Light propagation properties of two-dimensional photonic crystal channel filters with elliptical micro-cavities

Light propagation through a channel filter based on two-dimensional photonic crystals with elliptical-rod defects is studied by the finite-difference time-domain method. Shape alteration of the defects from the usual circle to an ellipse offers a powerful approach to engineer the resonant frequency of channel filters. It is found that the resonant frequency can be flexibly adjusted by just changing the orientation angle of the elliptical defects. The sensitivity of the resonant wavelength to the alteration of the oval rods＇ shape is also studied. This kind of multi-channel filter is very suitable for systems requiring a large number of output channel filters.

A dual channel perturbation particle filter algorithm based on GPU acceleration

The particle filter（PF） algorithm is one of the most commonly used algorithms for maneuvering target tracking. The traditional PF maps from multi-dimensional information to onedimensional information during particle weight calculation, and the incorrect transmission of information leads to the fact that the particle prediction information does not match the weight information, and its essence is the reduction of the information entropy of the useful information. To solve this problem, a dual channel independent filtering method is proposed based on the idea of equalization mapping. Firstly, the particle prediction performance is described by particle manipulations of different dimensions, and the accuracy of particle prediction is improved. The improvement of particle degradation of this algorithm is analyzed in the aspects of particle weight and effective particle number. Secondly, according to the problem of lack of particle samples, the new particles are generated based on the filtering results, and the particle diversity is increased. Finally, the introduction of the graphics processing unit（GPU） parallel computing the platform, the “channel-level” and “particlelevel” parallel computing the program are designed to accelerate the algorithm. The simulation results show that the algorithm has the advantages of better filtering precision, higher particle efficiency and faster calculation speed compared with the traditional algorithm of the CPU platform.

Tunable multiple-channel filters based on photonic heterostructures using single-negative materials

We studied the multiple-channel filters based on photonic heterostructures consisting of single-negative permittivity and single-negative permeability media. The results showed that the number of resonance modes inside the zero-φeff gap increases as the number of heterogenous interface M increases. The number of resonance modes inside the zero-φeff gap is equal to that of heterogenous interface M, and it can be used as M channels filter. This result provides a feasible method to adjust the channel number of multiple-channel filters. When losses are involved, the results showed that the electric fields of the resonance modes decay largely with the increase of the number of heterogenous interface and damping factors. Besides, the relationship between the quality factor of multiple-channel filters and the number of heterogenous interface M is linear, and the quality factor of multiple-channel filters decreases with the increase of the damping factor. These results provide feasible methods to adjust the quality factor of multiple-channel filters.

Design and analysis of multi-channel drop filter using dual L defected hexagonal photonic crystal ring resonator

In this paper,dual L defected hexagonal Photonic Crystal Ring Resonator(PCRR)using Channel Drop Filter(CDF)is designed for Coarse Wavelength Division Multiplexing(CWDM)systems.In this structure,the external rods of the ring resonator are arranged in a hexagon and the internal rods are removed in L arrangement for introducing defects.Scatter rods are used to prevent leakage.By using the L defected hexagonal resonator,a multi-channel CDF is designed,which exhibits multiple wavelengths of CWDM(1500 nm–1600 nm)region.In addition,the selection of rod size and the position of rods in the proposed multi-channel CDF are validated by varying the radius of coupling and scattering rods,as well as the position of resonators,respectively.By using plane wave expansion and opti Finite Difference Time Domain(FDTD)method,the electromagnetic wave propagation and the photonic band gap are obtained.

Extended Kalman filtering-based channel estimation for space-time coded MIMO-OFDM systems

A space-time coded multiple-input multiple-output orthogonal frequency-division multiplexing （MIMO-OFDM） system is considered as a solution to the future wideband wireless communication system. This paper proposes an extended Kalman filtering-based （EKF-based） channel estimation method for space-time coded MIMO-OFDM systems. The proposed method can exploit pilot symbols and an extended Kalman filter to estimate channel without any prior knowledge of channel statistics. In comparison with the least square （LS） and the least mean square （LMS） methods, the EKF-based approach has a better performance in theory. Computer simulations demonstrate the proposed method outperforms the LS and LMS methods. Therefore it can offer draznatic system performance improvement at a modest cost of computational complexity.

