《ZTE Communications》
2022—2023年文章列表(参考文献格式)
(按Ctrl并单击可链接原文)
2022年
第S1期:
Research Paper
[1] L. Y.
Duan, H. L. Lu, J. J. Qi, et al., “An improved parasitic parameters extraction
method for InP HEMT,” ZTE Communications,
vol. 20, no. S1, pp. 01–06, Jan. 2022. doi: 10.12142/ZTECOM.2022S1001.
[2] Z. P.
Zhao, Y. L. Zhao, B. Y. Yan, et al., “Auxiliary fault location on commercial
equipment based on supervised machine learning,” ZTE Communications, vol. 20, no. S1, pp. 07–15, Jan. 2022. doi:
10.12142/ZTECOM.2022S1002.
[3] X. Y.
Shi, T. Z. Han, H. Tian, et al., “Design of raptor-like rate compatible SC-LDPC
codes,” ZTE Communications, vol. 20,
no. S1, pp. 16–21, Jan. 2022. doi: 10.12142/ZTECOM.2022S1003.
[4] X. Y. Yi,
J. X. Chen, P. Chen, et al., “Derivative-based envelope design technique for
wideband envelope tracking power amplifier with digital predistortion,” ZTE Communications, vol. 20, no. S1, pp.
22–26, Jan. 2022. doi: 10.12142/ZTECOM.2022S1004.
[5] D. Y. Li,
Y. F. Tu, X. S. Zhou, et al., “End-to-end Chinese entity recognition based on
BERT-BiLSTM-ATT-CRF,” ZTE Communications,
vol. 20, no. S1, pp. 27–35, Jan. 2022. doi: 10.12142/ZTECOM.2022S1005.
[6] G. T. Fan
and Z. B. Wang, “Intelligent antenna attitude parameters measurement based on
deep learning SSD model,” ZTE
Communications, vol. 20, no. S1, pp. 36–43, Jan. 2022. doi:
10.12142/ZTECOM.2022S1006.
[7] Y. J. Xu,
Q. C. Zhao, X. D. Xu, et al., “Multi-task learning with dynamic splitting for
open-set wireless signal recognition,” ZTE
Communications, vol. 19, no. 4, pp. 44–55, Jan. 2022. doi:
10.12142/ZTECOM.2022S1007.
[8] H. M. Hu,
Y. Liu, Y. Y. Ge, et al., “Multi-cell uplink interference management: A
distributed power control method,” ZTE
Communications, vol. 20, no. S1, pp. 56–63, Jan. 2022. doi:
10.12142/ZTECOM.2022S1008.
[9] Z. Y. Li,
R. Chen, X. G. Huang, et al., “SVM for constellation shaped 8QAM PON system,” ZTE Communications, vol. 20, no. S1,
pp. 64–71, Jan. 2022. doi: 10.12142/ZTECOM.2022S1009.
Review
[10] J. R.
Han and Y. Gao, “General introduction of non-terrestrial networks for new
radio,” ZTE Communications, vol. 20, no.
S1, pp. 72–78, Jan. 2022. doi: 10.12142/ZTECOM.2022S1000.
第1期:
Special Topic:
Reconfigurable Intelligent Surface (RIS)
[1] Y. F.
Yuan, S. Jin, and M. Di Renzo, “Editorial: special topic on reconfigurable
intelligent surface (RIS),” ZTE
Communications, vol. 20, no. 1, pp. 1?2, Mar. 2022. doi:
10.12142/ZTECOM.202201001.
[2] Y. F.
Yuan, Q. Gu, A. N. Wang, et al“. Recent progress in research and development of
reconfigurable intelligent surface, ” ZTE
Communications, vol. 20, no. 1, pp. 3–13, Mar. 2022. doi:
10.12142/ZTECOM.202201002.
[3] X. L.
Hou, X. Li, X. Wang, et al.,“Some observations and thoughts about
reconfigurable intelligent surface application for 5G evolution and 6G,” ZTE Communications, vol. 20, no. 1, pp.
