认知超密集网络用户关联与资源分配联合优化遗传算法

针对下行的异构认知超密集异构网络（UDN）的多维资源配置问题,提出一种以毫微微小区用户最大吞吐量为目标的联合优化用户关联和资源分配的改进遗传算法。首先,在算法开始之前进行预处理,初始化用户可达基站和可用信道矩阵;其次,采用符号编码,将用户与基站以及用户与信道的匹配关系编码为一个二维的染色体;然后,将动态择优复制+轮盘赌作为选择算法,以加快种群的收敛;最后,为避免算法陷入局部最优,在变异阶段加入早熟判决的变异算子,从而在有限次迭代下求得基站、用户、信道的连接策略。实验结果表明,在基站与信道数量一定时,所提算法与三维匹配的遗传算法相比在用户总吞吐量方面提高了7.2%,在认知用户吞吐量方面提高了1.2%,且计算复杂度更低。所提算法缩小了可行解的搜索空间,能在较低复杂度下有效提高认知UDN的总吞吐量。     

Joint user knowledge and matrix factorization for recommender systems

Currently, most of the existing recommendation methods treat social network users equally, which assume that the effect of recommendation on a user is decided by the user’s own preferences and social influence. However, a user’s own knowledge in a field has not been considered. In other words, to what extent does a user accept recommendations in social networks need to consider the user’s own knowledge or expertise in the field. In this paper, we propose a novel matrix factorization recommendation algorithm based on integrating social network information such as trust relationships, rating information of users and users’ own knowledge. Specifically, since we cannot directly measure a user’s knowledge in the field, we first use a user’s status in a social network to indicate a user’s knowledge in a field, and users’ status is inferred from the distributions of users’ ratings and followers across fields or the structure of domain-specific social network. Then, we model the final rating of decision-making as a linear combination of the user’s own preferences, social influence and user’s own knowledge. Experimental results on real world data sets show that our proposed approach generally outperforms the state-of-the-art recommendation algorithms that do not consider the knowledge level difference between the users.     

A flexible mid-term frequency domain scheduler for resource allocation in HetNets based on the SINR requested by users

This article treats the resource allocation problem for the downlink of a multi-cell, multiservice Wireless Mobile Communications System (WMCS) with heterogeneous architecture deployed into an urban environment using Long Term Evolution (LTE) and Orthogonal Frequency Division Multiple Access (OFDMA) in its physical level.The optimization model aims to satisfy services to users by making an efficient use of the available resources, and a fair frequency block allocation, using the Signal to Interference-plus-Noise Ratio (SINR). It is a Mixed Integer Nonlinear Programming (MINLP) model whose solution is complex to obtain directly. The proposed solution algorithm decouples the solution space of the problem and uses an iterative and semi-distributed approach to implement a frequency-domain scheduler in the medium term that uses a global vision of the system to allocate resources trying to obtain the SINR required for all users (the proposed goal). Since it is not always possible to achieve it, we take advantage of the elasticity of some of the services offered and incorporate a slack variable to solve it.The approach allows selecting the frequency allocation strategy, the exploration focus of the search space and the system administrator’s vision. The results obtained show that the implementation that uses a coordinated frequency allocation obtains better results in the amount of users with full satisfaction and in the use of power when compared to implementations using other frequency allocation strategies. In scenarios with heterogeneous architecture, the combined effect of picocells and coordinated frequency allocation improves the value for the defined performance metrics.     

