To improve the similarity measurement between users, a similarity measurement approach incorporating clusters of intrinsic user groups（ SMCUG） is proposed considering the social information of users. The approach co...To improve the similarity measurement between users, a similarity measurement approach incorporating clusters of intrinsic user groups（ SMCUG） is proposed considering the social information of users. The approach constructs the taxonomy trees for each categorical attribute of users. Based on the taxonomy trees, the distance between numerical and categorical attributes is computed in a unified framework via a proper weight. Then, using the proposed distance method, the nave k-means cluster method is modified to compute the intrinsic user groups. Finally, the user group information is incorporated to improve the performance of traditional similarity measurement. A series of experiments are performed on a real world dataset, M ovie Lens. Results demonstrate that the proposed approach considerably outperforms the traditional approaches in the prediction accuracy in collaborative filtering.展开更多
Traditional researches on user preferences mining mainly explore the user’s overall preferences on the project,but ignore that the fundamental motivation of user preferences comes from their attitudes on some attribu...Traditional researches on user preferences mining mainly explore the user’s overall preferences on the project,but ignore that the fundamental motivation of user preferences comes from their attitudes on some attributes of the project.In addition,traditional researches seldom consider the typical preferences combination of group users,which may have influence on the personalized service for group users.To solve this problem,a method with noise reduction for group user preferences mining is proposed,which focuses on mining the multi-attribute preference tendency of group users.Firstly,both the availability of data and the noise interference on preferences mining are considered in the algorithm design.In the process of generating group user preferences,a new path is used to generate preference keywords so as to reduce the noise interference.Secondly,the Gibbs sampling algorithm is used to estimate the parameters of the model.Finally,using the user comment data of several online shopping websites as experimental objects,the method is used to mine the multi-attribute preferences of different groups.The proposed method is compared with other methods from three aspects of predictive ability,preference mining ability and preference topic similarity.Experimental results show that the method is significantly better thap other existing methods.展开更多
Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear ...Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.展开更多
The joint spatial division and multiplexing(JSDM)is a two-phase precoding scheme for massive multiple-input-multiple-output(MIMO)system under frequency division duplex(FDD)mode to reduce the amount of channel state in...The joint spatial division and multiplexing(JSDM)is a two-phase precoding scheme for massive multiple-input-multiple-output(MIMO)system under frequency division duplex(FDD)mode to reduce the amount of channel state information(CSI)feedback.To apply this scheme,users need to be partitioned into groups so that users in the same group have similar channel covariance eigenvectors while users in different groups have almost orthogonal eigenvectors.In this paper,taking the clustered user model into account,we consider the user grouping of JSDM for the downlink massive MIMO system with uniform planar antenna array(UPA)at base station(BS).A deep learning based user grouping algorithm is proposed to improve the efficiency of the user grouping process.The proposed grouping algorithm transfers the statistical CSI of all users into a picture,and utilizes the deep learning enabled objective detection model you look only once(YOLO)to divide the users into different groups rapidly.Simulation results show that the proposed user grouping scheme can achieve higher sum rate with less time delay.展开更多