One-step synthesis of organic microwire-disk interconnected structure for miniaturized channel filters

Miniaturized channel filters are in high demand for many applications such as photonic integrated circuits, information-based technology, and platforms for investigation of light–matter interactions.Recently, several photonic schemes have been proposed to achieve nanofilters, which require sophisticated growth techniques. Here, we have fabricated microdisk whispering-gallery-mode（WGM） resonators through controlled assembly of organic materials with an emulsion-solventevaporation method. Based on this emulsion assembly method, the diameters of microdisks can be easily controlled, and more importantly, a microwire-disk interconnected structure is able to be constructed via one-step assembly. This microwire-waveguide-connected microdisk heterostructure can be utilized as a channel drop filter. Our results have demonstrated a facile way to achieve flexible WGM-based photonic components which can be integrated with other functional devices.

PARTICLE FILTERING BASED AUTOREGRESSIVE CHANNEL PREDICTION MODEL

A particle filtering based AutoRegressive (AR) channel prediction model is presented for cognitive radio systems. Firstly, this paper introduces the particle filtering and the system model. Secondly, the AR model of order p is used to approximate the flat Rayleigh fading channels; its stability is discussed, and an algorithm for solving the AR model parameters is also given. Finally, an AR channel prediction model based on particle filtering and second-order AR model is presented. Simulation results show that the performance of the proposed AR channel prediction model based on particle filtering is better than that of Kalman filtering.

A Non-Maximally Decimated Dynamic Reconfigurable Channelized Structure Based on Modulated Filter Bank

A structure of dynamic reconfigurable channelized filter bank is proposed in order to solve the problem that the uniform channelized receiver cannot receive the cross-channel and wideband signal. The dynamic reconfigurable channelized filter bank is divided into two parts-the analysis filter bank and the synthesis filter bank. The function of the analysis filter bank is to divide the received signal into several sub-signals according to the channel division. Then the sub-signals of each channel need to be detected and discriminated. At last, we use the sub-signals to reconstruct the original received signal by the synthesis filter bank. The analysis filter and the synthesis filter bank of the dynamic reconfigurable channelized filter bank are all efficient polyphase structures, so it can save more hardware resources and has extensive applicability. The structure is simulated by MATLAB and the simulation results verify the correctness of this structure.

Channel Estimation for SCM-OFDM Systems by Modified Kalman Filter

In this paper, the problem of channel estimation for superposition coded modulation-orthogonal frequency division multiplexing (SCM-OFDM) systems over frequency selective channels is investigated. Assuming that the path delays are known, a new channel estimator based on modified Kalman filter algorithms is introduced for the estimation of the multipath Rayleigh channel complex gains (CG). In the simulation, the mean square error (MSE) and bit-error-rate (BER) performances are given to verify the effectiveness of the Kalman estimation algorithms for SCM-OFDM systems.

Nonstationary Filtering for Markov Jumping Systems with Fading Channel and Multiplicative Noises

This paper studies the nonstationary filtering problem of Markov jump system under l2 - l∞ performance. Due to the difference in propagation channels, signal strength and phase will inevitably change randomly and cause the waste of signals resources. In response to this problem, a channel fading model with multiplicative noise is introduced. And then a nonstationary filter, which receives signals more efficiently is designed. Meanwhile Lyapunov function is constructed for error analysis. Finally, the gain matrix for filtering is obtained by solving the matrix inequality, and the results showed that the nonstationary filter converges to the stable point more quickly than the traditional asynchronous filter, the stability of the designed filter is verified.

ZigBee Signal Phase Tracking Using a Discrete Kalman Filter Estimator under AWGN Channel

The objective of this research is to track the phase changes in Binary Phase Shift Keying (BPSK) modulated signal in ZigBee communication systems using discrete Kalman Filter (KF). Therefore, Kalman Filtering is used to estimate and optimize the carrier phase of BPSK modulated signal, in the presence of Additive White Gaussian Noise (AWGN) channel, by minimizing the phase deviation error. Therefore, a simulation model, using MATLAB, will be created to demonstrate ZigBee transmission system with the impact of the integrated filter. The expected results will show that Kalman Filter tracks the phase of BPSK modulated signal correctly and the performance of tracking will be measured by Mean Square Error (MSE) with respect to Signal to Noise Ratio (SNR). This study proposes a new method of phase tracking in ZigBee receivers in the presence of AWGN channel which can be extended to Internet of Things (IoT) applications.