MU-MIMO系统的用户调度和预编码联合优化

提出了一种有限反馈下多用户MIMO下行链路中用户调度和预编码的联合设计方法。在该方法中,每个用户向基站反馈自己希望的预编码向量序号,以及量化误差和自己信道的最大奇异值这两个标量。基站根据估计的信干噪比,选取信干噪比最大的一个用户,利用该用户反馈的预编码向量来生成一个预编码矩阵。基站继续逐个地选择其他用户,选择的准则是用户的信道能更好地匹配这个生成的预编码矩阵中的向量。该方法同时解决了多用户MIMO系统中,用户调度和预编码向量的设计这两个问题,属于一种跨层设计。仿真结果验证了所提方法的有效性。     

Joint user profiling with hierarchical attention networks

User profiling by inferring user personality traits, such as age and gender, plays an increasingly important role in many real-world applications. Most existing methods for user profiling either use only one type of data or ignore handling the noisy information of data. Moreover, they usually consider this problem from only one perspective. In this paper, we propose a joint user profiling model with hierarchical attention networks (JUHA) to learn informative user representations for user profiling. Our JUHA method does user profiling based on both inner-user and inter-user features. We explore inner-user features from user behaviors (e.g., purchased items and posted blogs), and inter-user features from a user-user graph (where similar users could be connected to each other). JUHA learns basic sentence and bag representations from multiple separate sources of data (user behaviors) as the first round of data preparation. In this module, convolutional neural networks (CNNs) are introduced to capture word and sentence features of age and gender while the self-attention mechanism is exploited to weaken the noisy data. Following this, we build another bag which contains a user-user graph. Inter-user features are learned from this bag using propagation information between linked users in the graph. To acquire more robust data, inter-user features and other inner-user bag representations are joined into each sentence in the current bag to learn the final bag representation. Subsequently, all of the bag representations are integrated to lean comprehensive user representation by the self-attention mechanism. Our experimental results demonstrate that our approach outperforms several state-of-the-art methods and improves prediction performance.     

Impact of user patience on auto-scaling resource capacity for cloud services

An important feature of most cloud computing solutions is auto-scaling, an operation that enables dynamic changes on resource capacity. Auto-scaling algorithms generally take into account aspects such as system load and response time to determine when and by how much a resource pool capacity should be extended or shrunk. In this article, we propose a scheduling algorithm and auto-scaling triggering strategies that explore user patience, a metric that estimates the perception end-users have from the Quality of Service (QoS) delivered by a service provider based on the ratio between expected and actual response times for each request. The proposed strategies help reduce costs with resource allocation while maintaining perceived QoS at adequate levels. Results show reductions on resource-hour consumption by up to approximately 9% compared to traditional approaches.     

Pastiche scenarios: Fiction as a resource for user centred design

Pastiche scenarios draw on fiction as a resource to explore the interior ‘felt-life’ aspects of user experience and the complex social and cultural issues raised by technological innovations. This paper sets out an approach for their use, outlining techniques for the location of source material and presenting three case studies of pastiche scenario use. The first case study is an evaluation of the Apple iPod that explores the socio-cultural meanings of the technology. The second case study focuses on the participatory design of Net Neighbours, an online shopping system where volunteers shop as intermediaries for older people who do not have access to computers. The third is an in depth consideration of a conceptual design, the ‘cambadge’ a wearable lightweight web cam which, upon activation broadcasts to police or public websites intended to reduce older people's fear of crime. This design concept is explored in depth in pastiche scenarios of the Miss Marple stories, A Clockwork Orange and Nineteen Eighty-four that reflect on how the device might be experienced not only by users but also by those it is used against. It is argued that pastiche scenarios are a useful complementary method for designers to reason about user experience as well as the broad social and cultural impacts of new technologies.     

多层中继协作HetNets用户对关联方案

基于K层中继协作异构网络,提出一种改进的用户对关联方案,使中继广播信号到每层网络的信源和信宿,信源和信宿通过估计接收信号强度(RSS)将用户对关联到最大RSS的中继。基于最大-最大用户对关联(MM-UP-A)准则,结合随机几何理论将每层网络中继的位置建模为齐次泊松点过程,得到任一用户对关联到第k层中继的概率解析式,并给出信源和信宿到关联中继距离的统计描述。数值分析和仿真结果验证了MM-UP-A准则的正确性,并表明关联概率同时受到信源与中继发送功率的影响。     

