Deep Channel Learning for Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems

• Published in: IEEE wireless communications letters Vol. 9; no. 9; pp. 1447 - 1451
• Main Authors: Elbir, Ahmet M., Papazafeiropoulos, Anastasios, Kourtessis, Pandelis, Chatzinotas, Symeon
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Array signal processing; Artificial neural networks; Channel estimation; Complexity theory; Deep learning; large intelligent surfaces; Machine learning; massive MIMO; Millimeter waves; MIMO (control systems); MIMO communication; Surface waves; Training
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2020.2993699

This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated.