A Deep Learning Based Broadcast Approach for Image Semantic Communication over Fading Channels

We consider an image semantic communication system in a time-varying fading Gaussian MIMO channel,with a finite number of channel states.A deep learning-aided broadcast approach scheme is proposed to benefit the adaptive semantic transmission in terms of different channel states.We combine the classic broadcast approach with the image transformer to implement this adaptive joint source and channel coding(JSCC)scheme.Specifically,we utilize the neural network(NN)to jointly optimize the hierarchical image compression and superposition code mapping within this scheme.The learned transformers and codebooks allow recovering of the image with an adaptive quality and low error rate at the receiver side,in each channel state.The simulation results exhibit our proposed scheme can dynamically adapt the coding to the current channel state and outperform some existing intelligent schemes with the fixed coding block.

Effective Capacity of URLLC over Parallel Fading Channels with Imperfect Channel State Information

This paper investigates the effective capacity of a point-to-point ultra-reliable low latency communication(URLLC)transmission over multiple parallel sub-channels at finite blocklength(FBL)with imperfect channel state information(CSI).Based on reasonable assumptions and approximations,we derive the effective capacity as a function of the pilot length,decoding error probability,transmit power and the sub-channel number.Then we reveal significant impact of the above parameters on the effective capacity.A closed-form lower bound of the effective capacity is derived and an alternating optimization based algorithm is proposed to find the optimal pilot length and decoding error probability.Simulation results validate our theoretical analysis and show that the closedform lower bound is very tight.In addition,through the simulations of the optimized effective capacity,insights for pilot length and decoding error probability optimization are provided to evaluate the optimal parameters in realistic systems.

Energy efficiency aware dynamic rate and power adaptation in carrier sensing based WLANs under Rayleigh fading and shadowing

We consider the problem of energy efficiency aware dynamic adaptation of data transmission rate and transmission power of the users in carrier sensing based Wireless Local Area Networks(WLANs)in the presence of path loss,Rayleigh fading and log-normal shadowing.For a data packet transmission,we formulate an optimization problem,solve the problem,and propose a rate and transmission power adaptation scheme with a restriction methodology of data packet transmission for achieving the optimal energy efficiency.In the restriction methodology of data packet transmission,a user does not transmit a data packet if the instantaneous channel gain of the user is lower than a threshold.To evaluate the performance of the proposed scheme,we develop analytical models for computing the throughput and energy efficiency of WLANs under the proposed scheme considering a saturation traffic condition.We then validate the analytical models via simulation.We find that the proposed scheme provides better throughput and energy efficiency with acceptable throughput fairness if the restriction methodology of data packet transmission is included.By means of the analytical models and simulations,we demonstrate that the proposed scheme provides significantly higher throughput,energy efficiency and fairness index than a traditional non-adaptive scheme and an existing most relevant adaptive scheme.Throughput and energy efficiency gains obtained by the proposed scheme with respect to the existing adapting scheme are about 75%and 103%,respectively,for a fairness index of 0.8.We also study the effect of various system parameters on throughput and energy efficiency and provide various engineering insights.

Optimizing Power Allocation for D2D Communication with URLLC under Rician Fading Channel:A Learning-to-Optimize Approach

To meet the high-performance requirements of fifth-generation(5G)and sixth-generation(6G)wireless networks,in particular,ultra-reliable and low-latency communication(URLLC)is considered to be one of the most important communication scenarios in a wireless network.In this paper,we consider the effects of the Rician fading channel on the performance of cooperative device-to-device(D2D)communication with URLLC.For better performance,we maximize and examine the system’s minimal rate of D2D communication.Due to the interference in D2D communication,the problem of maximizing the minimum rate becomes non-convex and difficult to solve.To solve this problem,a learning-to-optimize-based algorithm is proposed to find the optimal power allocation.The conventional branch and bound(BB)algorithm are used to learn the optimal pruning policy with supervised learning.Ensemble learning is used to train the multiple classifiers.To address the imbalanced problem,we used the supervised undersampling technique.Comparisons are made with the conventional BB algorithm and the heuristic algorithm.The outcome of the simulation demonstrates a notable performance improvement in power consumption.The proposed algorithm has significantly low computational complexity and runs faster as compared to the conventional BB algorithm and a heuristic algorithm.

