Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

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.

A novel similarity measurement approach considering intrinsic user groups in collaborative filtering

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.

Deep Learning Based User Grouping for FD-MIMO Systems Exploiting Statistical Channel State Information

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.

A pilot allocation method for multi-cell multi-user massive MIMO system

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.

Grouping pilot allocation scheme based on matching algorithm in massive MIMO system

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.

Research on User Pairing Techniques Based on NOMA in Cognitive Radio Networks

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.

融合群体兴趣和核心用户影响的群组推荐方法研究

[目的/意义]为提高群组推荐准确率和可解释性,针对现有的群组推荐方法较少关注群组内核心用户对群体兴趣的影响,综合考虑群组整体兴趣和群组内核心用户对其他用户的影响,提出融合群体兴趣和核心用户影响的群组推荐方法。[方法/过程]首先,借鉴h指数思想计算群组内每个用户的影响力,识别群组内核心用户;其次,利用LDA主题模型进行群体兴趣建模和核心用户兴趣建模;最后,综合考虑群组中用户自身兴趣和其受核心用户影响而产生的兴趣进行资源推荐服务。[结果/结论]利用微博数据进行实证,验证了此方法的有效性。

基于XML索引动态分组的P2P信息检索机制

针对中心服务器P2P存在瓶颈和单点失效以及无结构P2P易产生网络阻塞的问题,提出一种基于XML索引动态分组的P2P信息检索机制,解决了P2P系统多层次分组不能及时更新以及分组依据片面性的问题。方案中基于发掘用户兴趣设计了动态分组的IBDG算法,该算法通过实时更新分组提高了搜索效率。仿真实验结果证明该算法有效。

有向网络兴趣社区的快速挖掘算法及其在僵尸粉检测中的应用

针对传统的无向网络社区挖掘方法无法实现大规模有向网络中社区有效发现的问题,提出了一种新的有向图社区及其兴趣特征快速挖掘算法。采用贪心算法求解社区划分模块性最大化的优化问题,较好地平衡了有向图社区挖掘中准确性与有效性之间的矛盾,实现对大规模微博类有向网络社区结构的有效识别;基于发现的社区,采用tf-idf算法进一步挖掘社区用户的兴趣爱好,实现了对微博网络中兴趣小组的精确挖掘。基于新浪微博的实验结果表明:所提算法不仅可以快速有效地挖掘有向网络中的社区结构及其用户的兴趣特征,还能够准确地检测出微博网络中的僵尸粉社区,研究结果对微博系统的净化、谣言控制、网络广告的精准投放等研究具有重要的参考价值。

基于混合Markov模型的用户浏览预测

建立有效的用户浏览预测模型,准确的预测用户的浏览路径,是开发各种Web站点导航工具的关键。传统的Markov模型是一种简单而有效的预测模型,但它存在测准确率低、预测覆盖率低以及存储复杂度高等缺点。通过对传统Markov模型的扩展,并研究了群体用户在Web上的浏览特性,采用了基于混合Markov模型的用户群兴趣导航发现方法。实验结果表明,该方法比传统Markov模型更准确的反映了用户的访问兴趣,可以获得更高的预测准确率与覆盖率,以及有效地降低存储复杂度。

基于粗粒度程序切片的软件度量

软件度量是保证软件质量的重要方法,程序切片是一种针对切片标准简化程序的技术。提出了一种基于粗粒度程序切片技术度量面向对象程序模块耦合度和类内聚度的量化方法,扩展了粗粒度程序切片的含义,基于面向对象系统依赖图进行程序切片,给出了对应的算法实现,利用切片技术度量了模块耦合度和类内聚度。

基于概率矩阵分解算法的社交网络用户兴趣点个性化推荐

在社交网络环境中,传统社交网络用户兴趣点的个性化推荐方法存在网络用户兴趣行为的预测精准性低、用户社交数据覆盖率低的问题,不能充分挖掘用户兴趣点的时空序列特征,为此提出了一种基于概率矩阵分解算法的社交网络用户兴趣点个性化推荐方法。在模型训练的伪代码群中,计算与矩阵概率的变异算子相关的数值结果,实现社交关系网络的物理分割,完成基于概率矩阵分解算法的社交网络节点建模。在此基础上,搭建个性化社交网络框架,按照用户兴趣行为的特征挖掘结果,选择个性化的用户来推荐节点,完成社交网络用户兴趣点个性化推荐方法的建立。实用性检测结果表明,与传统方法相比,应用新型个性化推荐方法后,网络用户兴趣行为的预测精准度最高可达100%,用户社交数据覆盖率约为75%,提高了网络用户兴趣行为的预测精准性和用户社交数据覆盖率,社交网络用户兴趣点的时空序列特征得到了充分挖掘。

融合多维特征与兴趣漂移的虚拟学术社区群推荐模型

[目的/意义]为促进虚拟学术社区知识流转效率,弥补当前针对核心用户群组识别及动态兴趣漂移关注不足的问题,本文构建融合多维特征与兴趣漂移的虚拟学术社区群推荐模型。[方法/过程]以“科学网”为研究对象,从网络传播维度和网络结构维度出发,运用改进的信息熵公式综合识别核心用户并聚类发现用户群组。在此基础上,融合群组兴趣特征向量,基于滑动时间窗口和非线性遗忘曲线分析群组兴趣漂移过程,根据动态兴趣漂移结果进行群组推荐并验证该模型的适用性。[结果/结论]实验结果表明,该模型基于用户多维特征可以准确识别核心用户并能很好地反映群组兴趣漂移特征,同时,本文提出的群组推荐算法相比传统算法在推荐结果的准确率上明显提升。

兴趣偏移校正的互联网媒体深度信息分析模型

针对目前的互联网媒体深度信息分析方法忽略了用户之间的相关关系和相互影响,导致分析结果不够精确的问题,针对互联网媒体的海量数据,提出了一种兴趣偏移校正的互联网媒体深度信息分析模型。首先分析社交网络环境下用户兴趣多元化的特点,结合用户受其它成员的影响而发生的兴趣偏移的情况来建立用户兴趣模型,然后再结合用户社区兴趣最终得到完整的用户兴趣模型。实例仿真实验结果表明,结合评价指标发现,该方法在深度信息分析结果中具有很好的性能。

基于联邦学习的在线短视频内容分发策略

为提升短视频内容分发的精度,分析用户所属社交群体的兴趣倾向和对短视频内容的个性化需求,在基于主动推荐方式的短视频应用场景中,以视频内容提供商利润最大化为优化目标,设计了一种短视频内容分发策略。首先,基于联邦学习,利用用户群本地相册数据训练兴趣预测模型,提出用户群兴趣向量预测算法并得到用户群的兴趣向量表示;然后,以用户群的兴趣向量作为输入,基于组合置信上界(CUCB)算法实时设计相应的短视频内容分发策略,从而使视频内容提供商获取的长期利润最大化。所提策略获得的平均利润相对稳定且明显优于单纯基于CUCB的短视频分发策略得到的平均利润;与置信上界(UCB)策略和随机策略相比,所提策略使得视频内容提供商获得的总利润分别提高了12%和30%。实验结果表明,所提短视频内容分发策略能有效地提升短视频分发的精度,从而进一步提高视频内容提供商获取的利润。

基于项目分类和用户群体兴趣的协同过滤算法

由于用户评分数据在极端稀疏的情况下会导致传统协同过滤算法的推荐质量下降,针对该问题,提出一种基于项目分类和用户群体兴趣的协同过滤算法。该算法根据项目类别信息对项目进行分类,相同分类的项目具有较高的相似性;利用评分数据计算各个项目分类上的用户相似性矩阵,并计算用户群体在各个分类上的兴趣,通过二者构造加权的用户相似性矩阵;利用用户加权相似性矩阵寻找用户的最近邻以获得最佳的推荐效果。实验结果表明,该算法能有效提高推荐质量。

基于网络趣缘群体视角的知识付费产品需求研究

基于网络趣缘群体视角,研究了知识付费产品的用户特征及需求,发现用户使用知识付费产品的需求是利用碎片化时间积累跨领域知识技能,以解决知识焦虑、筛选优质内容和服务,获取专家在专业领域的认知和见解、寻求感情寄托,缓解来自社会的巨大压力。通过借鉴典型案例的知识付费产品需求满足设计,提出了知识付费产品发展建议。

考虑相似兴趣的科研人员群体画像构建方法

[目的/意义]《在科学家座谈会上的讲话》指出要对创新体系进行优化组合,加强科技力量统筹。当前我国科研人员总量稳居世界第一,对这一群体进行全面、客观、准确地了解是依靠新型举国体制攻关卡脖子技术、推进有组织的科研、实现科研力量统筹管理的重要基础,科研人员群体画像是实现此目标的有效途径之一。[方法/过程]首先,明确科研人员群体画像的概念,并根据科研信息的数据类型设计标签体系。其次,依据"角色法"等理论基础,提出考虑相似兴趣的科研人员群体画像构建方法,即通过关键词共现和社团划分得到具有相似研究兴趣的科研人员群体。最后,再对群体进行多角度的描述和呈现。[结果/结论]以某大学的学院为例,群体画像能够直观有效地描述科研兴趣群体并具有实践意义。[局限]词主题分析还需人工参与,词网络划分不能兼顾规模和效果。

Joint resource allocation for hybrid NOMA-assisted MEC in 6G networks

Multi-access Edge Computing(MEC)is an essential technology for expanding computing power of mobile devices,which can combine the Non-Orthogonal Multiple Access(NOMA)in the power domain to multiplex signals to improve spectral efficiency.We study the integration of the MEC with the NOMA to improve the computation service for the Beyond Fifth-Generation(B5G)and the Sixth-Generation(6G)wireless networks.This paper aims to minimize the energy consumption of a hybrid NOMA-assisted MEC system.In a hybrid NOMA system,a user can offload its task during a time slot shared with another user by the NOMA,and then upload the remaining data during an exclusive time duration served by Orthogonal Multiple Access(OMA).The original energy minimization problem is non-convex.To efficiently solve it,we first assume that the user grouping is given,and focuses on the one group case.Then,a multilevel programming method is proposed to solve the non-convex problem by decomposing it into three subproblems,i.e.,power allocation,time slot scheduling,and offloading task assignment,which are solved optimally by carefully studying their convexity and monotonicity.The derived solution is optimal to the original problem by substituting the closed expressions obtained from those decomposed subproblems.Furthermore,we investigate the multi-user case,in which a close-to-optimal algorithm with lowcomplexity is proposed to form users into different groups with unique time slots.The simulation results verify the superior performance of the proposed scheme compared with some benchmarks,such as OMA and pure NOMA.

Matching theory based user-grouping for indoor non-orthogonal multiple access visible light communication heterogeneous networks

This Letter proposes a model of indoor visible light communication(VLC)heterogeneous networks entirely based on LEDs with different specifications and applies non-orthogonal multiple access(NOMA)to it because of the narrow modulation bandwidth of LEDs.Moreover,a user-grouping scheme that is based on matching theory is proposed to improve the network achievable sum rate.Simulation results indicate that when each NOMA cluster contains 6 users,the proposed scheme has a 49.54%sum-rate enhancement compared with the traditional user-grouping scheme.As the number of users in each NOMA cluster increases,the proposed scheme performs better at the cost of computational complexity.