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Transformer neural operators enhance self-interference cancellation in full-duplex systems

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel approach to digital self-interference cancellation in in-band full-duplex systems by framing it as an operator-learning problem. This method utilizes a Transformer neural operator to learn the temporal evolution of residual self-interference from historical data. Unlike conventional methods that can degrade performance with strong desired signals, this new framework is trained in a signal-free setting and then applied, demonstrating stable cancellation performance across various interference levels and improving cancellation by 5-15 dB at a 30 dB SIR. AI

IMPACT This new method could improve the efficiency and reliability of full-duplex communication systems, potentially impacting future wireless network infrastructure.

RANK_REASON Academic paper detailing a new technical approach. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Transformer neural operators enhance self-interference cancellation in full-duplex systems

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Academic paper detailing a new technical approach. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zhijie Su, Shiquan Zhang, Qiaolin He ·

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

    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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