/\alpha)>0.
\end{align*}
Choose
\begin{align*}
p:=2+L.
\end{align*}
This choice is admissible because $p\geq 2$. It also gives
\begin{align*}
\alpha^{-1/p}
=
\exp(L/p)
\leq e,
\end{align*}
since $L/p\leq 1$. Therefore
\begin{align*}
m\rho
\leq
eC_R\sqrt{pm}.
\end{align*}
If $m\geq 1$, we divide by $\sqrt{m}$ to get
\begin{align*}
\sqrt{m}\rho
\leq
eC_R\sqrt{p}.
\end{align*}
Squaring yields
\begin{align*}
m
\leq
e^2C_R^2\rho^{-2}p
=
e^2C_R^2\rho^{-2}(2+\log(1/\alpha)).
\end{align*}
If $0<\alpha\leq 1/2$, then $L\geq\log 2$, so
\begin{align*}
2+L
\leq
\left(1+\frac{2}{\log 2}\right)L.
\end{align*}
Thus in this density range,
\begin{align*}
m
\leq
e^2C_R^2\left(1+\frac{2}{\log 2}\right)\rho^{-2}\log(1/\alpha).
\end{align*}
This is the only place where the additive $2$ can be absorbed; it works because $\log(1/\alpha)$ is bounded below on $0<\alpha\leq 1/2$.
For the high-density range $1/2<\alpha<1$, we use a different estimate. A dissociated set cannot contain the trivial character $1_{\widehat{G}}$, because the one-term relation $1_{\widehat{G}}^1=1_{\widehat{G}}$ would violate dissociativity. Hence every $\lambda\in\Lambda$ is non-trivial. For such $\lambda$, orthogonality of characters gives $\widehat{\mathbb{1}_G}(\lambda)=0$, and since $\mathbb{1}_A=\mathbb{1}_G-\mathbb{1}_{G\setminus A}$,
\begin{align*}
\widehat{\mathbb{1}_A}(\lambda)
=
-\widehat{\mathbb{1}_{G\setminus A}}(\lambda).
\end{align*}
Now apply Parseval's identity on the finite probability group $(G,\mu_G)$ to the function $\mathbb{1}_{G\setminus A}:G\to\mathbb{C}$:
\begin{align*}
\sum_{\gamma\in\widehat{G}}|\widehat{\mathbb{1}_{G\setminus A}}(\gamma)|^2
=
\int_G \mathbb{1}_{G\setminus A}(x)\,d\mu_G(x)
=
1-\alpha.
\end{align*}
Restricting this non-negative sum to $\Lambda$ and using the large-spectrum lower bound gives
\begin{align*}
m\rho^2\alpha^2
\leq
\sum_{\lambda\in\Lambda}|\widehat{\mathbb{1}_A}(\lambda)|^2
=
\sum_{\lambda\in\Lambda}|\widehat{\mathbb{1}_{G\setminus A}}(\lambda)|^2
\leq
1-\alpha.
\end{align*}
Since $\alpha>1/2$, we have $\alpha^{-2}<4$, and since $1-\alpha\leq\log(1/\alpha)$ for $0<\alpha<1$, this yields
\begin{align*}
m
\leq
4\rho^{-2}\log(1/\alpha).
\end{align*}
Combining the two regimes and taking
\begin{align*}
C:=\max\left\{e^2C_R^2\left(1+\frac{2}{\log 2}\right),4\right\}
\end{align*}
gives
\begin{align*}
m=|\Lambda|\leq C\rho^{-2}\log(1/\alpha).
\end{align*}
This is the desired size estimate.[/guided]