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基于多时隙业务联合整形的低能耗资源调度方法
作者:李建东,牛淳隆,赵晨曦,刘俊宇
[简介] 面向未来6G移动通信系统超高数据密度的业务需求场景,为保障用户服务质量(QoS)并降低系统能耗,首先分析了移动通信系统的能耗构成,发现了系统能耗的非线性特征。然后在此基础上,设计了多时隙业务联合整形的低能耗资源调度方法。该方法通过感知用户业务流量和时延要求等需求侧的数据特征,利用深度强化学习算法在给定的多个时隙内动态调整基站资源分配策略。该资源分配策略降低了用户业务请求的非平稳性,从而减少了基站的非线性传输特性产生的额外能耗。最后通过软件仿真对比不同方法,验证了基于多时隙业务联合整形的理论和算法的正确性和有效性。
面向5G-A的无线网络节能关键技术
作者:郭诚,陈梦竹
[简介] 建立网络能耗模型,对基站在数据发送状态、接收状态、休眠模式及模式转换的相对功耗进行建模,可以评估各种网络节能技术的节能增益。面向5G-A,从时域、空域、频域和功率域等方面,研究包括公共信号轻量化设计、极简小区架构、小区不连续发送/接收模式、基站唤醒机制、节能测量上报增强等网络节能的关键技术,使能更动态、高效、精准的无线传输,并通过终端和基站间信息交互的方式提升节能效果。随后,仿真验证了这些技术的正确性和有效性。
Perceptual Quality Assessment for Point Clouds : A Survey
ZHOU Yingjie, ZHANG Zicheng, SUN Wei, MIN Xiongkuo, ZHAI Guangtao
[Introduction] A point cloud is considered a promising 3D representation that has achieved wide applications in several fields. However, quality degradation inevitably occurs during its acquisition and generation, communication and transmission, and rendering and display. Therefore, how to accurately perceive the visual quality of point clouds is a meaningful topic. In this survey, we first introduce the point cloud to emphasize the importance of point cloud quality assessment (PCQA). A review of subjective PCQA is followed, including common point cloud distortions, subjective experimental setups and subjective databases. Then we review and compare objective PCQA methods in terms of model-based and projection-based. Finally, we provide evaluation criteria for objective PCQA methods and compare the performances of various methods across multiple databases. This survey provides an overview of classical methods and recent advances in PCQA.
Spatio-Temporal Context-Guided Algorithm for Lossless Point Cloud Geometry Compression
ZHANG Huiran, DONG Zhen, WANG Mingsheng
[Introduction] Point cloud compression is critical to deploy 3D representation of the physical world such as 3D immersive telepresence, autonomous driving, and cultural heritage preservation. However, point cloud data are distributed irregularly and discontinuously in spatial and temporal domain, where redundant unoccupied voxels and weak correlations in 3D space make achieving efficient compression a challenging problem. In this paper, we propose a spatio-temporal context-guided algorithm for lossless geometry point cloud compression. The proposed scheme starts with dividing the point cloud into sliced layers of unit thickness along the longest axis. Then, it introduces a prediction method where both intra-frame and inter-frame point clouds are available, by determining correspondences between adjacent layers and estimating the shortest path using the travelling salesman algorithm. Finally, the few prediction residual is efficiently compressed with optimal context-guided and adaptive fast-mode arithmetic coding techniques. Experiments prove that the proposed method can effectively achieve low bit rate lossless compression of point cloud geometric information, and is suitable for 3D point cloud compression applicable to various types of scenes.