# DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Detection
###### Abstract  
Generalized optical multiple-input multiple-output (GOMIMO) techniques have been recently shown to be promising for high-speed optical wireless communication (OWC) systems. In this paper, we propose a novel deep learning-aided GOMIMO (DeepGOMIMO) framework for GOMIMO systems, wherein channel state information (CSI)-free detection can be enabled by employing a specially designed deep neural network (DNN)-based MIMO detector. The CSI-free DNN detector mainly consists of two modules: one is the preprocessing module, which is designed to address both the path loss and channel crosstalk issues caused by MIMO transmission, and the other is the feedforward DNN module, which is used for joint detection of spatial and constellation information by learning the statistics of both the input signal and the additive noise. Our simulation results clearly verify that, in a typical indoor 4 \(\times\) 4 MIMO-OWC system using both generalized optical spatial modulation (GOSM) and generalized optical spatial multiplexing (GOSMP) with unipolar nonzero 4-level pulse-amplitude modulation (4-PAM) modulation, the proposed CSI-free DNN detector achieves near the same bit error rate (BER) performance as the optimal joint maximum-likelihood (ML) detector, but with much-reduced computational complexity. Moreover, because the CSI-free DNN detector does not require instantaneous channel estimation to obtain accurate CSI, it enjoys the unique advantages of improved achievable data rate and reduced communication time delay in comparison to the CSI-based zero-forcing DNN (ZF-DNN) detector. optical wireless communication; multiple-input multiple-output; deep learning +
Footnote †: journal: _Photonics_  
high spectral efficiency, but suffers from severe interchannel interference (ICI) [10]. In contrast, OSM can remove ICI by activating a single LED to transmit signal at each time slot. Although OSM can transmit additional index bits, only one constellation symbol can be transmitted at each time slot, and hence it is challenging for OSM systems to achieve high spectral efficiency [11]. Lately, generalized optical MIMO (GOMIMO) techniques, including generalized OSM (GOSM) and generalized OSMP (GOSMP), have been further proposed to boost the capacity of MIMO-OWC systems [12; 13; 14; 15]. In GOSM systems, multiple LEDs are activated to transmit the same signal, and therefore more index bits can be transmitted and the diversity gain can also be increased. In GOSMP systems, only a subset of LEDs are activated to transmit different signals, resulting in reduced multiplexing gain. However, additional index bits can be transmitted, and the ICI can also been reduced in GOSMP systems. In order to successfully implement GOMIMO systems, an efficient MIMO detection scheme should be adopted. Generally, the joint maximum-likelihood (ML) detector serves as the optimal detector for GOMIMO systems [16]. Nevertheless, the ML detector usually has high computational complexity, making it infeasible in practical applications. Instead, the combination of zero-forcing (ZF) equalization and ML detection can be a practical low-complexity detection scheme for GOMIMO systems [16]. However, ZF equalization inevitably leads to noise amplification due to high channel correlation in typical indoor MIMO-OWC systems. Moreover, the ZF-ML detector also suffers from the adverse effect of error propagation, because the detection error of spatial symbols might propagate to the estimation of constellation symbols. With the rapid development of machine learning technology, machine learning has revealed its great potential in wireless communication systems [17; 18]. Moreover, machine learning techniques have also been widely applied in optical communication systems. In [19], a distributed collaborative learning approach was proposed for cognitive and autonomous multidomain elastic optical networking. In [20], two machine learning algorithms were proposed for bit error rate (BER) degradation detection and failure identification in elastic optical networks.