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MAJSCC: Mamba-based adaptive joint source channel coding for wireless image transmission
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DOI：10.1117/1.JEI.35.2.023005.png)
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Abstract
En 中文
Lightweight and efficient neural network models for joint source-channel coding (JSCC) are critical for advancing semantic communication. We propose an adaptive JSCC architecture, named Mamba-based adaptive joint source-channel coding (MAJSCC), which is explicitly designed for wireless image transmission tasks. By integrating the Mamba architecture into both the encoder and decoder, the architecture enhances local feature representation and incorporates an adaptive mechanism to flexibly adjust to diverse channel conditions and transmission rates. Furthermore, the proposed network utilizes wavelet convolution to exploit a broader spectrum of signal information during training, thereby improving its ability to capture high-resolution image details. Comprehensive experimental evaluations demonstrate that the proposed MAJSCC achieves comparable or superior performance in large-scale, high-resolution image transmission tasks. Compared with the state-of-the-art BPG + 5G LDPC-coded systems (executed on CPU), it delivers faster end-to-end encoding speeds (accelerated on graphics processing unit), with a compact model design that ensures higher efficiency than traditional CNN-based JSCC methods.
Keywords:
semantic communication
Mamba architecture
adaptive mechanism
convolutional neural network
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