Outage Performance and Optimal Design of MIMO-NOMA Enhanced Small Cell Networks with Imperfect Channel-State Information

This paper focuses on boosting the performance of small cell networks(SCNs)by integrating multiple-input multiple-output(MIMO)and nonorthogonal multiple access(NOMA)in consideration of imperfect channel-state information(CSI).The estimation error and the spatial randomness of base stations(BSs)are characterized by using Kronecker model and Poisson point process(PPP),respectively.The outage probabilities of MIMO-NOMA enhanced SCNs are first derived in closed-form by taking into account two grouping policies,including random grouping and distance-based grouping.It is revealed that the average outage probabilities are irrelevant to the intensity of BSs in the interference-limited regime,while the outage performance deteriorates if the intensity is sufficiently low.Besides,as the channel uncertainty lessens,the asymptotic analyses manifest that the target rates must be restricted up to a bound to achieve an arbitrarily low outage probability in the absence of the inter-cell interference.Moreover,highly correlated estimation error ameliorates the outage performance under a low quality of CSI,otherwise it behaves oppositely.Afterwards,the goodput is maximized by choosing appropriate precoding matrix,receiver filters and transmission rates.In the end,the numerical results verify our analysis and corroborate the superiority of our proposed algorithm.

User Selection and Precoding Schemes Based on Partial Channel Information for Broadcast MIMO Systems

Due to the large number of users and the time-varying characteristics of wireless channels, it is very tough to inform the transmitter of full channel information in real multi-user MIMO broadcast systems. On the other hand, the capacity of multi-user systems greatly depends on the knowledge of the channel at the transmitter while this is not always the case in single-user MIMO systems. In this paper, we investigate combined user selection and zero-forcing precoding schemes that use partial channel information, i.e., very low amount of channel information at the base station. We show that while greatly reducing the complexity and channel knowledge feedback load, the proposed schemes preserve the optimality of zero-forcing scheme in term of achievable ergodic sum capacity in limit of large number of active users.

MODEL SELECTION METHOD BASED ON MAXIMAL INFORMATION COEFFICIENT OF RESIDUALS

The traditional model selection criterions try to make a balance between fitted error and model complexity. Assumptions on the distribution of the response or the noise, which may be misspecified, should be made before using the traditional ones. In this ar- ticle, we give a new model selection criterion, based on the assumption that noise term in the model is independent with explanatory variables, of minimizing the association strength between regression residuals and the response, with fewer assumptions. Maximal Information Coe^cient （MIC）, a recently proposed dependence measure, captures a wide range of associ- ations, and gives almost the same score to different type of relationships with equal noise, so MIC is used to measure the association strength. Furthermore, partial maximal information coefficient （PMIC） is introduced to capture the association between two variables removing a third controlling random variable. In addition, the definition of general partial relationship is given.

Wind Power Bidding Strategy Based on the Minimax Regret Criterion with Limited Distribution Information

In optimal wind bidding strategy related literatures, it is usually assumed that the full distribution information (for example, the cumulative distribution function or the probability density function) of wind power output is known. In real world applications, however, only very limited distribution information can be obtained. Therefore, the “optimal bidding strategy” obtained based on the hypothetical distribution may be far away from the true optimal one. In this paper, an optimal bidding strategy is obtained based on the minimax regret criterion. The salient feature of the new approach is that it requires only partial information of wind power distribution, for example, the expectation and the support set. Numerical test is then performed and the results suggest that the method established in this paper is effective.

Statistical Analysis for Constant-stress Partially Accelerated Life Test with Interval Censored Data

In this paper, a statistical analysis method is proposed to research life characteristics of products based on the partially accelerated life test. We discuss the statistical analysis for constant-stress partially accelerated life tests with Lomax distribution based on interval censored samples. The EM algorithm is used to obtain the maximum likelihood estimations(MLEs) and interval estimations for the shape parameter and acceleration factor.The average relative errors(AREs), mean square errors(MSEs), the confidence intervals for the parameters, and the influence of the sample size are discussed. The results show that the AREs and MSEs of the MLEs decrease with the increase of sample size. Finally, a simulation sample is used to estimate the reliability under different stress levels.

