… signal detection, this paper presents a model-driven deep learning method for joint channel
estimation and signal detection … , then derive a generalized expectation maximization (GEM) …

… Xu, “Deep expectationmaximization for joint MIMO channel estimation and signal
detection,” IEEE Trans. Signal Process., vol. 70, pp. 4483–4497, 2022. …

… Furthermore we develop a deep EM algorithm, wherein we … apply deep unfolding to train an
efficient structured deep net. … and that the deep EM algorithm gives promising detection and …

… We introduce a deep residual AE architecture motivated by EM in Section VI. In Section VII,
we compare the EM-inspired deep residual AE to existing algorithms for CDL and apply it to …

Deep Generative Networks (DGNs) with probabilistic modeling of their output and latent
space are currently trained via Variational Autoencoders (VAEs). In the absence of a known …

… In this paper, a radius-based expectation maximization (REM) algorithm is proposed for
SDI with phase rotated signals in a flexible PS-MQAM transceiver. To eliminate the impact of …

… -amplitude noise detection and attenuation. … signal amplitudes to construct a detection
criterion. A model that consists of a mixtureof two statistical distributions, representing the signal …

… mixture of experts, where experts are considered as deep neural network (DNN), is difficult
… for individual DNN in deep mixture of experts. We use hard expectation maximization (EM) to …

… iterative expectation maximization-based least square (LS) channel estimation, MMSE
equalization based on inter-frequency interference (IFI), time-domain IFI cancellation, and data …

… The physical analysis of huge volume of EEG signal by a human … work Expectation
Maximization Extreme Learning Machine (EM-ELM) based technique is adapted for perfect detection …