Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems

Elwekeil Mohamed; Jiang Shibao; Wang Taotao; Zhang Shengli

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Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems

机译：深度卷积神经网络用于MIMO-OFDM无线系统中的链路自适应

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This letter proposes a deep convolutional neural network (DCNN) approach for adaptive modulation and coding in practical multiple-input, multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Our target is to maximize the throughput and fulfill a packet error rate constraint. We consider practical impairments of MIMO-OFDM receiver, such as imperfect timing synchronization, carrier frequency offset correction, and channel estimation. We treat the estimated channel state information and the noise standard deviation as input features to the DCNN. The main advantages of the proposed approach are: 1) it learns the characteristics of the MIMO-OFDM channel properly and predicts the suitable modulation and coding scheme and 2) it does not need complex features selection.

机译：这封信提出了一种深度卷积神经网络（DCNN）方法，用于在实际的多输入，多输出正交频分复用（MIMO-OFDM）系统中进行自适应调制和编码。我们的目标是最大化吞吐量并满足分组错误率约束。我们考虑了MIMO-OFDM接收机的实际缺陷，例如不完善的时序同步，载波频率偏移校正和信道估计。我们将估计的信道状态信息和噪声标准偏差作为DCNN的输入特征。所提出的方法的主要优点是：1）它可以适当地学习MIMO-OFDM信道的特性并预测合适的调制和编码方案； 2）它不需要复杂的特征选择。

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来源
《Wireless Communications Letters, IEEE》 |2019年第3期|665-668|共4页
作者
Elwekeil Mohamed; Jiang Shibao; Wang Taotao; Zhang Shengli;
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作者单位

Shenzhen Univ, Coll Informat Engn, Shenzhen 518060, Peoples R China|Menoufia Univ, Fac Elect Engn, Dept Elect & Elect Commun Engn, Menoufia 32952, Egypt;

Shenzhen Univ, Coll Informat Engn, Shenzhen 518060, Peoples R China;

Shenzhen Univ, Coll Informat Engn, Shenzhen 518060, Peoples R China;

Shenzhen Univ, Coll Informat Engn, Shenzhen 518060, Peoples R China;

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原文格式 PDF
正文语种 eng
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关键词
Multiple-input; multiple-output (MIMO); orthogonal frequency division multiplexing (OFDM); adaptive modulation and coding (AMC); deep convolutional neural networks;

机译：多输入;多输出（MIMO）;正交频分复用（OFDM）;自适应调制和编码（AMC）;深度卷积神经网络;

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3. Adaptation in Convolutionally Coded MIMO-OFDM Wireless Systems Through Supervised Learning and SNR Ordering [J] . Daniels R. C., Caramanis C. M., Heath R. W. Vehicular Technology, IEEE Transactions on . 2010,第1期

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机译：MIMO-OFDM无线网络的链路自适应增强型动态信道分配

Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems

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