Channel Estimation and IQ Imbalance Compensation for Uplink Massive MIMO Systems With Low-Resolution ADCs

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

doi:10.1109/access.2017.2690439

Cited by 30 publications

(27 citation statements)

References 46 publications

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“…Noting it, sum rate limits in downlink (DL) MU MISO systems under IQI and CE errors were derived in [10]. In contrast [11], [12] were targeted towards the joint CE and IQI compensation in uplink (UL) MISO systems. More recently, performance analysis of dual-hop statistical channel state information (CSI) assisted cooperative communications was conducted via simulations in [13] to incorporate the effect of IQI.…”

Section: A State-of-the-artmentioning

confidence: 99%

“…, and n J R S1 n + R S2 n * . We recall that for addressing the demands of low-rate IoT settings using narrow band signals [10], [12], we have adopted this frequencyindependent-IQI model [3]. Furthermore, as the IQI parameters change very slowly as compared to the channel estimates, we assume their perfect knowledge availability at S [3], [4], [9]- [12].…”

Section: Signal Model For Characterizing Iq Impairmentsmentioning

confidence: 99%

“…We recall that for addressing the demands of low-rate IoT settings using narrow band signals [10], [12], we have adopted this frequencyindependent-IQI model [3]. Furthermore, as the IQI parameters change very slowly as compared to the channel estimates, we assume their perfect knowledge availability at S [3], [4], [9]- [12]. Using this practically-motivated assumption, we optimally exploit the information available in the IQI-based virtual signal term h B s * for designing the LSE and precoder at S. Moreover, using this IQI-knowledge, our proposed solution methodology can also be applied to the frequency-dependent-IQI scenarios.…”

Section: Signal Model For Characterizing Iq Impairmentsmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Least Squares Estimator and Precoder for Energy Beamforming Over IQ-Impaired Channels

Mishra

Johansson

2019

IEEE Signal Process. Lett.

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Usage of low-cost hardware in large antenna arrays and low-power wireless devices in Internet-of-Things (IoT) has led to the degradation of practical beamforming gains due to the underlying hardware impairments like in-phase-and-quadraturephase imbalance (IQI). To address this timely concern, we present a new nontrivial closed-form expression for the globally-optimal least-squares estimator (LSE) for the IQI-influenced channel between a multiantenna transmitter and single-antenna IoT device. Thereafter, to maximize the realistic transmit beamforming gains, a novel precoder design is derived that accounts for the underlying IQI for maximizing received power in both single and multiuser settings. Lastly, the simulation results, demonstrating a significant −8dB improvement in the mean-squared error of the proposed LSE over existing benchmarks, show that the optimal precoder designing is more critical than accurately estimating IQI-impaired channels. Also, the proposed jointly-optimal LSE and beamformer outperforms the existing designs by providing 24% enhancement in the mean signal power received under IQI.

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Section: A State-of-the-artmentioning

confidence: 99%

Section: Signal Model For Characterizing Iq Impairmentsmentioning

confidence: 99%

Section: Signal Model For Characterizing Iq Impairmentsmentioning

confidence: 99%

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Optimal Least Squares Estimator and Precoder for Energy Beamforming Over IQ-Impaired Channels

Mishra

Johansson

2019

IEEE Signal Process. Lett.

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“…The only way to reduce the energy consumption is to reduce the resolution, i.e., to use low-bit ADCs. [5][6][7][8][9] Furthermore, low-bit ADCs allow complexity reduction and faster signal processing. Low-bit ADCs are also extensively used for digitalization of parameters of neural networks.…”

Section: Introductionmentioning

confidence: 99%

Design of Robust Quantizers for Low-Bit Analog-to-Digital Converters for Gaussian Source

Dincic

Perić

Denić

et al. 2019

J CIRCUIT SYST COMP

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This paper considers the design of robust logarithmic [Formula: see text]-law companding quantizers for the use in analog-to-digital converters (ADCs) in communication system receivers. The quantizers are designed for signals with the Gaussian distribution, since signals at the receivers of communication systems can be very well modeled by this type of distribution. Furthermore, linearization of the logarithmic [Formula: see text]-law companding function is performed to simplify hardware implementation of the quantizers. In order to reduce energy consumption, low-resolution quantizers are considered (up to 5 bits per sample). The main advantage of these quantizers is high robustness — they can provide approximately constant SNR in a wide range of signal power (this is very important since the signal power at receivers can vary in wide range, due to fading and other transmission effects). Using the logarithmic [Formula: see text]-law companding quantizers there is no need for using automatic gain control (AGC), which reduces the implementation complexity and increases the speed of the ADCs due to the absence of AGC delay. Numerical results show that the proposed model achieves good performances, better than a uniform quantizer, especially in a wide range of signal power. The proposed low-bit ADCs can be used in MIMO and 5G massive MIMO systems, where due to very high operating frequencies and a large number of receiving channels (and consequently a large number of ADCs), the reduction of ADC complexity and energy consumption becomes a significant goal.

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“…This technique consists of the employment of a large number of antennas in the BS to fully exploit the degrees of freedom and reduce the transmit power [15]. However, the high number of antenna elements increases considerably the hardware cost and the energy consumption due to the presence of several A/D and D/A converters [16][17][18]. The energy consumption in an uplink receiver design is mainly from the analog-todigital converter (ADC) processing unit and the digital baseband processing unit, which are both affected by the precision of the ADC.…”

Section: Motivation and Problemsmentioning

confidence: 99%

Joint Automatic Gain Control and Receiver Design for Quantized Large-Scale Mu-Mimo Systems

CUNHA¹

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Channel Estimation and IQ Imbalance Compensation for Uplink Massive MIMO Systems With Low-Resolution ADCs

Cited by 30 publications

References 46 publications

Optimal Least Squares Estimator and Precoder for Energy Beamforming Over IQ-Impaired Channels

Optimal Least Squares Estimator and Precoder for Energy Beamforming Over IQ-Impaired Channels

Design of Robust Quantizers for Low-Bit Analog-to-Digital Converters for Gaussian Source

Joint Automatic Gain Control and Receiver Design for Quantized Large-Scale Mu-Mimo Systems

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