2D Generalized Optical Spatial Modulation for MIMO-OWC Systems
2D Generalized Optical Spatial Modulation for MIMO-OWC Systems
复制标题
MIMO-OWC 系统的 2D 广义光空间调制
DOI：
10.1109/jphot.2022.3192651
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发表时间：
2022-08
影响因子：
2.4
通讯作者：
Harald Haas
中科院分区：
文献类型：
--
作者：
Chen Chen;Lin Zeng;Xin Zhong;Shu Fu;Zhihong Zeng;Min Liu;Harald Haas
提出了一种新的多输入多输出光无线通信（MIMO-OWC）系统中的二维广义光空间调制（GOSM）方案。通过将多个连续的时隙分组为一个时间块，不仅可以在空间域而且可以在时域中执行2D GOSM映射。具体而言，设计了两种类型的2D GOSM映射方案，包括2D-1和2D-2 GOSM映射。此外，为了解决最佳联合最大似然（ML）检测的高复杂度问题以及基于迫零的ML（ZF-ML）检测的噪声放大和错误传播问题，还为2D GOSM系统设计了深度神经网络（DNN）辅助检测方案。仿真结果表明，所提出的2D GOSM方案与深度学习辅助检测的高速和低复杂度的MIMO-OWC系统的优越性。更具体地说，当应用DNN辅助检测时，与传统的一维（1D）GOSM相比，2D GOSM可以实现显着的3.4dB信噪比（SNR）增益。
In this paper, a novel two-dimensional (2D) generalized optical spatial modulation (GOSM) scheme is proposed for multiple-input multiple-output optical wireless communication (MIMO-OWC) systems. By grouping multiple successive time slots as one time block, 2D GOSM mapping can be performed not only in the space domain but also in the time domain. Specifically, two types of 2D GOSM mapping schemes are designed, including 2D-1 and 2D-2 GOSM mappings. Moreover, to address the high complexity issue of optimal joint maximum-likelihood (ML) detection and the noise amplification and error propagation issues of zero-forcing-based ML (ZF-ML) detection, a deep neural network (DNN)-aided detection scheme is further designed for 2D GOSM systems. Simulation results demonstrate the superiority of the proposed 2D GOSM scheme with deep learning-aided detection for high-speed and low-complexity MIMO-OWC systems. More specifically, a remarkable 3.4-dB signal-to-noise ratio (SNR) gain can be achieved by 2D GOSM in comparison to the conventional one-dimensional (1D) GOSM, when applying the DNN-aided detection.