A new prompt 2-D attitude steering approach for zero Doppler centroid of GEOsynchronous SAR (GEOSAR) is proposed. Large yaw angle of GEOSAR in traditional 2-D yaw steering condition can be solved by this method. It is suited to the large satellite such as GEOSAR. The GEOSAR can achieve broadside imaging when this method is applied. Compared to the traditional attitude steering approach, the steering angle and time are just 1/10 of it, and the developing difficulty of GEOSAR becomes lower through this new method. This approach is propitious to GEOSAR. When it is employed to SAR satellites with different altitudes, the residual Doppler centroid is accurate zero in all the conditions. Besides, an attitude selection reference standard is illustrated for different altitude orbital satellites.

The traditional methods based on CFAR and Kernel Density Estimation (KDE) for SAR ship candidate region extraction has the following defects: The choice of false alarm rate of CFAR depends on artificial experience; CFAR only models the sea clutter distribution, which poses a certain risk of missing detection to the target; When KDE is used to filter strong sea clutter, the threshold must be selected by artificial experience. These defects make the traditional method unable to adapt to complex scene, such as multi-satellite and multi-resolution. A candidate region extraction method for multi-satellite and multi-resolution SAR ships is proposed. In view of the defects of CFAR, an iterative method of mean dichotomy is proposed to approximate the target and calculate the segmentation threshold. The calculation efficiency of this method is more than 10 times higher than that of CFAR while overcoming the defects of CFAR; In view of the defects of KDE, block KDE combined with large threshold is used to filter strong sea clutter, and then seed point growth algorithm is used to reconstruct target. Because the large threshold has enough thresholds, the method can adapt to more complex scenarios. Experiments show that the proposed method has the advantages of no missed detection, self-adaptive threshold, high computational efficiency, and low false alarm rate. It has excellent multi-satellite and multi-resolution SAR ship candidate region extraction capability.

Due to the selection of dominant scatterers is easy to be affected by noise, a novel Inverse Synthetic Aperture Radar (ISAR) cross-range scaling algorithm based on image contrast maximization is proposed, which can realize the cross-range scaling while achieving the range spatial-variant phase autofocus. With the image contrast as cost function, the cross-range chirp rate of received signal can be estimated accurately using Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Based on the estimated results, the cross-range scaling of ISAR image and precise phase autofocus can be implemented. Both simulated and real data experiments confirm the effectiveness and robustness of the proposed algorithm.

Three dimensional interferometry of wide-band radar can provide crucial information for estimating the micro-motion and geometric parameters of targets. For estimation of the micro-motion parameters via three dimensional interferometry in the case of squint observing mode, an algorithm for micro-motion and geometric parameters based on squint calibration is proposed. The algorithm performs ranging and angle measuring for each antenna receiving echo in an L formation array. Moreover, the squint distortion is calibrated and three dimensional trajectories of scattering centers are obtained via establishing two elements and quadratic nonlinear equations and coordinate transformation. In addition, smoothing filtering and optimization are used to retrieve micro-motion and geometry parameters. The effectiveness and robustness of the proposed algorithm is confirmed via extensive experiments.

Focusing on the clutter suppression problem of the airborne Multiple Input Multiple Output (MIMO) radar, an improved method based on Knowledge-Aided Space-Time Adaptive signal Processing (KA-STAP) algorithm is proposed. The clutter subspace is constructed offline according to the prior distribution of the clutter in the space-time plane, to replace that of estimation based on the Prolate Spheroidal Wave Function (PSWF), so that complex operations are avoided. Simulation results show that the proposed approach can not only reduce the computational complexity, but can obtain deeper notch and better side-lobe performance.

Integration of radar and communication on the electronic war platform is an effective method to reduce volume and enhance spectrum usage and efficiency. A transmitted pattern based on OFDM-LFM MIMO radar is designed to realize the integration of radar and communication by changing initial frequency. The communication receiver interpretation of the bit is based on the initial frequency of the signal. In radar receiver, the same range resolution as tradition OFDM-LFM MIMO radar can be get with get the time domain synthetic bandwidth methods. The proposed method changes the initial frequency without changing the omnidirectional pattern because the orthogonal transmitted signals are nonoverlapping in the spectrum. Simulation examples are provided for performance evaluation and to demonstrate the effectiveness of the proposed information embedding technique.

