Uplink Performance Analysis in Multi-Tier Heterogeneous Cellular Networks with Power Control and Biased User Association

A K-tier uplink heterogeneous cellular network is modelled and analysed by accounting for both truncated channel inversion power control and biased user association. Each user has a maximum transmit power constraint and transmits data when it has sufficient transmit power to perform channel inversion. With biased user association, each user is associated with a base station(BS) that provides the maximum received power weighted by a bias factor, but not their nearest BS. Stochastic geometry is used to evaluate the performances of the proposed system model in terms of the outage probability and ergodic rate for each tier as functions of the biased and power control parameters. Simulations validate our analytical derivations. Numerical results show that there exists a trade-off introduced by the power cut-off threshold and the maximum user transmit power constraint. When the maximum user transmit power becomes a binding constraint, the overall performance is independent of BS densities. In addition, we have shown that it is beneficial for the outage and rate performances by optimizing different network parameters such as the power cut-off threshold as well as the biased factors.

Energy-Efficient User Association in Heterogeneous Networks with M2M/H2H Coexistence under QoS Guarantees

In this paper, we propose an energy efficient user association scheme for uplink heterogeneous networks with machine-to-machine(M2M) and human-to-human(H2H) coexistence. A group based random access protocol is designed for massive number of machine-typecommunications(MTC) user equipments'(UEs) transmissions. A user association problem for UEs' energy efficiency maximization is formulated considering the HTC UEs' quality of service(QoS) guarantees and load balance among multiple BSs, simultaneously. A distributed iterative algorithm is developed to solve the optimization problem. In addition, the convergence of the proposed algorithm is proved. Simulation results show that our proposed scheme outperforms other schemes in terms of energy efficiency and QoS guarantees.

User growth and penetration modeling for converged heterogeneous network

To evaluate the trail potential of converged heterogeneous network （CHN） market, the logistic method for adoption modeling of CHN is used. User growth ＆ penetration have been taken as two variants to find saturation condition in market. Model is continuous in time but modifications are done for discrete recurrence equation, commonly known as logistic map. Dynamic and static phases are taken into consideration while penetration decay is not covered in this model.

Delay-Based User Association in Heterogeneous Networks with Backhaul

The Internet of things(IoT) as an important application of future communication networks puts a high premium on delay issues. Thus when Io T applications meet heterogeneous networks(HetNets) where macro cells are overlaid with small cells, some traditional problems need rethinking. In this paper, we investigate the delay-addressed association problem in two-tier Het Nets considering different backhaul technologies. Specifically, millimeter wave and fiber links are used to provide high-capacity backhaul for small cells. We first formulate the user association problem to minimize the total delay which depends on the probability of successful transmission, the number of user terminals(UTs), and the number of base stations(BSs). And then two algorithms for active mode and mixed mode are proposed to minimize the network delay. Simulation results show that algorithms based on mutual selection between UTs and BSs have better performance than those based on distance. And algorithms for mixed modes have less delay than those for active mode when the number of BSs is large enough, compared to the number of UTs.

Resource Allocation and User Association for HTTP Adaptive Streaming in Heterogeneous Cellular Networks with Small Cells

Video streaming,especially hypertext transfer protocol based(HTTP) adaptive streaming(HAS) of video,has been expected to be a dominant application over mobile networks in the near future,which brings huge challenge for the mobile networks.Although some works have been done for video streaming delivery in heterogeneous cellular networks,most of them focus on the video streaming scheduling or the caching strategy design.The problem of joint user association and rate allocation to maximize the system utility while satisfying the requirement of the quality of experience of users is largely ignored.In this paper,the problem of joint user association and rate allocation for HTTP adaptive streaming in heterogeneous cellular networks is studied,we model the optimization problem as a mixed integer programming problem.And to reduce the computational complexity,an optimal rate allocation using the Lagrangian dual method under the assumption of knowing user association for BSs is first solved.Then we use the many-to-one matching model to analyze the user association problem,and the joint user association and rate allocation based on the distributed greedy matching algorithm is proposed.Finally,extensive simulation results are illustrated to demonstrate the performance of the proposed scheme.

