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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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11071691/ |
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| author | Aravind R. Voggu Kanish R Nishith Akula Lohitaksh Maruvada Takanori Shimizu Madhav Rao |
| author_facet | Aravind R. Voggu Kanish R Nishith Akula Lohitaksh Maruvada Takanori Shimizu Madhav Rao |
| author_sort | Aravind R. Voggu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fe47a0bea4524bd0b15a3f8a61bbf63c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fe47a0bea4524bd0b15a3f8a61bbf63c2025-08-25T23:18:39ZengIEEEIEEE Access2169-35362025-01-011311707811708910.1109/ACCESS.2025.358604311071691SIGNETS: Neural Network Architectures for m-QAM Soft DemodulationAravind R. Voggu0https://orcid.org/0000-0002-6242-1164Kanish R1https://orcid.org/0009-0000-1640-5852Nishith Akula2https://orcid.org/0009-0005-6299-0825Lohitaksh Maruvada3https://orcid.org/0009-0004-8244-6846Takanori Shimizu4https://orcid.org/0009-0000-3920-2652Madhav Rao5https://orcid.org/0000-0003-2278-9148International Institute of Information Technology Bangalore, Bengaluru, Karnataka, IndiaInternational Institute of Information Technology Bangalore, Bengaluru, Karnataka, IndiaInternational Institute of Information Technology Bangalore, Bengaluru, Karnataka, IndiaInternational Institute of Information Technology Bangalore, Bengaluru, Karnataka, IndiaSony India Software Centre, Bengaluru, Karnataka, IndiaInternational Institute of Information Technology Bangalore, Bengaluru, Karnataka, IndiaThis 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.https://ieeexplore.ieee.org/document/11071691/CNNcommunicationFPGALLRmachine learningmultipath components |
| spellingShingle | Aravind R. Voggu Kanish R Nishith Akula Lohitaksh Maruvada Takanori Shimizu Madhav Rao SIGNETS: Neural Network Architectures for m-QAM Soft Demodulation IEEE Access CNN communication FPGA LLR machine learning multipath components |
| title | SIGNETS: Neural Network Architectures for m-QAM Soft Demodulation |
| title_full | SIGNETS: Neural Network Architectures for m-QAM Soft Demodulation |
| title_fullStr | SIGNETS: Neural Network Architectures for m-QAM Soft Demodulation |
| title_full_unstemmed | SIGNETS: Neural Network Architectures for m-QAM Soft Demodulation |
| title_short | SIGNETS: Neural Network Architectures for m-QAM Soft Demodulation |
| title_sort | signets neural network architectures for m qam soft demodulation |
| topic | CNN communication FPGA LLR machine learning multipath components |
| url | https://ieeexplore.ieee.org/document/11071691/ |
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