Downlink Achievable Rate Analysis in Massive MIMO Systems With One-Bit DACs

Li, Yongzhi; Cheng, Tao; Swindlehurst, A. Lee; Mezghani, Amine; Liu, Liu

doi:10.1109/lcomm.2017.2687871

Cited by 109 publications

(83 citation statements)

References 17 publications

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“…Massive MIMO systems operating with low-resolution Digital-to-Analog (D/A) converters at the base station in the downlink transmission have also been studied. There is some evidence that they are sufficient to attain a good performance in terms of achievable link rate [29], [30]. Also, while these analyses are independent of the actual modulation and coding used in the system, numerical end-to-end link simulations have independently arrived at essentially the same conclusion that the degradation of BER performance due to low-precision (< 6 bits) D/A converters is negligible [31].…”

Section: B Coarse and Lean Convertorsmentioning

confidence: 99%

Efficient DSP and Circuit Architectures for Massive MIMO: State of the Art and Future Directions

Perre¹,

Liu²,

Larsson³

2018

IEEE Trans. Signal Process.

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Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio (NR) and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency.Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices.

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Section: B Coarse and Lean Convertorsmentioning

confidence: 99%

Efficient DSP and Circuit Architectures for Massive MIMO: State of the Art and Future Directions

Perre¹,

Liu²,

Larsson³

2018

IEEE Trans. Signal Process.

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“…This is because the random AN degrades the achievable rate of the legitimate users while the null-space AN only causes interference to the eavesdropper. From (41),γ R 0 is calculated as 6.1303 dB. In order to enhance the secrecy rate, increasing the DAC resolution is recommended if γ 0 > 6.1303 dB but not if γ 0 ≤ 6.1303 dB.…”

Section: B Achievable Ergodic Secrecy Ratementioning

confidence: 99%

“…An alternative simpler approach is to quantize the output of standard linear precoders, which is referred to as quantized linear precoding [39]- [41]. Although it is generally difficult to analytically characterize the performance degradation due to nonlinear quantization, the well-known Bussgang theorem can be applied to develop an approximate linear model [42], [43] .…”

Section: Introductionmentioning

confidence: 99%

Secure Massive MIMO Communication With Low-Resolution DACs

Zhu

et al. 2019

IEEE Trans. Commun.

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In this paper, we investigate secure transmission in a massive multiple-input multiple-output (MIMO) system adopting low-resolution digital-to-analog converters (DACs). Artificial noise (AN) is deliberately transmitted simultaneously with the confidential signals to degrade the eavesdropper's channel quality. By applying the Bussgang theorem, a DAC quantization model is developed which facilitates the analysis of the asymptotic achievable secrecy rate. Interestingly, for a fixed power allocation factor φ, low-resolution DACs typically result in a secrecy rate loss, but in certain cases they provide superior performance, e.g., at low signal-to-noise ratio (SNR). Specifically, we derive a closed-form SNR threshold which determines whether low-resolution or highresolution DACs are preferable for improving the secrecy rate. Furthermore, a closed-form expression for the optimal φ is derived. With AN generated in the null-space of the user channel and the optimal φ, low-resolution DACs inevitably cause secrecy rate loss. On the other hand, for random AN with the optimal φ, the secrecy rate is hardly affected by the DAC resolution because the negative impact of the quantization noise can be compensated for by reducing the AN power. All the derived analytical results are verified by numerical simulations. Index TermsPhysical layer security, massive multiple-input multiple-output (MIMO), digital-to-analog converter (DAC), artificial noise (AN)

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“…The value of (9) is calculated term-by-term. First concerning the numerator in (9), the energy of the desired signal follows…”

Section: Appendix C Proof Of Theoremmentioning

confidence: 99%

Optimal Multiuser Loading in Quantized Massive MIMO Under Spatially Correlated Channels

Gong

et al. 2019

IEEE Trans. Veh. Technol.

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Low-resolution digital-to-analog converter (DAC) has shown great potential in facilitating cost-and powerefficient implementation of massive multiple-input multiple-output (MIMO) systems. We investigate the performance of a massive MIMO downlink network with low-resolution DACs using regularized zero-forcing (RZF) precoding. It serves multiple receivers equipped with finite-resolution analog-to-digital converters (ADCs). By taking the quantization errors at both the transmitter and receivers into account under spatially correlated channels, the regularization parameter for RZF is optimized with a closed-form solution by applying the asymptotic random matrix theory. The optimal regularization parameter increases linearly with respect to the user loading ratio while independent of the ADC quantization resolution and the channel correlation. Furthermore, asymptotic sum rate performance is characterized and a closed-form expression for the optimal user loading ratio is obtained at low signal-to-noise ratio. The optimal ratio increases with the DAC resolution while it decreases with the ADC resolution. Numerical simulations verify our observations. Index TermsMassive multiple-input multiple-output (MIMO), digital-to-analog converter (DAC), analog-to-digital converter (ADC), spatial correlation, user loading ratio. I. INTRODUCTIONMassive multiple-input multiple-output (MIMO) has gained significant attention as a candidate technique for the next generation wireless system [2]- [4]. In massive MIMO, a large amount of antennas Part of this work was presented in [1] at the IEEE VTC-Fall in Toronto, Canada, Sept. 2017. 2 equipped at base station (BS) can provide high spectral and energy efficiencies [5]. Despite these merits of massive MIMO, it suffers from a challenging issue of high cost and power consumption for applications even at the BS. This is due to the fact that each antenna has to be driven by a separate radio-frequency (RF) chain.Considering that the power consumption of each RF chain can decrease dramatically by reducing the resolutions of digital-to-analog converters (DACs), one of the potential solutions is to employ lowresolution DACs for downlink transmissions [6]- [11]. More specifically in [6]-[8], nonlinear precoding schemes were proposed for massive MIMO downlink transmission with low-resolution DACs. In [6], a novel precoding technique using 1-bit DACs was presented to mitigate multiuser interference and quantization distortions. In [7], a computationally-efficient 1-bit beamforming algorithm, named POKEMON (short for PrOjected downlinK bEaMfOrmiNg), was proposed. The authors of [8] studied perturbation methods which minimize the probability of errors at receivers in a massive MIMO downlink with 1bit DAC. Alternatively, linear precoding techniques were studied for the downlink transmissions using low-precision DACs [9]- [11]. A linear precoding approach was studied in [9], where the output data of conventional linear precoders were directly quantized by low-resolution DACs. Considering 1-bit DACs and z...

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Downlink Achievable Rate Analysis in Massive MIMO Systems With One-Bit DACs

Cited by 109 publications

References 17 publications

Efficient DSP and Circuit Architectures for Massive MIMO: State of the Art and Future Directions

Efficient DSP and Circuit Architectures for Massive MIMO: State of the Art and Future Directions

Secure Massive MIMO Communication With Low-Resolution DACs

Optimal Multiuser Loading in Quantized Massive MIMO Under Spatially Correlated Channels

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