14–20, Mar. 2022. doi: 10.12142/ZTECOM.202201003.
[4] J. W.
Tang, S. H. Xu, F. Yang, et al, “Recent developments of transmissive
reconfigurable intelligent surfaces: a review, ” ZTE Communications, vol. 20, no. 1, pp. 21–27, Mar. 2022. doi:
10.12142/ZTECOM.202201004.
[5] X. R.
Guan and Q. Q. Wu“, IRS-enabled spectrum sharing: interference modeling,
channel estimation and robust passive beamforming, ” ZTE Communications, vol. 20, no. 1, pp. 28–35, Mar. 2022. doi:
10.12142/ZTECOM.202201005.
[6] Y. J. Xu,
Z. H. Yang, C. W. Huang, et al., “Resource allocation for two-tier RIS-assisted
heterogeneous NOMA networks,” ZTE
Communications, vol. 20, no. 1, pp. 36–47, Mar. 2022. doi:
10.12142/ZTECOM.202201006.
[7] Z. C.
Shao, W. J. Yan, and X. J. Yuan,“ Markovian cascaded channel estimation for RIS
aided massive MIMO using 1-bit ADCs and oversampling,” ZTE Communications, vol. 20, no. 1, pp. 48–56, Mar. 2022. doi:
10.12142/ZTECOM.202201007.
[8] M. N.
Jian, N. Zhang, and Y. J. Chen, “RIS: spatial wideband effect analysis and off-grid
channel estimation,” ZTE Communications,
vol. 20, no. 1, pp. 57–62, Mar. 2022. doi: 10.12142/ZTECOM.202201008.
[9] M. Y.
Zhou, X. Y. Chen, W. K. Tang, et al., “Dual-polarized RIS-based STBC
transmission with polarization coupling analysis,” ZTE Communications, vol. 20, no. 1, pp. 63–75, Mar. 2022. doi:
10.12142/ZTECOM.202201009.
Research Paper
[10] B. Yang,
C. L. Guo, and Z. Li, “Metric learning for semantic-based clothes retrieval,”ZTE Communications, vol. 20, no. 1, pp.
76–82, Mar. 2022. doi: 10.12142/ZTECOM.202201010.
第2期:
Special Topic:
Simultaneous Wireless Information and Power Transfer: Technology and
Practice
[1] Q. W.
Yuan and F.-L. Luo,“Editorial: special topic on simultaneous wireless information
and power transfer: technology and practice,” ZTE Communications, vol. 20, no. 2, pp. 1–2, Jun. 2022. doi:
10.12142/ZTECOM.202202001.
[2] B. Yang,
T. Mitani, N. Shinohara, et al., “High-power simultaneous wireless information
and power transfer: injection-locked magnetron technology,” ZTE Communications, vol. 20, no. 2, pp. 3–12, Jun. 2022. doi:
10.12142/ZTECOM.202202002.
[3] R.
Torres, D. Matos, F. Pereira, et al., “An overview of SWIPT circuits and
systems,” ZTE Communications, vol.
20, no. 2, pp. 13–18, Jun. 2022. doi: 10.12142/ZTECOM.202202003.
[4] S. Y. Sun
and G. Y. Wen, “Optimal design of wireless power transmission systems using
antenna arrays,” ZTE Communications,
vol. 20, no. 2, pp. 19–27, Jun. 2022. doi: 10.12142/ZTECOM.202202004.
[5] Q.-T.
Duong, Q.-T. Vo, T.-P. Phan, et al., “Dynamic power transmission using common
RF feeder with dual supply,” ZTE Communications, vol. 20, no. 2, pp.
28–36, Jun. 2022. doi: 10.12142/ZTECOM.202202005.
[6] J. Shen,
T. X. Zhao, and X. G. Liu, “Polarization reconfigurable patch antenna for wireless
power transfer related applications,” ZTE Communications, vol. 20, no. 2, pp.
