Abstract
Fifth generation wireless communication requires the higher data rates to meet the requirements of real-world applications. However, the conventional multiple-input-multiple output (MIMO) technology unable to meet these requirements due to low performance channel estimation methods. Therefore, this article is focused on implementation of Massive MIMO technology with artificial intelligence assisted deep learning convolutional neural network (DLCNN)-based channel estimation. In Massive MIMO environment, channel is affecting by various types of uncertainties like noise, fading effects, and multipath propagations, which resulting reduced channel estimation performance in receiver side. Thus, the DLCNN model is trained with the different types of channel conditions and estimated the perfect channel response matrix. The simulations performed using MatllabR2022a shows that the proposed DLCNN channel estimation resulted in superior spectrum efficiency, energy efficiency, and base station density performance as compared to traditional channel estimators.
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Navitha, C., Anuradha, P. (2023). Implementation of Massive MIMO Technology with Artificial Intelligence Assisted Deep Learning Convolutional Neural Network (DLCNN)-Based Channel Estimation. In: Kumar, A., Ghinea, G., Merugu, S. (eds) Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing. ICCIC 2022. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-99-2742-5_31
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DOI: https://doi.org/10.1007/978-981-99-2742-5_31
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