In this letter, we propose a deep learning-based dual mode orthogonal frequency division multiplexing with index modulation (DM-OFDM-IM) detector called DeepDM, which is close to optimal bit error rate (BER) performance with low computational complexity. DeepDM adopts a concatenation of convolutional neural network (CNN) and deep neural network (DNN) to detect index bits and carrier bits separately. A loss function is proposed to train the CNN and the DNN to approach the BER performance of the maximum likelihood detector. In addition, we propose a training method with selected data samples to make the neural networks converge fast. It is shown via simulations that DeepDM shows advantages over conventional detectors in terms of the BER performance and the computational complexity under the Rayleigh fading channel.

中文翻译：

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基于深度学习的双模 OFDM 索引调制检测器


在这封信中，我们提出了一种基于深度学习的双模正交频分复用索引调制（DM-OFDM-IM）检测器，称为 DeepDM，它接近最佳误码率（BER）性能，且计算复杂度较低。 DeepDM采用卷积神经网络（CNN）和深度神经网络（DNN）的串联来分别检测索引位和载波位。提出了损失函数来训练 CNN 和 DNN，以接近最大似然检测器的 BER 性能。此外，我们提出了一种使用选定数据样本的训练方法，以使神经网络快速收敛。仿真结果表明，DeepDM 在瑞利衰落信道下的 BER 性能和计算复杂度方面优于传统检测器。