Examinando por Autor "Carro-Calvo, Leopoldo"

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Deep Neural Network: An Alternative to Traditional Channel Estimators in Massive MIMO Systems
(IEEE, 2022-04-05) Melgar, Antonio; de la Fuente, Alejandro; Carro-Calvo, Leopoldo; Barquero-Pérez, Óscar; Morgado, Eduardo
Fifth-generation (5G) requires a highly accurate estimate of the channel state information (CSI) to exploit the benefits of massive multiple-input-multiple-output (MaMIMO) systems. 5G systems use pilot sequences to estimate channel behaviour using traditional methods like least squares (LS), or minimum mean square error (MMSE) estimation. However, traditional methods do not always obtain reliable estimations: LS exhibits a poor estimation when inadequate channel conditions (i.e., low- signal-to-noise ratio (SNR) region) and MMSE requires prior statistical knowledge of the channel and noise (complex to implement in practice). We present a deep learning framework based on deep neural networks (DNNs) for fifth-generation (5G) MaMIMO channel estimation. After a first preliminary scheme with which we verify the good estimation capacity of our DNN-based approach, we propose two different models, which differ in the information processed by the DNN and benefit from lower computational complexity or greater flexibility for any reference signal pattern, respectively. The results show that, compared to the LS-based channel estimation, the DNN approach decreases the mean square error (MSE) and the system’s spectral efficiency (SE) increases, especially in the low- SNR region. Our approach provides results close to optimal MMSE estimation but benefits from not requiring any prior channel statistics information.
Massive MIMO Channel Estimation With Convolutional Neural Network Structures
(Institute of Electrical and Electronics Engineers, 2024-07-29) Carro-Calvo, Leopoldo; de la Fuente, Alejandro; Melgar, Antonio; Morgado, Eduardo
Massive multiple-input-multiple-output (mMIMO) enables a significant increase in capacity in fifth-generation (5G) communications systems, both in beamforming and spatial multiplexing scenarios, demanding highly accurate channel estimates. We present two models based on convolutional neural networks (CNNs) for 5G mMIMO channel estimation that differ in complexity and flexibility. The results achieved with both models are competitive compared to traditional methods, such as least squares (LS) which presents a poor estimate in the low signal-to-noise ratio (SNR) region, or minimum mean square error (MMSE) which requires prior statistical knowledge of the channel and noise estimation. Furthermore, the proposed CNN models outperform estimation structures based on conventional deep neural networks (DNNs). Our approach achieves results close to the MMSE estimates, improving them in the low SNR regime, and enabling them to a wide range of channel conditions, i.e., variability in time, frequency, and SNR, not requiring any prior channel statistics information. Furthermore, we present a deep analysis of the computational and cost complexity, demonstrating the suitability of the proposed models for real hardware structure implementation