论文标题
双模式索引调制3D-OFDM的深度学习信号检测
Deep Learning-Based Signal Detection for Dual-Mode Index Modulation 3D-OFDM
论文作者
论文摘要
在本文中，我们提出了一个基于Duaim-3DNet的深度学习信号检测器，用于基于双模式调制的三维（3D）正交频施加多路复用（DM-IM-IM-3D-OFDM）。此处，DM-IM-3D-OFDM是一个子载波索引调制方案，它通过双模式3D星座符号和活动子载波的索引传达数据位。因此，使用常规最大似然（ML）检测器时，该方案比现有IM方案获得了更好的错误性能，但是，该检测器的计算复杂性较高，尤其是当系统参数增加时。为了解决这个基本问题，我们建议以数据驱动的方式共同且可靠地检测到接收者的深神经网络（DNN）的使用，以共同且可靠地检测DM-IM-3D-OFDM的符号和索引位。仿真结果表明，与ML检测器相比，我们提出的DNN检测器在运行时复杂性下的运行时复杂性差不多。
In this paper, we propose a deep learning-based signal detector called DuaIM-3DNet for dual-mode index modulation-based three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM). Herein, DM-IM-3D- OFDM is a subcarrier index modulation scheme which conveys data bits via both dual-mode 3D constellation symbols and indices of active subcarriers. Thus, this scheme obtains better error performance than the existing IM schemes when using the conventional maximum likelihood (ML) detector, which, however, suffers from high computational complexity, especially when the system parameters increase. In order to address this fundamental issue, we propose the usage of a deep neural network (DNN) at the receiver to jointly and reliably detect both symbols and index bits of DM-IM-3D-OFDM under Rayleigh fading channels in a data-driven manner. Simulation results demonstrate that our proposed DNN detector achieves near-optimal performance at significantly lower runtime complexity compared to the ML detector.
