Jian Xiao

Jian Xiao

A Ph.D. student at CCNU

Publications

You can also find the full list of my articles on my Google Scholar profile or IEEE author page .

Journal Articles

Channel Estimation for Pinching-Antenna Systems (PASS)

Published in IEEE Communications Letters , 2025

This letter is the first to explore channel estimation for Pinching-Antenna SyStems (PASS), addressing their uniquely ill-conditioned and underdetermined channel characteristics. In particular, two efficient deep learning-based channel estimators are proposed. 1) PAMoE: This estimator incorporates dynamic padding, feature embedding, fusion, and mixture of experts (MoE) modules, which effectively leverage the positional information of PAs and exploit expert diversity. 2) PAformer: This Transformer-style estimator employs the self-attention mechanism to predict channel coefficients in a per-antenna manner, which offers more flexibility to adaptively deal with dynamic numbers of PAs in practical deployment. The code is available at PASS

Recommended citation: J. Xiao, J. Wang, and Y. Liu, "Channel Estimation for Pinching-Antenna Systems (PASS)," in IEEE Communications Letters. 2025.
Download Paper

Hybrid Near/Far-Field Channel Prediction for RIS-Aided LEO Satellite Networks

Published in IEEE Communications Letters , 2024

A hybrid near- and far- field cascaded channel prediction scheme is proposed for reconfigurable intelligent surface (RIS) assisted low earth orbit (LEO) satellite networks. The code is available at SIN

Recommended citation: J. Xiao, J. Wang, X. Li, W. Xie, N. C. Luong and A. Nallanathan, "Hybrid Near/Far-Field Channel Prediction for RIS-Aided LEO Satellite Networks," in IEEE Communications Letters, doi: 10.1109/LCOMM.2024.3489579, 2024.
Download Paper

Wideband Beamforming for RIS Assisted Near-Field Communications

Published in IEEE Transactions on Wireless Communications , 2024

A near-field wideband beamforming scheme is investigated for reconfigurable intelligent surface (RIS) assisted multiple-input multiple-output (MIMO) systems, in which a deep learning-based end-to-end (E2E) optimization framework is proposed to maximize the system spectral efficiency. To deal with the near-field double beam split effect, the base station is equipped with frequency-dependent hybrid precoding architecture by introducing sub-connected true time delay (TTD) units, while two specific RIS architectures, namely true time delay-based RIS (TTD-RIS) and virtual subarray-based RIS (SA-RIS), are exploited to realize the frequency-dependent passive beamforming at the RIS. [Code]

Recommended citation: J. Wang, J. Xiao, Y. Zou, W. Xie and Y. Liu, "Wideband Beamforming for RIS Assisted Near-Field Communications," in IEEE Transactions on Wireless Communications, 2024, accept to appear.
Download Paper

Multi-Task Learning for Near/Far Field Channel Estimation in STAR-RIS Networks

Published in IEEE Transactions on Communications , 2024

A joint cascaded channel estimation scheme is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) systems with hardware imperfections. The practical hybrid near- and far-field electromagnetic radiation with spatial non-stationarity is investigated. By exploiting the cascaded channel correlations between different users and between different STAR-RIS elements, a multi-task learning (MTL)-based channel estimation framework is proposed. The code is available at MTN

Recommended citation: J. Xiao, J. Wang, Z. Wang, J. Wang, W. Xie and Y. Liu, "Multi-Task Learning for Near/Far Field Channel Estimation in STAR-RIS Networks," in IEEE Transactions on Communications, vol. 72, no. 10, pp. 6344-6359, Oct. 2024.
Download Paper

Multi-Scale Attention Based Channel Estimation for RIS-Aided Massive MIMO Systems

Published in IEEE Transactions on Wireless Communications, 2024

A multi-scale attention based channel estimation framework is proposed for reconfigurable intelligent surface (RIS) aided massive multiple-input multiple-output systems, in which hardware imperfections and time-varying characteristics of the cascaded channel are investigated. By exploiting the spatial correlations of different scales in the RIS reflection element domain, we construct a Laplacian pyramid attention network (LPAN) to realize the high-dimensional cascaded channel reconstruction with limited pilot overhead. Furthermore, we leverage parameter sharing and recursion strategy to reduce the space complexity. Moreover, a selective fine-tuning strategy is developed to realize the domain adaption. The code is available at LPAN