The rapid development of mega low earth orbit(LEO)satellite networks is expected to have a significant impact on 6G networks.Unlike terrestrial networks,due to the high-speed movement of satellites,users will frequent...The rapid development of mega low earth orbit(LEO)satellite networks is expected to have a significant impact on 6G networks.Unlike terrestrial networks,due to the high-speed movement of satellites,users will frequently hand over between satellites even if their positions remain unchanged.Furthermore,the extensive coverage characteristic of satellites leads to massive users executing handovers simultaneously.To address these challenges,we propose a novel double grouping-based group handover scheme(DGGH)specifically tailored for mega LEO satellite networks.First,we develop a user grouping strategy based on beam-limited hierarchical clustering to divide users into distinct groups.Next,we reframe the challenge of managing multiple users’simultaneous handovers as a single-objective optimization problem,solving it with a satellite grouping strategy that leverages the accuracy of greedy algorithms and the simplicity of dynamic programming.Additionally,we develop a group handover algorithm based on minimal handover waiting time to improve the satellite grouping process further.The detailed steps of the DGGH scheme’s handover procedure are meticulously outlined.Comprehensive simulations show that the proposed DGGH scheme outperforms single-user handover schemes in terms of handover signaling over-head and handover success rate.展开更多
Pilot contamination can spoil the accuracy of channel estimation and then has become one of the key problems influencing the performance of massive multiple input multiple output(MIMO)systems.This paper proposes a met...Pilot contamination can spoil the accuracy of channel estimation and then has become one of the key problems influencing the performance of massive multiple input multiple output(MIMO)systems.This paper proposes a method based on cell classification and users grouping to mitigate the pilot contamination in multi-cell massive MIMO systems and improve the spectral efficiency.The pilots of the terminals are allocated onebit orthogonal identifier to diminish the cell categories by the operation of exclusive OR(XOR).At the same time,the users are divided into edge user groups and central user groups according to the large-scale fading coefficients by the clustering algorithm,and different pilot sequences are assigned to different groups.The simulation results show that the proposed method can effectively improve the spectral efficiency of multi-cell massive MIMO systems.展开更多
Pilot allocation is one of the effective means to reduce pilot pollution in massive multiple-input multiple-output(MIMO)systems.The goal of this paper is to improve the uplink achievable sum rates of strong users,and ...Pilot allocation is one of the effective means to reduce pilot pollution in massive multiple-input multiple-output(MIMO)systems.The goal of this paper is to improve the uplink achievable sum rates of strong users,and ensure the quality of service(QoS)requirements of weak users at the same time,so that the sum rates of system can be improved.Combining with the technical advantage of pilot grouping,a low complexity pilot allocation scheme based on matching algorithm is proposed,which divides the users in the target cell into weak user group and strong user group,and adopts the minimum-maximum matching method to allocate pilots in weak user group.Through the introduction of Hungarian algorithm,a pilot allocation method is designed to ensure the fairness of the strong users.The simulation results show that,compared with the smart pilot allocation scheme,the pilot allocation scheme based on Hungarian algorithm,the pilot allocation scheme based on user grouping and the random pilot allocation scheme,the system performance of the proposed scheme has been effectively improved.展开更多
This paper outlined a Non-Orthogonal Multiple Access (NOMA) grouping transmission scheme for cognitive radio networks. To address the problems of small channel gain difference of the middle part users caused by the tr...This paper outlined a Non-Orthogonal Multiple Access (NOMA) grouping transmission scheme for cognitive radio networks. To address the problems of small channel gain difference of the middle part users caused by the traditional far-near pairing algorithm, and the low transmission rate of the traditional Orthogonal Multiple Access (OMA) transmission, a joint pairing algorithm was proposed, which provided multiple pairing schemes according to the actual scene. Firstly, the secondary users were sorted according to their channel gain, and then different secondary user groups were divided, and the far-near pairing combined with (Uniform Channel Gain Difference (UCGD) algorithm was used to group the secondary users. After completing the user pairing, the power allocation problem was solved. Finally, the simulation data results showed that the proposed algorithm can effectively improve the system transmission rate.展开更多
