I'm trying to wrap my head around power spectral density on a conceptual level, but I am having some difficulty. Suppose I have a communication system where I am receiving and sampling white Gaussian noise, which has a uniform PSD. Increasing the bandwidth of my receiver results in greater noise power across all frequency components. I can follow the math of how this occurs: integrate the PSD across more frequencies and you will increase the total power, which is uniform for white noise. However, how does it conceptually make sense that letting in higher frequency power components directly increases the power of the low frequency components?
EDIT: Some commentators have pointed out that my question was worded poorly. I apologize. I will try to clarify what I mean by turning it into a concrete thought experiment. Suppose I have a LPF with bandwidth $B_1$. Then, the variance of the DC component in the DFT domain after sampling WGN will be some $\sigma_1^2$. Now, compare that with a wider LPF with bandwidth $B_2$, where $B_2 > B_1$. Then the DC component variance would be greater, $\sigma_2^2 > \sigma_1^2$. Now, what if instead I implemented this second LPF filter a different way, with a parallelized structure: the sum of 2 different filter paths. The first filter path is the LPF with bandwidth $B_1$ and the second is a bandpass filter with passband $[B_1, B_2]$. This is followed by sampling. This is an equivalent structure to the LPF with bandwidth $B_2$, so the output statistics should be the same. However, notice that the second path is DC blocking, so it will never have a DC component. This should mean that the variance of the DC of the sum of the paths is equal to just the variance of LPF path, $\sigma_1^2$. But clearly, despite this being an equivalent filter structure, the output statistics are not the same. Why?