arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.11757cs.ITcs.LGmath.IT
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基于深度学习的循环前缀不足MIMO-OFDM系统解扩器

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

Huawei Technology(华为技术有限公司);Southeast University(东南大学)

Yan Sun, Chao Wang, Huan Cai, Chunming Zhao, Yiqun Wu, Yan Chen

摘要:

研究了循环前缀不足时多输入多输出(MIMO)正交频分复用(OFDM)系统的均衡设计问题。特别是当多径时延扩展超过CP长度时,载波间干扰(ICI)和符号间干扰(ISI)会严重影响信号检测性能。为了解决这个问题,提出了一种基于深度学习的均衡器来近似最大似然检测。受相邻子载波之间的依赖性的启发,开发了一种计算效率高的联合检测方案。采用建议的均衡器,一个迭代接收机也被构造和检测性能进行了评估,通过模拟测量多径信道。我们的研究结果表明,建议的接收机可以实现显着的性能改善相比,两个传统的基线计划。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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