可重构智能表面辅助通信系统时变级联信道估计

针对可重构智能表面(RIS)辅助通信系统时变级联信道的估计中需解决的级联信道稀疏表示、时变信道参数跟踪和信号重构等关键问题,提出了一种结合Khatri-Rao积的分层贝叶斯卡尔曼滤波(KR-HBKF)算法。该算法首先利用信道的稀疏特性,通过Khatri-Rao积和克罗内克积变换得到RIS级联信道的稀疏表示,将RIS级联信道估计问题转化为低维度的稀疏信号恢复问题。然后,根据RIS级联信道的状态演化模型,在HBKF算法的预测模型中引入了时间相关性参数,应用改进的HBKF解决时变信道参数跟踪和信号重构问题,完成时变级联信道的估计。KR-HBKF算法综合利用了信道的稀疏性和时间相关性,能以较小的导频开销获得更好的估计精度。仿真结果表明,与传统压缩感知算法相比,所提算法具有约5 dB的估计性能提升,且在不同的时变信道条件下具有更好的鲁棒性。

Particle filter for joint frequency offset and channel estimation in MIMO-OFDM systems

A particle filter is proposed to perform joint estimation of the carrier frequency offset （CFO） and the channel in multiple-input multiple-output orthogonal frequency division multiplexing （MIMO-OFDM） wireless communication systems. It marginalizes out the channel parameters from the sampling space in sequential importance sampling （SIS）, and propagates them with the Kalman filter. Then the importance weights of the CFO particles are evaluated according to the imaginary part of the error between measurement and estimation. The varieties of particles are maintained by sequential importance resampling （SIR）. Simulation results demonstrate this algorithm can estimate the CFO and the channel parameters with high accuracy. At the same time, some robustness is kept when the channel model has small variations.

基于空时域滤波的欺骗式干扰抑制技术

基于欺骗式干扰信号在空间上具有很强的方向性,通过利用SAR信号的角度-多普勒关系,可在多普勒域实现对其有效分解(投影)的特性,提出了一种基于多多普勒约束的空-时联合自适应滤波器,结合多通道SAR(MSAR)系统可实现对欺骗式干扰信号的有效抑制。首先分析和估计有效信号和转发式欺骗干扰(DFI)的时空频谱,然后对斜视MSAR数据进行处理,利用子空间投影矩阵去除主瓣DFI并利用多多普勒方向线性约束最小方差(MDD-LCMV)波束成形器实现对旁瓣DFI的抑制以及信号频谱重建。文中通过点仿真实验验证了所提算法的有效性。

UFMC系统中基于阈值的改进DFT信道估计算法

针对通用滤波多载波(Universal Filtered Multi-Carrier,UFMC)系统中传统的基于离散傅里叶变换(Discrete Fourier Transform,DFT)的信道估计算法存在噪声区间不连续、信道长度内噪声消除不彻底、信号经DFT运算后频谱泄露高等问题,提出一种基于阈值的改进DFT信道估计算法。算法在频域通过加窗以降低信道冲激响应(Channel Impulse Response,CIR)能量泄露,在时域通过设置阈值和指数平滑可最大限度滤除样本内噪声。理论分析与仿真结果表明,与传统的DFT估计算法相比,所提算法误比特率(Bit Error Ratio,BER)更低,在高信噪比下有1.5 dB左右的性能提升,能够进一步减少噪声对系统性能的影响,可有效提高信道估计的准确度。

基于参数估计和Kalman滤波的单通道盲源分离算法

针对存在频谱混叠通信信号的单通道盲源分离(single channel blind source separation,SCBSS)问题,提出一种基于参数估计和Kalman滤波的SCBSS算法。首先,针对根多重信号分类(root multiple signal classification,Root-MUSIC)算法在相近载频估计方面的局限性,提出一种自适应的Root-MUSIC算法,对接收到的盲混合信号的源信号数目和载频进行估计;其次,将Kalman滤波的思想引入到SCBSS算法中,根据估计得到的源信号参数构造信号模型,将其作为Kalman滤波系统的观测向量,执行“时间更新”和“测量更新”两个过程,得到源信号的最佳估计,实现单通道盲源分离。仿真结果表明,所提算法能够有效地从存在频谱混叠的单路接收信号中准确地分离出多路源信号,比传统的算法分离精度高,运算速度快。