37–42, Jun. 2022. doi: 10.12142/ZTECOM.202202006.
[7] X. Wang,
W. B. Li, and M. Y. Lu, “A radio-frequency loop resonator for short-range
wireless power transmission,” ZTE Communications, vol. 20, no. 2, pp.
43–47, Jun. 2022. doi: 10.12142/ZTECOM.202202007.
Review
[8] M. Y.
Chang, J. Q. Han, X. J. Ma, et al., “Programmable metasurface for simultaneously
wireless information and power transfer system,” ZTE Communications, vol.
20, no. 2, pp. 48–62, Jun. 2022. doi: 10.12142/ZTECOM.202202008.
第3期:
Special Topic: Federated Learning for IoT and Edge Computing
[1] Y. Pan, L.
Z. Cui, Z. P. Cai, and W. Li, “Editorial: special topic on federated learning
for IoT and edge computing,” ZTE
Communications, vol. 20, no. 3, pp. 1–2, Sept. 2022. doi: 10.12142/ZTECOM.202203001.
[2] Y. C.
Nan, M. H. Fang, X. J. Zou, et al., “A collaborative medical diagnosis system
without sharing patient data,” ZTE
Communications, vol. 20, no. 3, pp. 3–16, Sept. 2022. doi: 10.12142/ZTECOM.202203002.
[3] X. M.
Han, M. H. Gao, L. M. Wang, et al., “A survey of federated learning on non-IID
data,” ZTE Communications, vol. 20,
no. 3, pp. 17–26, Sept. 2022. doi: 10.12142/ZTECOM.202203003.
[4] F. Lu, L.
Gu, X. H. Tian, et al.,“Federated learning based on extremely sparse series
clinic monitoring data,” ZTE
Communications, vol. 20, no. 3, pp. 27–34, Sept. 2022. doi:
10.12142/ZTECOM.202203004.
[5] Q. B. Liu,
Z. H. Jin, J. B. Wang, et al., “MSRA-Fed: a communication-efficient federated
learning method based on model split and representation aggregate,” ZTE Communications, vol. 20, no. 3, pp.
35–42, Sept. 2022. doi: 10.12142/ZTECOM.202203005.
[6] B. Tang,
C. M. Zhang, K. W. Wang, et al., “Neursafe-FL: a reliable, efficient,
easy-to-use federated learning framework,” ZTE
Communications, vol. 20, no. 3, pp. 43–53, Sept. 2022. doi:
10.12142/ZTECOM.202203006.
Review
[7] B. L.
Yan, Q. Wu, H. Shi, et al., “Toward low-cost flexible intelligent OAM in
optical fiber communication networks,” ZTE
Communications, vol. 20, no. 3, pp. 54–60, Sept. 2022. doi:
10.12142/ZTECOM.202203007.
Research Paper
[8] J. T. Zhang,
Z. Q. He, H. Rui, et al., “Spectrum sensing for OFDMA using multicarrier
covariance matrix aware CNN,” ZTE Communications,
vol. 20, no. 3, pp. 61–69, Sept. 2022. doi: 10.12142/ZTECOM.202203008.
[9] Q. Q. Wu, J.
Z. Chen, Z. Q. Wu, et al, “Synthesis and design of 5G duplexer based on optimization
method,” ZTE Communications, vol. 20,
no. 3, pp. 70–76, Sept. 2022. doi: 10.12142/ZTECOM.202203009.
[10] X. M.
Lyu, H. Chen, Z. Y. Wu, et al., “Alarm-based root cause analysis based on
weighted fault propagation topology for distributed information network,” ZTE Communications, vol. 20, no. 3, pp.
77–84, Sept. 2022. doi: 10.12142/ZTECOM.202203010.
[11] Q. X.
Zhang, J. Han, L. Cheng, et al., “Approach to anomaly detection in microservice
system with multi-source data streams,” ZTE
Communications, vol. 20, no. 3, pp. 85–92, Sept. 2022. doi:
10.12142/ZTECOM.202203011.