ESR-PINNs:Physics-informed neural networks with expansion-shrinkage resampling selection strategies

Neural network methods have been widely used in many fields of scientific research with the rapid increase of computing power.The physics-informed neural networks(PINNs)have received much attention as a major breakthrough in solving partial differential equations using neural networks.In this paper,a resampling technique based on the expansion-shrinkage point(ESP)selection strategy is developed to dynamically modify the distribution of training points in accordance with the performance of the neural networks.In this new approach both training sites with slight changes in residual values and training points with large residuals are taken into account.In order to make the distribution of training points more uniform,the concept of continuity is further introduced and incorporated.This method successfully addresses the issue that the neural network becomes ill or even crashes due to the extensive alteration of training point distribution.The effectiveness of the improved physics-informed neural networks with expansion-shrinkage resampling is demonstrated through a series of numerical experiments.

融合灭弧信息的有源串联接地配电网故障检测方法

为了优化有源全补偿消弧设备和挖掘灭弧状态信息包含的故障特征,该文提出基于串联分压的配电网消弧原理及融合灭弧状态信息的接地故障检测方法。提出采用单个H桥变流器与消弧线圈串联的有源降压消弧全补偿方法和灵活接入保护电容的有源消弧装置保护方案,在消弧原理与控制、设备承压与安全、功率输出与过压防护等方面表现出良好性能。提出融合有源降压消弧全补偿“伪正常”状态信息的接地故障检测方法,利用系统正常-故障-灭弧3种状态信息构造故障检测判据,在免受过渡电阻和分布参数不对称影响的基础上优化故障检测方式和提高故障检测效率,无需线路间信息的横向比较即可实现系统全域零序导纳测量、故障馈线独立判别和故障程度跟踪识别。仿真实验证实了方法的有效性,为优化配电网有源消弧和接地故障检测提供创新方案。

基于部分信息的SLT-LT联合码防窃听方案设计

近年来,信息的安全传输备受人们关注,现有的物理层安全技术从信息论的角度出发,将物理层安全编码与传输信道的动态物理特性进行结合,实现信息的保密传输。作为一种纠删码,LT(Luby transform)码由于其编码随机性、码率不固定等特性,使得窃听者不能直接从泄露的编码符号中得到有用信息,只要合法用户在窃听者之前接收到足够数量的编码符号,便可实现信息的安全传输。而作为一种转移LT(Shifted LT,SLT)码,SLT码能高效恢复信息的同时具有更小的译码开销。因此,我们将SLT码应用于Wyner降阶窃听信道模型进行研究,提出一种基于部分信息转移的SLT-LT联合码防窃听方案,信源利用接收者已知的部分信息对度分布进行调整,并对信源符号进行SLT-LT码级联编码。由于窃听信道是合法信道的降阶信道,因此外在的窃听者截获到的消息符号是合法接收者的降阶版本,在相同时间内,合法接收者能够收到更多消息符号,随着编解码过程的不断进行,合法接收者的优势不断累积,能够优先完成解码,而度分布的调整以及级联编码方案使得编码符号的平均度进一步增大,窃听者难以完成解码,进一步降低了窃听者译出率;之后,对所提方案的编解码性能以及安全性进行理论分析,并通过实验仿真进行验证,仿真结果表明,与其他防窃听LT方案相比,本文所提方案仅增加少量的译码开销但具有更好的安全性能。

部分信息下带时滞的鲁棒资产负债博弈问题研究

研究了部分可观测信息下带时滞的鲁棒资产负债博弈问题,运用滤波理论,将不完全信息转化为完全信息,以终端相对财富的效用最大化为目标,构建了不完全信息带时滞的鲁棒资产负债博弈问题,借助动态规划原理,得到了均衡投资策略和值函数的显示表达式。最后,通过数值模拟验证了参数对均衡投资策略和值函数的影响,主要得出了不完全信息下投资者的效用低于完全信息。所以,投资者应尽可能的搜集与投资相关的更多信息,以便于做出更加明智的决策。

