TDJSCC: Low Complexity and Bandwidth Efficient Deep Joint Source-Channel Coding With OFDM

The practical deployment of deep joint source-channel coding (DJSCC) in edge devices faces two critical limitations. First, the prohibitive computational complexity of deep neural networks hinders efficiency. Second, existing OFDM-based systems suffer from bandwidth inefficiency due to dedicated pil...

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Vydáno v:IEEE access Ročník 13; s. 150244 - 150257
Hlavní autoři: Xu, Man, Lam, Chan-Tong, Liang, Yuanhui, Ng, Benjamin, Yuan, Xiaochen, Im, Sio-Kei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Artificial neural networks
Bandwidth
Bandwidths
Channel coding
Channel estimation
Coding
Complexity
Computational complexity
Computational modeling
Computer architecture
Datasets
Decomposition
Deep joint source-channel coding
Efficiency
Floating point arithmetic
Fourier transforms
in-band pilots
Neural networks
OFDM
Orthogonal Frequency Division Multiplexing
Singular value decomposition
Spectral efficiency
Symbols
tensorized convolutional neural network
Tensors
ISSN:2169-3536, 2169-3536
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Shrnutí:The practical deployment of deep joint source-channel coding (DJSCC) in edge devices faces two critical limitations. First, the prohibitive computational complexity of deep neural networks hinders efficiency. Second, existing OFDM-based systems suffer from bandwidth inefficiency due to dedicated pilot symbol allocation. To address these challenges, we propose tensorized deep joint source-channel coding (TDJSCC), a novel DJSCC framework that integrates a tensorized convolutional neural network (TCNN) with in-band pilot-augmented orthogonal frequency-division multiplexing (OFDM). The TCNN decomposes high-dimensional convolution kernels into cascaded low-rank tensor operations through singular value decomposition (SVD). Simultaneously, our in-band pilot design eliminates dedicated pilot symbols by strategically replacing data subcarriers with pilot tones. This approach achieves 100% bandwidth efficiency while maintaining channel estimation accuracy through optimized discrete Fourier transform (DFT) interpolation. The simulation results demonstrate that the proposed TDJSCC model outperforms the existing DJSCC model on low-resolution datasets and achieves comparable performance for high-resolution datasets, with 87% fewer parameters and <inline-formula> <tex-math notation="LaTeX">3.1\times </tex-math></inline-formula> floating-point operations (FLOPs) reduction. Furthermore, the proposed TDJSCC achieves improved performance, significantly lower computational complexity, and full bandwidth efficiency.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3602059