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.

翻译：本文分析了多细胞大规模多输出和多输出(MIMO)系统的性能,并配有可变分辨率模拟数字转换器(ADCs ) 。 在这样一个结构中, 每个 ADC 使用任意量化分辨率来节省电力和硬件成本。 沿着这个方向, 我们首先引入一个基于添加量化噪音模型(AQNM) 和线性最低平均偏差(LMMSSE) 估计理论的量化- 已知频道大规模多输出( MIMIMO) 。 之后, 我们通过利用估计的频道状态信息( CSI), 得出可实现的上链接光谱率(SE) 的无症状表达方式, 而不是空间关联的雷利平流光速淡淡化渠道, 以节省电和硬件成本成本。 最后, 量化多细胞最低平均误差(QA- M- M- MMSSE) 和 单细胞单项平均偏差(QA- SMMSE) 的合并。 在推算过程中, 我们考虑了量化错误的影响, 以及使用随机矩阵理论理论理论理论, 来实现 的平流化( ) A- 模拟结果, 以显示, 以最小的平流化为基础的平整级平级平的平的平流的平流的平流的平。