How to obtain tail bounds for a linear combination of dependent and bounded random variables?

I am looking for tail bounds (preferably exponential) for a linear combination of dependent and bounded random variables.

consider $$K_{ij}=\sum_{r=1}^N\sum_{c=1}^N W_{ir}C_{rc}W_{jc}$$ where $i \neq j$, $W\in \{+1, -1\}$ and $W$ follows $\operatorname{Bernoulli}(0.5)$, and $C=\operatorname{Toeplitz}(1, \rho, \rho^2, \ldots, \rho^{N-1})$, $0 \leq \rho < 1$.

I will be to happy if you give me any pointer to how I can evaluate the moment generating function of $K_{ij}$ to have bound for $Pr\{K_{ij} \geq \epsilon\}\leq \min_s\exp(-s\epsilon)E[\exp(K_{ij}s)]$ based on chernoff bound ans $s \geq 0$.

For a hint you can look at my another question entitled as How to obtain tail bounds for a sum of dependent and bounded random variables?'' which is a special case of this problem where $C_{rc}=1, 1 \leq c \leq N, 1 \leq r \leq N$. Thanks a lot in advance.

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