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.     

云社交电视的用户视频质量体验

近年来,多屏云社交电视将社交体验引入了传统的电视领域中.观看同一内容社交电视的观众虽然来自不同的地方,但是仍然可以通过文本、图像、音频和视频来进行互动,这就在传统体验上增强了社会属性.笔者将社交电视用户体验分为3个要素(用户延迟、视频质量体验和资源利用率),并且提供一个通用算法来提高用户体验.具体来说就是提出了一个基于云计算的最佳播放速率分配方案,最大限度提高整体视频质量体验(QoE)和用户间的延迟上限.实验结果表明,该算法实现了用户间延迟和视频质量的最优组合,并论证了无线信道快速衰弱下的最优性能.     

基于最大值判决的OCDMA并行干扰抑制系统

提出一种适用于光正交码作为地址码的光码分多址多用户干扰并行干扰抑制系统.该系统通过在发送端将比特"0"和比特"1"分别用一个光正交码和其移位变形来映射,接收端采用APD作为光电检测器,并采用最大值判决即通过比较比特"0"和比特"1"支路的大小来识别比特"1"和比特"0",从而实现用户间干扰的并行干扰抑制.分析了系统性能并进行仿真,仿真结果表明该系统的误码性能和常规光码分多址(OCDMA)系统相比,当接收光功率为-50dBW时,可以改善5个数量级,并且接收光功率越大,改善量越大.     

一种新的多用户波束赋形算法

为了抑制多用户多输入多输出(Multi-user multiple input multiple output,MU-MIMO)系统中的用户间干扰,提出一种新的算法。在本用户消除对其他用户的干扰的前提下选择波束赋形矢量,如果所有用户都采取这种措施,各个用户受到其他用户的干扰将降低至最低,从而提高整个系统的性能。仿真结果表明,随着用户数增加,采用本文中的算法较传统的信漏噪比(Signal-to-leakage-and-noise ratio,SLNR)算法可以降低系统误码率(Bit error rate,BER)。     

毫微微小区中一种基于分组的资源分配方法

为有效解决毫微微小区间( Femtocell)干扰,采用分布式方式对毫微微小区进行资源管理。首先,对毫微微接入点( FAPs)进行分组。基于Lingo数学建模的思想,提出了一种解决分组优化问题的算法。该算法在使用分支定界算法寻找最优解的同时,通过建立单纯形表剪去偏离最优解方向的分支;其次,每组选择一个簇头为本组内FAPs分配资源,为此,提出了新的子信道分配方法,该方法根据干扰指示矩阵修正子信道分配的情况。仿真结果表明：和其他算法相比,提出的算法不仅能找到分组优化问题的最优解,并且效率更高;另外,提出的资源分配算法不仅减小了用户间干扰,而且提高了户间速率公平。     

CDMA 2000 1xEVDO Rev.A系统的QoS应用

随着数据用户和数据业务的迅猛发展,运营商网络的承载压力不断加大,如何综合考虑网络资源利用最大化和用户间公平性,是目前网络运行商面临的主要问题。文章对QoS概念、QoS模型、3G业务的QoS分类以及CDMA 2000 1xEVDORev.A端到端QoS的体系结构进行了说明,并对EVDORev.A系统中端到端QoS保证的系统实现进行了介绍。文章认为第三代无线通信技术CDMA 2000 1xEVDORev.A能提供端到端的QoS解决方案,它不仅增强了对实时业务的支持,还可以大大提升网络服务性能。