RSSI和PC-CSI加权融合的指纹定位方法

针对基于RSSI和CSI的指纹定位技术易受环境干扰、定位精度较低的问题,提出了一种基于RSSI指纹和相位修正信道状态信息(phase correct based channel state information, PC-CSI)指纹的加权融合指纹定位技术。基于PC-CSI的指纹定位在传统基于CSI幅值的指纹定位基础上增加相位信息对定位结果进行修正,之后对RSSI指纹和PC-CSI指纹的定位结果加权重定位。实验结果表明,提出的加权融合指纹定位算法与基于CSI的主动定位算法相比,平均定位误差(mean position error,MPE)降低了36.2%,能满足室内定位需求。     

基于贝叶斯公式的信息反馈机制

提出了一种基于贝叶斯公式的信息反馈模型BIF.当系统拒绝用户对资源的访问时,BIF通过计算在当前的条件下改变某些访问条件的可能性,把用户的访问条件的替代方案限制到一个小而有用的集合,系统将具有最大可能性的选项反馈给用户.用户根据系统的反馈改变访问条件从而成功访问资源.BIF引入附加策略来保护敏感策略,在此过程中不会泄漏任何危及到系统安全和机密性的信息.实验证明,BIF模型在保护了系统敏感信息的同时,也提高了系统的可用性及访问成功的几率.     

WLAN流媒体传输速率控制

针对IEEE 802.11的链路层重传机制和分包策略的不足,研究了基于重传时延最小约束的传输层包长调整策略,提出一种适合于无线局域网(WLAN)流媒体传输的可变包长TCP友好速率控制机制(VPTFRC).与其他方案不同,该机制进一步考查了无线包错误率对包长调整和速率控制的影响.仿真试验结果验证了采用该方法可获得优于TFRC的有效吞吐量、传输延时及抖动等指标,其中,帧传输时延、时延抖动和丢包率的最大改善程度分别可达58%,42%和85%.     

基于信度赋值技术的统一融合框架研究

从信度赋值技术的角度,提出了统一融合框架(UFA)的理论机制,它是基于证据支持贴近度(ESMS)前置过滤器耦合包含冲突分配器在内的融合机,在给出一个ESMS函数的同时,也考虑了不同信息源(可靠、非可靠、不完全、冗余及互补)信度赋值中心,克服了错误、虚假和不一致等信息对融合机的干扰,充分发挥了融合机优势,减少了融合计算的复杂度,大大提高了融合效果.将统一融合框架应用于Pioneer Ⅱ移动机器人地图创建方面,通过仿真分析,并与单一融合机的仿真结果作了对比,充分说明了统一融合框架的优越性.     

基于5G的无线携能通信技术研究与发展

无线携能通信(Simultaneous Wireless Information and Power Transfer,SWIPT)技术提高了频谱的利用率,同时可实现传统网络的节能减排,为网络提供了更智能的自治能力、更广泛的覆盖范围、更高度集成的系统能力等美好前景.针对这一新的研究领域,本文从资源优化分配和安全传输的角...     

M(o)bius数字基带通信系统的性能仿真

在一定信噪比的前提下,通过计算机仿真和编程计算,对M(o)bius数字基带通信系统中偶对称三角波、奇对称三角波、偶对称方波、奇对称方波和锯齿波这5种常用的调制解调函数族及其逆变换进行仿真1万次,观察错误概率(Pb)的波动范围.仿真结果表明,系统是完全可行的.对系统在不同信噪比情况下的错误概率进行计算,绘制错误概率曲线.结果表明,新型M(o)bius数字基带通信系统比传统系统性能卓越,可降低系统的初始投资及提高系统的性价比.     

对包含劳动参与率因素的内生增长模型的探讨

基于内生增长模型的建模思想,通过改进Romer的内生增长模型,建立包含劳动参与率因素的内生增长模型.同时,对总产出方程、知识的增长方程和资本的增长方程,进行了不同程度的改进.通过严密的数理分析,得到经济系统趋于稳定增长状态的一般条件和长期经济增长率的表达式,进而分析生产过程中,规模报酬与经济系统稳定增长状态的关系.最后,分析与讨论长期均衡经济增长率及其影响因素的变动关系.     

Robust model rankings of forecasting performance

This paper investigates robust model rankings in out‐of‐sample, short‐horizon forecasting. We provide strong evidence that rolling window averaging consistently produces robust model rankings while improving the forecasting performance of both individual models and model averaging. The rolling window averaging outperforms the (ex post) “optimal” window forecasts in more than 50% of the times across all rolling windows.     

Exchange rate forecasting and the performance of currency portfolios

We examine the potential gains of using exchange rate forecast models and forecast combination methods in the management of currency portfolios for three exchange rates: the euro versus the US dollar, the British pound, and the Japanese yen. We use a battery of econometric specifications to evaluate whether optimal currency portfolios implied by trading strategies based on exchange rate forecasts outperform single currencies and the equally weighted portfolio. We assess the differences in profitability of optimal currency portfolios for different types of investor preferences, two trading strategies, mean squared error‐based composite forecasts, and different forecast horizons. Our results indicate that there are clear benefits of integrating exchange rate forecasts from state‐of‐the‐art econometric models in currency portfolios. These benefits vary across investor preferences and prediction horizons but are rather similar across trading strategies.     

Google Trends and the forecasting performance of exchange rate models

In this paper, we use Google Trends data for exchange rate forecasting in the context of a broad literature review that ties the exchange rate movements with macroeconomic fundamentals. The sample covers 11 OECD countries’ exchange rates for the period from January 2004 to June 2014. In out‐of‐sample forecasting of monthly returns on exchange rates, our findings indicate that the Google Trends search query data do a better job than the structural models in predicting the true direction of changes in nominal exchange rates. We also observed that Google Trends‐based forecasts are better at picking up the direction of the changes in the monthly nominal exchange rates after the Great Recession era (2008–2009). Based on the Clark and West inference procedure of equal predictive accuracy testing, we found that the relative performance of Google Trends‐based exchange rate predictions against the null of a random walk model is no worse than the purchasing power parity model. On the other hand, although the monetary model fundamentals could beat the random walk null only in one out of 11 currency pairs, with Google Trends predictors we found evidence of better performance for five currency pairs. We believe that these findings necessitate further research in this area to investigate the extravalue one can get from Google search query data.