Joint user activity and signal detection for massive multiple-input multiple-output by Xiaoqun SONG, Ming JIN, Zhongjie JIA

“…In uplink grant-free massive multiple-input multiple-output (mMIMO) systems, the performance of available methods for joint user activity and signal detection deteriorates when the correlation of receiving antennas or the number of active devices increases.Moreover, the available methods require the knowledge of noise power, which is often practically unknown.To address the above issues, combining approximate message passing with unitary transformation and expectation maximization algorithm to jointly implement user activity and signal detection was proposed.Different from the conventional approximate message passing algorithm, the proposed one assumes that the noise power was unknown.Firstly, by exploiting the approximate message passing algorithm with unitary transform, the distribution of transmitted symbols together with the distribution of noise power was obtained.Secondly, expectation maximization algorithm was applied to estimate the user activity.Finally, the signal detection was implemented by deriving the posterior distribution of the decoupled signal belongs.Simulation results show that the proposed method is better than the traditional method in joint user activity and signal detection.…”
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Distributed parameter estimation in wireless sensor networks in the presence of fading channels and unknown noise variance by Shoujun Liu, Kezhong Liu, Jie Ma, Wei Chen

“…A new estimator based on the expectation maximization algorithm is subsequently proposed. Simulation results show that this estimator exhibits superior performance compared to the method of moments estimator in both parallel- and multiple-access schemes. …”
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A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data. by Lulu Pan, Qian Gao, Kecheng Wei, Yongfu Yu, Guoyou Qin, Tong Wang

“…Data integration, Variable selection, T distribution, Expectation maximization algorithm, Genotype-Tissue Expression, Cross validation.…”
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Channel Estimation for Relay-Based M2M Two-Way Communications Using Expectation-Maximization by Xiaoyan Xu, Jianjun Wu, Chen Chen, Wenyang Guan, Haige Xiang

“…As the closed-form solution of maximum likelihood channel estimation does not exist, and the superimposed signal structure at the receiver is conducive to the expectation-maximization application, the expectation-maximization algorithm is utilized to provide the maximum likelihood solution in the presence of unobserved data through stable iterations. …”
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Estimation and Analysis of Sports Energy Consumption Based on Gait Tactile Parameters by Wang Yan, Zang Jian-Cheng, Li Bi-Tao

“…The combination of expectation-maximization algorithm and MapReduce computing model realizes the migration of traditional algorithm to “cloud computing” platform. …”
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OLED Panel Radiation Pattern and Its Impact on VLC Channel Characteristics by Hanjie Chen, Zhengyuan Xu

“…Different from a Lambertian radiation pattern, we propose an improved analytic mixed Gaussian model to describe the rotational radiation asymmetry, whose parameter values are found by applying an expectation-maximization algorithm for curve fitting with measurements. …”
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How a distractor influences fixations during the exploration of natural scenes by Hélène Devillez, Anne Guérin-Dugué, Nathalie Guyader

“…We also propose a simple mixture model evaluated using the Expectation-Maximization algorithm to test the distractor effect on fixation locations, including fixations which did not land on the distractor. …”
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A Novel Directionlet-Based Image Denoising Method Using MMSE Estimator and Laplacian Mixture Distribution by Yixiang Lu, Qingwei Gao, Dong Sun, Dexiang Zhang, Yi Xia, Hui Wang

“…In order to achieve noise removal, the directionlet coefficients of the uncorrupted image are modeled independently and identically by a two-state Laplacian mixture model with zero mean. The expectation-maximization algorithm is used to estimate the parameters that characterize the assumed prior model. …”
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Parameter Estimation of the Lomax Lifetime Distribution Based on Middle-Censored Data: Methodology, Applications, and Comparative Analysis by Peiyao Ren, Wenhao Gui, Shan Liang

“…This paper studies the parameter estimation of the Lomax distribution based on middle-censored data. The expectation–maximization algorithm is employed to compute the maximum likelihood estimates of the two unknown parameters of the Lomax distribution. …”
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Gaussian decomposition method for full waveform data of LiDAR base on neural network by Jie Liu, Xinjie Zhang, Jing Lv, Xinyu Li, Libin Du