For the problem of hydrometeor classification in the presence of ground clutter, traditional methods produce large classification errors under different weather and environmental conditions. A new method for the classification of Hydrometeor based on Fuzzy Neural Network-Fuzzy C-Means (FNN-FCM) is proposed. Firstly, the FNN is trained by the clutter data received by the Dual-polarization weather radar in the clear sky mode. The parameters of the membership function of each polarization parameter of the clutter are calculated adaptively. Then the ground clutter in the rainfall mode is suppressed by the ground clutter membership function obtained by the training. Finally, FCM clustering algorithm is used to classify the Hydrometeor after clutter suppression. The processing results of the measured data show that the proposed method can effectively suppress ground clutter and obtain finer hydrometeor classification results.

In view of the strong anti-jamming capability of monopulse radar, the cross eye is used to interfere with monopulse radar. Monopulse radar is widely used in missile terminal guidance for precise attack on aircraft and ship targets. Based on the radar equation and the principle of monopulse radar angle measurement, the retrodirective cross-eye interference of the isolated target and the two point source under the target echo are modeled. Based on the analysis method of linear fitting, the general formula of two source reverse cross eye parameters and monopulse radar indicating angle are obtained. The influence of jammer power, signal phase difference, signal amplitude ratio and echo signal phase on the angle deception effect of monopulse radar is discussed through case simulation. The results show that: The phase difference of the two signals emitted by the jammer is closer to 180° and the amplitude ratio is closer to 1, the better the angle deception effect of the jammer is to the monopulse radar; with the increase of jammer power, the parameter tolerance of jammers is more relaxed when JSR (10～25 dB) is increased; and due to the influence of target echeo phase, the jamming effect of jammer is unstable; the mathematical model is consistent with the simulation model of monopulse radar receiver. This study can provide reference for the design of reverse cross eye jammers for aircraft and ships.

A multi-parameter convolutional neural network method is proposed for gesture recognition based on Frequency Modulated Continuous Wave (FMCW) radar. A multidimensional parameter dataset is constructed for gestures by performing time-frequency analysis of the radar signal to estimate the distance, Doppler and angle parameters of the gesture target. To realize feature extraction and classification accurately, an end-to-end structured Range-Doppler-Angle of Time (RDA-T) multi-dimensional parameter convolutional neural network scheme is further proposed using multi-branch network structure and high-dimensional feature fusion. The experimental results reveal that using the combined gestures information of distance, Doppler and angle for multi-parameter learning, the proposed scheme resolves the problem of low information quantity of single-dimensional gesture recognition methods, and its accuracy outperforms the single-dimensional methods in terms of gesture recognition by 5%～8%.

To improve the location resolution of electromagnetic radiation source, a ultra-short baseline network CASMA (Mini-Array by Chinese Academy of Sciences) is proposed for detection, utilizing optical fiber for timing. CASMA contains 5 electromagnetic detection stations and a control unit. The distance between each pair of stations is about 1 km, meaning that the length of baseline to the wavelength is about 0.1. The timing accuracy is about 10 ns. CASMA is applied to record the vertical electric field emitting by radio transmitters. CASMA utilizes interferometric imaging algorithm to calculate the transmitters’ azimuth. By experiment, the calculated azimuths approach the expected azimuths with deviations are less than 0.2°, showing many advantages over traditional systems or methods. Consequently, CASMA has accuracy direction finding resolution for electromagnetic radiation source. According to the results, the location accuracy may be expected to be 0.5%·R in a 2500 km scope where R is the distance between the electromagnetic radiation source and CASMA using two sets of CASMA for intersection positioning.

To test the radiated interference E-field threshold of Equipment Under Test (EUT) with common-mode interference of transmission lines in reverberation chambers and unify the test results with the open areas, the range of the maximum directivity of the lines with random loads is calculated by the derivation of the equation of the common-mode currents and decomposition of the currents into the corresponding characteristic ones. The calculated results are validated with the experiments performed in a reverberation chamber and an open area, respectively, with a single conductor line and a coaxial cable as the EUT. The theoretical and experimental results show that the test results in the two different areas can be unified with the calculated results. The common mode interference of two conductor lines and coaxial cables can be equivalent to single conductor lines and the bend of the lines almost has no influence on the test results.

