SIGNETS: Neural Network Architectures for m-QAM Soft Demodulation
This paper presents a novel approach to Quadrature Amplitude Modulation (QAM) demodulation using neural networks, addressing the limitations of traditional demodulation techniques in complex channel conditions. Through systematic Neural Architecture Search and Hyper-parameter optimization, we develo...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11071691/ |
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| Summary: | This paper presents a novel approach to Quadrature Amplitude Modulation (QAM) demodulation using neural networks, addressing the limitations of traditional demodulation techniques in complex channel conditions. Through systematic Neural Architecture Search and Hyper-parameter optimization, we develop a family of Convolutional Neural Network architectures that demonstrate robust performance across challenging channel conditions, including multi-path fading, inter-symbol interference, and non-linear distortions, without requiring explicit channel estimation. We comparatively evaluate the members of the family of networks to find a Pareto-optimal Neural Network Demodulator with a balance of demodulation accuracy and computational cost, achieving an average accuracy of 99.658% across 4 dB to 24 dB SNR while requiring less than 16,000 Floating-Point Operations (FLOPs) for every demodulated QAM-16 symbol. A practical Field Programmable Gate Array (FPGA) implementation that achieves 2.52 Million bits per second throughput while maintaining 99.55% demodulation accuracy through structured pruning and quantization-aware training is presented. Experimental validation over acoustic channels demonstrates superior performance compared to traditional techniques, with performance further enhanced through fine-tuning. |
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| ISSN: | 2169-3536 |