“…The method employs an improved densely connected convolutional neural network and the EM (Expectation Maximization) algorithm to extract information from the data. …”
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Tracking maneuver target using interacting multiple model-square root cubature Kalman filter based on range rate measurement by Hongqiang Liu, Zhongliang Zhou, Haiyan Yang

“…Their approximate distribution functions are obtained by the use of the expectation maximization algorithm with Gaussian mixture model. …”
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TabMoE: A General Framework for Diverse Table-Based Reasoning with Mixture-of-Experts by Jie Wu, Mengshu Hou

“…Each expert within the model specializes in a distinct logical function and is trained through the utilization of a hard Expectation–Maximization algorithm. Remarkably, this framework eliminates the necessity of dependency on tabular pre-training, instead exclusively employing limited task-specific data to significantly enhance models’ inferential capabilities. …”
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First in Vivo SPECT Imaging of Mouse Femorotibial Cartilage Using Tc-NTP 15-5 by Elisabeth Miot-Noirault, Aurélien Vidal, Philippe Auzeloux, Jean-Claude Madelmont, Jean Maublant, Nicole Moins

“…Tomographic reconstruction of SPECT data was performed with a three-dimensional ordered subset expectation maximization algorithm, and slices were reconstructed in three axes. 99m Tc-NTP 15-5 rapidly accumulated in the joint, with a peak of radioactivity being reached from 5 minutes postinjection and maintained for at least 90 minutes. …”
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Fuzzy Data Modeling and Parameter Estimation in Two Gamma Populations by Vijay Kumar Lingutla, Nagamani Nadiminti

“…Because of the absence of closed-form solutions for the Maximum Likelihood estimators, the Expectation-Maximization algorithm is utilized, and asymptotic confidence intervals are constructed based on the observed information matrix. …”
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Inference in skew generalized t-link models for clustered binary outcome via a parameter-expanded EM algorithm. by Chénangnon Frédéric Tovissodé, Aliou Diop, Romain Glèlè Kakaï

“…We have taken advantage of the Expectation-Maximization algorithm accelerated via parameter-expansion for model fitting. …”
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Modeling the Inter-Arrival Time Between Severe Storms in the United States Using Finite Mixtures by Ilana Vinnik, Tatjana Miljkovic

“…Parameter estimation is performed using the Expectation–Maximization algorithm, with model selection validated via the Bayesian information criterion (BIC). …”
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Modelling and Prediction of Random Delays in NCSs Using Double-Chain HMMs by Yuan Ge, Yan Zhang, Wengen Gao, Fanyong Cheng, Nuo Yu, Jincenzi Wu

“…The initialization and optimization problems of the model parameters are solved by using the segmental K-mean clustering algorithm and the expectation maximization algorithm, respectively. Based on the model, the prediction of the controller-to-actuator (CA) delay in the current sampling period is obtained. …”
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Distributed Monitoring of Moving Thermal Targets Using Unmanned Aerial Vehicles and Gaussian Mixture Models by Yuanji Huang, Pavithra Sripathanallur Murali, Gustavo Vejarano

“…A distributed expectation-maximization algorithm is developed for this purpose, and it operates on local data and data exchanged with one-hop neighbors only. …”
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A novel trajectory learning method for robotic arms based on Gaussian Mixture Model and k-value selection algorithm. by Jingnan Yan, Yue Wu, Kexin Ji, Cheng Cheng, Yili Zheng

“…Next, k-means clustering is applied with the optimal k-value to initialize the parameters of the Gaussian Mixture Model, which are then refined and trained through the Expectation-Maximization algorithm. The resulting model parameters are then employed in Gaussian Mixture Regression to generate the robotic arm trajectories. …”
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A novel expectation-maximization approach to infer general diploid selection from time-series genetic data. by Adam G Fine, Matthias Steinrücken

“…Here, we extend a previously introduced expectation-maximization algorithm for the inference of additive selection coefficients to the case of general diploid selection, in which the heterozygote and homozygote fitness are parameterized independently. …”
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