Secure Wireless Multicasting through Nakagami-m Fading Channels with Multi-Hop Relaying

The additional diversity gain provided by the relays improves the secrecy capacity of communications system significantly. The multiple hops in the relaying system is an important technique to improve this diversity gain. The development of an analytical mathematical model of ensuring security in multicasting through fading channels incorporating this benefit of multi-hop relaying is still an open problem. Motivated by this issue, this paper considers a secure wireless multicasting scenario employing multi-hop relaying technique over frequency selective Nakagami-m fading channel and develops an analytical mathematical model to ensure the security against multiple eavesdroppers. This mathematical model has been developed based on the closed-form analytical expressions of the probability of non-zero secrecy multicast capacity (PNSMC) and the secure outage probability for multicasting (SOPM) to ensure the security in the presence of multiple eavesdroppers. Moreover, the effects of the fading parameter of multicast channel, the number of hops and eavesdropper are investigated. The results show that the security in multicasting through Nakagami-m fading channel with multi-hop relaying system is more sensitive to the number of hops and eavesdroppers. The fading of multicast channel helps to improve the secrecy multicast capacity and is not the enemy of security in multicasting.

相干衰落抑制Φ-OTDR的分布式光纤周界安防技术

基于相位敏感光时域反射计(Φ-OTDR)的分布式光纤振动传感系统在铁路周界安防中有着重要的应用前景.为降低Φ-OTDR中固有的相干衰落对相位解调的影响,提高扰动信号识别率,提出一种基于矢量旋转滑动平均(MVRA)的相干衰落抑制方法.首先,对探测信号复矢量化,并对各位置的复矢量信号按初相角进行旋转对齐;然后,采用滑动平均的方法缓和信号幅度起伏以及减小噪声功率,提高信噪比,进而抑制相干衰落;其次,从衰落抑制信号解调出扰动信号,将MVRA与频谱提取重组(SERM)、数字向移变换(DPST)方法进行对比,通过差分相位标准差验证抗衰落效果;最后,通过搭建分布式光纤周界入侵检测实验平台,模拟环境噪声、应力破坏、攀爬、剪网4种防护扰动信号,以解调的相位灰度图作为特征图像,使用卷积神经网络进行模式识别.实验结果表明:相比SERM、DPST,MVRA能更高效地抑制衰落,当滑动窗长50 ns时,MVRA提高11.2 dB信噪比;扰动信号的识别率由衰落抑制前的88%提高到衰落抑制后的92%.

基于强跟踪的自适应PHD-SLAM算法

同时定位与建图(Simultaneous Localization and Mapping,SLAM)技术使移动机器人在缺乏先验环境信息的条件下,能够在估计自身位姿的同时构建环境地图。然而,在海洋、矿洞等复杂环境中,移动机器人容易受到随机突变噪声的干扰,进而导致SLAM性能下降。现有的概率假设密度(Probability Hypothesis Density,PHD)SLAM算法未考虑随机突变噪声,受到干扰时在线自适应调整能力较弱。为解决移动机器人因随机突变噪声导致状态估计和建图精度降低的问题,本文结合强跟踪滤波器(Strong Tracking Filter,STF)与PHD滤波器,提出了一种基于强跟踪的自适应PHD-SLAM滤波算法(Strong Tracking Probability Hypothesis Density Simultaneous Localization and Mapping,STPHD-SLAM)。该算法以PHD-SLAM为框架,针对过程噪声协方差和量测噪声协方差随机突变问题,本文通过在特征预测协方差中引入STF中的渐消因子,实现了对特征预测的自适应修正和卡尔曼增益的动态调整,从而增强了算法的自适应能力。其中渐消因子根据量测新息递归更新,确保噪声突变时每个时刻的量测新息保持正交,从而充分利用量测信息,准确并且快速地跟踪突变噪声。针对渐消因子激增导致的滤波器发散问题,本文对渐消因子进行边界约束,提高算法的鲁棒性。仿真结果表明,在量测噪声协方差和过程噪声协方差随机突变的情况下,所提算法相较于PHD-SLAM 1.0和PHD-SLAM 2.0的定位和建图精度都得到了提高,同时保证了计算效率。

IRS辅助的车联网相移设计和信道对齐策略

针对车联网中车辆的动态移动以及信号的随机散射所导致的信道快速变化问题,研究了智能反射面(intelligent reflecting surface,IRS)辅助的车联网通信并提出了联合相移设计和信道对齐策略,以削减车联网中多普勒频移的影响并提升通信性能。提出的策略由两个阶段构成。在第一阶段设计IRS的相移以改善级联信道的衰落状态,在策略的第二阶段进行修正函数的设计,实现了直接信道和级联信道的信道对齐,从而减少直接信道快衰落状态对通信性能的影响。通过联合相移设计和信道对齐策略中设计的反射相移以及修正函数,实现了通信性能的提升。仿真结果表明,所提策略相比于相移优化策略可以提升8.8%的频谱效率。