Context-Oriented Multi-RAT User Association and Resource Allocation with Triple Decision in 5G Heterogeneous Networks

In the upcoming 5 G heterogeneous networks, leveraging multiple radio access technologies(RATs) shows to be a crucial issue in achieving RAT multiplexing gain to meet the explosive traffic demand. For always best connection(ABC), users tend to activate parallel transmission across all available RATs. However from a system-wide perspective, this might not be optimal given the context of network load, interference and diverse service requirements. To intelligently determine how to use these multi-RAT access resources concurrently, this paper proposes a joint multi-RAT user association and resource allocation strategy with triple decision and integrated context awareness of users and networks. A dynamic game based ant colony algorithm(GACA) is designed to simultaneously maximize the system utility and the fairness of resource allocation. Simulation results show that it's more reasonable to make multi-RAT association decision from a system-wide viewpoint than from an individual one. Compared to max-SNR based and ABC based strategies, the proposed method alleviates network congestion and optimizes resource allocation. It obtains 39%~70% performance improvement.

User Selection Algorithms for Lowering Cross-Tier Interference in Heterogeneous Network

To lower the cross-tier intercell interference(ICI) between macrocell and microcell,three user selection algorithms for the heterogeneous network were proposed in this paper, assuming full knowledge of channelstate information at the transmitter. Algorithm 1 chooses microcell users whose interference channel matrix is parallel to that of a known user and targets at increasing user SINR. Algorithm 2 takes effect of chordal distance-channel norm balance on the system into account and predetermines the available user set from which it can choose service users. With comprehensive considerations to effect of interference signal and useful signal on system, Algorithm 3 set a weighting function as the objective function of user selection. Simulation results demonstrated that all three proposed algorithms could achieve user diversity gain while lowering cross-tier interference.

Terminal-Modality-Based Joint Call Admission Control Algorithm for Fair Radio Resource Allocation in Heterogeneous Cellular Networks

There is a problem of unfairness in allocation of radio resources among heterogeneous mobile terminals in heterogeneous wireless networks. Low-capability mobile terminals (such as single-mode terminals) suffer high call blocking probability whereas high-capability mobile terminals (such as quad-mode terminals) experience very low call blocking probability, in the same heterogeneous wireless network. This paper proposes a Terminal-Modality-Based Joint Call Admission Control (TJCAC) algorithm to reduce this problem of unfairness. The proposed TJCAC algorithm makes call admission decisions based on mobile terminal modality (capability), network load, and radio access technology (RAT) terminal support index. The objectives of the proposed TJCAC algorithm are to reduce call blocking/dropping probability, and ensure fairness in allocation of radio resources among heterogeneous mobile terminals in heterogeneous networks. An analytical model is developed to evaluate the performance of the proposed TJCAC scheme in terms of call blocking/dropping probability in a heterogeneous wireless network. The performance of the proposed TJCAC algorithm is compared with that of other JCAC algorithms. Results show that the proposed algorithm reduces call blocking/dropping probability in the networks, and ensure fairness in allocation of radio resources among heterogeneous terminals.

User Heterogeneity and Individualized Recommender

Previous works on personalized recommendation mostly emphasize modeling peoples＇ diversity in potential fa- vorites into a uniform recommender. However, these recommenders always ignore the heterogeneity of users at an individual level. In this study, we propose an individualized recommender that can satisfy every user with a customized parameter. Experimental results on four benchmark datasets demonstrate that the individualized recommender can significantly improve the accuracy of recommendation. The work highlights the importance of the user heterogeneity in recommender design.

非正交多址接入异构网络用户公平性问题研究

针对5G非正交多址接入异构网络主要以传输总速率最大化为优化目标带来的资源分配不平衡,导致部分信道最小速率过低,造成用户服务质量下降,从而带来用户之间不公平问题,文中基于非正交接入异构网络模型,综合考虑传输总速率和用户最小速率,提出将整体优化问题解耦,利用最佳信道配对选择和优化用户功率分配方法最大化最小用户速率,解决用户公平性问题。仿真结果表明,提出的算法在较低的复杂度下,用户最小速率明显优于传统方法,用户公平性明显提升。

