作者： 张坤;高磊;袁艺双;彭世新;徐如意

期刊： 广西师范大学学报(哲学社会科学版),2020年56(3):120-127 ISSN：1001-6597

作者机构： 华中师范大学 国家数字化学习工程技术研究中心,湖北 武汉 430079;华中师范大学 教育大数据国家工程实验室,湖北 武汉 430079;[徐如意; 彭世新; 袁艺双; 张坤; 高磊] 华中师范大学

关键词： 自闭症儿童;动态社会情境;视觉注意;眼动追踪

摘要： 自闭症儿童存在严重的沟通与社交障碍。以合适的学习材料,开展社会情境教育干预,对提高自闭症儿童社交能力非常重要。为了探究自闭症儿童观看不同类型社会情境学习材料的视觉注意特点,研究方法以卡通与真人动态社会情境视频为实验材料,以28名3-6岁自闭症儿童为研究对象,采用单因素被试内设计,利用眼动追踪技术与SPSS进行分析。实验结果表明,自闭症儿童对卡通情境中人物头部、眼睛等区域给予更多注视,而对真实情境中人物的身体区域给予更多注视;自闭症儿童对卡通情境中人物情绪的指认得分显著高于真实情境中的指认得分。这一研究结果能为自闭症儿童教育干预提供理论支撑,为设计教育干预学习材料提供指导依据。

语种： 中文

作者： Lu, Wei*;Wang, Yongliang;Fang, Qiqing;Peng, Shixin

期刊： Wireless Personal Communications,2019年105(1):257-266 ISSN：0929-6212

通讯作者： Lu, Wei

作者机构： [Lu, Wei; Fang, Qiqing; Wang, Yongliang] Air Force Early Warning Acad, Wuhan, Hubei, Peoples R China.;[Peng, Shixin] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.

通讯机构： [Lu, Wei] A;Air Force Early Warning Acad, Wuhan, Hubei, Peoples R China.

关键词： Weighted IRLS;Channel estimation;Massive MIMO;FDD

摘要： In this letter a weighted iteratively reweighted least-square (IRLS) algorithm is proposed for FDD massive MIMO channel estimation. The priori support information is merged into the weighted IRLS to improve the recovery performance. The priori support information is obtained from the uplink channel by reciprocity in angle domain, and a support estimation algorithm is proposed from the analysis of basis mismatch and angle deviation between uplink and downlink which is more practical in the real scenario. A brief convergence analysis of weighted IRLS is given out. Simulations show that the proposed weighted IRLS outperforms the standard IRLS, subspace pursuit (SP) and weighted SP.

语种： 英文

作者： Lu, Wei*;Wang, Yongliang;Wen, Xiaoqiao;Hua, Xiaoqiang;Peng, Shixin;...

期刊： IEEE ACCESS,2019年7:86964-86978 ISSN：2169-3536

通讯作者： Lu, Wei

作者机构： [Lu, Wei; Wen, Xiaoqiao; Wang, Yongliang] Air Force Early Warning Acad, Wuhan 430072, Hubei, Peoples R China.;[Hua, Xiaoqiang] Natl Univ Def Technol, Changsha 410073, Hunan, Peoples R China.;[Peng, Shixin] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Zhong, Liang] China Univ Geosci, Dept Commun Syst, Wuhan 430074, Hubei, Peoples R China.

通讯机构： [Lu, Wei] A;Air Force Early Warning Acad, Wuhan 430072, Hubei, Peoples R China.

