major conclusions and implications of this paper are drawn in Section \[sec:conclusion\].

The following notation is used throughout the paper. The notation $\mathbb{E}\{ \cdot \}$ indicates the expectation with respect to a random variable, whereas $\| \cdot \|$ and $| \cdot |$ stand for the Euclidean norm and absolute value, respectively. We let ${\mathbf{I}}_{M}$ denote the $M \times M$ identity matrix, whereas we use $\mathcal{CN}(\cdot,\cdot)$ to denote a multi-variate circularly-symmetric complex Gaussian distribution. We use $ \mathbb{C}$, $ \mathbb{Z}_+$, and $\mathbb{R}$ to denote the sets of all complex-valued numbers, all positive integers, and all real-valued numbers, respectively. The Gamma function is denoted by $\Gamma(\cdot)$.

System Model {#sec:system}
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![Illustration of one realization of BS positions from the homogeneous PPP $\Phi_{\lambda}$ and of UEs uniformly distributed in the corresponding Poisson-Voronoi cells.[]{data-label="figureVoronoi"}](figureCells){width="0.95\columnwidth"}

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We consider the UL of a cellular network that is designed to serve a dense heterogeneous distribution of UEs. This is modeled using the stochastic geometry framework adopted in [@Andrews2011a] in which the BSs are distributed spatially in $\mathbb{R}^2$ according to a homogeneous PPP $\Phi_{\lambda}$ of intensity $\lambda$ (measured in BSs per $\textrm{km}^2$). More precisely, this means that in any area of size $A$ (measured in $\textrm{km}^2$), the 