Symbol Detection and Channel Estimation using Neural Networks in Optical Communication Systems

Aboutaleb, Ahmed; Fatnassi, Wael; Soltani, Mehdi; Rezki, Zouheir

doi:10.1109/icc.2019.8761449

Cited by 2 publications

(13 citation statements)

References 17 publications

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“…The NN estimator in [26] is designed for specific peak intensity level, leading to poor performance when tested with different intensities or corresponding SNRs. This complicates adaptive systems and requires frequent retraining for varying intensities.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…To undertake this issue, the authors in [25] proposed a DL-enabled image denoising network to acquire knowledge from a huge set of training data and to compute an estimate of the massive MIMO visible light communication (VLC) channel. Furthermore, it was shown in [26] that a NN with one hidden layer and sigmoid activation functions can be trained to get an accurate channel state information (CSI) estimates in a Log-normal fading. However, the system therein is not practical as it needs a NN for every training SNR.…”

Section: ) Channel Estimationmentioning

confidence: 99%

“…In [23], the authors propose employing only one NN to rectify the LS estimation error. The results in [23] show that their NN design outperforms LS estimator but it is simpler in implementation compared with [26]. Despite their accurate CSI prediction results, the authors in [23] relied on an unrealistic assumption that all the input samples are already known in advance for the testing phase.…”

Section: ) Channel Estimationmentioning

confidence: 99%

“…Achieving an adequate MSE while maintaining a low complexity model in SOC is a challenging task. While several research studies based on learning frameworks achieved good MSE results in OWC, their designs involved high complexity schemes since it is necessary to create a NN for each SNR value [26].…”

Section: Challenge and Contributionmentioning

confidence: 99%

“…However, the resulting performance was found to be inferior to that of the optimal MMSE estimator. Accordingly, the results in [23], [26] inspired us to develop a channel estimator which involves a single NN with a non-uniform strategy that is robust along a wide range of SNRs and achieves equivalent performance as the MMSE channel estimator and outperforms the LS channel estimator. In addition, our implementation does not require the knowledge of all input samples in advance for the testing phase, as required in [23].…”

Section: Challenge and Contributionmentioning

confidence: 99%

See 4 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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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: Simulation Resultsmentioning

confidence: 99%

Section: ) Channel Estimationmentioning

confidence: 99%