缓存与MIMO回程联合的内容传递方法

毫米波小蜂窝网络可提供较高的区域吞吐量,但其回程链路容量受限易造成阻塞。针对该问题,提出一种缓存小基站(SBS)与无线自回程SBS联合的内容传递方法。将部分流行内容预存储在缓存中,未存储内容由部署多输入多输出的宏基站传送。采用毫米波天线模式和视距球模型并借助随机几何工具,推导出缓存与回程联合内容传递异构网络的覆盖概率、平均区域速率和平均区域能量效率的表达式。实验结果表明,该方法可使波束变窄且传输更具有方向性,能够提高自回程传输的平均区域速率及接入链路的性能。

全毫米波异构网络混合回程及缓存协助内容传递方案

针对部分流行多媒体文件重复下载产生的额外流量消耗引起的网络阻塞问题,提出了一种混合自回程及缓存(hybrid self-backhaul and cache, HSBC)协助的内容传递方案。在同信道和全毫米波部署的异构网络中,基于配置因子η,部分小基站(small base station, SBS)配备缓存并按照流行度等级存储多媒体内容,而无缓存配备的SBS通过多天线宏基站的自回程为用户提供请求内容,毫米波技术的引入有效缓解了网络频谱稀缺问题。利用实际天线阵列方向图模型,研究了系统的覆盖概率、平均面积吞吐量和平均延迟。结果表明,HSBC协助系统相较传统自回程(traditional self-backhaul, TSB)协助系统获得的性能增益很大程度上取决于系统参数。据此,提出了一种自适应TSB-HSBC-SBS协助内容传递模型。当缓存SBS的当前比率因子小于阈值时,选择TSB-SBS协助模型;否则使用HSBC-SBS协助模型。

Integrated Statistical and Engineering Process Control Based on Smooth Transition Autoregressive Model

Traditional studies on integrated statistical process control and engineering process control (SPC-EPC) are based on linear autoregressive integrated moving average (ARIMA) time series models to describe the dynamic noise of the system.However,linear models sometimes are unable to model complex nonlinear autocorrelation.To solve this problem,this paper presents an integrated SPC-EPC method based on smooth transition autoregressive (STAR) time series model,and builds a minimum mean squared error (MMSE) controller as well as an integrated SPC-EPC control system.The performance of this method for checking the trend and sustained shift is analyzed.The simulation results indicate that this integrated SPC-EPC control method based on STAR model is effective in controlling complex nonlinear systems.

Online process monitoring for complex systems with dynamic weighted principal component analysis

Conventional multivariate statistical methods for process monitoring may not be suitable for dynamic processes since they usually rely on assumptions such as time invariance or uncorrelation. We are therefore motivated to propose a new monitoring method by compensating the principal component analysis with a weight approach.The proposed monitor consists of two tiers. The first tier uses the principal component analysis method to extract cross-correlation structure among process data, expressed by independent components. The second tier estimates auto-correlation structure among the extracted components as auto-regressive models. It is therefore named a dynamic weighted principal component analysis with hybrid correlation structure. The essential of the proposed method is to incorporate a weight approach into principal component analysis to construct two new subspaces, namely the important component subspace and the residual subspace, and two new statistics are defined to monitor them respectively. Through computing the weight values upon a new observation, the proposed method increases the weights along directions of components that have large estimation errors while reduces the influences of other directions. The rationale behind comes from the observations that the fault information is associated with online estimation errors of auto-regressive models. The proposed monitoring method is exemplified by the Tennessee Eastman process. The monitoring results show that the proposed method outperforms conventional principal component analysis, dynamic principal component analysis and dynamic latent variable.

Research on the Shaanxi Urbanization and Agricultural Modernization Coordinated Development Pattern based on Game Theory

In this paper, we research on the Shaanxi urbanization and agricultural modernization coordinated development pattern based on the game theory. At present, the industrialization and the urbanization, agricultural modernization in our country obviously lags behind, which hindered the synchronous development of them. Therefore, with the internal relations between the empirical researches, we explore influence of industrialization and urbanization of agricultural modernization and path, to promote three synchronous developments which have important practical significance. Based on vector autoregressive model and game thinking, at the same time, we analysis the influence degree of the industrialization and urbanization of agricultural modernization and path in order to provide experience for the synchronous development of reference.

密集网络下基于Self-Backhaul感知的用户接入负载均衡算法

针对密集异构网络自回程场景中带宽分配不合理引起的负载不均衡问题,提出一种基于self-backhaul感知的用户接入负载均衡方案.首先根据密集异构网络下各个小基站接入与回程资源的负载状态提出一种用户接入负载均衡策略;其次利用Q-Learning算法对各个小基站带内无线接入与回程带宽分配进行学习,用户在不同带宽分配因子下,根据用户接入负载均衡策略进行重新接入,得到不同接入情况下的系统效用,进而得到最优带宽分配策略,保证负载均衡性的同时实现系统效用最大化.仿真结果表明,该方案在密集异构网络自回程场景中提高了网络负载均衡性,同时提升了用户速率体验.

AN EXPONENTIAL INEQUALITY FOR AUTOREGRESSIVE PROCESSES IN ADAPTIVE TRACKING

A wide range of literature concerning classical asymptotic properties for linear models with adaptive control is available, such as strong laws of large numbers or central limit theorems. Unfortunately, in contrast with the situation without control, it appears to be impossible to find sharp asymptotic or nonasymptotic properties such as large deviation principles or exponential inequalities. Our purpose is to provide a first step towards that direction by proving a very simple exponential inequality for the standard least squares estimator of the unknown parameter of Gaussian autoregressive process in adaptive tracking.