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Federated Learning for Channel Estimation in Conventional and IRS-Assisted Massive MIMO
Ahmet M. Elbir,Sinem Coleri +1 more
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TLDR
A convolutional neural network (CNN) trained on the local datasets of the users without sending them to the BS is designed and the proposed CNN architecture exhibits lower estimation error than the state-of-the-art ML-based channel estimation schemes.Abstract:
Machine learning (ML) has attracted a great research interest for the problems in the physical layer of wireless communications, such as channel estimation, thanks to its low computational complexity and robustness against imperfect channel data. Channel estimation via ML requires model training on a dataset, which usually includes the received pilot signals as input and channel data as output. In previous works, model training is mostly done in a centralized manner, where the whole training dataset is collected from the users at the base station (BS). This approach introduces huge transmission overhead for data collection from the users. In this paper, to address this challenge, we propose a federated learning (FL) framework for channel estimation. We design a convolutional neural network (CNN) trained on the local datasets of the users without sending them to the BS. We develop FL-based channel estimation schemes for both conventional and IRS (intelligent reflecting surface) assisted massive MIMO (multiple-input multiple-output) systems, where a single CNN is trained for two different datasets for both scenarios. Even if the IRS-assisted massive MIMO includes two different channels, namely the direct and cascaded channels, their estimation is performed with a single CNN, without using multiple CNNs for each task. Via numerical simulations, we evaluate the performance for noisy and quantized model transmission and show that the proposed approach provides approximately 16 times lower transmission overhead than the centralized learning (CL) schemes, while maintaining satisfactory channel estimation performance close to CL. Furthermore, the proposed CNN architecture exhibits lower estimation error than the state-of-the-art ML-based channel estimation schemes.read more
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Federated Learning for 6G: Applications, Challenges, and Opportunities.
TL;DR: In this article, the authors provide a comprehensive study on the applications of federated learning for 6G wireless networks and discuss the key requirements in applying FL for wireless communications, identifying the main problems, challenges, and providing a comprehensive treatment of implementing FL techniques for wireless communication.
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A Survey on Channel Estimation and Practical Passive Beamforming Design for Intelligent Reflecting Surface Aided Wireless Communications.
TL;DR: In this paper, the authors provide a comprehensive survey on the up-to-date research in IRS-aided wireless communications, with an emphasis on the promising solutions to tackle practical design issues.
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AIRIS: Artificial intelligence enhanced signal processing in reconfigurable intelligent surface communications
TL;DR: In this paper, the merging of artificial intelligence and reconfigurable intelligent surface (RIS) has been discussed, including environmental sensing, channel acquisition, beamforming design, and resource scheduling.
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A Survey of Deep Learning Architectures for Intelligent Reflecting Surfaces.
TL;DR: This article provides a synopsis of these techniques for designing DL-based IRS-assisted wireless systems using architectures such as supervised, unsupervised and reinforcement learning.
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Interplay Between RIS and AI in Wireless Communications: Fundamentals, Architectures, Applications, and Open Research Problems
TL;DR: The road to implementing the combination of RIS and AI is explored, more specifically, integrating AI-enabled technologies into RIS-based frameworks for maximizing the practicality of RIS to facilitate the realization of smart radio propagation environments.
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
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Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays
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TL;DR: The gains in multiuser systems are even more impressive, because such systems offer the possibility to transmit simultaneously to several users and the flexibility to select what users to schedule for reception at any given point in time.
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