面向多源异构数据的个性化搜索和推荐算法综述

高效精准的个性化搜索、推荐等服务可为人们生产生活带来极大便利,而随着互联网技术的迅猛发展,面向多源异构数据的个性化搜索和推荐任务逐渐变得日趋复杂,也是当前大数据分析及个性化服务领域的研究热点和难点.个性化搜索和推荐算法广泛收集多源异构数据,获取用户偏好信息,利用各类机器学习、深度学习等技术,构建用户兴趣偏好模型,预测用户偏好,推荐满足用户个性化需求和偏好的项目或内容,提升用户的使用体验和网站平台的商业利益.本文介绍面向多源异构数据的个性化搜索问题的数学描述,综述面向多源异构数据的个性化搜索和推荐算法的相关研究工作,包括:传统个性化搜索和推荐算法、融合多源异构数据的个性化搜索和推荐算法以及动态个性化搜索和推荐算法等相关研究现状,整理了算法常用数据集、性能评价指标及评估体系,进一步阐明了目前面向多源异构数据的个性化搜索和推荐方法的实际应用场景及今后研究的发展方向,并讨论了存在的不足及所面临的严峻挑战,期望为相关领域的研究人员提供有益帮助.

网络结构视角下用户异质性对知识协作效果的影响研究

[目的/意义]探讨如何优化在线知识协作平台网络结构和平衡团队内部的知识与经验,为促进跨领域信息流通,避免信息孤岛的形成,通过集体知识协作,推动知识的创造、传播和应用。[方法/过程]本研究利用编程问答网站Stack Overflow的数据,筛选出含“Python”标签的问题帖和对应的回答帖,结合负二项回归模型探讨了网络结构特征和用户异质性对知识协作质量和效率的影响。[结果/结论]网络结构特征方面,节点中心性显著提升了协作的质量和效率,较高的聚集系数和较大的连边跨度限制了信息流通,不利于知识协作效率。用户异质性方面,知识背景和注册时长的高异质性通常阻碍协作,经验异质性中注册时长异质性对协作效果均产生负面影响,回答被采纳率异质性仅对协作质量产生了负面影响,而活动强度异质性则产生了积极影响。

改进Petri网下异构跨域用户身份验证方法

在异构跨域用户身份验证时,由于用户身份存在属性特征难以捕捉、状态描述误差大等问题,导致验证精准度较低,为有效解决这一问题,文中提出一种基于改进Petri网的用户身份验证算法。利用改进Petri网反映不同局域网中事物间依赖关系,将异构跨域用户活动模型与Petri网模型转换,采用同步用户的状态变化提取实时状态特征值。通过建立云间身份认证中心,计算用户不同身份代表性词语在一定周期内出现的次数和频率值。提取频率最高的词语,并计算该词语在待验证用户全部代表词语库中的权重值,对比权重值与验证熵值,将得到的验证参数输入到认证中心,输出验证结果。实验结果表明,所提方法在先验节点比例为8%时,验证准确率达到0.9,且召回率和综合指标均在0.6以上,具备一定的抗攻击性,整体实用性能强、利用价值高。

5G异构网络中基于多目标Actor-Critic的资源分配

在5G异构网络(heterogeneous network,HetNet)中广泛部署小基站可以提高网络容量和用户速率,但密集部署也会产生严重干扰和更高能耗问题。为了最大化网络能量效率(energy efficiency,EE)并保证用户服务质量(quality of service,QoS),提出了一种在小蜂窝基站中嵌入能量收集器供电的资源分配方案。首先,针对网络系统的下行链路,将频谱和小基站发射功率分配问题建模为联合优化系统能效和用户满意度的多目标优化问题。其次,提出了基于深度强化学习的多目标演员-评论家(multi-objective actor-critic,MAC)资源分配算法求解所建立的优化模型。最后,仿真结果表明,相比于其他传统学习算法,能量效率提高了11.96%~12.37%,用户满意度提高了11.45%~27.37%。

行为增强的多层次协同Top-N推荐

传统推荐系统只利用单一用户行为,然而用户行为间是具有关联性,忽视用户行为会丢失辅助行为对目标行为的影响。本文提出了一种行为增强的多层次协同Top-N推荐,在推荐二分图和元路径图上利用注意力机制传播信息,学习多层次高阶和异质协同信号(包括用户-项目间的和项目间的)以提高推荐性能,这样可以更好地利用推荐图结构,并充分考虑到推荐图结构上各种行为间的相互影响。在经典数据集上做了全方位实验验证模型有效性,在电商推荐数据上取得了很好效果。