In order to realize safety, reliability and self-control of electromagnetic computing, the large-scale parallel MoM is studied based on domestically-made many-core supercomputer platform named " Tianhe-2”. A new LU decomposition algorithm named Block Diagonal matrix Pivoting LU decomposition (BDPLU) algorithm, is proposed by analyzing the diagonally dominant characteristics of the matrix generated through dispersing electric field integral equation of MoM, for the purpose of communication pressure reduction to computer cluster and solution acceleration to MoM integral equation during large-scale parallel computation. The BDPLU algorithm reduces the amount of calculation in the process of panel factorization. More importantly, the algorithm completely eliminates MPI communication when pivoting. Using BDPLU algorithm, the maximum number of CPU cores break through 6×10⁵ CPU cores, which is the largest scale of parallel MoM computation in domestically-made and many-core supercomputing platform at present, and the parallel efficiency of solving matrix can reach 51.95%. Numerical results show that parallel MoM can accurately and efficiently solve large-scale electromagnetic field problems on domestic supercomputing platform.

For the requirement of broadband interference suppression for passive sonar, a robust broadband interference suppression algorithm using few snapshots is proposed. Based on the estimated bearing of the broadband interference, the algorithm obtains the steered cross-spectral density matrix through multi-frequency data in the bandwidth and estimates the signal subspace, then uses the projection approach to correct the unit vector, and estimates the steering vector of interference through inversely transforming. Repeating above steps can obtain the interference steering vector set, thereby constructing the suppression matrix. The interference component of array data is eliminated by suppression matrix processing, and the final spatial spectrum can be obtained after spatial processing. The theoretical analysis, simulation and processing of sea trial data show that the proposed algorithm uses few, even single frequency domain snapshots processing, and still has good performance in environments where target motion, conditions rapid change and other conditions that time integration is unsuitable, at the same time, algorithm is robust for mismatches faced by space processing.

In view of the problem that the existing methods are not applicable or are only feasible to the case where only a low ratio of data are missing in multivariable time series, a missing data prediction algorithm is proposed based on Kronecker Compressed Sensing (KCS) theory. Firstly, the sparse representation basis is designed to largely utilize both the temporal smoothness characteristic of time series and potential correlation between multiple time series. In this way, the missing data prediction problem is modeled into the problem of sparse vector recovery. In the solution part of the model, according to the location of missing data, the measurement matrix is designed suitable for the current application scenario and low correlation with the sparse representation basis. Then, the validity of the model is verified from two aspects: Whether the sparse representation vector is sufficiently sparse and the sensing matrix satisfies the restricted isometry property. Simulation results show that the proposed algorithm has good performance in the case where a high ratio of data are missing.

In order to solve the dictionary mismatch problem of Compressive Sensing (CS) based multi-target Device-Free Localization (DFL) under the wireless localization environments, a Variational Expectation Maximization (VEM) based dictionary refinement method is proposed. Firstly, this method builds the dictionary based on the saddle surface model, and models the environment-related dictionary parameters as tunable parameters. Then, a two-layer hierarchical Gaussian prior model is imposed on the location vector to induce its sparsity. Finally, the VEM algorithm is adopted to estimate the posteriors of hidden variables and optimize the environment-related dictionary parameter, thus the estimation of target locations and dictionary refinement can be realized jointly. Compared with the conventional CS based multi-target DFL schemes, the simulation results demonstrate that the performance of the proposed algorithm is especially excellent in changing wireless localization environments.

