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Deep-Learning Based Channel Estimation for OFDM Wireless Communications
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- Tian, Guoda (author)
- Lund University,Lunds universitet,Kommunikationsteknologi,Forskargrupper vid Lunds universitet,Communications Engineering,Lund University Research Groups
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- Cai, Xuesong (author)
- Lund University,Lunds universitet,Kommunikationsteknologi,Forskargrupper vid Lunds universitet,Communications Engineering,Lund University Research Groups
- Zhou, Tian (author)
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- Wang, Weinan (author)
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- (creator_code:org_t)
- 2022
- 2022
- English.
- In: 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC).
- Conference paper (peer-reviewed)
Abstract
Subject headings
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- Multi-carrier technique is a backbone for modern commercial networks. However, the performances of multi-carrier systems in general depend greatly on the qualities of acquired channel state information (CSI). In this paper, we propose a novel deep-learning based processing pipeline to estimate CSI for payload time-frequency resource elements. The proposed pipeline contains two cascaded subblocks, namely, an initial denoise network (IDN), and a resolution enhancement network (REN). In brief, IDN applies a novel two-steps denoising structure while REN consists of pure fully-connected layers. Compared to existing works, our proposed processing architecture is more robust under lower signal-to-noise scenarios and delivers generally a significant gain.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)
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