Real-time complex-valued RF blind source separation via in-physics computing

The rise of edge-centric 6G networks demands real-time intelligent processing of heterogeneous radio-frequency (RF) signals. Complex-valued blind source separation (CV-BSS) provides a physical-layer capability, enabling the blind extraction of source components from overlapping RF signals. Here, we demonstrate an integrated photonic vector processor (IPVP) that implements real-time CV-BSS via in-physics computation directly on raw RF signals. The IPVP operates across multiple RF bands up to 24 GHz and achieves blind, in-hardware adaptation to unknown sources and channels via in situ learning. Experiments demonstrate the efficacy of IPVP in multiple-input multiple-output (MIMO) communications and multi-band radar interference suppression. The IPVP reduces the EVMs of the two recovered signals from over 50% to 6.18% and 5.3%, respectively, while enhancing the SIR by up to 26 dB. This work demonstrates, to our knowledge, the first photonic processor capable of real-time CV-BSS at RF, highlighting the potential of integrated photonic hardware for ultralow-latency, edge-intelligent signal processing.

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Publication Details

Journal
Science Advances
Published
2026-11-11
DOI
https://doi.org/10.1126/sciadv.aeg1515
Primary Topic
Advanced Photonic Communication Systems
Type
article
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article

Real-time complex-valued RF blind source separation via in-physics computing

Xingyuan Xu, Yunping Bai, Kun Xu, Sai Tak Chu et al.
Science Advances
Advanced Photonic Communication Systems
article

Real-time complex-valued RF blind source separation via in-physics computing

Xingyuan Xu, Yunping Bai, Kun Xu, Sai Tak Chu, Sha Zhu, Chaoran Huang, Xiaotian Zhu, Shifan Chen, Shuying Li, David Moss, Jiajia Wang, Chao Wang, Yifu Xu, Zhihui Liu, 张姝然 Zhang Shuran, Haoran Zhang, Yuyang Liu, Peiming Yan
article en

Abstract

The rise of edge-centric 6G networks demands real-time intelligent processing of heterogeneous radio-frequency (RF) signals. Complex-valued blind source separation (CV-BSS) provides a physical-layer capability, enabling the blind extraction of source components from overlapping RF signals. Here, we demonstrate an integrated photonic vector processor (IPVP) that implements real-time CV-BSS via in-physics computation directly on raw RF signals. The IPVP operates across multiple RF bands up to 24 GHz and achieves blind, in-hardware adaptation to unknown sources and channels via in situ learning. Experiments demonstrate the efficacy of IPVP in multiple-input multiple-output (MIMO) communications and multi-band radar interference suppression. The IPVP reduces the EVMs of the two recovered signals from over 50% to 6.18% and 5.3%, respectively, while enhancing the SIR by up to 26 dB. This work demonstrates, to our knowledge, the first photonic processor capable of real-time CV-BSS at RF, highlighting the potential of integrated photonic hardware for ultralow-latency, edge-intelligent signal processing.

Science AdvancesVol. 12(46)
Beijing University of Posts and Telecommunications (CN), City University of Hong Kong (HK), Chinese University of Hong Kong (HK), Nankai University (CN), China Telecom (CN), State Key Laboratory of Information Photonics and Optical Communications (CN), Swinburne University of Technology (AU)
Advanced Photonic Communication Systems
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