[12] Z. Q.
Cui, G. P. Wang, Z. G. Wang, et al., “Symbiotic radio systems: detection and
performance analysis,” ZTE Communications, vol. 20, no. 3, pp.
93–98, Sept. 2022. doi: 10.12142/ZTECOM.202203012.
第4期:
Special Topic: Wireless Communication and Its Security:
Challenges and Solutions
[1] K. Ren
and Z. B. Wang, “Editorial: wireless communication and its security: challenges
and solutions,” ZTE Communications, vol. 20, no. 4, pp. 1–2,
Dec. 2022. doi: 10.12142/ZTECOM.202204001.
[2] Y. F.
Cao, J. N. Cao, Y. Q. Wang, et al., “Security in edge blockchains: attacks and
countermeasures,” ZTE Communications, vol. 20, no. 4, pp.
3–14, Dec. 2022. doi: 10.12142/ZTECOM.202204002.
[3] Z. Tong,
B. W. Deng, L. L. Zheng, et al., “Utility-improved key-value data collection
with local differential privacy for mobile devices,” ZTE Communications, vol. 20, no. 4, pp. 15–21, Dec. 2022. doi:
10.12142/ZTECOM.202204003.
[4] H. T. Lu,
X. C. Yan, Q. Zhou, et al., “Key intrinsic security technologies in 6G
networks,” ZTE Communications, vol. 20,
no. 4, pp. 22–31, Dec. 2022. doi: 10.12142/ZTECOM.202204004.
[5] P. F.
Wang, W. Song, G. Sun, et al., “Air-ground integrated low-energy federated
learning for secure 6G communications,” ZTE
Communications, vol. 20, no. 4, pp. 32–40, Dec. 2022. doi:
10.12142/ZTECOM.202204005.
[6] M. He, X.
M. Li, and J. B. Ni, “Physical layer security for mmwave communications:
challenges and solutions,” ZTE Communications, vol. 20, no. 4, pp.
41–51, Dec. 2022. doi: 10.12142/ZTECOM.202204006.
Review
[7] X. Y.
Duan, H. H. Kang, and J. J. Zhang, “Autonomous network technology innovation in
digital and intelligent era,” ZTE
Communications, vol. 20, no. 4, pp. 52–61, Dec. 2022. doi:
10.12142/ZTECOM.202204007.
Research Paper
[8] X. B.
Ran, Z. J. Dai, K. Zhong, et al., “Broadband sequential load-modulated balanced
amplifier using coupler-PA codesign approach,” ZTE Communications, vol. 20, no. 4, pp. 62–68, Dec. 2022. doi:
10.12142/ZTECOM.202204008.
[9] H. N.
Jia, Z. Q. He, W. L. Tan, et al., “Distributed multi-cell multi-user miso
downlink beamforming via deep reinforcement learning,” ZTE Communications, vol. 20, no. 4, pp. 69–77, Dec. 2022. doi:
10.12142/ZTECOM.202204009.
[10] Z. H.
Li, S. Q. Yang, J. H. Yu, et al., “Predictive scheme for mixed transmission in
time-sensitive networking,” ZTE Communications, vol. 20, no. 4, pp.
78–88, Dec. 2022. doi: 10.12142/ZTECOM.202204010.
[11] J. J.
Mei, T. Guan, and J. W. Tong, “Label enhancement for scene text detection,” ZTE Communications, vol. 20, no. 4, pp.
89–95, Dec. 2022. doi: 10.12142/ZTECOM.202204011.
[12] N. Z.
Gao, Y. F. Yu, X. H. Hua, et al., “A content-aware bitrate selection method
using multi-step prediction for 360-degree video streaming,” ZTE Communications, vol. 20, no. 4, pp.
96–109, Dec. 2022. doi: 10.12142/ZTECOM.202204012.
2023年
第1期:
Special
Topic: Federated Learning over Wireless Networks
[1] S. G.
