Deep Learning Based Equalizer for MIMO-OFDM Systems with Insufficient Cyclic Prefix

• URL: http://arxiv.org/abs/2007.11757v1
• Date: Thu, 23 Jul 2020 02:21:24 GMT
• Title: Deep Learning Based Equalizer for MIMO-OFDM Systems with Insufficient Cyclic Prefix
• Authors: Yan Sun, Chao Wang, Huan Cai, Chunming Zhao, Yiqun Wu, Yan Chen
• Abstract summary: In particular, the signal detection performance is severely impaired by inter-carrier interference (ICI) and inter-symbol interference (ISI) To tackle this problem, a deep learning-based equalizer is proposed for approximating the maximum likelihood detection. Our results reveal that the proposed receiver can achieve significant performance improvement compared to two traditional baseline schemes.
• Score: 11.11468231197267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study the equalization design for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with insufficient cyclic prefix (CP). In particular, the signal detection performance is severely impaired by inter-carrier interference (ICI) and inter-symbol interference (ISI) when the multipath delay spread exceeding the length of CP. To tackle this problem, a deep learning-based equalizer is proposed for approximating the maximum likelihood detection. Inspired by the dependency between the adjacent subcarriers, a computationally efficient joint detection scheme is developed. Employing the proposed equalizer, an iterative receiver is also constructed and the detection performance is evaluated through simulations over measured multipath channels. Our results reveal that the proposed receiver can achieve significant performance improvement compared to two traditional baseline schemes.

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