The Grey Wolf Optimizer (GWO) algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature, and it is an algorithm with high level of exploration and exploitation capability. This algorithm has good performance in searching for the global optimum, but it suffers from unbalance between exploitation and exploration. An improved Chaos Grey Wolf Optimizer called CGWO is proposed, for solving complex classification problem. In the proposed algorithm, Cubic chaos theory is used to modify the position equation of GWO, which strengthens the diversity of individuals in the iterative search process. A novel nonlinear convergence factor is designed to replace the linear convergence factor of GWO, so that it can coordinate the balance of exploration and exploitation in the CGWO algorithm. The CGWO algorithm is used as the trainer of the Multi-Layer Perceptrons (MLPs), and 3 complex classification problems are classified. The statistical results prove the CGWO algorithm is able to provide very competitive results in terms of avoiding local minima, solution precision, converging speed and robustness.

When modeling user preferences, the current researches of recommendation ignore the problem of modeling initialization and the review information accompanied with rating information for recommender models, integrating deep learning into the recommendation system becomes a hotspot of Point-Of-Interest (POI) recommendation. In this paper, a new POI recommendation model called Matrix Factorization Model integrated with Hybrid Neural Networks (MFM-HNN) is proposed. The model improves the performance of POI recommendation by fusing review text and check-in information based on Neural Network (NN). Specifically, the convolutional neural network is used to learn the feature representation of the review text and the check-in information is initialized by using the stacked denoising autoencoder. Furthermore, the extended matrix factorization model is exploited to fuse the review information feature and the initial value of the check-in information for POI recommendation. As is shown in the experimental results on real datasets, the proposed MFM-HNN achieves better recommendation performances than the other state-of-the-art POI recommendation algorithms.

In order to improve the ability of anti-chosen plaintext attack and decryption quality under unknown attack in current optical encryption technology, an optical image encryption algorithm based on chaotic Gyrator transform and differential mixed mask is proposed. The input plaintext is converted into its corresponding Quick Response (QR) code. The chaotic phase mask is generated according to the Logistic map. At the same time, the radial Hilbert and the zone plate phase function are combined to fuse with the chaotic phase mask for constructing the mixed phase mask. Then, a random sequence of Logistic chaotic maps is used to calculate the rotation angle of the Gyrator transformation, and the QR code is modulated to form Gyrator spectrum by combining the mixed phase mask. The Gyrator spectrum is divided into two components by introducing the equivalent decomposition technique, and two differential spiral phase masks are obtained by setting up different orders. Then, the Singular Value Decomposition (SVD) is introduced to process one of the Gyrator spectral components so that its corresponding orthogonal matrix is encoded by combining two differential phase masks. Finally, by combining the encoded orthogonal matrix and diagonal matrix, the  encrypted cipher is outputted based on thereversible SVD technology. The ability of resisting plaintext attack and clipping attack, as well as the sensitivity level of the encryption results to key change is analyzed theoretically. Experimental results show that the algorithm has good security performance.

By means of Zero-mean Microstructure Pattern Binarization (ZMPB), an image representation method based on image local microstructure binary pattern extraction is proposed. The method can express all the important patterns with visual meaning that may occur in the image. Moreover, through the dominant binary pattern learning model, the dominant feature pattern set adapted to the different data sets is obtained, which not noly achieves excellent ability in feature robustness, discriminative and representation, but also can greatly reduce the dimension of feature coding and improve the execution speed of the algorithm. The experimental results show that the proposed method has strong discriminative power and outperformes the traditional LBP and GIMMRP methods. Compared with many recent algorithms, the proposed method also presents a competitive advantage.

Considering the low recognition accuracy of behavior recognition from different perspectives at present, this paper presents a perspective-independent method for depth videos. Firstly, the fully connected layer of depth Convolution Neural Network (CNN) is creatively used to map human posture in different perspectives to high-dimensional space that is independent with perspective to achieve the Human Posture Modeling (HPM) of deep-performance video in spatial domain. Secondly, considering temporal-spatial correlation between video sequence frames, the Rank Pooling (RP) function is applied to the series of each neuron activated time to encode the video time sub-sequence, and then the Fourier Time Pyramid (FTP) is used to each pooled time series to produce the final spatio-temporal feature representation. Finally, different methods of behavior recognition classification are tested on several datasets. Experimental results show that the proposed method improves the accuracy of depth video recognition in different perspectives. In the UWA3DII datasets, the proposed method is 18% higher than the most recent method. The proposed method  (HPM+RP+FTP) has a good generalization performance, achieving a 82.5% accuracy on dataset of MSR Daily Activity3D.

