English(EN) Digital Self-Interference Cancellation for In-Band Full-Duplex Systems Based on Transformer Neural Operators

Transformer神经网络算子增强全双工系统中的自干扰消除

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了一种新颖的带内全双工系统数字自干扰消除方法，将其视为一个算子学习问题。该方法利用Transformer神经网络算子从历史数据中学习残余自干扰的时间演变。与可能因强期望信号而降低性能的传统方法不同，该新框架在无信号环境下进行训练，然后应用，在各种干扰水平下均表现出稳定的消除性能，并将SIR为30 dB时的消除效果提高了5-15 dB。 AI

影响这种新方法可以提高全双工通信系统的效率和可靠性，可能对未来的无线网络基础设施产生影响。

排序理由详细介绍新技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Zhijie Su, Shiquan Zhang, Qiaolin He · 2026-10-15 04:00

基于Transformer神经网络算子的带内全双工系统的数字自干扰消除

arXiv:2610.19490v1 Announce Type: cross Abstract: Digital-domain self-interference cancellation is essential in in-band full-duplex radios after passive suppression and analog cancellation. It is particularly challenging when the desired signal is strong relative to the residual …

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基于Transformer神经网络算子的带内全双工系统的数字自干扰消除

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