Performance Analysis of Massive MIMO Relay Systems With Variable-Resolution ADCs/DACs Over Spatially Correlated Channels

Xiong, Youzhi; Sun, Sanshan; Wei, Ning; Liu, Li; Zhang, Zhongpei

doi:10.1109/tvt.2021.3061699

Cited by 9 publications

(8 citation statements)

References 51 publications

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“…By taking into account the spatial correlation and low-resolution ADCs used at the relay and BS, Dong et al in [27] derived the closed-form expression of achievable rate and discussed the power scaling law for the massive MIMO relay system. Considering that ADCs with arbitrary resolution profile are employed at the relay, Xiong et al [28] derived the closedform expression of achievable rate over spatially correlated Rayleigh fading channels under both perfect and imperfect channel state information (CSI). This study also provided the condition under which low-resolution ADCs and receive spatial correlation dominate the performance loss.…”

Section: A Prior Relevant Workmentioning

confidence: 99%

“…As a matter of fact, it is essential to analyze the performance of MMSE-based receivers over multicell massive MIMO. Additionally, as mentioned in [28]- [32], it is of interest to consider a variable-resolution quantization, which can provides extra degrees-of-freedom for the design and optimization. Furthermore, in practical scenarios, spatial correlation calls for considerations during performance analysis.…”

Section: B Contributionsmentioning

confidence: 99%

“…In contrast to [6]- [24], considering spatial correlation is particularly imperative for large arrays since practical channels are generally spatially correlated. Moreover, along our previous work [28], we also consider variable-resolution ADC architecture, a more general case that includes 1-bit ADCs, pure low-resolution ADCs and mixed-ADC architectures discussed in the majority of existing works.…”

Section: B Contributionsmentioning

confidence: 99%

“…Thus, Y p j is quantized by where Q(Y p j ) is an element-wise quantization applied to the real and imaginary parts of Y p j , respectively. Obeying to the majority of works [7]- [28], we also adopt the AQNM to obtain a linearized approximation of the quantization Q(Y p j ) . Under the consideration of AQNM, the quantized version of Y p j can further be given by…”

Section: A Uplink Pilot Transmissionmentioning

confidence: 99%

“…Proof: The proof is available in Appendix B. Substituting (28) to (30) back into (20) yields the asymptotic closed-form SE for the MR combining.…”

Section: A Asymptotic Spectral Efficiency Of Mrcmentioning

confidence: 99%

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Spectral and Energy Efficiency of Multicell Massive MIMO With Variable-Resolution ADCs Over Correlated Rayleigh Fading Channels

Xiong¹,

Sun²,

Wei³

et al. 2021

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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.

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Section: A Prior Relevant Workmentioning

confidence: 99%

Section: B Contributionsmentioning

confidence: 99%