Abstract
In this paper, we propose an orthogonal frequency division multiplexing (OFDM)-based generalized optical quadrature spatial modulation (GOQSM) technique for multiple-input multiple-output optical wireless communication (MIMO-OWC) systems. Considering the error propagation and noise amplification effects when applying maximum likelihood and maximum ratio combining (ML-MRC)-based detection, we further propose a deep neural network (DNN)-aided detection for OFDM-based GOQSM systems. The proposed DNN-aided detection scheme performs the GOQSM detection in a joint manner, which can efficiently eliminate the adverse effects of both error propagation and noise amplification. The obtained simulation results successfully verify the superiority of the deep learning-aided OFDM-based GOQSM technique for high-speed MIMO-OWC systems.
Highlights
W ITH the explosive increasing of mobile data traffic in recent years, traditional radio frequency communication technologies such as WiFi might not be able to support the heavy mobile data traffic in the near future
Note that the number of hidden layers and the number of neurons in each hidden layer adopted in the deep neural network (DNN) are obtained after multiple trials, which can ensure that the DNN achieves satisfactory performance with relatively low computational complexity
In order to address the adverse effects of error propagation and noise amplification in the orthogonal frequency division multiplexing (OFDM)-based generalized optical quadrature spatial modulation (GOQSM) system using the two-step maximum likelihood and maximum ratio combining (MLMRC) detection, we have further proposed a DNN-aided detection scheme
Summary
W ITH the explosive increasing of mobile data traffic in recent years, traditional radio frequency communication technologies such as WiFi might not be able to support the heavy mobile data traffic in the near future. To increase the spectral efficiency contributed by the spatial index symbols and enhance the overall spectral efficiency of OSM systems, several modified OSM schemes have been proposed in the literature. Combining spectral-efficient OFDM modulation with OSM schemes can be an efficient and low-complexity way to increase the overall spectral efficiency of OWC systems. We propose an OFDM-based GOQSM technique for substantial spectral efficiency enhancement of bandlimited OWC systems. We further propose two detection schemes for OFDM-based GOQSM systems, including maximum likelihood and maximum ratio combining (MLMRC)-based detection and deep neural network (DNN)-aided detection. Simulation results show that the proposed OFDMbased GOQSM technique greatly outperforms OFDM-based GOSM when applying deep learning-aided detection
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