Optimal Per-Antenna ADC Bit Allocation in Correlated and Cell-Free Massive MIMO

Verenzuela, Daniel; Björnson, Emil; Matthaiou, Michail

doi:10.1109/tcomm.2021.3067699

Cited by 22 publications

(15 citation statements)

References 28 publications

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“…Max product SINR power allocation algorithm is considered in Massive MIMO systems to increase SE. Because, SE depends on a logarithmic expression of the SINR (Verenzuela et al, 2021). Detailed analyzes of the SE expressions can be examined in the study in (Björnson et al, 2017).…”

Section: Max Product Sinr Power Allocationmentioning

confidence: 99%

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Power Allocation Algorithms for Massive MIMO System

Dikmen¹,

Kulac²

2021

European Journal of Science and Technology

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Massive multiple-input multiple-output (MIMO) is still valid as an important system to increase performance of fifth generation (5G) and beyond wireless communication technologies. Spectrum efficiency (SE), high data rate and energy efficiency (EE) are among these performances. Recently, due to the increase in interconnected devices, the spread of internet of things (IoT) systems and the limited resources, various performance improvements have become inevitable. It is seen that there are various studies to realize such improvements with Massive MIMO. There are many researches especially for spectrum efficiency and energy efficiency. Because issue of energy and bandwidth problem are among the issues that need to be solved and developed first. In recent years, it is understood that power allocation algorithms have been focused on solving these two problems. In this study, researches on power allocation algorithms for MIMO systems are examined. The main points of the studies are emphasized. In addition, the comparison of three different power allocation algorithms, which will be among the basic power allocation algorithms, are carried out in terms of spectrum efficiency.

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Section: Max Product Sinr Power Allocationmentioning

confidence: 99%

“…The aim is to maximize the SINR products of users across system. The mathematical expression of the max product SINR power allocation method can be written as follows, similar to the work in (Verenzuela et al, 2021).…”

Section: Max Product Sinr Power Allocationmentioning

confidence: 99%

Power Allocation Algorithms for Massive MIMO System

Dikmen¹,

Kulac²

2021

European Journal of Science and Technology

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“…Previous work [17]- [19] focused on adapting the resolution of each individual ADC to the channel state. In contrast, we (i) adapt the resolutions of both the ADC array and the spatial equalizer to the instantaneous communication scenario and (ii) study their individual impact on power consumption.…”

Section: A Contributionsmentioning

confidence: 99%

Resolution-Adaptive All-Digital Spatial Equalization for mmWave Massive MU-MIMO

Castañeda

Mirfarshbafan

Ghajari

et al. 2021

2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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“…In particular, A j,k and F j,k are given by ( 28) and (31), respectively. B j,k + C j,k + D j,k + E j,k and G j,k are, respectively, given by ( 29) and (30) at the top of next page.…”

Section: A Asymptotic Spectral Efficiency Of Mrcmentioning

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%

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

Xiong¹,

Sun²,

Wei³

et al. 2021

Preprint

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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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Optimal Per-Antenna ADC Bit Allocation in Correlated and Cell-Free Massive MIMO

Cited by 22 publications

References 28 publications

Power Allocation Algorithms for Massive MIMO System

Power Allocation Algorithms for Massive MIMO System

Resolution-Adaptive All-Digital Spatial Equalization for mmWave Massive MU-MIMO

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

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