One common model for a wireless channel is the Rayleigh model where the channel coefficient is modeled as a circularly-symmetric complex Gaussian random variable with mean zero and variance $\sigma^{2}$ denoted by ${\cal{CN}}(0,\sigma^{2})$. Typically, it's assumed that the channel has a unit variance as its impact can be lumped into the transmitted power or to the additive white Gaussian noise (AGWN) that define the overall system SNR.
For the SIMO communication system at your hand, the received signal at the $n$th base station antenna can be written as $y_{n} = h_{n}d + z_{n}$ where $d$ is the transmitted symbol by the user, $h_{n}\sim{\cal{CN}}(0,1)$ is the channel between the user and the $n$th receiver antenna at BS, and $z_{n}$ is the AWGN noise at the $n$th received antenna. In a vector format, the received signal at base station, stacking all the received signals at $N$ base station antenna, you can write
$$
\boldsymbol{y} = \begin{bmatrix}y_{1} \\ \vdots \\ y_{N}\end{bmatrix} = d\underbrace{\begin{bmatrix}h_{1} \\ \vdots \\ h_{N}\end{bmatrix}}_{\boldsymbol{h}} + \boldsymbol{z}
$$
From physics point of view, the channel between the transmitter and the $i$th and $j$th receiver antenna are somewhat correlated if these antenna are relatively close to each other (relative to the wavelength of transmitting wave). If the antennas are co-located and are placed at a proper distance, on can assume that the channels seen by different receiving antenna are statistcally independent which means that the joint probability density $f_{h_{i},h_{j}}(x,y) = f_{h_{i}}(x)f_{h_{j}}(y), \forall i,j = 1,\ldots,N$. Since each channel coefficient is distributed as a Gaussian random variable, we have $\mathbb{E}\{h_{i}h_{j}^{*}\} = \delta_{ij}\sigma_{i}^{2}$ where $\delta_{ij}$ is the Kronecker delta
$$
\delta_{ij} = \left\{\begin{array}{ll} 1 & i = j\\ 0 & i\neq j\end{array}\right .
$$
Hence,
$$
\mathbb{E}\{\boldsymbol{h}\boldsymbol{h}^{H}\} =
\begin{bmatrix}
\sigma_{1}^{2} & 0 & \cdots & 0\\
0 & \sigma_{2}^{2} & \cdots & 0\\
\vdots & \vdots & \ddots & \cdots \\
0 & 0 & \cdots & \sigma_{N}^{2}
\end{bmatrix}
$$
If all channels have unit power, $\sigma_{i} = 1,\forall i = 1,\ldots, N$, then $\mathbb{E}\{\boldsymbol{h}\boldsymbol{h}^{H}\} = \boldsymbol{I}$ where $\boldsymbol{I}$ is the $N\times N$ identity matrix.
Note that Gaussian random vectors are fully defined by their mean and covariance matrix, hence, $\boldsymbol{h}\sim{\cal{CN}}(\boldsymbol{\mu} = \boldsymbol{0},\boldsymbol{\Sigma} = \mathbb{E}\{(\boldsymbol{h}-\boldsymbol{\mu})(\boldsymbol{h}-\boldsymbol{\mu})^{H}\} = \boldsymbol{I})$.
