以用户为中心的可见光通信协作传输中训练资源分配算法

以用户为中心的可见光通信协作传输是近年来出现的新架构,这导致虚拟小区之间出现重叠。为避免导频污染问题,每个虚拟小区中的光接入点(AP)或者虚拟小区中选择相同AP的用户发送的训练序列应该是正交的。针对可见光通信中以用户为中心的协作网络,研究训练资源的正交分配问题,提出了一种新的导频分配算法,联合导频分配和用户选择问题,以期最大限度地增加虚拟小区内可被接入的用户数。分析和仿真结果表明,该导频分配方案可以有效改善导频污染问题,提高训练资源利用率,并且相比已有的导频分配方案,性能有所改进。     

5G网络技术研究现状和发展趋势

摘要：首先分析了5G 网络中的新需求对移动通信网络的挑战,从而说明进行5G 网络技术研究的必要性。随后,介绍了目前国内外研究组织在5G 网络相关技术的研究进展,包括NGMN、5GPPP、IMT-2020等,同时也介绍了国内外标准化组织对5G网络技术的标准化研究进展。基于对5G网络中的新需求、新挑战的分析,结合国内外关于5G网络技术研究现状的总结,预测了未来5G网络技术的发展趋势并给出了相关的技术发展方向。最后对5G网络的发展进行了总结和展望。     

以用户为中心的社会多媒体计算

从用户的角度解读社会多媒体计算框架,提出一种以用户为中心的社会多媒体计算框架。在这个框架下,重新探讨了以用户为中心的社会多媒体计算3方面的任务:用户感知的多媒体内容理解、基于网络行为的用户建模、社会媒体网络用户关系分析。     

移动泛在融合的通信业务发展趋势

融合作为下一代通信系统发展潮流,将发生在多个方面。从业务体验角度出发,“泛在”将被引入,与移动发生融合,成为一个活跃业务发展方向。本文从业务提供模式以用户需求角度出发,对移动泛在业务环境进行展望,并着重分析了周边泛在环境的特点及其在协同方面的功能需求。最后,对泛在系统的体系结构设计中的自组织特征进行简单分析。     

User-centric energy efficiency fairness in backscatter-assisted wireless powered communication network

In order to address the unfair user-centric energy efficiency (EE) problem caused by channel difference in the backscatter-assisted wireless powered communication network,a resource allocation scheme was proposed.Firstly,a mixed integer nonconvex fractional programming problem was formulated to maximize the minimum user-centric EE,subject to the quality of service and energy-causality constraints.Based on the generalized fractional programming theory,the original problem was transformed into a mixed integer nonconvex subtraction problem.With the aid of the slack variable,the proof by contradiction,the auxiliary variable and the mixed integer nonconvex subtraction problem were further transformed into an equivalent convex problem.Finally,an iterative algorithm was proposed to obtain the optimal solutions.Computer simulations validated the quick convergence of the proposed iterative algorithm,and that the developed resource allocation scheme efficiently guarantees the fairness among users in terms of EE.     

An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT

Social Edge Service (SES) is an emerging mechanism in the Social Internet of Things (SIoT) orchestration for effective user-centric reliable communication and computation. The services are affected by active and/or passive attacks such as replay attacks, message tampering because of sharing the same spectrum, as well as inadequate trust measurement methods among intelligent devices (roadside units, mobile edge devices, servers) during computing and content-sharing. These issues lead to computation and communication overhead of servers and computation nodes. To address this issue, we propose the HybridgrAph-Deep-learning (HAD) approach in two stages for secure communication and computation. First, the Adaptive Trust Weight (ATW) model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication overhead. Second, a Quotient User-centric Coeval-Learning (QUCL) mechanism to formulate secure channel selection, and Nash equilibrium method for optimizing the communication to share data over edge devices. The simulation results confirm that our proposed approach has achieved effective communication and computation performance, and enhanced Social Edge Services (SES) reliability than state-of-the-art approaches.     

一种适用于超密集网络下行链路的干扰抑制方案

在超密集网络（UDN）下行链路场景中,为了有效克服因节点间距减小、邻近节点传输损耗相差不大而产生多个强度相近的干扰源对用户性能的影响,提出了一种基于优先级以用户为中心的小区间干扰抑制方案。为了便于量化基站协作取得的带宽性能增益,采用了一种计算带宽盈亏率的定量分析指标。通过该指标,易于确定在特定UDN场景下干扰协调方案是否处于盈利状态,进而通过系统能够接受的最大损耗比优化参数配置。仿真结果表明,该干扰抑制方案能显著提升用户的信号干扰噪声比（SINR）性能,且能更好地保障用户公平性,有效地克服了用户SINR性能在基站数较多时受天线数限制的缺陷,为提升UDN网络用户性能和系统性能提供了一种解决方案。