NeuromorphicRx: From Neural to Spiking Receiver
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908694777692160 |
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| author | Gupta, Ankit Dizdar, Onur Chen, Yun Kadan, Fehmi Emre Sattarzadeh, Ata Wang, Stephen |
| author_facet | Gupta, Ankit Dizdar, Onur Chen, Yun Kadan, Fehmi Emre Sattarzadeh, Ata Wang, Stephen |
| contents | In this work, we propose a novel energy-efficient spiking neural network (SNN)-based receiver for 5G-NR OFDM system, called neuromorphic receiver (NeuromorphicRx), replacing the channel estimation, equalization and symbol demapping blocks. We leverage domain knowledge to design the input with spiking encoding and propose a deep convolutional SNN with spike-element-wise residual connections. We integrate an SNN with artificial neural network (ANN) hybrid architecture to obtain soft outputs and employ surrogate gradient descent for training. We focus on generalization across diverse scenarios and robustness through quantized aware training. We focus on interpretability of NeuromorphicRx for 5G-NR signals and perform detailed ablation study for 5G-NR signals. Our extensive numerical simulations show that NeuromorphicRx is capable of achieving significant block error rate performance gain compared to 5G-NR receivers and similar performance compared to its ANN-based counterparts with 7.6x less energy consumption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_05246 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NeuromorphicRx: From Neural to Spiking Receiver Gupta, Ankit Dizdar, Onur Chen, Yun Kadan, Fehmi Emre Sattarzadeh, Ata Wang, Stephen Neural and Evolutionary Computing Information Theory In this work, we propose a novel energy-efficient spiking neural network (SNN)-based receiver for 5G-NR OFDM system, called neuromorphic receiver (NeuromorphicRx), replacing the channel estimation, equalization and symbol demapping blocks. We leverage domain knowledge to design the input with spiking encoding and propose a deep convolutional SNN with spike-element-wise residual connections. We integrate an SNN with artificial neural network (ANN) hybrid architecture to obtain soft outputs and employ surrogate gradient descent for training. We focus on generalization across diverse scenarios and robustness through quantized aware training. We focus on interpretability of NeuromorphicRx for 5G-NR signals and perform detailed ablation study for 5G-NR signals. Our extensive numerical simulations show that NeuromorphicRx is capable of achieving significant block error rate performance gain compared to 5G-NR receivers and similar performance compared to its ANN-based counterparts with 7.6x less energy consumption. |
| title | NeuromorphicRx: From Neural to Spiking Receiver |
| topic | Neural and Evolutionary Computing Information Theory |
| url | https://arxiv.org/abs/2512.05246 |