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本期推荐
低轨卫星网络接入与传输技术
作者:申佳伟,洪涛,张更新
[简介] 针对低轨卫星网络空口传输特征,在地面5G NR体制的基础上利用智能化网络与轻量化终端间的协同,实现用户的广域无感接入与适变传输。梳理了低轨卫星网络空口传输关键技术,认为需要采用内生智能的方法辅助空口设计,从空口赋能AI和AI辅助空口两个方面构建完整闭环的研究体系,最后展望了未来低轨卫星网络的研究趋势。
5G电源模组高精度3D结构光测量技术
作者:邓芳伟,黄石军
[简介]提出了多视角大景深高精度3D视觉传感技术。运用高清数字光处理(DLP)结构光投影技术和高速高分辨工业相机,从板级集成电路设计、控制驱动软件研发、3D点云重建与处理算法等方面深入研究,自主研发亚微米级/低成本多视角大景深高精度3D视觉测量技术。主要包括:采用高分辨率、高帧率的工业摄像机,实现高精度的三维重建;采用多波长相位扩展法,实现高精度的相位计算;利用多工业摄像机解决视场隐藏,扩大了三维重构视场,最终实现了0.48μm Z轴的重复精度。
On Normalized Least Mean Square Based Interference Cancellation Algorithm for Integrated Sensing and Communication Systems
YU Xiaohui, YU Shucheng, LIU Xiqing, PENG Mugen
[Introduction] Integrated sensing and communication (ISAC) technology is a promising candidate for next-generation communication systems. However, severe co-site interference in existing ISAC systems limits the communication and sensing performance, posing significant challenges for ISAC interference management. In this work, we propose a novel interference management scheme based on the normalized least mean square (NLMS) algorithm, which mitigates the impact of co-site interference by reconstructing the interference from the local transmitter and canceling it from the received signal. Simulation results demonstrate that, compared to typical adaptive interference management schemes based on recursive least square (RLS) and stochastic gradient descent (SGD) algorithms, the proposed NLMS algorithm effectively cancels co-site interference and achieves a good balance between computational complexity and convergence performance.
Intelligence Driven Wireless Networks in B5G and 6G Era: A Survey
GAO Yin, CHEN Jiajun, LI Dapeng, XIA Xiang, HE Zuyuan
[Introduction]As the wireless communication network undergoes continuous expansion, the challenges associated with network management and optimization are becoming increasingly complex. To address these challenges, the emerging artificial intelligence (AI) and machine learning (ML) technologies have been introduced as a powerful solution. They empower wireless networks to operate autonomously, predictively, on-demand, and with smart functionality, offering a promising resolution to intricate optimization problems. This paper aims to delve into the prevalent applications of AI/ML technologies in the optimization of wireless networks. The paper not only provides insights into the current landscape but also outlines our vision for the future and considerations regarding the development of an intelligent 6G network.