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DNN-Aided Optimized Constellation Schemes for Coded Modulation
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DOI：10.1587/transfun.2025eal2058.png)
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Abstract
En 中文
Modulation is crucial for multiplexing, reducing bandwidth, and improving transmission efficiency. By incorporating neural networks, we can optimize modulation for specific communication channels. We propose two neural network optimized modulation schemes: a regular constellation mapping for 16-ary modulation and an irregular mapping for 2m-ary modulation. These maintain gradient flow during backpropagation, allowing adjustments to constellation points to minimize bit error rates (BER) while keeping system complexity manageable. The results show our polar-coded modulation schemes outperform traditional uniform QAM with about a 0.5 dB gain under low SNR. Additionally, these schemes can also be applied to LDPC-coded modulation systems to improve BER performance.
Keywords:
constellation mapping
modulation
neural networks
polar codes
Journal
IF:
0.4
Papers:
210
Citations:
1.3K