Cui, C. C. Yin, and G. X. Zhu, “Editorial: federated learning over wireless
networks,” ZTE Communications, vol. 21, no. 1, pp. 1–2, Mar. 2023. doi: 10.12142/ZTECOM.202301001.
[2] X. Y. Xu,
S. L. Liu, and G. D. Yu, “Adaptive retransmission design for wireless federated
edge learning,” ZTE Communications,
vol. 21, no. 1, pp. 3–14, Mar. 2023. doi: 10.12142/ZTECOM.202301002.
[3] W. T.
Zhang, H. T. Liang, Y. H. Xu, et al., “Reliable and privacy-preserving federated learning with anomalous users,” ZTE Communications, vol. 21, no. 1,
pp. 15–24, Mar. 2023. doi: 10.12142/ZTECOM.202301003.
[4] Y. J.
Wang, D. Z. Wen, Y. J. Mao, et al., “RIS-assisted federated learning in multi-cell
wireless networks,” ZTE Communications,
vol. 21, no. 1, pp. 25–37, Mar. 2022. doi: 10.12142/ZTECOM.202301004.
[5] J. T.
Yan, T. Chen, B. W. Xie, et al., “Hierarchical
federated learning: architecture, challenges, and its implementation in
vehicular networks,” ZTE Communications,
vol. 21, no. 1, pp. 38–45, Mar. 2022. doi: 10.12142/ZTECOM.202301005.
[6] Y. H.
Ding, M. Shikh-Bahaei, Z. H. Yang, et al., “Secure federated learning over
wireless communication networks with
model compression,” ZTE Communications,
vol. 21, no. 1, pp. 46–54, Mar. 2022. doi: 10.12142/ZTECOM.202301006
Research Paper
[7] R. Huang,
H. L. Li, and Y. M. Zhang, “Efficient bandwidth allocation and computation
configuration in industrial IoT,” ZTE Communications, vol. 21, no. 1,
pp. 55–63, Mar. 20223. doi: 10.12142/ZTECOM.202301007.
[8] J.
G. Lu and Q. F. Zheng, “Ultra-lightweight face animation method for ultra-low bitrate
video conferencing,” ZTE Communications,
vol. 21, no. 1, pp. 64–71, Mar. 2022. doi: 10.12142/ZTECOM.202301008.
[9] W. B.
Cai, S. L. Yang, G. Sun, et al., “Adaptive load balancing for parameter servers
in distributed machine learning over
heterogeneous networks,” ZTE
Communications, vol. 21, no. 1, pp. 72–80, Mar. 2023. doi:
10.12142/ZTECOM.202301009.
[10] P. Lu,
B. Sheng, and W. Z. Shi, “Scene visual perception and AR navigation applications,” ZTE Communications, vol. 21, no. 1, pp. 81–88, Mar.
2023. doi: 10.12142/ZTECOM.202301010.
[11] Y. F. Tu, B.
H. Zhu, H. Z. Yang, et al., “RCache:
a read-intensive workload-aware page cache for NVM filesystem,” ZTE Communications, vol. 21, no. 1, pp.
89–94, Mar. 2023. doi: 10.12142/ZTECOM.202301011.
第2期:
Special
Topic: Evolution of AI Enabled Wireless Networks
[1] L. Wang
and Y. Gao, “Editorial: evolution of AI enabled wireless networks,” ZTE Communications, vol. 21, no. 2, pp.
1–2, Jun. 2023. doi: 10.12142/ZTECOM.202302001.
[2] B. Yang,
X. Liang, S. N. Liu, et al., “Intelligent 6G wireless network with
multi-dimensional information perception,” ZTE
Communications, vol. 21, no. 2, pp. 3–10, Jun. 2023. doi:
10.12142/ZTECOM.202302002.
[3] L. T.
Deng and Y. R. Zhao, “Deep learning-based semantic feature extraction: a
literature review and future directions,” ZTE Communications,
vol. 21, no. 2, pp. 11–17, Jun. 2023. doi: 10.12142/ZTECOM.202302003.
