Deep Channel Learning for Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
Ahmet M. Elbir, Anastasios Papazafeiropoulos, Pandelis Kourtessis, Symeon Chatzinotas
Abstract
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.
Topics & Concepts
Computer scienceMIMOConvolutional neural networkDeep learningChannel (broadcasting)Artificial intelligenceComputer engineeringMulti-user MIMOArtificial neural networkArchitecture3G MIMOSurface (topology)Deep neural networksElectronic engineeringReal-time computingComputer architectureKey (lock)Signal-to-noise ratio (imaging)Solid modelingSignal processingAdvanced Wireless Communication TechnologiesMillimeter-Wave Propagation and ModelingAdvanced Data and IoT Technologies