Query-based association rule mining supporting user perspective

Two parameters, namely support and confidence, in association rule mining, are used to arrange association rules in either increasing or decreasing order. These two parameters are assigned values by counting the number of transactions satisfying the rule without considering user perspective. Hence, an association rule, with low values of support and confidence, but meaningful to the user, does not receive the same importance as is perceived by the user. Reflecting user perspective is of paramount importance in light of improving user satisfaction for a given recommendation system. In this paper, we propose a model and an algorithm to extract association rules, meaningful to a user, with an ad-hoc support and confidence by allowing the user to specify the importance of each transaction. In addition, we apply the characteristics of a concept lattice, a core data structure of Formal Concept Analysis (FCA) to reflect subsumption relation of association rules when assigning the priority to each rule. Finally, we describe experiment results to verify the potential and efficiency of the proposed method.     

Joint resource allocation for lifetime-aware cooperative multi-carrier DF system

In this paper,we study the problem of optimal resource allocation for lifetime maximization in an orthogonal-frequencydivision multiplexing(OFDM)system with decode-and-forward relay.The goal is to minimize total energy cost of the system by jointly optimizing power allocation,subcarrier pairing and relay selection.We present a heuristic solution that is composed of two parts.The first part is an optimal power allocation approach to allocate power to a subcarrier pair of the source and the relay.The second part is a modified Hungarian algorithm to make subcarrier pairing and relay selection.Evaluations show that the presented scheme outperforms other schemes in the total transmitted data and the network lifetime.     

Joint optimization of UAV position and user grouping for UAV-assisted hybrid NOMA systems

This article investigates the use of unmanned aerial vehicles (UAVs) in assisting hybrid non-orthogonal multiple access (NOMA) systems to enhance spectrum efficiency and communication connectivity. A joint optimization problem is formulated for UAV positioning and user grouping to maximize the sum rate. The formulated problem exhibits non-convexity, calling for an effective solution. To address this issue, a two-stage approach is proposed. In the first stage, a particle swarm optimization algorithm is employed to optimize the UAV positions without considering user grouping. With the UAV positions optimized, a game theory-based approach is utilized in the second stage to optimize user grouping and improve the sum rate of the hybrid NOMA system. Simulation results demonstrate that the proposed two-stage method achieves solutions close to the global optimum of the original problem. By optimizing the positions of UAVs and user groups, the sum rate can be effectively improved. Additionally, optimizing the deployment of UAVs ensures better fairness in providing communication services to multiple users.     

基于FP-Growth的智能家居用户时序关联操控习惯挖掘方法

针对传统关联规则挖掘算法无法高效且准确地挖掘出隐含于用户操作记录中的时序关联操控习惯,提出一种基于FP-Growth的智能家居用户时序关联操控习惯挖掘算法。该算法分为三个阶段,分别为基于用户操控动作森林、改进的FP-Growth算法和一种时间约束规则进行事务集的生成、时序频繁项集的生成以及最终时序关联操控习惯的生成。最后,使用真实用户操控记录进行对比实验,结果表明该算法能提高生成事务集的效率,并能更准确地发现用户操控家居设备的时序关联习惯。     

D2D通信中联合资源分配与功率控制算法

终端直通(D2D)技术引入移动蜂窝网络虽然能够提高蜂窝系统性能,但却带来了很大的干扰和能量消耗.为了降低干扰,提升频谱效率,同时又兼顾能量效率,提出了一种联合资源分配和功率控制的方法,用以实现高能效的D2D通信.仿真结果表明:本文提出的迭代资源分配和功率控制方案,相比已有方案能量效率有了明显提高.     

