On the Performance of Autoencoder-Based Space Optical Communications

El-Rahman, Abd; El-Fikky, Abd El-Rahman A.; Rezki, Zouheir

doi:10.1109/globecom48099.2022.10001440

Cited by 6 publications

(15 citation statements)

References 14 publications

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“…Accordingly, it is essential to also take into consideration higher frequency spectrums such as the optical spectrum for wireless communication. When compared to RF communications, optical wireless communications (OWC) and space optical communications (SOC) offer several benefits over their RF counterparts, including lower transmission power, licensefree spectrum, higher throughput, and cost-effective installation [1].…”

Section: A Backgroundmentioning

confidence: 99%

“…Large information bandwidth, low transmitted power, improved directionality, and immunity to jamming are the obvious benefits for SOC. SOC has been widely considered by many space agencies worldwide in a variety of practical applications [1]- [3]. One of the most common application scenarios for SOC includes communication between a geostationary Earth orbit (GEO) satellite, and ground stations [2].…”

Section: A Backgroundmentioning

confidence: 99%

“…This work is a substantial extension of [1]. In [1], we assumed a perfect CSI in symbol detection and excluded any channel estimation calculations for simplicity.…”

Section: Challenge and Contributionmentioning

confidence: 99%

“…This work is a substantial extension of [1]. In [1], we assumed a perfect CSI in symbol detection and excluded any channel estimation calculations for simplicity. However, the assumption that receiver knows the fading coefficients perfectly may not be viable in practical scenarios.…”

Section: Challenge and Contributionmentioning

confidence: 99%

“…This is the author's version which has not been fully edited an content may change prior to final publication. Draw m minibatch samples (h (1) , h (2) , . .…”

Section: A Learning Algorithmmentioning

confidence: 99%

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

Elfikky,

Rezki

2024

Trans. Mach. Learn. Comm. Netw.

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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.

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Section: A Backgroundmentioning

confidence: 99%

Section: A Backgroundmentioning

confidence: 99%

“…This work is a substantial extension of [1]. In [1], we assumed a perfect CSI in symbol detection and excluded any channel estimation calculations for simplicity.…”

Section: Challenge and Contributionmentioning

confidence: 99%

Section: Challenge and Contributionmentioning

confidence: 99%

“…This is the author's version which has not been fully edited an content may change prior to final publication. Draw m minibatch samples (h (1) , h (2) , . .…”

Section: A Learning Algorithmmentioning

confidence: 99%

See 3 more Smart Citations

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

Elfikky,

Rezki

2024

Trans. Mach. Learn. Comm. Netw.

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Learning-Based Autoencoder for Multiple Access and Interference Channels in Space Optical Communications

Elfikky,

Soltani,

Rezki

2023

IEEE Commun. Lett.

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Deep Learning Based End-to-End Optical Wireless Communication Systems With Autoencoders

Safi,

Tavakkolnia,

Haas

2024

IEEE Commun. Lett.

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The utilization of neural network-based autoencoders (AEs) for the implementation of the physical layer in communication systems has recently emerged as a promising technique for achieving end-to-end optimization of communication links. However, applying conventional AE architecture to intensity modulation/direct detection optical wireless systems is challenging due to positive real-value constraint, eye safety standards, and the limited dynamic range of light sources. To address these issues, in this paper we propose a practical architecture, namely differential AE, that incorporates the concept of differential signaling. This approach allows the transmission of negative encoder output elements. In a shot-noise limited scenario, we evaluate and contrast the performance of differential AE, conventional AE, and a non-AE classical system, showcasing the superior bit-error rate achieved by the differential AE.

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On the Performance of Autoencoder-Based Space Optical Communications

Cited by 6 publications

References 14 publications

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

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

Learning-Based Autoencoder for Multiple Access and Interference Channels in Space Optical Communications

Deep Learning Based End-to-End Optical Wireless Communication Systems With Autoencoders

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