Channel Prediction Using a System of Ordinary Differential Equation

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@InProceedings{Wang:2022:ICCT,

• author = "Lei Wang2 and Guanzhang Liu and Jiang Xue",
• booktitle = "2022 IEEE 22nd International Conference on Communication Technology (ICCT)",
• title = "Channel Prediction Using a System of Ordinary Differential Equation",
• year = "2022",
• pages = "1009--1014",
• abstract = "For massive Multiple-input Multiple-output (MIMO) systems, it is crucial to predict channel state information (CSI) at future moments. Outdated CSI in mobile scenarios will have a serious negative impact on the transmission system, resulting in system performance degradation. Timely and accurate channel prediction can compensate for the loss of system performance caused by mobility. We propose a hybrid evolutionary method to identify ordinary differential equation (ODE) systems to predict CSI, called HEODE. First of all, the evolution algorithm based on tree structure is used to evolve the system architecture, and the explicit ODE model is obtained. Then, the parameters of ODEs are optimised by optimisation algorithm. Finally, the optimal ODE model for CSI prediction is obtained. Besides, the effective of interval prediction is given. Compared to the autoregressive (AR) and genetic programming (GP) based prediction methods, simulation results show that the proposed method is robust and effective.",
• keywords = "genetic algorithms, genetic programming, System performance, Simulation, Systems architecture, Massive MIMO, Predictive models, Ordinary differential equations, Prediction algorithms, Massive MIMO, Channel prediction, Hybrid evolutionary method, Optimisation, Ordinary differential equation",
• DOI = "10.1109/ICCT56141.2022.10073053",
• ISSN = "2576-7828",
• month = nov,
• notes = "Also known as \cite{10073053}",

}

Genetic Programming entries for Lei Wang2 Guanzhang Liu Jiang Xue

Citations