Channel Estimation for One-Bit Massive MIMO Systems Exploiting Spatio-Temporal Correlations

Kim, Hwanjin; Choi, Junil

doi:10.1109/glocom.2018.8647574

Cited by 14 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But in the regime above −20 dB the correlated quantization noise accros the antennas is considerably not Gaussian which leads to mismatch in the estimation. Similar behaviour is also observed in [8], where one-bit channel estimation is performed for co-located massive MIMO with spatio and temporal correlation. The effect is also explained by the mismatch of the quantization noise to the Gaussian assumption of the estimator:…”

Section: Numerical Resultssupporting

confidence: 75%

See 1 more Smart Citation

Few-Bit CSI Acquisition for Centralized Cell-Free Massive MIMO with Spatial Correlation

Maryopi

Burr

2019

2019 IEEE Wireless Communications and Networking Conference (WCNC)

View full text Add to dashboard Cite

The availability and accuracy of Channel State Information (CSI) play a crucial role for coherent detection in almost every communication system. Particularly in the recently proposed cell-free massive MIMO system, in which a large number of distributed Access Points (APs) is connected to a Central processing Unit (CPU) for joint decoding, acquiring CSI at the CPU may improve performance through the use of detection algorithms such as minimum mean square error (MMSE) or zero forcing (ZF). There are also significant challenges, especially the increase in fronthaul load arising from the transfer of high precision CSI, with the resulting complexity and scalability issues. In this paper, we address these CSI acquisition problems by utilizing vector quantization with precision of only a few bits and we show that the accuracy of the channel estimate at the CPU can be increased by exploiting the spatial correlation subject to this limited fronthaul load. Further, we derive an estimator for the simple Quantize-and-Estimate (QE) strategy based on the Bussgang theorem and compare its performance to Estimate-and-Quantize (EQ) in terms of Mean Squared Error (MSE). Our simulation results indicate that the QE with few-bit vector quantization can outperform EQ and individual scalar quantization at moderate SNR for small numbers of bits per dimension.

show abstract

Section: Numerical Resultssupporting

confidence: 75%

“…Nevertheless, the above works do not explicitly address the channel correlation. For one-bit co-located massive MIMO the problem of channel estimation with correlation was studied among others in [8].…”

Section: Introductionmentioning

confidence: 99%

Few-Bit CSI Acquisition for Centralized Cell-Free Massive MIMO with Spatial Correlation

Maryopi

Burr

2019

2019 IEEE Wireless Communications and Networking Conference (WCNC)

View full text Add to dashboard Cite

show abstract

“…All the previous work, however, has not considered the temporal correlation, which is inherent in all communication channels. [102] developed a novel channel estimator which exploits both the spatial and temporal correlations of channels. In their approach, the Kalman filter is adopted to exploit the temporal correlation and perform successive channel estimation.…”

Section: Channel Estimationmentioning

confidence: 99%

Low-Resolution ADCs for Wireless Communication: A Comprehensive Survey

Luo

Xiong

2019

IEEE Access

View full text Add to dashboard Cite

With the rapid growth of wireless data traffic and antennas configuration, higher spectrum efficiency and lower power consumption processing have evoked remarkable attention from the research and industry community for the deployment of future wireless communication. It has become a heated topic quickly in recent years and gives rise to the widespread interest around the world. As a core technology of the fifth-generation (5G) mobile communication, massive multi-input multi-output (MIMO) technology can fully exploit the space resources and greatly improve the spectral and energy efficiency. However, massive MIMO systems are faced with the problems of mass data processing, high hardware cost, and huge total power consumption. To cope with these problems, a useful solution is that the receiver equips with finite resolution analog-to-digital (ADC) converters. A large number of research results show that the low-resolution quantization technology brings significant performance within the allowable loss of capacity. This promising technique has attracted many scholars to do tremendous endeavor on it. As a motivation, we make a comprehensive survey about low-resolution ADCs for wireless communication. This paper summarizes the latest developments in the design of low-resolution communication systems, focusing on system performance analysis, some key technologies of the receiver, and typical application scenarios for the low-resolution ADCs. In view of the adverse effects caused by coarse quantization, some potential implementations are presented to alleviate this dilemma. Future research directions are also given and suggested in this paper. This overview contributes significantly to providing an informative and tutorial reference for the key technologies of low-resolution ADCs as well as its applications in practical systems.INDEX TERMS Low resolution quantization, multi-input and multi-output, channel capacity, channel estimation, automatic gain control, synchronization, receiver, relay system. JUN LIU received the B.S. degree in communication engineering from the Sichuan University of Science and Engineering, Zigong, China, in 2017, where he is currently pursuing the M.S. degree. From 2018 to 2020, he will be a Visiting Scholar with the Department of Aeronautics and Astronautics, Shanghai Jiao Tong University (SJTU). His research interests include low-resolution quantization, signal processing for wireless communications, intelligent signal processing, computer vision, and information fusion.

show abstract

“…Using low-resolution analog-to-digital converters (ADCs) is an effective way of mitigating the power consumption problem in massive MIMO systems because the ADC power consumption exponentially decreases as its resolution level [6]. However, symbol detection and channel estimation in massive MIMO systems with low-resolution ADCs become difficult This paper was presented in part at the IEEE Global Communications Conference, 2018 [1]. tasks because the quantization process using low-resolution ADCs becomes highly nonlinear.…”

Section: Introductionmentioning

confidence: 99%

Channel estimation for spatially/temporally correlated massive MIMO systems with one-bit ADCs

Kim

Choi

2019

J Wireless Com Network

Self Cite

View full text Add to dashboard Cite

This paper considers the channel estimation problem for massive multiple-input multiple-output (MIMO) systems that use one-bit analog-to-digital converters (ADCs). Previous channel estimation techniques for massive MIMO using one-bit ADCs are all based on single-shot estimation without exploiting the inherent temporal correlation in wireless channels. In this paper, we propose an adaptive channel estimation technique taking the spatial and temporal correlations into account for massive MIMO with one-bit ADCs. We first use the Bussgang decomposition to linearize the one-bit quantized received signals. Then, we adopt the Kalman filter to estimate the spatially and temporally correlated channels. Since the quantization noise is not Gaussian, we assume the effective noise as a Gaussian noise with the same statistics to apply the Kalman filtering. We also implement the truncated polynomial expansion-based low complexity channel estimator with negligible performance loss. Numerical results reveal that the proposed channel estimators can improve the estimation accuracy significantly by using the spatial and temporal correlations of channels.

show abstract

Channel Estimation for One-Bit Massive MIMO Systems Exploiting Spatio-Temporal Correlations

Cited by 14 publications

References 15 publications

Few-Bit CSI Acquisition for Centralized Cell-Free Massive MIMO with Spatial Correlation

Few-Bit CSI Acquisition for Centralized Cell-Free Massive MIMO with Spatial Correlation

Low-Resolution ADCs for Wireless Communication: A Comprehensive Survey

Channel estimation for spatially/temporally correlated massive MIMO systems with one-bit ADCs

Contact Info

Product

Resources

About