H.264/AVC技术进展及其务实发展策略思考

结合NGN及3G演进的固定、移动视频业务实际需求状况，论述H．264／AVC的重要技术进展及其务实发展策略。     

5G核心网业务模型的智能化预测研究

核心网业务模型的建立是5G网络容量规划和网络建设的基础,通过现有方法得到的理论业务模型是静态不可变的且与实际网络存在偏离。为了克服现有5G核心网业务模型与现网模型适配性较差以及规划设备无法满足用户实际业务需求的问题,提出了一种长短期记忆(long short-term memory,LSTM)网络与卷积LSTM(convolution LSTM,ConvLSTM)网络双通道融合的5G核心网业务模型预测方法。该方法基于人工智能(artificial intelligence,AI)技术以实现高质量的核心网业务模型的智能预测,形成数据反馈闭环,实现网络自优化调整,助力网络智能化建设。     

使能未来工厂的5G能力综述

5G系统将移动通信服务从移动电话、移动宽带和大规模机器通信扩展到新的应用领域，即所谓对通信服务有特殊要求的垂直领域。对使能未来工厂的5G能力进行了全面的分析总结，包括弹性网络架构、灵活频谱、超可靠低时延通信、时间敏感网络、安全和定位，而弹性网络架构又包括对网络切片、非公共网络、5G局域网和边缘计算的支持。希望从广度到深度，对相关的理论及技术应用做透彻、全面的梳理，对其挑战做清晰的总结，从而为相关研究和工程技术人员提供借鉴。     

基于5G边缘云的港口统一通信方案研究与实践

5G边缘云具有分布式、灵活组网、快速部署和本地流量分流等优势,在港口等垂直工业领域有很广泛的应用前景,同时对实现港口统一通信也提出了一定的要求。在研究基于5G边缘云的港口统一通信架构的基础上,提出了针对港口现场全域各类要素实现全面感知、支撑自动化业务改造的5G边缘云功能框架。此功能框架能很好地适用于港口场景的有线无线网络融合的统一承载,可作为目前复杂环境下的港口网络统一通信的网络基础设施。最后在某港口码头上对该方案进行了实际验证。     

雾网络中基于统计分布的内容缓存与交付方案

作为5G中的一种重要模型,雾无线接入网络(Fog Radio Access Network, F-RAN)通过设备到设备通信和无线中继等技术获得了显著的性能增益,而边缘设备中合适的缓存则可以让内容缓存用户(Caching Users, CUs)向内容请求用户(Requesting Users, RUs)直接发送缓存内容,有效减小前传链路的负担和下载延迟。考虑一个F-RAN模型下用户发出请求并获得交付的场景,将每个CU的内容请求队列建模为独立的M/D/1模型,分析导出CUs缓存命中率和平均下载延迟关于内容缓存与交付方案的表达式,证明CUs缓存命中率与内容统计分布之间的联系有助于实现前者的近似最优解。针对在一段时间内的期望视角下建立的优化问题,提出了基于统计分布的算法并注意了执行时的交付控制。仿真结果表明,相较于现有缓存策略,优化内容整体统计分布的方案能够最大化CUs缓存命中率,同时减小平均下载延迟。     

Bi-directional LSTM based channel estimation in 5G massive MIMO OFDM systems over TDL-C model with Rayleigh fading distribution

In this work, a deep learning (DL)-based massive multiple-input multiple-output (mMIMO) orthogonal frequency division multiplexing (OFDM) system is investigated over the tapped delay line type C (TDL-C) model with a Rayleigh fading distribution at frequencies ranging from 0.5 to 100 GHz. The proposed bi-directional long short-term memory (Bi-LSTM) channel state information (CSI) estimator uses online learning during training and offline learning during the practical implementation phase. The design of the estimator takes into account situations in which prior knowledge of channel statistics is limited and targets excellent performance, even with limited pilot symbols (PS). Three separate loss functions (mean square logarithmic error [MSLE], Huber, and Kullback–Leibler Distance [KLD]) are assessed in three classification layers. The symbol error rate (SER) and outage probability performance of the proposed estimator are evaluated using a number of optimization techniques, such as stochastic gradient descent (SGD), momentum, and the adaptive gradient (AdaGrad) algorithm. The Bi-LSTM-based CSI estimator is trained considering a specific number of PS. It can be readily seen that by incorporating a cyclic prefix (CP), the system becomes more resilient to channel impairments, resulting in a lower SER. Simulations show that the SGD optimization approach and Huber loss function-trained Bi-LSTM-based CSI estimator have the lowest SER and very high estimation accuracy. By using deep neural networks (DNNs), the Bi-LSTM method for CSI estimation achieves a superior channel capacity (in bps/Hz) at 10 dB than long short-term memory (LSTM) and other conventional CSI estimators, such as minimum mean square error (MMSE) and least squares (LS). The simulation results validate the analytical results in the study.     

Hierarchical and Hybrid Cell Load Balancing in 5G Heterogeneous Mobile Networks

In Fifth Generation (5G) Heterogeneous Mobile Networks (HetNets), deploying dense small cell networks makes user association more challenging. The process of collecting cell load information from the User Equipments (UEs) and broadcasting the feedback message involves significant overhead and time complexity. Moreover, the UEs may not know the optimum cell to reselect, satisfying its data rate requirements. In order to overcome these drawbacks, in this paper, we propose to design an Hierarchical and Hybrid Cell Load Balancing (HHCLB) technique using Selective Handoff. In this technique, the UEs of each cell are grouped into clusters depending on their proximity distance. Each cluster contains a cluster controller (CC) which is in charge of determining the intra-cell load and redirecting the cell-reselection request of a UE. If the data rate of any UE in a cluster becomes less than its required rate, then the cell reselection process is performed. By simulation results, it is shown that load balancing can be done proactively (implicitly) by the CCs when the load is unbalanced or can be done on demand (explicitly) when a UE send a request for cell reselection. In the case of Macro cells, HHCLB attains 71% higher throughput for low load scenario and 59% higher throughput for high load scenario. Similarly, in the case of Femto cells, HHCLB attains 19% higher throughput for low load scenario and 27% higher throughput for high load scenario.     

Mesh结构P/G布线网络层次化快速分析方法

本文提出了一个基于网络划分的P／G布线网络层次化快速分析方法。其中，对于子网运算，通过对Cholesky分解法三角化对称正定阵的图模型分析，并基于Mesh结构网络的自身特点，提出了一个基于图顶点排序的加速子网分析运算策略；并用基于MPI的并行结构实现了P／G布线网络分析的并行运算。     

国内3G网络共享问题之研究

随着国内3G时代的来临，3G网络共享问题越来越受到人们的关注。本文结合国内移动通信的现状，着重分析了3G网络共享的模式及经济效益，论述了3G网络共享的相应策略。     

波长脉宽可调的ps钛宝石自锁模激光器

利用Ｇｉｒｅｓ－Ｔｏｕｒｎｏｉｓ干涉仪提供负群速度色散补偿钛宝石棒产生的正色散，以获得稳定的ｐｓ自锁模脉冲。腔内插入双折射滤光片实现波长调谐。