随着通信市场饱和、新用户增长放缓,运营商基础业务收入增长乏力,存量用户促活、提升用户忠诚度及每用户平均收入(Average Revenue Per User,ARPU)值成为关键需求。运营商虽已推出全光WiFi(Fiber to the Room,FTTR)、权益类等新兴业务...随着通信市场饱和、新用户增长放缓,运营商基础业务收入增长乏力,存量用户促活、提升用户忠诚度及每用户平均收入(Average Revenue Per User,ARPU)值成为关键需求。运营商虽已推出全光WiFi(Fiber to the Room,FTTR)、权益类等新兴业务以满足用户多样化需求,但传统促活方法依赖固定模式与经验判断,难以实现策略精准匹配。本文引入元学习概念搭建一个两阶段框架,基于用户维系与营销运营积累的数据(如用户点击、缴费、流量使用、营销活动参与等行为),分析不同用户群体对促活活动的响应模式与行为特征,训练能够快速适应不同用户群体和推荐活动的模型。该模型有助于更精准地识别用户潜在需求与偏好,辅助运营商选择并实施最优的推荐活动。经与传统方案对比验证,本文方案可使用户活跃度提升19%、转化率提升8%。展开更多
基金The National High Technology Research and Development Program of China(863 Program)(No.2013AA013503)the National Natural Science Foundation of China(No.61472080+3 种基金6137020661300200)the Consulting Project of Chinese Academy of Engineering(No.2015-XY-04)the Foundation of Collaborative Innovation Center of Novel Software Technology and Industrialization
文摘To improve the similarity measurement between users, a similarity measurement approach incorporating clusters of intrinsic user groups（ SMCUG） is proposed considering the social information of users. The approach constructs the taxonomy trees for each categorical attribute of users. Based on the taxonomy trees, the distance between numerical and categorical attributes is computed in a unified framework via a proper weight. Then, using the proposed distance method, the nave k-means cluster method is modified to compute the intrinsic user groups. Finally, the user group information is incorporated to improve the performance of traditional similarity measurement. A series of experiments are performed on a real world dataset, M ovie Lens. Results demonstrate that the proposed approach considerably outperforms the traditional approaches in the prediction accuracy in collaborative filtering.
基金the Major Project of National Social Science Foundation of China under Grant No.20&ZD127.
文摘Traditional researches on user preferences mining mainly explore the user’s overall preferences on the project,but ignore that the fundamental motivation of user preferences comes from their attitudes on some attributes of the project.In addition,traditional researches seldom consider the typical preferences combination of group users,which may have influence on the personalized service for group users.To solve this problem,a method with noise reduction for group user preferences mining is proposed,which focuses on mining the multi-attribute preference tendency of group users.Firstly,both the availability of data and the noise interference on preferences mining are considered in the algorithm design.In the process of generating group user preferences,a new path is used to generate preference keywords so as to reduce the noise interference.Secondly,the Gibbs sampling algorithm is used to estimate the parameters of the model.Finally,using the user comment data of several online shopping websites as experimental objects,the method is used to mine the multi-attribute preferences of different groups.The proposed method is compared with other methods from three aspects of predictive ability,preference mining ability and preference topic similarity.Experimental results show that the method is significantly better thap other existing methods.
基金supported by the Key R&D Project of the Ministry of Science and Technology of China(2020YFB1808005)。
文摘Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.
基金This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFE0121500in part by the National Natural Science Foundation of China under Grants 61971126 and 61831013.
文摘The joint spatial division and multiplexing(JSDM)is a two-phase precoding scheme for massive multiple-input-multiple-output(MIMO)system under frequency division duplex(FDD)mode to reduce the amount of channel state information(CSI)feedback.To apply this scheme,users need to be partitioned into groups so that users in the same group have similar channel covariance eigenvectors while users in different groups have almost orthogonal eigenvectors.In this paper,taking the clustered user model into account,we consider the user grouping of JSDM for the downlink massive MIMO system with uniform planar antenna array(UPA)at base station(BS).A deep learning based user grouping algorithm is proposed to improve the efficiency of the user grouping process.The proposed grouping algorithm transfers the statistical CSI of all users into a picture,and utilizes the deep learning enabled objective detection model you look only once(YOLO)to divide the users into different groups rapidly.Simulation results show that the proposed user grouping scheme can achieve higher sum rate with less time delay.
基金supported in part by the State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster(No.MS01240103)the National Natural Science Foundation of China(No.62071146,No.62431009)+1 种基金the National 2011 Collaborative Innovation Center of Wireless Communication Technologies(No.2242022k60006)the Research Project Fund of Songjiang Laboratory(No.SL20230104).
