| Low complexity receiver for uplink SCMA system via expectation propagation X Meng, Y Wu, Y Chen, M Cheng 2017 IEEE wireless communications and networking conference (WCNC), 1-5, 2017 | 104 | 2017 |
| A unified Bayesian inference framework for generalized linear models X Meng, S Wu, J Zhu IEEE Signal Processing Letters 25 (3), 398-402, 2018 | 74 | 2018 |
| Unitary approximate message passing for sparse Bayesian learning M Luo, Q Guo, M Jin, YC Eldar, D Huang, X Meng IEEE transactions on signal processing 69, 6023-6039, 2021 | 72 | 2021 |
| A generalized sparse Bayesian learning algorithm for 1-bit DOA estimation X Meng, J Zhu IEEE Communications Letters 22 (7), 1414-1417, 2018 | 66 | 2018 |
| An expectation propagation perspective on approximate message passing X Meng, S Wu, L Kuang, J Lu IEEE Signal Processing Letters 22 (8), 1194-1197, 2015 | 64 | 2015 |
| Training binary neural networks using the bayesian learning rule X Meng, R Bachmann, ME Khan International conference on machine learning, 6852-6861, 2020 | 50 | 2020 |
| Diffusion model based posterior sampling for noisy linear inverse problems X Meng, Y Kabashima arXiv preprint arXiv:2211.12343, 2022 | 46 | 2022 |
| Estimation of sparse massive MIMO-OFDM channels with approximately common support X Lin, S Wu, L Kuang, Z Ni, X Meng, C Jiang IEEE Communications Letters 21 (5), 1179-1182, 2017 | 41 | 2017 |
| Bilinear adaptive generalized vector approximate message passing X Meng, J Zhu IEEE Access 7, 4807-4815, 2018 | 40 | 2018 |
| Block expectation propagation for downlink channel estimation in massive MIMO systems S Wu, Z Ni, X Meng, L Kuang IEEE communications letters 20 (11), 2225-2228, 2016 | 30 | 2016 |
| Multi-user detection for spatial modulation via structured approximate message passing X Meng, S Wu, L Kuang, D Huang, J Lu IEEE Communications Letters 20 (8), 1527-1530, 2016 | 30 | 2016 |
| Advanced NOMA receivers from a unified variational inference perspective X Meng, L Zhang, C Wang, L Wang, Y Wu, Y Chen, W Wang IEEE Journal on Selected Areas in Communications 39 (4), 934-948, 2020 | 28 | 2020 |
| An AMP-based low complexity generalized sparse Bayesian learning algorithm J Zhu, L Han, X Meng IEEE Access 7, 7965-7976, 2018 | 26 | 2018 |
| Expectation propagation based iterative multi-user detection for MIMO-IDMA systems X Meng, S Wu, L Kuang, Z Ni, J Lu 2014 IEEE 79th Vehicular Technology Conference (VTC Spring), 1-5, 2014 | 24 | 2014 |
| Exact solutions of a deep linear network L Ziyin, B Li, X Meng NeurIPS2022 (arXiv preprint arXiv:2202.04777), 2022 | 20 | 2022 |
| Concise derivation of complex Bayesian approximate message passing via expectation propagation X Meng, S Wu, L Kuang, J Lu arXiv preprint arXiv:1509.08658, 2015 | 18 | 2015 |
| Efficient recovery of structured sparse signals via approximate message passing with structured spike and slab prior X Meng, S Wu, MR Andersen, J Zhu, Z Ni China Communications 15 (6), 1-17, 2018 | 11 | 2018 |
| Approximate message passing with nearest neighbor sparsity pattern learning X Meng, S Wu, L Kuang, J Lu arXiv preprint arXiv:1601.00543, 2016 | 10 | 2016 |
| Vector approximate message passing algorithm for compressed sensing with structured matrix perturbation J Zhu, Q Zhang, X Meng, Z Xu Signal Processing 166, 107248, 2020 | 9 | 2020 |
| Compressive massive random access for massive machine-type communications (mMTC) M Ke, Z Gao, Y Wu, X Meng 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP …, 2018 | 9 | 2018 |
LinLing KuangTsinghua UniversityBestätigte E-Mail-Adresse bei tsinghua.edu.cn
