Deep learning aided beam vector assignment for massive MIMO maritime communication considering location information and handover impact
Tài liệu tham khảo
Duan, 2020, Joint multicast beamforming and relay design for maritime communication systems, IEEE Trans. Green Commun. Netw., 4, 139, 10.1109/TGCN.2019.2947469
Yang, 2015, Green energy and content-aware data transmissions in maritime wireless communication networks, IEEE Trans. Intell. Transp. Syst., 16, 751
Pelich, 2015, AIS-Based evaluation of target detectors and SAR sensors characteristics for maritime surveillance, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8, 3892, 10.1109/JSTARS.2014.2319195
Zhang, 2021, Integrated localization and communication aided multi-USV network, 1
Lu, 2014, An overview of massive MIMO: benefits and challenges, IEEE J. Sel. Top. Sign. Proces., 8, 742, 10.1109/JSTSP.2014.2317671
Choi, 2021, Large-scale beamforming for massive MIMO via randomized sketching, IEEE Trans. Veh. Technol., 70, 4669, 10.1109/TVT.2021.3071543
Zhou, 2022, AoA-Based positioning for aerial intelligent reflecting surface-aided wireless communications: an angle-domain approach, IEEE Wirel. Commun. Lett., 1
Shen, 2021, 2D fingerprinting-based localization for mmwave cell-free massive MIMO systems, IEEE Commun. Lett., 25, 3556, 10.1109/LCOMM.2021.3109645
Qiu, 2022, Secure transmission scheme based on fingerprint positioning in cell-free massive MIMO systems, IEEE Trans. Signal Inf. Process. Netw., 8, 92
Shen, 2021, Beam-domain anti-jamming transmission for downlink massive MIMO systems: a Stackelberg game perspective, IEEE Trans. Inf. Forensics Secur., 16, 2727, 10.1109/TIFS.2021.3063632
Yan, 2019, Machine learning-based handovers for sub-6 GHz and mmwave integrated vehicular networks, IEEE Trans. Wireless Commun., 18, 4873, 10.1109/TWC.2019.2930193
Di Taranto, 2014, Location-aware communications for 5G networks: how location information can improve scalability, latency, and robustness of 5G, IEEE Signal Process. Mag., 31, 102, 10.1109/MSP.2014.2332611
Lin, 2014, Improving handover and drop-off performance on high-speed trains with multi-RAT, IEEE Trans. Intell. Transp. Syst., 15, 2720, 10.1109/TITS.2014.2316499
Xiong, 2017, A broad beamforming approach for high-mobility communications, IEEE Trans. Veh. Technol., 66, 10546, 10.1109/TVT.2017.2734944
Maiberger, 2010, Location based beamforming, 000184
Chen, 2017, Massive MIMO beamforming with transmit diversity for high mobility wireless communications, IEEE Access, 5, 23032, 10.1109/ACCESS.2017.2766157
Koda, 2018, Reinforcement learning based predictive handover for pedestrian-aware mmwave networks, 692
Sana, 2020, Multi-agent deep reinforcement learning for distributed handover management in dense mmwave networks, 8976
Pang, 2018, Multiple-output-Gaussian-process regression-based anomaly detection for multivariate monitoring series, 326
Cho, 2020, Hierarchical anomaly detection using a multioutput Gaussian process, IEEE Trans. Autom. Sci. Eng., 17, 261, 10.1109/TASE.2019.2917887
Saci, 2021, Autocorrelation integrated Gaussian based anomaly detection using sensory data in industrial manufacturing, IEEE Sens. J., 21, 9231, 10.1109/JSEN.2021.3053039
Wang, 2020, Anomaly monitoring in high-density data centers based on Gaussian distribution anomaly detection algorithm, 836
Song, 2020, Fast beamforming strategy: learning the AoD of the dominant path, 267
Tauqir, 2019, Deep learning based beam allocation in switched-beam multiuser massive MIMO systems, 1
Rezaie, 2020, Location- and orientation-aided millimeter wave beam selection using deep learning, 1
H. Lee, 2009, Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, 609
H. Lee, 2011, Unsupervised learning of hierarchical representations with convolutional deep belief networks, Commun. ACM, 54, 95, 10.1145/2001269.2001295
Hua, 2015, Deep belief networks and deep learning, 1
Huang, 2018, Big data analysis on beam spectrum for handover optimization in massive-MIMO cellular systems, 1
Chen, 2016, Location-aided umbrella-shaped massive MIMO beamforming scheme with transmit diversity for high speed railway communications, 1
Nonaka, 2020, 28 GHZ-band experimental trial at 283 km/h using the Shinkansen for 5g evolution, 1
Mazin, 2018, Accelerating beam sweeping in mmwave standalone 5G new radios using recurrent neural networks, 1
Chang, 2019, An accelerated linearly convergent stochastic L-BFGS algorithm, IEEE Trans. Neural Netw. Learn. Syst., 30, 3338, 10.1109/TNNLS.2019.2891088
Li, 2019, Large-margin regularized softmax cross-entropy loss, IEEE Access, 7, 19572, 10.1109/ACCESS.2019.2897692
Chiu, 2000, Predictive schemes for handoff prioritization in cellular networks based on mobile positioning, IEEE J. Sel. Areas Commun., 18, 510, 10.1109/49.840208