Symbol Detection and Channel Estimation for Space Optical Communications Using Neural Network and Autoencoder

Abstract

Optical wireless communications in space are degraded by atmospheric turbulence, light attenuation, and detector noise. In this paper, we develop a neural network (NN) channel estimator that is optimized across a wide range of signal-to-noise ratio levels during the training stage. In addition, we propose a novel autoencoder (AE) model to develop a complete physical layer communication system in space optical communications (SOC). The AE is designed to work with both perfect and imperfect channel state information (CSI), providing a flexible and versatile solution for SOC. Batch normalization and multiple-decoders are incorporated into the proposed AE, which improves receiver learning capabilities by allowing the use of more than one path to update encoder and decoder weights. This novel approach can reduce the error in detection relative to state-of-the-art models. Using the system tool kit simulator, we examine our system's performance in a downlink SOC channel that connects a geostationary satellite to a ground station in Log-normal fading channel. Furthermore, we evaluate the performance of our system in a downlink channel that establishes a connection between a Low Earth Orbit satellite and a ground station, operating in Gamma-Gamma fading channel. The numerical results show that the proposed channel estimator NN is superior to state-of-the-art learning-based frameworks and achieves the same level of performance as the minimum mean square error estimator. Additionally, with no fading and for both perfect and imperfect CSI with different code rates and fading channels, the proposed AE-based detection outperforms both benchmark learning frameworks and most popular convolutional codes.INDEX TERMS Deep learning, channel estimation, symbol detection, space optical communications, system tool kit.

The Past, Present, and Future of the Internet: A Statistical, Technical, and Functional Comparison of Wired/Wireless Fixed/Mobile Internet

“…ML and AI will play a key role in automating network operations and optimizing the user experience. Some of these actions required for broadband high-speed Internet are end-to-end learning frameworks for channel estimation and symbol detection [117], ML for localization and positioning in Internet-based applications [118], improving data speed and quality, interference mitigation in multiple access [119], and prediction and balancing traffic. Service providers can remotely manage networks and troubleshoot problems in real-time with AI-ML.…”

The Past, Present, and Future of the Internet: A Statistical, Technical, and Functional Comparison of Wired/Wireless Fixed/Mobile Internet

“…ML and AI will play a key role in automating network operations and optimizing the user experience. Some of these actions required for broadband high-speed Internet are end-to-end learning frameworks for channel estimation and symbol detection [117], ML for localization and positioning in Internet-based applications [118], improving data speed and quality, interference mitigation in multiple access [119], and prediction and balancing traffic. Service providers can remotely manage networks and troubleshoot problems in real-time with AI-ML.…”