I am using MATLAB to simulate transmission of binary data sequence, $\mathbf{x}={x_k\in\{+1,-1\}\forall 0\leq k\leq N}$, over an ISI channel whose impulse response $\mathbf{h}=[h_0 h_1 \cdots h_{\mu}]$ is perfectly known. My channel observation sequence is $\mathbf{y}$ is a noisy version of filtered $\mathbf{x}$ that can be expressed mathematically as
$$y_k=Hx + w_k,$$
where $w_k\sim\mathcal{N}(\mu,\sigma_w^2)$.
For such system, equalizers can be designed to mitigate the effect of ISI. This often includes computing quantities like autocorrelation matrices $R_{xx}=E[x^{}x^T]$ and $R_{yy}=E[y^{}y^{T}]$, and cross correlation $R_{yx}=E[y^{}x^{T}]$
In MATLAB, I have access to $\mathbf{x}$ and I know noise variance $\sigma_w^2$ so computing $R_{xx},R_{yy},$ and $R_{yx}$ is straight forward.
My question is how do we compute such quantities in practice when we only have access to $\mathbf{y}$, our channel observation and we don't know what input sequence $\mathbf{x}$ is?