  This paper analyzes the performance of multicell massive multiple-input and
multiple-output (MIMO) systems with variable-resolution analog-to-digital
converters (ADCs). In such an architecture, each ADC uses arbitrary
quantization resolution to save power and hardware cost. Along this direction,
we first introduce a quantization-aware channel estimator based on additive
quantization noise model (AQNM) and linear minimum mean-squared error (LMMSE)
estimate theory. Afterwards, by leveraging on the estimated channel state
information (CSI), we derive the asymptotic expressions of achievable uplink
spectral efficiency (SE) over spatially correlated Rayleigh fading channels for
maximal ratio combining (MRC), quantization-aware multicell minimum
mean-squared error (QA-M-MMSE) combining, and quantization-aware single-cell
MMSE (QA-S-MMSE) combining, respectively. During the derivations, we consider
the effect of quantization errors and resort to random matrix theory to achieve
the asymptotic results. Finally, simulation results demonstrate that our
theoretical analyses are correct and that the proposed quantization-aware
estimator and combiners are more beneficial than the quantization-unaware
counterparts. Besides, based on a generic power consumption model, it is shown
that low-resolution ADCs can obtain the best tradeoff between SE and energy
efficiency (EE) under multicell scenarios.