多光谱信息下的油纸绝缘局部放电深度学习融合诊断方法

为了获得更丰富的放电特征信息,提高局部放电诊断的效率,提出了一种多光谱信息下的油纸绝缘局部放电深度学习融合方法。首先,基于微型光学传感器件和局部放电光谱分布,构建了局部放电多光谱同步检测平台,通过实验获得了四种放电类型的多光谱数据;然后构建卷积神经网络模型,将不同光谱段的局部放电数据作为模型不同通道的输入,利用通道级融合提取多光谱信号中的有效信息,对油纸绝缘局部放电类型进行准确辨识。结果表明:不同放电类型的多光谱信息可以作为模式识别的有效特征;引入多光谱信息后,本文所提方法的识别准确率可以达到98%以上,相比于仅使用脉冲电流信号有着明显的提升;相比于统计特征参数分析法和深度神经网络,提出的方法对多光谱信息融合的效果更好,识别的准确率更高。

A maximum principle for optimal control problem of fully coupled forward-backward stochastic systems with partial information

The paper is concerned with a stochastic optimal control problem in which the controlled system is described by a fully coupled nonlinear forward-backward stochastic differential equation driven by a Brownian motion.It is required that all admissible control processes are adapted to a given subfiltration of the filtration generated by the underlying Brownian motion.For this type of partial information control,one sufficient(a verification theorem) and one necessary conditions of optimality are proved.The control domain need to be convex and the forward diffusion coefficient of the system can contain the control variable.

塔里木盆地鸟山地区铀矿化信息探测技术应用

塔里木盆地分布有我国最大的塔克拉玛干沙漠,石油、天然气资源丰富,铀矿产资源潜力巨大。当前,该盆地沙漠覆盖区铀矿勘查程度低,铀矿找矿确定亟待投入大量的铀矿勘查工作。在沙漠覆盖区开展放射性地球物理和穿透性地球化学方法勘探,可有效探测识别深部铀矿化信息异常,有助于定位预测深部铀矿体。为此,在塔里木盆地巴楚隆起区西南缘的鸟山沙漠覆盖区内,采用活性炭吸附测氡和分量化探方法,应用弱信息提取技术、组合、综合信息提取技术及方向导数,提取沙漠覆盖区深部铀矿化信息异常,异常受鸟山断裂带及鸟山东断裂带控制,鸟山沙漠覆盖区氡浓度平均值为910 Bq·m^(-3),其值约为同地区非沙漠覆盖区的二分之一,氡浓度在平面上呈现环状异常,分量铀平均值为90 ng·g^(-1),其值约为同地区非沙漠覆盖区的三分之一,分量含量在平面上呈团块状,并沿鸟山东断裂带向南延伸趋势,认为活性炭吸附测氡和分量化探组合探测技术用于沙漠覆盖区铀矿找矿工作可行且有效,氡的异常特征与非沙漠覆盖区异常特征一致,环状异常及构造断裂分布一致的氡、分量铀异常以及提取的弱信息异常、元素组合异常、元素及氡气综合异常反映鸟山沙漠覆盖区可能存在砂岩型铀矿化。

Probabilistic inference of fatigue damage propagation with limited and partial information

A general method of probabilistic fatigue damage prognostics using limited and partial information is developed.Limited and partial information refers to measurable data that are not enough or cannot directly be used to statistically identify model parameter using traditional regression analysis.In the proposed method, the prior probability distribution of model parameters is derived based on the principle of maximum entropy（Max Ent） using the limited and partial information as constraints.The posterior distribution is formulated using the principle of maximum relative entropy（MRE） to perform probability updating when new information is available and reduces uncertainty in prognosis results.It is shown that the posterior distribution is equivalent to a Bayesian posterior when the new information used for updating is point measurements.A numerical quadrature interpolating method is used to calculate the asymptotic approximation for the prior distribution.Once the prior is obtained, subsequent measurement data are used to perform updating using Markov chain Monte Carlo（MCMC） simulations.Fatigue crack prognosis problems with experimental data are presented for demonstration and validation.

