Efficient satellite communication is critical for ensuring seamless data
transmission across various applications, including remote sensing,
defense, and global connectivity. Traditional signal processing
techniques face challenges such as signal degradation, interference,
and bandwidth limitations, reducing overall transmission efficiency.
Advanced optimization algorithms can enhance signal integrity,
mitigate noise, and improve data throughput. This study proposes an
adaptive hybrid optimization framework integrating Deep Learning-
based Channel Estimation (DL-CE) with an Enhanced Error
Correction Model (EECM). The DL-CE employs a Convolutional
Neural Network (CNN) combined with a Recurrent Neural Network
(RNN) to predict channel variations dynamically, reducing
transmission errors by 32.5%. Meanwhile, the EECM incorporates
Low-Density Parity-Check (LDPC) codes optimized using a Genetic
Algorithm (GA) to enhance error correction efficiency, leading to a
27.8% reduction in bit error rate (BER) compared to conventional
LDPC codes. Experimental evaluations on real-time satellite
transmission datasets demonstrate a 21.3% improvement in spectral
efficiency and a 36.4% enhancement in data throughput. Comparative
analysis with traditional Orthogonal Frequency-Division Multiplexing
(OFDM) and Turbo coding-based error correction confirms that the
proposed method achieves a lower BER of 1.02 × 10?³, higher peak
signal-to-noise ratio (PSNR) of 42.8 dB, and increased data
transmission speed of 1.8 Gbps.
I. Gugan1, V. Praveena2, Seyed M. Buhari3
Dr. N.G.P. Institute of Technology, India1,2, Universiti Teknologi Brunei, Brunei3
Satellite Communication, Deep Learning, Error Correction, Spectral Efficiency, Data Throughput
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| Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 16 , Issue: 1 , Pages: 3449 - 3453 )
Date of Publication :
March 2025
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40
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