Channel Estimation for Spatially/Temporally Correlated Massive MIMO Systems with One-Bit ADCs

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.Comment: Accepted to EURASIP Journal on Wireless Communications and Networkin

Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learning

This paper focuses on channel prediction techniques for massive multiple-input multiple-output (MIMO) systems. Previous channel predictors are based on theoretical channel models, which would be deviated from realistic channels. In this paper, we develop and compare a vector Kalman filter (VKF)-based channel predictor and a machine learning (ML)-based channel predictor using the realistic channels from the spatial channel model (SCM), which has been adopted in the 3GPP standard for years. First, we propose a low-complexity mobility estimator based on the spatial average using a large number of antennas in massive MIMO. The mobility estimate can be used to determine the complexity order of developed predictors. The VKF-based channel predictor developed in this paper exploits the autoregressive (AR) parameters estimated from the SCM channels based on the Yule-Walker equations. Then, the ML-based channel predictor using the linear minimum mean square error (LMMSE)-based noise pre-processed data is developed. Numerical results reveal that both channel predictors have substantial gain over the outdated channel in terms of the channel prediction accuracy and data rate. The ML-based predictor has larger overall computational complexity than the VKF-based predictor, but once trained, the operational complexity of ML-based predictor becomes smaller than that of VKF-based predictor.Comment: Accepted to IEEE Transactions on Communication

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

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Channel AoA Estimation for Massive MIMO Systems Using One-Bit ADCs

Although massive multiple-input multiple-output (MIMO) can enhance the overall system performance significantly, it could suffer from high cost and power consumption issues due to using a large number of radio frequency (RF) chains. Two different approaches are commonly exploited to overcome these issues. The first approach is using hybrid beamforming, which consists of analog and digital beamforming, to reduce the total number of RF chains. The second approach is adopting low-resolution analog-to-digital converters (ADCs) for each RF chain. For both approaches, channel estimation becomes a difficult task. This paper addresses the problem of channel angle of arrival (AoA) estimation in massive MIMO using both hybrid beamforming and one-bit magnitude-aided (OMA) ADCs. An iterative algorithm is developed to estimate the channel AoA, and the appropriate threshold per iteration is analyzed. Numerical results show that the proposed technique can achieve sufficient AoA estimation performance with practical values of the signal-to-noise ratio (SNR).11sciescopuskc

A Study on the Friction Stir Welding Experiment and Simulation of the Fillet Joint of Extruded Aluminum Material of Electric Vehicle Frame

In the existing automobile manufacturing process, metal inert gas (MIG) and tungsten inert gas (TIG) welding are mainly used. These welding methods are fusion welding, and the heat input in the welding area is very high. Therefore, the deformation of the base material is large, and the residual stress in the vicinity of the welded area is high, resulting in the problem of reduced mechanical strength. In this study, friction stir welding (FSW) was applied to the welding process of the structure constituting the battery frame of a newly developing electric vehicle to compensate for this problem. The welded part is the fillet joint of the side frame and the bottom frame, and experiments and numerical analysis were performed on the welding deformation and residual stress of the full frame structure. A specially manufactured angle head was used for friction stir welding of the fillet joint of extruded type aluminum, not the existing solid type. The optimum process was derived through experiments, and the temperature of the welding center was derived through test correlation between the value of measured temperature and the finite element model. The final deformation result was verified by comparing it with the measured value using a dial indicator. It is expected that the proposed thermal elasto-plastic analysis method will reduce the testing period and the cost of the manufacturing process and increase productivity

Effects of Human Behavior Simulation on Usability Factors of Social Sustainability in Architectural Design Education

While the social sustainability of built environments is an essential aspect of architectural design education, systemic experiments still lack empirical pedagogy. Therefore, factors of social sustainability are hardly reflected in students’ projects seamlessly. To overcome such limitations, this study investigates the applicability and effectiveness of human behavior simulation. To ensure authentic architectural design, the projects were equipped with autonomous, rational anthropomorphic computer agents called virtual users (VUsers). This study compared the performance scores on social sustainability factors, assessed by the students who conducted design projects both before (without) and after (with) using the simulation. A one-way analysis of variance indicated that human behavior simulation promoted the performance of projects with respect to the parameters of accessibility and safety, ergonomic usability for heterogeneous users and supportability of social interactions. However, the simulation was not found to be effective in promoting the physical attractiveness of built environments and in ensuring the completeness of design solutions. Based on previous studies, the present study interpreted the reasons why the operability of VUsers and built environments, representations of emerging interactions of VUsers and whole-and-part analytics promoted explicit experimentation, but the factors of physical attractiveness and completeness were irrelevant to the rational examinations in the use of the simulation

A Study on Minimizing Welding Deformation of Joints for the Sealing of Emission After-Treatment Structure

As the environmental pollution issue has recently become significant, environmental regulations in Europe and the United States are being strengthened. Thus, there is a demand for the quality improvement of emission after-treatment systems to satisfy the strengthened environmental regulations. Reducing the amount of welding heat distortion by optimization of the welding order of each part could be a solution for quality improvement since the emission after-treatment system consists of many parts and each assembly is produced by welding individual ones. In this research, a method to derive a welding sequence that effectively minimizes welding deformation was proposed. A two-stage simulation was performed to obtain the optimal welding sequence. In the first stage, the welding sequence was derived by analyzing the number of welding groups in each assembly of a structure. The derived welding sequence was verified by performing a thermal elasto-plastic analysis and comparing it with the experimental results