Optimal Reinsurance and Investment Strategies Under Mean-Variance Criteria:Partial and Full Information

This paper is concerned with an optimal reinsurance and investment problem for an insurance firm under the criterion of mean-variance. The driving Brownian motion and the rate in return of the risky asset price dynamic equation cannot be directly observed. And the short-selling of stocks is prohibited. The problem is formulated as a stochastic linear-quadratic control problem where the control variables are constrained. Based on the separation principle and stochastic filtering theory, the partial information problem is solved. Efficient strategies and efficient frontier are presented in closed forms via solutions to two extended stochastic Riccati equations. As a comparison, the efficient strategies and efficient frontier are given by the viscosity solution to the HJB equation in the full information case. Some numerical illustrations are also provided.

使用基于物理信息的卷积神经网络求解Navier–Stokes方程的物理合理且守恒解

基于物理信息的神经网络方法(PINN)是一种使用神经网络有效求解偏微分方程(PDEs)的新兴方法。基于物理信息的卷积神经网络方法(PICNN)是一种由卷积神经网络(CNNs)增强的PINN的变体。由于卷积神经网络的参数共享特性可以有效地学习空间依赖关系,因此PICNN在一系列偏微分方程的求解问题上取得了更好的结果。然而,应用现有的基于PICNN的方法求解Navier–Stokes方程时会产生振荡的预测解,这违背了物理定律和守恒特性。为了解决这一问题,我们提出了一种将PICNN与有限体积法相结合的新方法,以获得Navier–Stokes方程的物理上合理且具有守恒特性的预测解。我们使用有限体积法推导了Navier–Stokes方程的二阶迎风差分格式。然后我们使用所推导的格式来计算偏导数并构造基于物理信息的损失函数。我们对以稳态Navier–Stokes方程作为控制方程的不同场景进行了实验以评估所提出的方法,包括对流传热问题和顶盖驱动流问题等。实验结果表明,我们的方法可以有效地提高PICNN预测解的物理合理性和准确性。

面向流体力学的物理神经网络综述

针对融合了物理控制方程,尤为适用于物理场预测的新兴神经网络方法——物理神经网络(PINN),开展深入的文献调研,形成对面向流体力学的物理神经网络方法发展趋势的研判。首先,对神经网络融合物理信息的思路进行溯源;其次,介绍当前物理神经网络基本架构,针对全连接型物理神经网络,从间断问题的高精度预测研究、偏微分方程(PDE)植入形式、流场重建问题、损失函数形式、多精度数据及多尺度问题以及训练控制等方面进行文献综述;再次,对于基于卷积神经网络(CNN)和其他新兴网络架构的物理神经网络进行文献梳理;最后,形成面向流体力学的物理神经网络发展趋势与思考。通过对2017年至2023年间近百篇文献的研究及相关数值实验可知,针对强间断的高分辨率预测是面向高速流动问题的物理神经网络研究中需要解决的重要问题;基于全连接网络的物理神经网络拥有无网格化的优势,可用于各类流动问题的求解;基于卷积网络的物理神经网络具备与已有传统数值方法深度融合的优势,可有效利用已有的流场图像、物理量云图等结构化数据,进行复杂流动问题的求解。

