Deep-Learning Based Channel Estimation for OFDM Wireless Communications

Guoda Tian, Xuesong Cai, Tian Zhou, Weinan Wang, Fredrik Tufvesson

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceedingPeer review


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.
Titel på värdpublikation2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)
StatusAccepted/In press - 2022

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