On Performance of Deep Learning for Harmonic Spur Cancellation in OFDM Systems

Ziming HE  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E103-A   No.2   pp.576-579
Publication Date: 2020/02/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.2019EAL2114
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 [5] 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.