基于Runge-Kutta的自回归物理信息神经网络求解偏微分方程

物理信息神经网络离散时间模型(PINN-RK)是深度学习技术与龙格库塔方法相结合的产物,在求解偏微分方程时具有非常出色的稳定性和较高的求解精度.但是,受到龙格库塔算法本身的限制,PINN-RK模型仅能实现单步时间预测,且计算效率较低.因此,为了实现多时间步长预测和提高模型的计算效率,提出了一种基于龙格库塔法的自回归物理信息神经网络模型(SR-PINN-RK).该模型基于自回归时间步进机制,改进了神经网络的训练流程和网络结构,相比PINN-RK模型,大幅减少了神经网络的训练参数,提高了模型的计算效率.此外,在自回归机制的作用下,该模型通过对标签数据的动态更新,成功实现了对偏微分方程解的多时间步长预测.为了验证文中模型的求解精度和计算效率,分别求解了Allen-Cahn方程和Burgers方程,并与文献中的基准解进行了对比.结果表明,模型预测解与基准解之间具有很高的一致性,求解Allen-Cahn方程和Burgers方程的最大相对误差均低于0.009.

宁夏草地绿度时空变化图谱及驱动因素分析

[目的]揭示宁夏草地绿度的时空变化规律,探明其驱动因素,为有效管理北方农牧交错生态脆弱区草地碳库和推动宁夏草地长效生态增汇提供决策参考。[方法]基于NDVI、气象及土地利用数据,采用地学信息图谱、Sen+Mann-Kendall趋势分析、变异系数、Hurst指数、偏相关分析及残差分析等方法,分析了宁夏草地绿度时空分布及变化特征,探究了气候变化和人类活动对草地绿度的影响。[结果]2000—2020年,宁夏草地绿度以速率为0.007/a波动上升,草甸草地绿度最高且增速最快为0.011/a,荒漠草地绿度最低且增速最慢为0.004/a;草地绿度月均变化呈倒“U”型,不同类型草地绿度最大值相差较大,最小值相近。草地绿度空间分异明显,呈“南高北低”的格局,3—8月绿度提升由南向北推进,9月至次年2月渐次降低由北向南推移。97.76%草地绿度呈增加趋势,极其显著增加比重最大为48.78%,荒漠草地和典型草地绿度变化为高波动且绿化趋势不持续,草甸草地绿度变化为中低波动且绿化趋势可持续。宁夏草地绿度与气温正负相关并存,与降水呈正相关,67.10%区域以降水驱动为主;残差分析显示人类活动对宁夏草地绿度以正效应为主,退耕还草等生态工程促进草地绿度提升,建设用地扩张导致草地绿度降低。[结论]2000年以来,宁夏草地绿度有了明显提升,且在宁夏南部地区增加成效尤为显著。荒漠草地和典型草地绿度波动较大,需要在后期管理中减少人类活动对其的扰动。

Optimal power and subcarrier allocation with partial side information for downlink OFDMA system

In this paper,we investigate the power and subcarrier allocation issue in the case of partial side information for downlink orthogonal frequency division multiple access （OFDMA） system.Relaxation method is utilized to characterize the necessary conditions of the optimal solution and the uniqueness of the optimal solution is proved.The game theoretical concept,surplus function is also introduced to analyze the optimal solution.Based on the theoretical analysis,we propose iterative surplus balancing algorithm （ISBA） that can jointly assign the power and subcarriers in multiple rounds,and then the optimality of ISBA is proved.Simulation results are presented to show the characteristics of the theoretical analysis and ISBA.

基于物理信息神经网络的船舶螺旋桨尾流场重构

将物理信息神经网络(PINN)应用于船舶螺旋桨尾流场的重建.介绍了PINN的原理和基本框架;应用PINN求解Burgers方程,对PINN求解偏微分方程的可行性进行验证.利用计算流体力学(CFD)软件STAR CCM+对KVLCC2螺旋桨的敞水特性进行了数值模拟,得到了该桨在敞水中运动的流场信息.基于数值模拟得到的敞水桨流场特性信息,构造PINN训练样本集对PINN进行训练;训练后的PINN用于推断控制方程在任意时间和空间坐标的近似解.将PINN得到的速度和压力分布与STAR CCM+模拟的速度和压力分布进行了比较,对比结果验证了PINN在尾流场重建中的可靠性.研究结果表明,PINN可以应用于船舶螺旋桨尾流场的重建.