Research from National Taipei University of Technology Has Provided New Study Fi ndings on Machine Learning (Machine Learning-Based Channel Estimation Techniques for ATSC 3.0)
Research from National Taipei University of Technology Has Provided New Study Fi ndings on Machine Learning (Machine Learning-Based Channel Estimation Techniques for ATSC 3.0)
台北工业大学的研究为机器学习提供了新的研究成果(ATSC 3.0基于机器学习的信道估计技术)
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摘要
由一名新闻记者兼机器人与机器学习每日新闻的新闻编辑-一项关于人工智能的新研究现在可用。根据NewsRx Jo Urnalists在台湾台北的新闻报道，研究表明：“信道估计精度对正交频分复用(OFDM)系统的性能有很大影响。”这项研究的资金支持者包括台湾国家科技委员会。新闻记者从台北理工大学的研究中得到一句话:「在文献中,有相当多的频道估计方法,然而,摘要:当无线信道受到非线性失真和干扰时,这些方法的性能会明显下降。机器学习(ML)在解决非参数问题方面显示出很大的潜力。本文针对梳形ype导频模式和随机导频符号系统,提出了基于ml的信道估计方法。以ATSC 3.0.为例，我们将其与ATSC 3.0系统中线性R和非线性信道模型的传统信道估计性能进行了比较，并评估了基于ML的方法对信道模型失配和信噪比(SNR)失配的鲁棒性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news reporting from Taipei, Taiwan, by NewsRx jo urnalists, research stated, “Channel estimation accuracy significantly affects t he performance of orthogonal frequency-division multiplexing (OFDM) systems.” Financial supporters for this research include National Science And Technology C ouncil, Taiwan. The news journalists obtained a quote from the research from National Taipei Uni versity of Technology: “In the literature, there are quite a few channel estimat ion methods. However, the performances of these methods deteriorate considerably when the wireless channels suffer from nonlinear distortions and interferences. Machine learning (ML) shows great potential for solving nonparametric problems. This paper proposes ML-based channel estimation methods for systems with comb-t ype pilot patterns and random pilot symbols, such as ATSC 3.0. We compare their performances with conventional channel estimations in ATSC 3.0 systems for linea r and nonlinear channel models. We also evaluate the robustness of the ML-based methods against channel model mismatch and signal-to-noise ratio (SNR) mismatch. ”
Key words
National Taipei University of Technology/Taipei/Taiwan/Asia/Cyborgs/Emerging Technologies/Machine Learning
NETL
NSTL