[4] Y. N.
Yan, Y. Liu, T. Ni, et al., “Content popularity prediction via federated
learning in cache-enabled wireless networks,” ZTE Communications,
vol. 21, no. 2, pp. 18–24, Jun. 2023. doi: 10.12142/ZTECOM.202302004.
[5] M. K.
Zhao, Y. S. Huang, and X. Li, “Federated learning for 6G: a survey from
perspective of integrated sensing, communication and computation,” ZTE Communications, vol. 21, no. 2, pp.
25–33, Jun. 2023. doi: 10.12142/ZTECOM.202302005.
[6] J. J.
Chen, Y. Gao, Z. Liu, et al.,“Future vision on artificial intelligence assisted
green energy efficiency network,” ZTE Communications, vol. 21, no. 2, pp.
34–39, Jun. 2023. doi: 10.12142/ZTECOM.202302006.
[7] U. Awada,
J. K. Zhang, S. Chen, et al.,“Machine learning driven latency optimization for
Internet of Things applications in edge computing,” ZTE Communications, vol. 21, no. 2, pp. 40–52, Jun 2022. doi:
10.12142/ZTECOM.202302007.
[8] F.
Meng, Y. M. Huang, Z. H. Lu, et al.,“Multi-user mmWave beam tracking via multi-agent
deep Q-learning,” ZTE Communications,
vol. 21, no. 2, pp. 53–60, Jun. 2023. doi: 10.12142/ZTECOM.202302008.
[9] Q. You,
Q. Xu, X. Yang, et al., “RIS-assisted UAV-D2D communications exploiting deep
reinforcement learning,” ZTE Communications, vol. 21, no. 2, pp.
61–69, Jun. 2023. doi: 10.12142/ZTECOM.202302009.
[10] C. Y.
Liu, J. J. Guo, Y. M. Zhang, et al., “SST-V: a scalable semantic transmission
framework for video,” ZTE Communications, vol. 21, no. 2, pp. 70–79, Mar. 2023. doi: 10.12142/ZTECOM.202302010.
[11] Y. T. Li, Y.
Ding, J. C. Gao, et al.,“UAV autonomous navigation for wireless powered data
collection with onboard deep Q-Network,” ZTE
Communications, vol. 21, no. 2, pp. 80–87, Jun. 2023. doi:
10.12142/ZTECOM.202302011.
Review
[12] J. X.
Chen, P. G. Zhou, J. Y. Yu, et al., “Research towards terahertz power
amplifiers in silicon-based process,” ZTE Communications, vol. 21, no. 2, pp.
88–94, Jun. 2023. doi: 10.12142/ZTECOM.202302012.
第3期:
Special
Topic: Reinforcement Learning and Intelligent Decision
[1] Y. Gao, “Special topic on reinforcement learning and intelligent decision,” ZTE Communications, vol. 21, no. 3, pp.
01–02, Sept. 2023. doi: 10.12142/ZTECOM.202303001.
[2] M. Ren,
R. Y. Xu, and T. Zhu“, Double deep Q-network decoder based on EEG
brain-computer interface,” ZTE Communications, vol. 21, no. 3, pp.
03–10, Sept. 2023. doi: 10.12142/ZTECOM.202303002.
[3] B. Y.
Feng, M. X. Feng, M. R. Wang, et al., “Multi-agent hierarchical graph attention
reinforcement learning for grid-aware energy management,” ZTE Communications,
vol. 21, no. 3, pp. 11–21, Sept. 2023. doi: 10.12142/ZTECOM.202303003.
[4] J. P. Yu
and Y. Y. Chen, “A practical reinforcement learning framework for automatic radar
detection,” ZTE Communications, vol. 21, no. 3, pp. 22–28, Sept. 2023. doi:
10.12142/ZTECOM.202303004.
[5] J. H.