纳米粉体TiO_(2)对靛蓝染色织物光褪色的影响

采用钛酸四正丁酯(TBOT)作为TiO_(2)的前驱体,通过水热反应合成单晶粒TiO_(2)粉体,制备了颗粒状的锐钛型纳米粉体TiO_(2)。结果表明:紫外光照条件下,纳米粉体二氧化钛可以使靛蓝织物表观深度发生大幅度下降,粉体TiO_(2)浓度为10%时,K/S值下降率达到51.81%;促进剂的参与有助于靛蓝织物的褪色效果;涂抹粉体TiO_(2)后的染色布样,在紫外线照射后,颜色值ΔL^(*)偏亮白,同时随着粉体TiO_(2)浓度的增大,ΔL*增大,颜色亮度增大,Δa^(*)初始为负值,颜色偏绿,随着粉体TiO_(2)浓度的增大,样品颜色绿光减少,红光增加;当粉体TiO_(2)浓度为10%,随着光照时间的增加,Δa^(*)值偏向正值,染色布样色光向偏红光偏移,紫外光照射6~8 h时Δa^(*)变化幅度较大,10 h之后,趋于缓慢。

基于代谢组和转录组的银缕梅叶片退红机制初探

为了探究银缕梅〔Parrotia subaequalis(H.T.Chang)R.M.Hao et H.T.Wei〕叶片在春季由红转绿的物质基础和分子机制,采用代谢组和转录组联合分析对银缕梅幼叶和成熟叶中差异花色苷类成分含量及差异表达基因进行比较分析,并对花色苷合成关键酶基因进行生物信息学分析。结果表明:幼叶中花色苷类成分的总相对含量约为成熟叶的27倍;幼叶中锦葵色素3-O-葡萄糖苷、锦葵色素3-O-半乳糖苷和芍药花素3-O-葡萄糖苷的相对含量明显高于其他花色苷类成分,且极显著(p<0.01)高于成熟叶中对应成分的相对含量。比较幼叶和成熟叶的转录组数据,共筛选出11000个差异表达基因。GO功能富集结果显示富集到催化活性的差异表达基因最多(3000个);KEGG富集分析结果显示差异表达基因显著富集在植物激素信号转导通路和苯丙烷生物合成通路。通过与葡萄(Vitis vinifera Linn.)中已知的花色苷合成关键酶氨基酸序列进行比对,共筛选出9个银缕梅花色苷合成酶基因,并且这9个基因在幼叶中的相对表达量明显高于成熟叶。qRT-PCR结果表明:PsCHS1、PsF3′H、Ps3GT和PsAOMT的相对表达量极显著高于成熟叶。PsCHS1、PsF3′H、Ps3GT和PsAOMT分别包含393、528、461和323个氨基酸残基;4个蛋白的二级结构均以α螺旋和无规卷曲为主,其中,PsF3′H不含β转角,其余3个蛋白均由α螺旋、β转角、延伸链和无规卷曲构成;4个蛋白的三级结构均含有相应蛋白家族的保守结构。系统进化树显示:PsCHS1与红花檵木(Loropetalum chinense var.rubrum Yieh)的LcCHS,PsF3′H与枫香树(Liquidambar formosana Hance)的LfF3′H,Ps3GT与可可(Theobroma cacao Linn.)的Tc3GT,以及PsAOMT与枫香树的LfAOMT分别首先聚在一起。综上所述,锦葵色素3-O-葡萄糖苷、锦葵色素3-O-半乳糖苷和芍药花素3-O-葡萄糖苷可能是银缕梅叶片退红的主效花色苷类成分,PsCHS1、PsF3′H、Ps3GT和PsAOMT可能是银缕梅叶片退红的关键基因。

无线光通信中大气湍流噪声测量研究

光信号的强度闪烁、光斑漂移、光束扩展、到达角起伏等大气湍流现象会影响光信号在大气中的传输性能。本文将这种闪烁效应看作是光通信系统传输中的乘性噪声,推导了大气湍流乘性噪声模型,并进行了实验测量研究。结果表明:按照晴天、雾霾天、雪天、小雨天、中雨天、大雨天及阴天的顺序,湍流乘性噪声对光通信的通信质量影响加剧,大气折射率结构常数逐渐增大,噪声信号的偏斜度与陡峭度随之增加,导致光强信号的衰落概率变大;同时随着信噪比(signal-to-noise ratio,SNR)的增大,系统的中断概率逐渐减小;而在相同的SNR下,中断概率随着大气折射率结构常数的减小而减小。研究大气湍流噪声对无线光通信系统的影响具有重要的意义,它为大范围、高速的无线光通信技术带来重要的技术突破,对无线光通信技术的发展起到重要的作用。