改进的局部最小像素先验遥感图像盲复原算法

为了解决遥感图像盲复原时模糊核估计不准确、复原图像存在振铃效应的问题,提出改进的局部最小像素先验遥感图像盲复原算法。该算法首先引入极端通道先验与局部最小像素先验结合,对图像的强度进行更好的约束,有利于得到更好的潜在清晰图像;然后采用基于梯度的方法估计模糊核,模糊核估计与中间潜在清晰图像估计交替迭代进行,获得较为理想的模糊核;最后引入联合双边滤波器,采用改进的拉普拉斯与正则化图像复原算法抑制图像复原的振铃效应。实验结果表明,本文方法对遥感图像复原效果较好,恢复的图像边缘清晰,振铃伪影得到抑制且模糊核较为理想;客观评价指标峰值信噪比(PSNR)较前沿复原算法平均提高约1.40 dB,结构相似度(SSIM)平均提高约0.02。

基于天空区域分割的快速去雾算法研究

针对传统暗通道先验去雾算法在处理户外含雾图像时,出现天空区域颜色失真和处理速度慢的问题,提出一种可自适应识别天空区域的快速去雾算法。在天空分割方面,选用图像中细节特点明显的亮度分量为研究对象,结合最大类间方差法(OTSU)和动态参数建立自适应识别天空区域算法模型,得到最佳分割阈值,分割出有雾图像的天空区域和非天空区域,并根据天空区域计算出大气光值。在提高处理速度方面,在使用引导滤波优化透射率过程中引入图像下采样算法,保证复原后图像质量的同时减少算法耗时。最后经过与多种经典算法对比,文中算法在视觉效果上细节更加自然,SSIM、PSNR和MSE的综合指标均超过其他算法,并且处理速度较快。主观和客观评价结果均表明,文中算法在视觉效果和时间效率方面都优于其他几种算法,具有一定的实用价值。

基于通道相似度熵的卷积神经网络裁剪

卷积神经网络(CNN)中包含大量滤波器,参数训练以及存储占用大量内存资源。裁剪滤波器是减小网络规模、释放内存、提高计算速度的有效方法。现有滤波器裁剪方法的主要问题是将滤波器权值作为孤立的数值计算,裁剪小权值滤波器,保留权值大的滤波器,忽视了部分小权值滤波器在特征提取过程中的重要性。通过分析滤波器通道之间的相似性,提出一种基于通道相似度的滤波器熵值计算方法(FEC)。针对滤波器结构特征,对权值张量进行均值压缩,并证明其合理性。先计算滤波器通道距离判断通道之间的相似性,再根据通道相似度计算滤波器熵,由熵值大小进行滤波器排序,删除一定比例熵值较小的滤波器。实验设计针对不同卷积层采用不同的裁剪比例,在CIFAR10以及Image Net标准数据集上对VGG-16和Res Net-34网络进行裁剪。实验结果表明:在基本保持原始准确度的情况下,分别减少了约94%和70%的参数数量;在目标检测网络SSD上参数数量减少了55.72%,平均精度均值(mAP)提高了1.04个百分点。

基于稀疏贝叶斯学习的GFDM系统联合迭代信道估计与符号检测

针对当前广义频分复用(Generalized Frequency Division Multiplexing,GFDM)系统时变信道估计精度低的问题,提出基于稀疏贝叶斯学习的GFDM系统联合信道估计与符号检测算法.具体地,采用无干扰导频插入的GFDM多重响应信号模型,在稀疏贝叶斯学习框架下,结合期望最大化算法(Expectation-Maximization,EM)和卡尔曼滤波与平滑算法实现块时变信道的最大似然估计;基于信道状态信息的估计值进行GFDM符号检测,并通过信道估计与符号检测的迭代处理逐步提高信道估计与符号检测的精度.仿真结果表明,所提算法能够获得接近完美信道状态信息条件下的误码率性能,且具有收敛速度快、对多普勒频移鲁棒性高等优点.