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

Xiong, Youzhi; Zhang, Zhongpei; Wei, Ning; Li, Binrui; Yang, Chen

doi:10.1002/ett.3549

Cited by 23 publications

(23 citation statements)

References 49 publications

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“…12 shows the total receiver power consumption as a function of the quantization bits b. The calculation of receiver power consumption is based on the work in [40] P total = P BB + P LO + N r (P LNA + P H + 2P M ) +2N r (cP AGC + P ADC ), (65) where P BB , P LO , P LNA , P H , P M and P AGC denote the power consumption in the baseband processor, local oscillator (LO), low noise amplifier (LNA), π 2 hybrid and LO buffer, Mixer and AGC, respectively. c is chosen as 0 for the 1-bit system and 1 for b-bit systems.…”

Section: B R H Is Unknown At the Receivermentioning

confidence: 99%

Channel Estimation for Large-Scale Multiple-Antenna Systems Using 1-Bit ADCs and Oversampling

2020

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Large-scale multiple-antenna systems with large bandwidth are fundamental for future wireless communications, where the base station employs a large antenna array. In this scenario, one problem faced is the large energy consumption as the number of receive antennas scales up. Recently, low-resolution analogto-digital converters (ADCs) have attracted much attention. Specifically, 1-bit ADCs are suitable for such systems due to their low cost and low energy consumption. This paper considers uplink large-scale multipleantenna systems with 1-bit ADCs on each receive antenna. We investigate the benefits of using oversampling for channel estimation in terms of the mean square error and symbol error rate performance. In particular, low-resolution aware channel estimators are developed based on the Bussgang decomposition for 1-bit oversampled systems and analytical bounds on the mean square error are also investigated. Numerical results are provided to illustrate the performance of the proposed channel estimation algorithms and the derived theoretical bounds.INDEX TERMS Large-scale multiple-antenna systems, 1-bit quantization, oversampling, channel estimation, Cramér-Rao bound.

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Section: B R H Is Unknown At the Receivermentioning

confidence: 99%

Channel Estimation for Large-Scale Multiple-Antenna Systems Using 1-Bit ADCs and Oversampling

2020

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“…Previous studies on LS-MU-MIMO deployments have addressed the design of the system in different scenarios. [14][15][16][17][18][19][20][21] The authors discussed several important aspects and key transceiver design challenges, such as transmit precoding, channel estimation, and detection process. However, most of them focus on the case when the channels between UTs and BSs are spatially uncorrelated.…”

Section: Introductionmentioning

confidence: 99%

Spatial channel correlation for local scattering with linear MMSE‐based estimator and detector in multi‐cell large scale MU‐MIMO networks

Alammari

Sharique

2021

Trans Emerging Tel Tech

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Scalable Multi-antenna systems are designed to gain improvement in energy and spectral efficiency under different propagation conditions by equipping the BSs with hundreds or even more antennas. A better understanding of such systems leads to overcome the essential challenges, which is important for the beneficial deployment in future networks. In wireless communication systems, channel models describe the main characteristics of the propagation environment and are essential for systems performance evaluation. This article considers a multi-cell large scale multiuser MIMO (LS-MU-MIMO) system with a physical correlated channel model and linear-based MMSE estimator and detector.A physical local scattering geometric-based stochastic channel model for dense urban and suburban scenarios is presented in this article to illustrate the impact of propagation environment on signal transmission in a general framework of antenna spatial correlation. Also, a pilot signaling-based channel estimation in the uplink scenario, the effect of pilot contamination (PC), and spatial correlation on the channel estimation are studied. The effect of PC and channel estimation on the performance of LS-MU-MIMO system is investigated by applying MMSE-based channel estimator and detector over uniform linear arrays (ULA) of BS antennas in different scattering environments. Moreover, different pilot reuse patterns are considered, and their impact on the average sum SE has been investigated. The simulation results show that LS-MU-MIMO system can provide higher performance for dense urban scenarios where the user terminals (UTs) have limited mobility and higher coherence time than suburban scenarios. Furthermore, while higher spatial correlation will result in improvement in the channel estimation quality, it contributes imperfectly to the channel hardening effect.

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“…The total number of analog to digital converters (ADCs) can be increased as each receiving antenna needs a pair of ADCs. 14 In Reference 15, the authors discussed the power consumption of DACs at the BS in downlink systems. The power dissipation of DACs raises exponentially with the resolution and linearly with the bandwidth.…”

Section: Introductionmentioning

confidence: 99%

Quantized precoding and companding schemes for PAPR reduction in UFMC‐based massive MIMO downlink systems

Durga

SundruAnuradha

2021

Trans Emerging Tel Tech

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Universal-filtered multi-carrier (UFMC) is a promising waveform technology, which inherits the robustness of FBMC and simplicity of OFDM. In this article, the UFMC waveform is combined with the massive multi-user multiple-input and multiple-output (MU-MIMO) technology to meet the desired needs of future wireless networks. In order to eliminate MU interference (MUI), the precoding is essential in massive MU-MIMO downlink. However, the transmitted signals produced by the precoders have high peak-to-average power ratio (PAPR), thus resulting in reduction of power efficiency of the system. Hence, this article proposes the quantized MU precoding and companding schemes to increase the power efficiency of UFMC-based massive MU-MIMO downlink systems. A nonlinear quantized precoder called squared-infinity norm Douglas-Rachford splitting (SQUID), which generates signals with constant envelope, is proposed. The proposed SQUID precoder reduces the power consumption and MUI of UFMC-based massive MU-MIMO systems. Moreover, a piecewise linear companding (PLC) scheme, which further reduces the PAPR by employing two threshold values and one companding parameter, is also proposed. The simulation results confirm that the proposed UFMC-based massive MU-MIMO with quantized nonlinear SQUID precoding provides better performance than quantized linear precoders. Besides, the proposed system with the PLC scheme offers better PAPR reduction of 4.5 dB when compared with linear nonsymmetrical transform and 𝜇-law schemes.

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

Cited by 23 publications

References 49 publications

Channel Estimation for Large-Scale Multiple-Antenna Systems Using 1-Bit ADCs and Oversampling

Channel Estimation for Large-Scale Multiple-Antenna Systems Using 1-Bit ADCs and Oversampling

Spatial channel correlation for local scattering with linear MMSE‐based estimator and detector in multi‐cell large scale MU‐MIMO networks

Quantized precoding and companding schemes for PAPR reduction in UFMC‐based massive MIMO downlink systems

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