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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
Basketball
Miami Heat & Dwyane Wade 
Chinese chess
A amateur 
My Creation
A casual thought 
Story
Memory & Gone 
Football
Arsenal & Ars è ne Wenger 
literature
Poet & Haizi 
publications
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]
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].
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
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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
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
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.
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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.
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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.
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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.
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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.
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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.
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talks
Awards in Academic Achievement
Published:
[2022-5] The first prize graduate scholarship (Pacemaker to Merit Student) in HNIST (Award-winning Proportion = 5/30). [Certificate]
[2021-10] The first prize graduate scholarship (Pacemaker to Merit Student) in HNIST (Award-winning Proportion = 5/30). [Certificate]
[2019-5] The first prize undergraduate academic scholarship (Pacemaker to Merit Student) in HNIST (Award-winning Proportion = 3/130). [Certificate]
Awards in Wireless AI Competition
Published:
[2021-9] The fourth place for AI-based channel estimation task in Wireless Communication AI Competition (WAIC) 2021 hosted by 5G+AI Work Group of IMT-2020 (5G) Promotion Group. Code.
[2022-4] The fourth place for AI based Channel Modeling and Generating task in 6G AI-Competition hosted by Guangdong OPPO Mobile Telecommunications Corp., Ltd. Code. (Contributators: Bingbing Ouyang, Jian Zou, Jian Xiao)
[2022-11] The tenth place for AI-based Joint Channel Estimation and Channel State Information Feedback task in Wireless Communication AI Competition (WAIC) 2022 hosted by 5G+AI Work Group of IMT-2020 (5G) Promotion Group. Code. (Contributators: Chunling Zhao, Jian Zou, Jian Xiao)
Awards in China mathematical contest in modeling
Published:
[2022] Our team (Jian Xiao, Wei Fang and Yuanjia Huang) was awarded the first prize for mmWave FMCW Radar-based position in the 18-th China post-graduate mathematical contest in modeling (Proportion of the first prize = 1.13%). [Certificate]
[2020] Our team (Jian Xiao, Aoqing Zhao and Pengda Ding) was awarded the second prize for Low-complexity computation and storage of correlation matrice in the 17-th China post-graduate mathematical contest in modeling ((Proportion of the second prize = 13.36 %)). [Certificate]
[2019] Our team (Jian Xiao, Lingbing He and Yiying Luo) was awarded the third prize for Carrier recovery for ODSP in the 17-th China post-graduate mathematical contest in modeling ((Proportion of the third prize = 18.58%)). [Certificate]
teaching
1234
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.