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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.12941 |
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| _version_ | 1866914380412616704 |
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| author | Yellapragada, SaiKrishna Saketh Kocharlakota, Atchutaram K. Costa, Mário Ollila, Esa Vorobyov, Sergiy A. |
| author_facet | Yellapragada, SaiKrishna Saketh Kocharlakota, Atchutaram K. Costa, Mário Ollila, Esa Vorobyov, Sergiy A. |
| contents | Deep learning-based neural receivers offer promising physical-layer solutions for next-generation wireless systems. We propose an axial self-attention transformer neural receiver that achieves state-of-the-art Block Error Rate (BLER) performance with significantly improved computational efficiency during inference and large-scale training. By factorizing attention operations along temporal and spectral axes, the proposed architecture reduces computational complexity from $O((TF)^2)$ to $O(T^2F+TF^2)$, yielding substantially fewer floating-point operations and attention matrix multiplications per transformer block. Experimental validation under 3GPP Clustered Delay Line (CDL) channels demonstrates consistent performance gains across varying mobility scenarios. Under non-line-of-sight conditions, our proposed axial neural receiver outperforms global self-attention and convolutional neural receiver baselines at 10% BLER and 1% BLER respectively, with reduced computational complexity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12941 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Computationally Efficient Neural Receivers via Axial Self-Attention Yellapragada, SaiKrishna Saketh Kocharlakota, Atchutaram K. Costa, Mário Ollila, Esa Vorobyov, Sergiy A. Signal Processing Deep learning-based neural receivers offer promising physical-layer solutions for next-generation wireless systems. We propose an axial self-attention transformer neural receiver that achieves state-of-the-art Block Error Rate (BLER) performance with significantly improved computational efficiency during inference and large-scale training. By factorizing attention operations along temporal and spectral axes, the proposed architecture reduces computational complexity from $O((TF)^2)$ to $O(T^2F+TF^2)$, yielding substantially fewer floating-point operations and attention matrix multiplications per transformer block. Experimental validation under 3GPP Clustered Delay Line (CDL) channels demonstrates consistent performance gains across varying mobility scenarios. Under non-line-of-sight conditions, our proposed axial neural receiver outperforms global self-attention and convolutional neural receiver baselines at 10% BLER and 1% BLER respectively, with reduced computational complexity. |
| title | Computationally Efficient Neural Receivers via Axial Self-Attention |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2510.12941 |