MaaS场景下考虑用户异质性的交通分配模型

MaaS场景下,通过问卷调查考虑用户异质性,构建广义出行费用函数确定不同年龄不同收入人群的出行费用、出行时间和出行舒适度权重,应用模糊综合评价法构建隶属度函数,对个体出行链多模式交通特征进行提取与融合,完成模型参数标定。该模型考虑不同类别人群出行对经济性、便捷度和舒适性的要求,使得模型更加精细。模型分别应用到广州市番禺广场片区和北京街片区,结果显示,模型计算各OD间主出行方式交通分配量与公交、地铁及驾车等方式实际交通量数据有较高适配,2个片区的多方式组合出行交通量平均分配精度达到86.8%,证明了模型的可行性和有效性。

User space transformation in deep learning based recommendation

Deep learning based recommendation methods, such as the recurrent neural network based recommendation method(RNNRec) and the gated recurrent unit(GRU) based recommendation method(GRURec), are proposed to solve the problem of time heterogeneous feedback recommendation. These methods out-perform several state-of-the-art methods. However, in RNNRec and GRURec, action vectors and item vectors are shared among users. The different meanings of the same action for different users are not considered. Similarly, different user preference for the same item is also ignored. To address this problem, the models of RNNRec and GRURec are modified in this paper. In the proposed methods, action vectors and item vectors are transformed into the user space for each user firstly, and then the transformed vectors are fed into the original neural networks of RNNRec and GRURec. The transformed action vectors and item vectors represent the user specified meaning of actions and the preference for items, which makes the proposed method obtain more accurate recommendation results. The experimental results on two real-life datasets indicate that the proposed method outperforms RNNRec and GRURec as well as other state-of-the-art approaches in most cases.

异构边缘环境下基于蚁群系统的用户分配

边缘计算是一种新兴的计算范式,将任务分配至边缘服务器可有效提升用户满意度。然而,如何将用户与合适的边缘服务器匹配实现特定优化目标是一项挑战。多数现有解决方案未能充分考虑设备异构性和网络复杂性对分配策略的影响。为此,首先,建立了设备异构和干扰感知的用户分配问题模型;然后,提出了双向排序贪婪算法和启发式蚁群系统算法,旨在提高用户分配率,同时降低平均任务延迟。实验结果表明,所提出的方法有效地解决了问题,并且在性能上优于其他三种代表性的方法。

双通道异构图神经网络序列推荐算法

基于用户行为序列的推荐系统的目的是根据上一次序列的顺序预测用户的下一次点击。目前的研究一般是根据用户行为序列中项目的转换来了解用户偏好。然而,行为序列中的其他有效信息被忽略,如用户配置文件,这会导致模型无法了解用户的特定偏好。提出了一种基于双通道异构图神经网络的用户行为序列推荐算法(DC-HetGNN),该方法通过异构图神经网络通道和异构图线图通道学习行为序列嵌入,并捕获用户的特定偏好。DC-HetGNN会根据行为序列构造包含各种类型节点的异构图,可以捕获项目、用户和序列之间的依赖关系。其次,异构图神经网络通道和异构图线图通道捕获物品复杂转换及序列之间的交互信息,并学习包含用户信息的物品嵌入。最后,考虑到用户长期和短期偏好的影响,将局部和全局序列嵌入与注意力网络相结合,得到最终的序列嵌入。在两个电商用户行为序列数据集Diginetica和Tmall上进行的实验表明,DC-HetGNN与新近模型FGNN相比在指标平均倒数排名(MRR)和召回率(Recall)中平均分别提升2.08%和0.78%,与TGSRec相比在指标MRR@n和Recall@n中平均分别提升2.70%和0.49%。

基于统一语义表征的多用户异构语义网络

多用户语义通信,特别是异构语义网络是目前研究较少的课题。提出一种基于统一语义表征的单编码器-单解码器框架,其中异构的语义知识作为引导语义提取和表达的秘钥——符号消息被映射到统一的本原语义空间,并根据接收端用户各自的语义知识特异化解码。所提掩膜推理和异构语义转述约束,相较常用的点对点自编码方案,给语义可解释性及拓展性带来显著增强。针对多用户语义安全性,还提出一种利用语义知识流形空间进行语义加密的方法。