锂电池自适应无迹H∞滤波SOC估计研究

荷电状态(state of charge,SOC)作为表征锂电池剩余电量的关键指标,其精确估计对于合理使用电池电量、保障电池安全具有重要意义。本文针对基于H∞滤波(H infinity filter,HIF)估计SOC时鲁棒性好但估计精度低的问题,提出一种自适应无迹H∞滤波(adaptive unscented H infinity filter,AU_HIF)SOC估计方法,以提高SOC估计精度。首先,选择能够在精度和复杂度间取得良好平衡的双极化(dual polarization,DP)等效电路模型进行新型估计算法的设计;其次,结合无迹卡尔曼滤波(unscented Kalman filter,UKF)算法相比于传统扩展卡尔曼滤波(extended Kalman filter,EKF)算法更适用于非线性系统状态估计的特点,文章基于先验误差协方差矩阵设计新型渐消因子,实现自适应无迹H∞滤波算法的设计,以减小陈旧测量值对估计结果的影响,提高滤波算法的跟踪能力及估计精度。最后,通过搭建自主实验平台获取实际模拟工况数据,验证了文章所提自适应无迹H∞滤波算法相比于传统H∞滤波算法、传统UKF算法和其他类型改进H∞滤波算法具有更高的估计精度及更好的鲁棒性。文章研究内容对提高新能源汽车、储能电站等电池系统的SOC估计精度具有重要意义。

无界域上具有记忆的非自治Plate方程随机吸引子的存在性

研究无界域上一类具有衰退记忆和加性噪声的非自治Plate方程解的长时间行为.利用一致估计验证了解的拉回渐近紧性,获得了其随机吸引子的存在性.

基于双参考优化的壁画图像色彩还原

褪变色壁画图像的色彩还原研究可以促进壁画的保护和展示。壁画图像色彩还原旨在将退化壁画图像的色彩褪变区域还原为原有色彩。常规的基于单幅参考壁画图像的色彩还原方法难以选取与退化壁画图像相似的参考壁画图像,进而影响色彩还原质量。为此,提出一种基于双参考优化的壁画图像色彩还原方法。采用双参考策略,即使用2幅参考壁画图像对退化壁画图像进行色彩还原,利用图像优化模块抑制褪变色壁画图像中普遍存在的噪声、划痕等多重退化,通过编码器-解码器网络编码提取壁画图像多尺度特征,并构建特征融合模块优化壁画图像的多尺度特征。采用双参考指导模块计算参考壁画图像与退化壁画图像的语义对应置信度,以实现图像区域间的相似性匹配,并实现2幅参考壁画图像的风格融合。在此基础上,利用融合特征实现退化壁画图像的色彩还原。实验结果表明,该方法可以较准确地还原退化壁画图像色彩,同时能较好保持壁画图像原有的边缘结构信息,并且使用无参考图像质量评估指标对各个方法的还原壁画图像进行客观评估,与对比方法相比,该方法在客观评估指标上最多可降低12.2%。

高原地区VHF地空通信覆盖范围影响因素研究

针对利用地形遮蔽法计算的民航VHF通信信号覆盖范围较粗略的问题,提出了一种在原有的算法下加入雾衰减从而提高信号覆盖精度的算法模型,并构建可视化的显示平台。首先,以西藏某高原地区VHF通信监视台站为例,利用地理信息系统从实际的地图中获取台站周围的数字高程模型(DEM),计算在地形影响下不同高度层上的信号传播截止距离;其次,根据国际电联无线电通信部门(ITU-R)P系列建议书中的雾衰减模型,构造在地形和雾共同影响下的新算法模型;最后,给定飞行高度、发射频率、发射功率等进行仿真。结果表明,多因素影响下的信号覆盖范围比单因素下的覆盖范围精确,可以为VHF地面通信监视台站选址、飞行航线规划以及西部地区民航发展提供有效支撑。