Recommended citation: J. Xiao, J. Wang, Z. Wang, W. Xie and Y. Liu, "Multi-Scale Attention Based Channel Estimation for RIS-Aided Massive MIMO Systems," IEEE Transactions on Wireless Communications, vol. 23, no. 6, pp. 5969-5984, June 2024.
Download Paper

Self-Supervised Learning for Few-Shot Positioning

Published in IEEE Transactions on Vehicular Technology, 2024

A signal-guided masked autoencoder (S-MAE) based semi-supervised learning framework is proposed for high-precision positioning with limited labeled channel impulse response (CIR) samples. To release the overfiting effect of the neural network under insufficient labeled samples, we design a two-stage training strategy based on the proposed S-MAE model, which can be divided into pre-training and fine-tuning stage. The code is available at MAE-Position

Recommended citation: J. Wang, W. Fang, J. Xiao, Y. Zheng, L. Zheng and F. Liu, "Signal-Guided Masked Autoencoder for Wireless Positioning With Limited Labeled Samples," in IEEE Transactions on Vehicular Technology, 2024.
Download Paper

U-MLP-Based Hybrid-Field Channel Estimation for XL-RIS Assisted Millimeter-Wave MIMO Systems

Published in IEEE Wireless Communications Letters, 2023

To adress the hybrid-field cascaded channel estimation with spatial non-stationarity in the extremely large-scale RIS (XL-RIS) assisted millimeter wave systems, a U-shaped network based on the dedicated multilayer perceptron (MLP) architecture, termed as U-MLP, is propose to capture the long-range dependency of non-stationary channel and realize the channel channel reconstruction with limited pilot overhead. The code is available at U-MLP

Recommended citation: J. Xiao, J. Wang, Z. Chen and G. Huang, "U-MLP-Based Hybrid-Field Channel Estimation for XL-RIS Assisted Millimeter-Wave MIMO Systems," in IEEE Wireless Communications Letters, vol. 12, no. 6, pp. 1042-1046, June 2023, doi: 10.1109/LWC.2023.3259465.
Download Paper

Deep Reinforcement Learning for Shared Offloading Strategy in Vehicle Edge Computing

Published in IEEE Systems Journal, 2022

In order to reduce the computing load of edge servers and improve the system response, a shared offloading strategy based on deep reinforcement learning is proposed for the complex computing environment of Internet of Vehicles (IoVs). The shared offloading strategy exploits the commonality of vehicles task requests, similar computing tasks coming from different vehicles can share the computing results of former task submitted.

Recommended citation: X Peng, Z. Han, W. Xie, C. Yu, P. Zhu, J. Xiao and J. Yang, "Deep Reinforcement Learning for Shared Offloading Strategy in Vehicle Edge Computing," in IEEE Systems Journal, vol. 17, no. 2, pp. 2089-2100, June 2023
Download Paper

Intelligent Channel Estimation, Feedback and Prediction for RIS Systems

Published in IEEE CL & WCL , 2022

1) We proposed a wavelet-driven learnable soft-thresholding network architecture to joint estimate the direct channel and cascaded channel in RIS systems at the same coherence time by learning the feature of shared pilots. 2) We proposed a deep compressed sensing framework to reduce the pilot overhead of cascaded channel estimation for RIS-aided Massive MIMO communication system. 3) We proposed a Transformer empowered quantized sample framework for CSI compression and reconstruction in FDD RIS systems. 4) We proposed a distributed learning-based joint channel estimation and feedback scheme for RIS-aided multi-user systems. 5) We proposed a linear network architecture to realize the channel prediction for RIS assisted UAV-LEO Communications.

