Mutli-Level Autoencoder: Deep Learning Based Channel Coding and Modulation
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| Format: | Preprint |
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2025
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| _version_ | 1866908428076580864 |
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| author | Abdel-Qader, Ahmad Chaaban, Anas Shehata, Mohamed S. |
| author_facet | Abdel-Qader, Ahmad Chaaban, Anas Shehata, Mohamed S. |
| contents | In this paper, we design a deep learning-based convolutional autoencoder for channel coding and modulation. The objective is to develop an adaptive scheme capable of operating at various signal-to-noise ratios (SNR)s without the need for re-training. Additionally, the proposed framework allows validation by testing all possible codes in the codebook, as opposed to previous AI-based encoder/decoder frameworks which relied on testing only a small subset of the available codes. This limitation in earlier methods often led to unreliable conclusions when generalized to larger codebooks. In contrast to previous methods, our multi-level encoding and decoding approach splits the message into blocks, where each encoder block processes a distinct group of $B$ bits. By doing so, the proposed scheme can exhaustively test $2^{B}$ possible codewords for each encoder/decoder level, constituting a layer of the overall scheme. The proposed model was compared to classical polar codes and TurboAE-MOD schemes, showing improved reliability with achieving comparable, or even superior results in some settings. Notably, the architecture can adapt to different SNRs by selectively removing one of the encoder/decoder layers without re-training, thus demonstrating flexibility and efficiency in practical wireless communication scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23511 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mutli-Level Autoencoder: Deep Learning Based Channel Coding and Modulation Abdel-Qader, Ahmad Chaaban, Anas Shehata, Mohamed S. Signal Processing Emerging Technologies In this paper, we design a deep learning-based convolutional autoencoder for channel coding and modulation. The objective is to develop an adaptive scheme capable of operating at various signal-to-noise ratios (SNR)s without the need for re-training. Additionally, the proposed framework allows validation by testing all possible codes in the codebook, as opposed to previous AI-based encoder/decoder frameworks which relied on testing only a small subset of the available codes. This limitation in earlier methods often led to unreliable conclusions when generalized to larger codebooks. In contrast to previous methods, our multi-level encoding and decoding approach splits the message into blocks, where each encoder block processes a distinct group of $B$ bits. By doing so, the proposed scheme can exhaustively test $2^{B}$ possible codewords for each encoder/decoder level, constituting a layer of the overall scheme. The proposed model was compared to classical polar codes and TurboAE-MOD schemes, showing improved reliability with achieving comparable, or even superior results in some settings. Notably, the architecture can adapt to different SNRs by selectively removing one of the encoder/decoder layers without re-training, thus demonstrating flexibility and efficiency in practical wireless communication scenarios. |
| title | Mutli-Level Autoencoder: Deep Learning Based Channel Coding and Modulation |
| topic | Signal Processing Emerging Technologies |
| url | https://arxiv.org/abs/2506.23511 |