Intelligent Surface Millimeter-wave Beamforming Based on Deep Learning
To deal with the problem of deep learning-based Large Intelligent Surface(LIS)millimeter-wave beamforming,a LIS with a limited number of active components is incorporated,then the performance of beamforming under the Convolutional Neural Network(CNN)algorithm is scrutinized through a series of experimental comparisons.The ray-tracing channel data from the DeepMIMO dataset are used to construct communication scenarios in specific environments,and the neural network models,CNN,Back Propagation(BP),and Multi-Layer Perceptron(MLP)algorithms are employed to learn the millimeter-wave communication environment.The experiments take various parameter conditions into consideration,including the number of paths,the quantity of active elements,transmit power and dataset size.The results indicate that the CNN algorithm outperforms the other two algorithms under all parameter conditions.Furthermore,as the number of paths and active elements increases,the performance advantage of the CNN algorithm becomes even more pronounced.Additionally,increasing the dataset size also contributes to enhancing the performance of the CNN algorithm.The experimental findings provide valuable references for the research and practical applications in related fields,and hold significant importance in enhancing the performance of millimeter-wave communication systems.
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