  Low-resolution digital-to-analog converter (DAC) has shown great potential in
facilitating cost- and power-efficient implementation of massive multiple-input
multiple-output (MIMO) systems. We investigate the performance of a massive
MIMO downlink network with low-resolution DACs using regularized zero-forcing
(RZF) precoding. It serves multiple receivers equipped with finite-resolution
analog-to-digital converters (ADCs). By taking the quantization errors at both
the transmitter and receivers into account under spatially correlated channels,
the regularization parameter for RZF is optimized with a closed-form solution
by applying the asymptotic random matrix theory. The optimal regularization
parameter increases linearly with respect to the user loading ratio while
independent of the ADC quantization resolution and the channel correlation.
Furthermore, asymptotic sum rate performance is characterized and a closed-form
expression for the optimal user loading ratio is obtained at low
signal-to-noise ratio. The optimal ratio increases with the DAC resolution
while it decreases with the ADC resolution. Numerical simulations verify our
observations.
