Channel estimation for reconfigurable intelligent surface‐aided millimeter‐wave massive multiple‐input multiple‐output system with deep residual attention network
IF 1.3 4区 计算机科学Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
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引用次数: 0
Abstract
We first model the channel estimation in sixth‐generation (6G) systems as a super‐resolution problem and adopt a deep residual attention approach to learn the nontrivial mapping from the received measurement to the reconfigurable intelligent surface (RIS) channel. Subsequently, we design a deep residual attention‐based channel estimation framework (DRA‐Net) to exploit the RIS channel distribution characteristics. Furthermore, to transfer the RIS channel feature maps extracted from the residual attention blocks (RABs) to the end of the estimator for accurate reconstruction, we propose a novel and effective feature fusion approach. The simulation results demonstrate that the proposed DRA‐Net‐based channel estimation method outperforms other deep learning‐based and conventional algorithms.
期刊介绍:
ETRI Journal is an international, peer-reviewed multidisciplinary journal published bimonthly in English. The main focus of the journal is to provide an open forum to exchange innovative ideas and technology in the fields of information, telecommunications, and electronics.
Key topics of interest include high-performance computing, big data analytics, cloud computing, multimedia technology, communication networks and services, wireless communications and mobile computing, material and component technology, as well as security.
With an international editorial committee and experts from around the world as reviewers, ETRI Journal publishes high-quality research papers on the latest and best developments from the global community.