Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems
Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems
复制标题
用于 MIMO-OFDM 无线系统中链路自适应的深度卷积神经网络
DOI：
10.1109/lwc.2018.2881978
复制
发表时间：
2019
影响因子：
6.3
通讯作者：
Zhang Shengli
中科院分区：
文献类型：
--
作者：
Elwekeil Mohamed;Jiang Shibao;Wang Taotao;Zhang Shengli
这封信提出了一种深度卷积神经网络（DCNN）方法，用于实际多输入多输出正交频分复用（MIMO-OFDM）系统中的自适应调制和编码。我们的目标是最大限度地提高吞吐量，并满足数据包错误率约束。我们考虑MIMO-OFDM接收机的实际损害，如不完美的定时同步，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差作为DCNN的输入特征。该方法的主要优点是：1）能够正确地学习MIMO-OFDM信道的特性并预测合适的调制和编码方案; 2）不需要复杂的特征选择。
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.