关键词： Channel estimation;massive MIMO;weighted l(p) minimization

摘要： We propose a weighted l-{p} minimization method for downlink channel estimation in frequency division duplexing massive multiple-input multiple-output (MIMO) systems. The proposed algorithm involves two stages, in which it first diagnoses the downlink supports by utilizing the channel sparsity in angular domain and angular reciprocity for uplink and downlink channels. In stage two, a weighted l-{p} minimization algorithm based on the diagnosed supports is used for downlink channel estimation. The diagnosed supports are used for generating the weighting matrix in the weighted l-{p} minimization. The restricted isometry property (RIP)-based guarantees and upper bound of the recovery error are derived. Our analytical results have the universal forms for the l-{p} (0 < p\leq 1) minimization and the weighted l-{p} (0 < p\leq 1) minimization, and can reduce to the RIP-based analysis results for the l-{1} minimization and the weighted l-{1} minimization which have been discussed in the previous literature. The discussion on the weight selection is also presented which is based on the derived upper bound. Simulations show that the weighted l-{p} minimization is preferred when the correct percentage of the estimated support is more than 0.5. For the channel estimation, the proposed method with support diagnosis and the weighted l-{p} minimization can achieve higher estimation accuracy compared with the l-{p} minimization, weighted subspace pursuit, weighted l-{1} minimization, general l-{1} minimization, joint orthogonal matching pursuit, and simultaneous orthogonal matching pursuit in the medium and high signal-to-noise-rate regions. © 2013 IEEE.

语种： 英文

作者： Lu, Wei*;Wang, Yongliang;Wen, Xiaoqiao;Peng, Shixin;Zhong, Liang

期刊： Electronics,2019年8(5) ISSN：2079-9292

通讯作者： Lu, Wei

作者机构： [Lu, Wei; Wen, Xiaoqiao; Wang, Yongliang] Air Force Early Warning Acad, Wuhan 430019, Hubei, Peoples R China.;[Peng, Shixin] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Zhong, Liang] China Univ Geosci, Dept Commun Syst, Wuhan 430074, Hubei, Peoples R China.

通讯机构： [Lu, Wei] A;Air Force Early Warning Acad, Wuhan 430019, Hubei, Peoples R China.

关键词： massive MIMO;channel estimation;Bayesian inference;overcomplete dictionary

摘要： We exploited the temporal correlation of channels in the angular domain for the downlink channel estimation in a massive multiple-input multiple-output (MIMO) system. Based on the slow time-varying channel supports in the angular domain, we combined the channel support information of the downlink angular channel in the previous timeslot into the channel estimation in the current timeslot. A downlink channel estimation method based on variational Bayesian inference (VBI) and overcomplete dictionary was proposed, in which the support prior information of the previous timeslot was merged into the VBI for the channel estimation in the current timeslot. Meanwhile the VBI was discussed for a complex value in our system model, and the structural sparsity was utilized in the Bayesian inference. The Bayesian Cramér-Rao bound for the channel estimation mean square error (MSE) was also given out. Compared with other algorithms, the proposed algorithm with overcomplete dictionary achieved a better performance in terms of channel estimation MSE in simulations. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

语种： 英文

作者： Lu, Wei*;Wang, Yongliang;Fang, Qiqing;Peng, Shixin

期刊： EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING,2018年2018(1):1-12 ISSN：1687-1472

通讯作者： Lu, Wei

作者机构： [Lu, Wei; Fang, Qiqing; Wang, Yongliang] Air Force Early Warning Acad, Wuhan, Hubei, Peoples R China.;[Peng, Shixin] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.

通讯机构： [Lu, Wei] A;Air Force Early Warning Acad, Wuhan, Hubei, Peoples R China.

关键词： FDD;Massive MIMO;Support diagnosis;Compressive channel estimation;Weighted subspace pursuit

摘要： Downlink channel state information (CSI) is critical in a frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system. We exploit the reciprocity between uplink and downlink channels in angular domain and diagnose the supports of downlink channel from the estimated uplink channel. While the basis mismatch effects will damage the sparsity level and the path angle deviations between uplink and downlink transmission paths will induce differences in channel supports, a downlink support diagnosis algorithm based on the DBSCAN (density-based spatial clustering of applications with noise) which is widely used in machine learning is presented. With the diagnosed supports of downlink channel in angular domain, a weighted subspace pursuit (SP) channel estimation algorithm for FDD massive MIMO is proposed. The restricted isometry property (RIP)-based performance analysis for the weighted SP algorithm is given out. Both the analysis and the simulation results show that the proposed downlink channel estimation with diagnosed supports is superior to the standard iteratively reweighted least squares (IRLS) and SP without channel priori or with the assumption of the common supports for uplink and downlink channels in angular domain. © 2018, The Author(s).

语种： 英文