SUBDIVISION建模在室内有机形态的应用研究

通过探索Subdivision建模技术在室内设计中的潜力,提高室内设计有机形态的吸引力和竞争力,并且有效提高设计师在有机形态设计中的效率。通过分析Subdivision建模原理,介绍其在室内运用的可能性、高效性、调节性、仿生性的特点。对其Subdivision建模在室内设计应用中的有机拓扑形态、空间界面软化、泰森多边形应用、渐消面的运用等几点优势展开讨论。结合乐嘉伦敦展厅和BarinSkiResort、Batwings案例,阐述其非线性美学和有机形态,解读其设计特点。进一步分析Subdivision建模的有机形态表达优势与局限性,探讨Subdivision建模在室内设计中的有机结构建模的可行性。探析出Subdivision建模技术能够更好设计具有非线性美学的有机形态,更有助于创造出独特的艺术形式和空间感受。为相关室内空间有机形态的设计提供了创新的设计思路。

散射通信自适应选频分集抗干扰性能分析

针对选频分集散射通信抗干扰性能评估准确性提升,提出了一种自适应选频分集抗干扰性能分析方法,基于抗衰落分集接收解调算法建立了选频抗干扰分析模型。通过仿真分析可获得选频分集准确的抗干扰性能,并根据不同的干扰特性得到了量化的分析结果。仿真结果表明,该自适应选频分集抗干扰分析方法优于现有的散射通信抗干扰分析方法,便捷有效,能够显著提升抗干扰性能评估的准确性。

基于平稳交错正交调制辅助的抗衰落低分辨毫米波定时恢复算法

信号衰落严重时,现有的低分辨毫米波定时恢复算法估计精度不足。针对上述问题,文中提出一种基于平稳交错正交调制辅助的抗衰落低分辨毫米波定时恢复算法。该算法设计了独特的二次重采样数据平稳器以及交错正交调制辅助的抗衰落估计器。二次重采样数据平稳器模块对信号二次重采样,实现对数据的非有理过采样。通过非有理过采样,该模块获得了具有均匀采样抖动的平稳基带信号。交错正交调制辅助的抗衰落估计器推导了平稳低位宽量化信号的定时误差估计公式,通过对平稳的基带信号低通滤波与延迟相关,最终导出定时误差估计值。仿真结果表明,所提算法的均方误差性能优于现有的其他算法,具有较高的定时恢复精度。

车联网V2I场景下基于GNN的SC-FDMA智能信道估计

随着车联网的迅猛发展,车对路基础设施(Vehicle to Infrastructure,V2I)通信对车联网的可靠性和时延提出了更高的要求,而信道估计是接收机高可靠低时延通信的重要保障.为解决传统信道插值算法不能有效拟合V2I信道快时变特性、自适应多普勒频移能力弱和传统神经网络可解释性不强的问题,本文提出基于图神经网络(Graph Neural Network,GNN)的单载波频分多址(Single Carrier-Frequency Division Multiple Access,SC-FDMA)智能信道估计算法.该算法将信道频率响应中的数据点作为图的节点、符号间时域相关性作为边,将图化后的数据送入GraphSAGE信道插值器(GraphSAGE Channel Interpolator,GCI)中,通过边更新、聚合操作、节点更新三大模块进行网络训练,同时采用多普勒频移矢量作为节点特征控制网络拟合不同多普勒条件的信道,使得网络具备可解释性.最后,系统仿真验证了在不同速度环境下算法的有效性和鲁棒性,较线性插值、样条插值以及全连接网络,本文所提GCI在低、中和高速移动环境下具有最优的误码率(Bit Error Rate,BER)和归一化均方误差(Normalized Mean Square Error,NMSE)性能,特别地,在200 km/h高速移动条件下GCI的优势更为明显.

基于卡尔曼滤波的慕课学习行为智能预测方法

为了降低慕课退课率,提升慕课教学质量,研究基于卡尔曼滤波的慕课学习行为智能预测方法。利用华为nova 5 pro的IMX582型摄像头,采集慕课环境中的学习行为数据;利用自回归积分滑动平均模型,依据采集的学习行为数据,建立慕课学习行为观测模型,并转换成状态空间模型;以状态空间模型的计算结果为卡尔曼滤波的初始状态向量;通过卡尔曼滤波更新初始状态向量,获取慕课学习行为的智能预测结果;在卡尔曼滤波内引入渐消因子,以及协方差匹配判据的方法,补偿卡尔曼滤波内后验协方差矩阵的不确定性,修正卡尔曼滤波输出的慕课学习行为智能预测结果。实验证明,该方法智能预测精度较高,应用该方法后可有效降低慕课退课率,提升学习者的学习活跃度。