基于关键帧节点自适应分区与关联的行为识别算法

基于视频的人体行为识别任务中由于大部分画面并不包含重要的判别信息,这对识别应用的准确性造成严重干扰。关键姿态帧既能表达视频又能降低计算量,且骨骼数据相比于图像包含更多维度的信息。因此,提出一种基于关键帧骨骼节点自适应分区与关联的行为识别算法。首先构建自适应池化深度网络以评估帧的重要性获取关键姿态帧序列;其次通过节点自学习模型建立非自然连接状态下的节点间关联;最后将改进的时空信息应用于STGCN并使用softmax分类识别。在开源的大规模数据集NTU-RGB+D和Kinetics上与几种典型技术进行比对,验证了所提方法在减少冗余数据量的同时能保留关键动作信息,且动作识别准确率平均提高了0.63%～11.81%。     

Joint admission control and resource allocation in virtualized servers

In service oriented architectures, Quality of Service (QoS) is a key issue. Service requestors evaluate QoS at run time to address their service invocation to the most suitable provider. Thus, QoS has a direct impact on the providers’ revenues. However, QoS requirements are difficult to satisfy because of the high variability of Internet workloads.     

Progressive partition miner: an efficient algorithm for mining general temporal association rules

We explore a new problem of mining general temporal association rules in publication databases. In essence, a publication database is a set of transactions where each transaction T is a set of items of which each item contains an individual exhibition period. The current model of association rule mining is not able to handle the publication database due to the following fundamental problems, i.e., 1) lack of consideration of the exhibition period of each individual item and 2) lack of an equitable support counting basis for each item. To remedy this, we propose an innovative algorithm progressive-partition-miner (abbreviated as PPM) to discover general temporal association rules in a publication database. The basic idea of PPM is to first partition the publication database in light of exhibition periods of items and then progressively accumulate the occurrence count of each candidate 2-itemset based on the intrinsic partitioning characteristics. Algorithm PPM is also designed to employ a filtering threshold in each partition to early prune out those cumulatively infrequent 2-itemsets. The feature that the number of candidate 2-itemsets generated by PPM is very close to the number of frequent 2-itemsets allows us to employ the scan reduction technique to effectively reduce the number of database scans. Explicitly, the execution time of PPM is, in orders of magnitude, smaller than those required by other competitive schemes that are directly extended from existing methods. The correctness of PPM is proven and some of its theoretical properties are derived. Sensitivity analysis of various parameters is conducted to provide many insights into Algorithm PPM.     

一种基于社区结构的用户兴趣关联规则发现方法

提出一种基于用户社区结构的用户兴趣关联规则发现方法,通过对用户按照兴趣进行社区划分,挖掘社区群体的共同兴趣,高效地发现兴趣之间的关联规则。对兴趣关联规则的特点进行了研究,分析发现有效关联规则均产生于社区内部的兴趣之间,不同社区之间的兴趣关联较少。     

能效优先的用户关联与功率控制协同优化机制

在异构蜂窝网络中,针对系统的能效,提出了一种基于效用函数最大化模型的用户关联与功率控制协同优化方案,该方案表示为非线性混合整数问题。为了求得该问题的最优解,设计了一种迭代算法,首先将原问题转换为带参数的多项式形式的问题,在外层循环利用Dinkelbach方法求得最佳的能效因子,然后在内层循环分别求得最佳的用户关联矩阵和传输功率。最终实验结果表明,用户关联与功率控制协同的优化方案在能效和负载平衡方面比固定功率条件下的用户关联策略的性能更优。     

Scalable video traffic offloading for streaming services in 5G HetNets

Multimedia Tools and Applications - In this paper, by combining scalable video coding (SVC) and traffic offloading, we propose a scalable video traffic offloading (SVO) approach to provide video...     

Joint cell selection and resource allocation games with backhaul constraints

In this work we study the problem of user association and resource allocation to maximize the proportional fairness of a wireless network with limited backhaul capacity. The optimal solution of this problem requires solving a mixed integer non-linear programming problem which generally cannot be solved in real time. We propose instead to model the problem as a potential game, which decreases dramatically the computational complexity and obtains a user association and resource allocation close to the optimal solution. Additionally, the use of a game-theoretic approach allows an efficient distribution of the computational burden among the computational resources of the network.