目的为满足动态化用户对产品数据管理的需求,解决现有产品数据管理效率低下,底层数据杂乱,数据管理平台技术受限的问题,对现有产品数据体系进行设计,并开发产品数据平台,保证产品数据的规范化与个性化要求。方法从动态客户需求的视角出...目的为满足动态化用户对产品数据管理的需求,解决现有产品数据管理效率低下,底层数据杂乱,数据管理平台技术受限的问题,对现有产品数据体系进行设计,并开发产品数据平台,保证产品数据的规范化与个性化要求。方法从动态客户需求的视角出发,提出结合模糊Kano模型和模糊层次分析法(FAHP)的产品数据体系设计方法,首先通过Voice of the Customer(VoC)法对用户需求进行收集与分析;其次通过模糊Kano模型对收集到的用户需求进行因子分类,找到关键需求因子;再次过FAHP模型对关键需求因子进行重要性排序;最后通过聚类分析需求关联性对因子进行修正。结果得到产品数据体系的底层设计依据,应用于技术数据管理平台设计中,以效率指标量化评价设计成果。结论该方法能够显著提升数据清洁度与有效性,满足多方用户对于产品数据的管理需求,同时也为产品数据管理平台国产化的建设提供一种可行且可靠的思路。展开更多
Holographic multiple-input multiple-output(HMIMO)has become an emerging technology for achieving ultra-high frequency spectral efficiency and spatial resolution in future wireless systems.The increasing antenna apertu...Holographic multiple-input multiple-output(HMIMO)has become an emerging technology for achieving ultra-high frequency spectral efficiency and spatial resolution in future wireless systems.The increasing antenna aperture leads to a more significant characterization of the spherical wavefront in near-field communications in HMIMO scenarios.Beam training as a key technique for wireless communication is worth exploring in this near-field scenario.Compared with the widely researched far-field beam training,the increased dimensionality of the search space for near-field beam training poses a challenge to the complexity and accuracy of the proposed algorithm.In this paper,we introduce several typical near-field beam training methods:exhaustive beam training,hierarchical beam training,and multi-beam training that includes equal interval multi-beam training and hash multi-beam training.The performances of these methods are compared through simulation analysis,and their effectiveness is verified on the hardware testbed as well.Additionally,we provide application scenarios,research challenges,and potential future research directions for near-field beam training.展开更多
文摘目的为满足动态化用户对产品数据管理的需求,解决现有产品数据管理效率低下,底层数据杂乱,数据管理平台技术受限的问题,对现有产品数据体系进行设计,并开发产品数据平台,保证产品数据的规范化与个性化要求。方法从动态客户需求的视角出发,提出结合模糊Kano模型和模糊层次分析法(FAHP)的产品数据体系设计方法,首先通过Voice of the Customer(VoC)法对用户需求进行收集与分析;其次通过模糊Kano模型对收集到的用户需求进行因子分类,找到关键需求因子;再次过FAHP模型对关键需求因子进行重要性排序;最后通过聚类分析需求关联性对因子进行修正。结果得到产品数据体系的底层设计依据,应用于技术数据管理平台设计中,以效率指标量化评价设计成果。结论该方法能够显著提升数据清洁度与有效性,满足多方用户对于产品数据的管理需求,同时也为产品数据管理平台国产化的建设提供一种可行且可靠的思路。
文摘Holographic multiple-input multiple-output(HMIMO)has become an emerging technology for achieving ultra-high frequency spectral efficiency and spatial resolution in future wireless systems.The increasing antenna aperture leads to a more significant characterization of the spherical wavefront in near-field communications in HMIMO scenarios.Beam training as a key technique for wireless communication is worth exploring in this near-field scenario.Compared with the widely researched far-field beam training,the increased dimensionality of the search space for near-field beam training poses a challenge to the complexity and accuracy of the proposed algorithm.In this paper,we introduce several typical near-field beam training methods:exhaustive beam training,hierarchical beam training,and multi-beam training that includes equal interval multi-beam training and hash multi-beam training.The performances of these methods are compared through simulation analysis,and their effectiveness is verified on the hardware testbed as well.Additionally,we provide application scenarios,research challenges,and potential future research directions for near-field beam training.
