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On Performance of Deep Learning for Harmonic Spur Cancellation in OFDM Systems
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2020/02/01
Online ISSN: 1745-1337
Type of Manuscript: LETTER
Category: Mobile Information Network and Personal Communications
deep learning (DL), deep neural network (DNN), orthogonal frequency-division multiplexing (OFDM), carrier frequency offset (CFO), harmonic spur,
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In this letter, the performance of a state-of-the-art deep learning (DL) algorithm in  is analyzed and evaluated for orthogonal frequency-division multiplexing (OFDM) receivers, in the presence of harmonic spur interference. Moreover, a novel spur cancellation receiver structure and algorithm are proposed to enhance the traditional OFDM receivers, and serve as a performance benchmark for the DL algorithm. It is found that the DL algorithm outperforms the traditional algorithm and is much more robust to spur carrier frequency offset.