Deep Learning-Based Detector for OFDM-IM
Deep Learning-Based Detector for OFDM-IM
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DOI：
10.1109/lwc.2019.2909893
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发表时间：
2019-08-01
影响因子：
6.3
通讯作者：
Matthaiou, Michail
中科院分区：
文献类型：
--
作者：
Thien Van Luong;Ko, Youngwook;Matthaiou, Michail
这封信首次尝试将深度学习（DL）用于正交频分复用（OFDM-IM）系统的信号检测。特别是，我们提出了一种新的基于DL的检测器称为DeepIM，它采用了一个深度神经网络与完全连接的层恢复数据位在OFDM-IM系统。为了提高DeepIM的性能，在进入网络之前，基于领域知识对接收的信号和信道向量进行预处理。使用模拟收集的数据集，DeepIM首先进行离线训练以最小化误码率（BER），然后将训练好的模型用于OFDM-IM的在线信号检测。仿真结果表明，DeepIM可以以比现有手工制作的检测器更低的运行时间实现接近最佳的BER。
This letter presents the first attempt of exploiting deep learning (DL) in the signal detection of orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems. Particularly, we propose a novel DL-based detector termed as DeepIM, which employs a deep neural network with fully connected layers to recover data bits in an OFDM-IM system. To enhance the performance of DeepIM, the received signal and channel vectors are pre-processed based on the domain knowledge before entering the network. Using datasets collected by simulations, DeepIM is first trained offline to minimize the bit error rate (BER) and then the trained model is deployed for the online signal detection of OFDM-IM. Simulation results show that DeepIM can achieve a near-optimal BER with a lower runtime than existing hand-crafted detectors.