Spectral and Energy Efficiency of Multicell Massive MIMO With Variable-Resolution ADCs Over Correlated Rayleigh Fading Channels
Youzhi Xiong, Sanshan Sun, Ning Wei, Li Liu, and Zhongpei Zhang

TL;DR
This paper investigates the spectral and energy efficiency of multicell massive MIMO systems with variable-resolution ADCs, introducing a quantization-aware channel estimator and analyzing achievable spectral efficiency with different combining schemes.
Contribution
It proposes a quantization-aware channel estimation method and derives asymptotic spectral efficiency expressions considering ADC resolution and channel correlation.
Findings
Low-resolution ADCs improve energy efficiency while maintaining spectral performance.
The proposed estimators outperform quantization-unaware methods in simulations.
Optimal ADC resolution balances spectral efficiency and energy consumption.
Abstract
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…
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Taxonomy
TopicsAdvanced MIMO Systems Optimization · Cooperative Communication and Network Coding · Energy Harvesting in Wireless Networks