文摘The rapid development of mega low earth orbit(LEO)satellite networks is expected to have a significant impact on 6G networks.Unlike terrestrial networks,due to the high-speed movement of satellites,users will frequently hand over between satellites even if their positions remain unchanged.Furthermore,the extensive coverage characteristic of satellites leads to massive users executing handovers simultaneously.To address these challenges,we propose a novel double grouping-based group handover scheme(DGGH)specifically tailored for mega LEO satellite networks.First,we develop a user grouping strategy based on beam-limited hierarchical clustering to divide users into distinct groups.Next,we reframe the challenge of managing multiple users’simultaneous handovers as a single-objective optimization problem,solving it with a satellite grouping strategy that leverages the accuracy of greedy algorithms and the simplicity of dynamic programming.Additionally,we develop a group handover algorithm based on minimal handover waiting time to improve the satellite grouping process further.The detailed steps of the DGGH scheme’s handover procedure are meticulously outlined.Comprehensive simulations show that the proposed DGGH scheme outperforms single-user handover schemes in terms of handover signaling over-head and handover success rate.
基金supported by the Scientific and Technological Innovation Foundation of Shunde Graduate School,USTB(BK19CF002).
文摘Pilot contamination can spoil the accuracy of channel estimation and then has become one of the key problems influencing the performance of massive multiple input multiple output(MIMO)systems.This paper proposes a method based on cell classification and users grouping to mitigate the pilot contamination in multi-cell massive MIMO systems and improve the spectral efficiency.The pilots of the terminals are allocated onebit orthogonal identifier to diminish the cell categories by the operation of exclusive OR(XOR).At the same time,the users are divided into edge user groups and central user groups according to the large-scale fading coefficients by the clustering algorithm,and different pilot sequences are assigned to different groups.The simulation results show that the proposed method can effectively improve the spectral efficiency of multi-cell massive MIMO systems.
基金the National Natural Science Foundation of China(No.62001001).
文摘Pilot allocation is one of the effective means to reduce pilot pollution in massive multiple-input multiple-output(MIMO)systems.The goal of this paper is to improve the uplink achievable sum rates of strong users,and ensure the quality of service(QoS)requirements of weak users at the same time,so that the sum rates of system can be improved.Combining with the technical advantage of pilot grouping,a low complexity pilot allocation scheme based on matching algorithm is proposed,which divides the users in the target cell into weak user group and strong user group,and adopts the minimum-maximum matching method to allocate pilots in weak user group.Through the introduction of Hungarian algorithm,a pilot allocation method is designed to ensure the fairness of the strong users.The simulation results show that,compared with the smart pilot allocation scheme,the pilot allocation scheme based on Hungarian algorithm,the pilot allocation scheme based on user grouping and the random pilot allocation scheme,the system performance of the proposed scheme has been effectively improved.
文摘This paper outlined a Non-Orthogonal Multiple Access (NOMA) grouping transmission scheme for cognitive radio networks. To address the problems of small channel gain difference of the middle part users caused by the traditional far-near pairing algorithm, and the low transmission rate of the traditional Orthogonal Multiple Access (OMA) transmission, a joint pairing algorithm was proposed, which provided multiple pairing schemes according to the actual scene. Firstly, the secondary users were sorted according to their channel gain, and then different secondary user groups were divided, and the far-near pairing combined with (Uniform Channel Gain Difference (UCGD) algorithm was used to group the secondary users. After completing the user pairing, the power allocation problem was solved. Finally, the simulation data results showed that the proposed algorithm can effectively improve the system transmission rate.
文摘随着通信市场饱和、新用户增长放缓,运营商基础业务收入增长乏力,存量用户促活、提升用户忠诚度及每用户平均收入(Average Revenue Per User,ARPU)值成为关键需求。运营商虽已推出全光WiFi(Fiber to the Room,FTTR)、权益类等新兴业务以满足用户多样化需求,但传统促活方法依赖固定模式与经验判断,难以实现策略精准匹配。本文引入元学习概念搭建一个两阶段框架,基于用户维系与营销运营积累的数据(如用户点击、缴费、流量使用、营销活动参与等行为),分析不同用户群体对促活活动的响应模式与行为特征,训练能够快速适应不同用户群体和推荐活动的模型。该模型有助于更精准地识别用户潜在需求与偏好,辅助运营商选择并实施最优的推荐活动。经与传统方案对比验证,本文方案可使用户活跃度提升19%、转化率提升8%。
