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

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.

Accepted paper in IEEE Wireless Communications Letters

References in corpus (4)

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• Intelligent Reflecting Surface-Enhanced OFDM: Channel Estimation and Reflection Optimization
• Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems
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• Deep Contextual Bandit and Reinforcement Learning for IRS-Assisted MU-MIMO Systems
• IRS-User Association in IRS-Aided MISO Wireless Networks: Convex Optimization and Machine Learning Approaches
• Deep-Learning-Based Channel Estimation for IRS-Assisted ISAC System
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• Federated Channel Learning for Intelligent Reflecting Surfaces With Fewer Pilot Signals