The channel estimation and signal detection are key issues in the Multi-Input Multi-Output and Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system. However, there exist severe impacts of phase noise (PN) on the estimations with the application of higher frequency in 5th Generation New Radio (5G-NR). In this paper, the joint channel estimation and signal detection (JCESD) method based on the maximum a posteriori (MAP) criterion under the assumption of Wiener process for PN is proposed in MIMO-OFDM system, which is called as the JCESD-PN-MAP method. Firstly, the MAP criterion is derived based on Bayesian theories and the structures of matrices in MAP criterion are analyzed to simplify the optimizations. Secondly, the optimizations for PN of receiving and transmitting antennas are translated into solving a tridiagonal linear equation and a sparse linear equation, respectively, which are optimized by the Gaussian elimination (GE) method with low computation complexity. To further reduce the computational complexity, the latter is solved by the alternating direction method of multipliers (ADMM) method. Thirdly, the optimizations for channel responses and transmitted signals are translated into solving two block diagonal linear equations, which are solved by calculating the inverse matrix with low computation complexity. The numerical results and complexity analysis confirm the effectiveness of our proposed method in terms of the accuracy and computation complexity.

Solving constrained nonlinear programs (NLPs) is of great importance in various domains such as power systems, robotics, and wireless communication networks. One widely used approach for addressing NLPs is the interior point method (IPM). The most computationally expensive procedure in IPMs is to solve systems of linear equations via matrix factorization. Recently, machine learning techniques have been adopted to expedite classic optimization algorithms. In this work, we propose using Long Short-Term Memory (LSTM) neural networks to approximate the solution of linear systems and integrate this approximating step into an IPM. The resulting approximate NLP solution is then utilized to warm-start an interior point solver. Experiments on various types of NLPs, including Quadratic Programs and Quadratically Constrained Quadratic Programs, show that our approach can significantly accelerate NLP solving, reducing iterations by up to 60% and solution time by up to 70% compared to the default solver.

Abstract Based on empirical data of an orthogonal frequency division multiplexing system in realistic environments of next‐generation cellular networks, a new analytical model of phase noise and numerical characteristics of the common phase error and intercarrier interference are derived. Applying an asymptotic theory of probability, analytical expressions are present to approximate the mean vector and the covariance matrix of the intercarrier interference. The approximation expression of the covariance matrix is accurate enough and only has three parameters. When applied to estimate original symbols based on additive white Gaussian noise channel, a Gibbs sampler performs better than the current estimation algorithm following Weiner process phase noise.

We build a Coxian random variable model to describe characterizations of a bathtub-shaped failure rate and present a perturbation to the Coxian random variable. In the perturbation, the corresponding probability density function is a linear combination of exponentials, and weights may be negative. The clear mechanism of the perturbation reveals that numerical error of the computation includes cancellation error. By studying weights in a perturbation, we can control absolute errors of all intermediate results. As the involving probability density function is a summation, the total absolute error of the final result is not larger than the sum of absolute errors of intermediate results. Hence, we can suggest the level of numerical precision to control errors. With an accurate likelihood function, some algorithms of maximum likelihood estimates can be established. We evaluate proposed algorithms through simulation examples and empirical data.

There have been various algorithms designed for simulating natural evolution. This paper proposes a new simulated evolutionary computation model called the abstract evolutionary algorithm (AEA), which unifies most of the currently known evolutionary algorithms and describes the evolution as an abstract stochastic process composed of two fundamental operators: selection and evolution operators. By axiomatically characterizing the properties of the fundamental selection and evolution operators, several general convergence theorems and convergence rate estimations for the AEA are established. The established theorems are applied to a series of known evolutionary algorithms, directly fielding new convergence conditions and convergence rate estimations of various specific genetic algorithms and evolutionary strategies. The present work provides a significant step toward the establishment of a unified theory of simulated evolutionary computation.