Shen, K. Jiang, and X. Y. Tan,“ Boundary data augmentation for offline reinforcement
learning,” ZTE Communications, vol. 21, no. 3, pp. 29–36, Sept. 2023. doi:
10.12142/ZTECOM.202303005.
Research Papers
[6] Z.
H. Zhu and Y. P. Zhang,“ Differential quasi-Yagi antenna and array,” ZTE Communications,
vol. 21, no. 3, pp. 37–44, Sept.
2023. doi: 10.12142/ZTECOM.202303006.
[7] X. Y.
Xie, Y. P. Wu, Z. F. Yuan, et al.,“ Massive unsourced random access under
carrier frequency offset,” ZTE Communications,
vol. 21, no. 3, pp. 45–53, Sept. 2023. doi: 10.12142/ZTECOM.202303007.
[8] L.
Cheng, S. Qin, and G. Feng, “Learning-based admission control for
low-earth-orbit satellite communication networks,” ZTE Communications, vol.
21, no. 3, pp. 54–62, Sept. 2023. doi: 10.12142/ZTECOM.202303008.
[9] B. Zhang,
Y. H. Wang, Y. N. Feng, et al.,“ A 220-GHz frequency-division multiplexing
wireless link with high data rate,” ZTE Communications, vol. 21, no. 3, pp.
63–69, Sept. 2023. doi: 10.12142/ZTECOM.202303009.
[10] Y. H.
Ji, J. Han, Y. X. Zhao, et al.,“ Log anomaly detection through GPT-2 for large
scale systems,” ZTE Communications, vol. 21, no. 3, pp. 70–76, Sept. 2023. doi:
10.12142/ZTECOM.202303010.
[11] Y. T. Zhu,
Z. Li, and H. T. Zhang, “Robust beamforming under channel prediction errors for
time-varying MIMO system,” ZTE Communications, vol. 21, no. 3, pp.
77–85, Sept. 2023. doi: 10.12142/ZTECOM.202303011.
[12] H. W.
Li, N. J. Bi, and J. Sha, “Design of raptor-like LDPC codes and high throughput
decoder towards 100 Gbit/s throughput,” ZTE Communications, vol. 21, no. 3, pp.
86–92, Sept. 2023. doi: 10.12142/ZTECOM.202303012.
[13] Y. Q.
Tang, H. M. Zhang, Z. Zheng, et al., “Hybrid architecture and beamforming
optimization for millimeter wave systems,” ZTE Communications, vol. 21, no. 3, pp.
93–104, Sept. 2023. doi: 10.12142/ZTECOM.202303013.
[14] W. Li,
J. K. Ji, Y. L. Liu, et al.,“ Simulation and modeling of common mode EMI noise
in planar transformers,” ZTE
Communications, vol. 21, no. 3, pp. 105–116, Sept. 2023. doi: 10.12142/ZTECOM.202303014.
[15] J. W.
Ding, Y. Liu, H. J. Liao, et al.,“ Statistical model of path loss for railway
5G marshalling yard scenario,” ZTE Communications, vol. 21, no. 3, pp.
117–122, Sept. 2023. doi: 10.12142/ZTECOM.202303015.
第4期:
Special
Topic: 3D Point Cloud Processing and Applications
[1] H. F. Sun, G. Li, S. H. Chen, et al., “Special topic on 3D point cloud processing and
applications,” ZTE Communications, vol.21, no. 4, pp. 1–2,
Dec. 2023. doi: 10.12142/ZTECOM.202304001.
[2] Y. J.
Zhou, Z. C. Zhang, W. Sun, et al., “Perceptual quality assessment for point
clouds: a survey,” ZTE Communications, vol. 21, no. 4, pp. 3–16, Dec. 2023. doi:
10.12142/ZTECOM.202304002.
[3] H. R.
Zhang, Z. Dong, and M. S. Wang, “Spatio-temporal context-guided algorithm for
lossless point cloud geometry compression,” ZTE Communications,
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