When Are Low Resolution ADCs Energy Efficient in Massive MIMO?

Sarajlić, Muris; Liu, Liang; Edfors, Ove

doi:10.1109/access.2017.2731420

Cited by 44 publications

(48 citation statements)

References 24 publications

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“…In this section, we evaluate the MMSE and MRC receiver in terms of EE for the massive MIMO systems with variable resolution ADCs. Base on the generic power consumption model, the EE is defined as follows:

η_{EE} = \frac{R W}{P_{total}} bits false/ Joule,

where P total is the receiver power consumption, R is the sum rate, and W is the transmission bandwidth.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

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Performance analysis of uplink massive MIMO systems with variable‐resolution ADCs using MMSE and MRC detection

Xiong

Zhang

Wei

et al. 2019

Trans Emerging Tel Tech

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In this paper, we analyze the performance of uplink massive multiple‐input–multiple‐output (MIMO) systems with variable‐resolution analog‐to‐digital converters (ADCs), ie, each receive antenna uses a pair of ADCs with arbitrary resolution. Given that the minimum mean square error (MMSE) or the maximum ratio combining (MRC) detection can be employed at the base station (BS); using perfect channel state information (CSI) and results from the random matrix theory, we first derive the closed‐form approximate expression of sum achievable rate for these two type detectors, respectively. Suppose that only imperfect CSI is available at the BS. We then provide a scheme to estimate the equivalent channel based on the classical linear MMSE technique and derive the sum achievable rate using the estimated equivalent channel. Moreover, to improve the system performance, we take the impacts of channel estimation error and quantization noise into account and achieve the corresponding analytical results. Finally, simulation results are presented to validate our theoretical analysis and provide some insights on the massive MIMO systems with variable resolution ADCs. Additionally, for the MMSE and MRC detector with perfect and imperfect CSI in the considered architecture, the trade‐off between the spectral efficiency and the energy efficiency is investigated as well.

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η_{EE} = \frac{R W}{P_{total}} bits false/ Joule,

where P total is the receiver power consumption, R is the sum rate, and W is the transmission bandwidth.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Energy Efficiencymentioning

confidence: 99%

Performance analysis of uplink massive MIMO systems with variable‐resolution ADCs using MMSE and MRC detection

Xiong

Zhang

Wei

et al. 2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…Several studies have investigated the effects of quantization bit-resolution on achievable rates [5], [12], [22] and energy efficiency (ratio of data rate to the power consumption) [23] recently, and some closed-form expressions are proposed for the achievable rate of quantized massive MIMO systems [5], [24].…”

Section: Introductionmentioning

confidence: 99%

BER performance analysis of coarsely quantized uplink massive MIMO

et al. 2019

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“…In fact, the CSI is not available at the base station. On the other hand, energy efficiency was studied in [13,[18][19][20]. The optimal number of quantized bits and antenna selection were considered to maximize the energy efficiency of general MIMO with low-resolution ADCs in [13].…”

Section: Introductionmentioning

confidence: 99%

Spectral and Energy Efficiency of Distributed Massive MIMO with Low-Resolution ADC

Lei

Yang

et al. 2018

Electronics

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In this paper, considering a more realistic case where the low-resolution analog-to-digital convertors (ADCs) are employed at receiver antennas, we investigate the spectral and energy efficiency in multi-cell multi-user distributed massive multi-input multi-output (MIMO) systems with two linear receivers. An additive quantization noise model is provided first to study the effects of quantization noise. Using the model provided, the closed-form expressions for the uplink achievable rates with a zero-forcing (ZF) receiver and a maximum ratio combination (MRC) receiver under quantization noise and pilot contamination are derived. Furthermore, the asymptotic achievable rates are also given when the number of quantization bits, the per user transmit power, and the number of antennas per remote antenna unit (RAU) go to infinity, respectively. Numerical results prove that the theoretical analysis is accurate and show that quantization noise degrades the performance in spectral efficiency, but the growth in the number of antennas can compensate for the degradation. Furthermore, low-resolution ADCs with 3 or 4 bits outperform perfect ADCs in energy efficiency. Numerical results imply that it is preferable to use low-resolution ADCs in distributed massive MIMO systems.

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When Are Low Resolution ADCs Energy Efficient in Massive MIMO?

Cited by 44 publications

References 24 publications

Performance analysis of uplink massive MIMO systems with variable‐resolution ADCs using MMSE and MRC detection

Performance analysis of uplink massive MIMO systems with variable‐resolution ADCs using MMSE and MRC detection

BER performance analysis of coarsely quantized uplink massive MIMO

Spectral and Energy Efficiency of Distributed Massive MIMO with Low-Resolution ADC

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