Recommended citation:
[1] W. Xie, J. Xiao, P. Zhu, C. Yu and L. Yang, "Deep Compressed Sensing-Based Cascaded Channel Estimation for RIS-Aided Communication Systems," in IEEE Wireless Communications Letters, vol. 11, no. 4, pp. 846-850, April 2022.[ Paper]
[2] W. Xie, J. Xiao, P. Zhu and C. Yu, "Multi-Task Learning-Based Channel Estimation for RIS Assisted Multi-User Communication Systems," in IEEE Communications Letters, vol. 26, no. 3, pp. 577-581, March 2022.[ Paper]
[3] W. Xie, J. Zou, J. Xiao, M. Li and X. Peng, "Quan-Transformer Based Channel Feedback for RIS-Aided Wireless Communication Systems," in IEEE Comzomunications Letters, vol. 26, no. 11, pp. 2631-2635, Nov. 2022.[ Paper]
[4] J. Zou, Q. Mao, J. Xiao, S. Liu, and Y. Liang, "Distributed Learning-Based Channel Estimation and Feedback for RIS-Aided Massive MIMO Systems," IEEE Wireless Communications Letters, DOI: 10.1109/LWC.2024.3509612, 2024.[ Paper]
[5] J. Wang, S. Gong, J. Xiao, J. Wang, and X. Li, "A lightweight channel prediction network for UAV-LEO Satellite Communications," IEEE Wireless Communications Letters, DOI: 10.1109/LWC.2024.3489677, 2024[ Paper].

Sustainable AI for Cooperative NOMA Networks

Published in Journal of Electronics & Information Technology (电子与信息学报) & Journal of Beijing University of Posts and Telecommunications (北京邮电大学学报), 2021

1) We proposed a deep adder network to achieve sustainable NOMA modulation detection in short packet transmission of mMTC scenarios, in which the convolution operations required by traditional CNN architecture are replaced by the adder operations with low energy consumption. 2) We proposed a multi-task federated learning framework to exploit a deep receiver architecture for cooperative MIMO-NOMA systems. The above works were independently accomplished during my master training period.

Recommended citation:
[1] J. Wang，Z. Li，J. Xiao，H. Li，W. Xie，C. Chao, “Deep adder network for NOMA modulation detection in short packet transmission” (in Chinese )，Journal of Electronics & Information Technology，2024. [ Paper]
[2] W. Xie，P. Li，J. Xiao，J. Wang，L. Yang, “Multi-task federated learning for deep receiver in cooperative MIMO-NOMA systems” (in Chinese )，Journal of Beijing University of Posts and Telecommunications，2024.[ Paper]

Conference Papers

Multi-Task Learning Based Channel Estimation for Hybrid-Field STAR-RIS Systems

Published in GLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2023

A joint cascaded channel estimation framework is proposed for simultaneously transmitting and reflecting recon-figurable intelligent surfaces (STAR-RIS) systems with hardware imperfection, in which practical the hybrid-field electromagnetic wave radiation with spatial non-stationarity is investigated. By exploiting the cascaded channel correlations in user domain and STAR-RIS element domain, we propose a multitask network (MTN) with multi-expert branches to simultaneously reconstruct the high-dimensional transmitting and reflecting channels from the observed mixture channel with noise.

Recommended citation: J. Xiao, J. Wang, Y. Liu, W. Xie, J. Wang and S. Liu, "Multi-Task Learning Based Channel Estimation for Hybrid-Field STAR-RIS Systems," GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023, pp. 6573-6578.
Download Paper | Download Slides

Multi-Scale Supervised Learning-Based Channel Estimation for RIS-Aided Communication Systems

Published in 2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023

Motivated by the development of single image super-resolution (SR) reconstruction in computer version, classic SR networks have been widely applied to the channel estimation of wireless communication system. To capture the spatial correlations in the reflection element-domain of reconfigurable intelligent surface (RIS), we propose a multi-scale supervised learning-based Laplacian pyramid wide residual network (LapWRes) to achieve the progressive reconstruction of cascaded channel in a coarse-to-fine fashion.

Recommended citation: J. Xiao, J. Wang, W. Xie, X. Wang, C. Wang and H. Xu, "Multi-Scale Supervised Learning-Based Channel Estimation for RIS-Aided Communication Systems," 2023 IEEE Wireless Communications and Networking Conference (WCNC), Glasgow, United Kingdom, 2023.
Download Paper | Download Slides