Truy cập mass liên tục theo thời gian: phát hiện hoạt động người dùng và ước lượng kênh qua truyền thông tin xấp xỉ theo vectơ
Tóm tắt
Từ khóa
Tài liệu tham khảo
Y. Wu, S. Tang, L. Zhang, Resilient machine learning based semantic-aware MEC networks for sustainable next-G consumer electronics. IEEE Trans. Consumer Electron. 99, 1–10 (2023)
S. Tang, Q. Yang, L. Fan, Contrastive learning based semantic communications. IEEE Trans. Commun. 99, 1–12 (2024)
X. Chen, D.W.K. Ng, W. Yu, E.G. Larsson, N. Al-Dhahir, R. Schober, Massive access for 5G and beyond. IEEE J. Sel. Areas Commun. 39(3), 615–637 (2020)
C. Bockelmann, N. Pratas, H. Nikopour, K. Au, T. Svensson, C. Stefanovic, P. Popovski, A. Dekorsy, Massive machine-type communications in 5G: physical and MAC-layer solutions. IEEE Commun. Mag. 54(9), 59–65 (2016)
L. Liu, E.G. Larsson, W. Yu, P. Popovski, C. Stefanovic, E. De Carvalho, Sparse signal processing for grant-free massive connectivity: a future paradigm for random access protocols in the Internet of Things. IEEE Signal Process. Mag. 35(5), 88–99 (2018)
L. Liu, W. Yu, Massive connectivity with massive MIMO-part I: device activity detection and channel estimation. IEEE Trans. Signal Process. 66(11), 2933–2946 (2018)
K. Senel, E.G. Larsson, Grant-free massive MTC-enabled massive MIMO: a compressive sensing approach. IEEE Trans. Commun. 66(12), 6164–6175 (2018)
Z. Zhang, Y. Li, C. Huang, Q. Guo, C. Yuen, Y.L. Guan, DNN-aided block sparse Bayesian learning for user activity detection and channel estimation in grant-free non-orthogonal random access. IEEE Trans. Veh. Technol. 68(12), 12000–12012 (2019)
Y. Li, M. Xia, Y.-C. Wu, Activity detection for massive connectivity under frequency offsets via first-order algorithms. IEEE Trans. Wireless Commun. 18(3), 1988–2002 (2019)
H.F. Schepker, C. Bockelmann, A. Dekorsy, Exploiting sparsity in channel and data estimation for sporadic multi-user communication. In: Proc. 10th Int. Symp. Wireless Commun. Syst., pp. 1–5 (2013). VDE
Z. Chen, F. Sohrabi, W. Yu, Sparse activity detection for massive connectivity. IEEE Trans. Signal Process. 66(7), 1890–1904 (2018)
Q. Zou, H. Zhang, D. Cai, H. Yang, A low-complexity joint user activity, channel and data estimation for grant-free massive MIMO systems. IEEE Signal Process. Lett. 27, 1290–1294 (2020)
Q. Zou, H. Zhang, D. Cai, H. Yang, Message passing based joint channel and user activity estimation for uplink grant-free massive MIMO systems with low-precision ADCs. IEEE Signal Process. Lett. 27, 506–510 (2020)
S. Liu, H. Zhang, Q. Zou, Decentralized channel estimation for the uplink of grant-free massive machine-type communications. IEEE Trans. Commun. 70(2), 967–979 (2021)
D. Angelosante, G.B. Giannakis, E. Grossi, Compressed sensing of time-varying signals. In: Proc. 16th Int. Conf. Digit. Signal Process., pp. 1–8 (2009). IEEE
J. Ziniel, P. Schniter, Dynamic compressive sensing of time-varying signals via approximate message passing. IEEE Trans. Signal Process. 61(21), 5270–5284 (2013)
B. Wang, L. Dai, Y. Zhang, T. Mir, J. Li, Dynamic compressive sensing-based multi-user detection for uplink grant-free NOMA. IEEE Commun. Lett. 20(11), 2320–2323 (2016)
N. Vaswani, W. Lu, Modified-CS: modifying compressive sensing for problems with partially known support. IEEE Trans. Signal Process. 58(9), 4595–4607 (2010)
D. Angelosante, S.I. Roumeliotis, G.B. Giannakis, Lasso-kalman smoother for tracking sparse signals. In: Proc. Asilomar Conf. Signals, Syst., Comput., pp. 181–185 (2009). IEEE
J.-C. Jiang, H.-M. Wang, Massive random access with sporadic short packets: Joint active user detection and channel estimation via sequential message passing. IEEE Trans. Wireless Commun. 20(7), 4541–4555 (2021)
Q. Wang, L. Liu, S. Zhang, F.C. Lau, On massive IoT connectivity with temporally-correlated user activity. In: IEEE Int. Symp. on Inf. Theory., pp. 3020–3025 (2021). IEEE
W. Zhu, M. Tao, X. Yuan, Y. Guan, Message Passing-Based Joint User Activity Detection and Channel Estimation for Temporally-Correlated Massive Access (IEEE Trans, Commun, 2023)
D.L. Donoho, A. Maleki, A. Montanari, Message-passing algorithms for compressed sensing. Proc. Nat. Acad. Sci. 106(45), 18914–18919 (2009)
Y. Kabashima, A CDMA multiuser detection algorithm on the basis of belief propagation. J. Phys. A: Math. Gen. 36(43), 11111 (2003)
S. Rangan, Generalized approximate message passing for estimation with random linear mixing. In: Proc. IEEE Int. Symp. Inf. Theory., pp. 2168–2172 (2011). IEEE
S. Rangan, P. Schniter, A.K. Fletcher, Vector approximate message passing. IEEE Trans. Inf. Theory 65(10), 6664–6684 (2019)
H. He, C.-K. Wen, S. Jin, Generalized expectation consistent signal recovery for nonlinear measurements. In: IEEE Int. Symp. on Inf. Theory., pp. 2333–2337 (2017). IEEE
Y. Cheng, L. Liu, L. Ping, Orthogonal AMP for massive access in channels with spatial and temporal correlations. IEEE J. Sel. Areas Commun. 39(3), 726–740 (2020)
T.P. Minka, Expectation propagation for approximate Bayesian inference. arXiv preprint arXiv:1301.2294 (2013)
M. Opper, O. Winther, M.J. Jordan, Expectation consistent approximate inference. J. Mach. Learn. Res. 6(12) (2005)
K. Murphy, Y. Weiss, M.I. Jordan, Loopy belief propagation for approximate inference: An empirical study. arXiv preprint arXiv:1301.6725 (2013)