… 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) …

… by employing ML signal detection,the impact of various linear and nonlinear signal impairments
can … We compare joint carrier synchronization and signal detection with the PLL based …

A common task in signal processing is the estimation of the parameters of a probability
distribution function. Perhaps the most frequently encountered estimation problem is the …

… We have introduced the expectation-maximization algorithm to the problem of sequence
detection when synchronization is not present. Results obtained show that the expectation step …

… detection of a direct-sequence spread-spectrum signal received in a pulsed noise jamming
environment is considered. The expectation-maximization … on the binary signal based on the …

… is used for a Constant False Alarm Rate (CFAR) threshold calculation for signal detection. …
In this paper, we used the Expectation Maximization algorithm to automatically extract useful …

… -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 …

… Therefore, we suggest the use of: 1) the expectation maximization (EM) algorithm, which
is an iterative computationally-efficient method used as an approximation of the maximum …

… Deinterleaving has applications in radar and signal detection. In [26], the problem of
deinterleaving time-interleaved pulse trains from a finite known number of periodic sources is …

… The Expectation Maximization (EM) algorithm has been widely used for parameter estimation
in data-driven process identification. EM is an algorithm for maximum likelihood estimation …