To solve sparse signal processing problem with structural noise interference, a method of sensing matrix optimization design based on sparse Bayesian theory is proposed. Combining the sparse signal model with additive interference, the design of the sensing matrix is realized by minimizing the trace of the posterior covariance matrix and the energy constraint of sensing matrix. The effects of sensing matrix optimization on the reconstruction error and reconstruction time are simulated using difference sparse signal and reconstruction algorithms, and the effects of the sensing matrix optimization on the reconstruction effect are analyzed when there is a bias in the prior information. The simulation results show that the optimized sensing matrix can obtain the important information in the sparse signal, the mean square error of the signal reconstruction accuracy is reduced by about 15～25 dB, and the reconstruction time is reduced by about 40%.

For the problems of the Composite Binary Offset Carrier (CBOC) signal pseudo code period and combination code sequence are difficult to estimate in a non-cooperative context, two blind methods are proposed based on power spectrum reprocessing and Radial Basis Function (RBF) neural networks. It can get the CBOC pseudo code period through two power spectrum calculations. Firstly, the received one pseudo code period is overlapped segmentation based on the estimated pseudo code period. Secondly, the learning coefficient is optimized selection and each segment of date vector as an input signal to the RBF neural networks to supervised adjustment. Finally, through the continuous input signal, it can restore the original combination code sequence according to the convergent weight vectors. Simulation results show that the pseudo code period can be estimated using the secondary power spectrum under low Signal-to-Noise Ratio (SNR). Compared with the Back Propagation (BP) neural networks and the Sanger neural networks, the proposed RBF neural networks improve the SNR by 1 dB and 3 dB respectively and the number of data groups required is less through RBF neural networks under the same condition.

Audio clipping distortion can be solved by the Consistent Iterative Hard Thresholding (CIHT) algorithm, but the performance of restoration will decrease when the clipping degree is large, so, an algorithm based on adaptive threshold is proposed. The method estimates automatically the clipping degree, and the factor of the clipping degree is adjusted in the algorithm according to the degree of clipping. Compared with the CIHT algorithm and the Consistent Dictionary Learning (CDL) algorithm, the performance of restoration by the proposed algorithm is much better than the other two, especially in the case of severe clipping distortion. Compared with CDL, the computational complexity of the proposed algorithm is low like CIHT, compared with CDL, it has faster processing speed, which is beneficial to the practicality of the algorithm.

For the problem of the finite word length effect of prototype filters in hardware implementation of the filter bank system, this paper studies how to improve the performance of roundoff noise caused by signal quantization for the FIR prototype filter, that is, to reduce the roundoff noise gain. An FIR filter optimization structure is proposed. By analyzing the source of roundoff noise, a polynomial parameterization method is used to derive the roundoff noise gain expression. The simulation example shows that the amplitude-frequency and phase-frequency response of the proposed structure filter are basically consistent with the ideal state under different constraint of word length. Compared with the existing algorithms, the proposed structure has a smaller roundoff noise gain.

FIR notch filter has many advantages such as linear phase, high precision and good stability. However, when the notch performance is required to be high, a higher order is usually required, resulting in increased greatly hardware complexity of the FIR notch filter. Based on sparse FIR filter design algorithm and common subexpression elimination, a novel algorithm is proposed for the design of low complexity sparse FIR notch filter. First, a sparse FIR notch benchmark filter that fulfills frequency response specifications is obtained from the sparse filter design algorithm. Then, each quantized filter coefficient is represented in Canonical Signed Digit (CSD). The sensitivities of all weight-two subexpressions and isolated nonzero digits of the quantized coefficient set are analyzed. Finally, the filter coefficient set with lower implementation cost is constructed by iteratively admitting subexpressions and isolated nonzero digits according to their sensitivities. Simulation results show that the proposed algorithm can save about 51% of adder compared with other low complexity filter design algorithms, which reduces effectively the implementation complexity and saves greatly the hardware cost.

To establish effective backhaul connection in multi-tiers Heterogeneous Network (HetNet), by exploiting advanced Non-Orthogonal Multiple Access (NOMA) a novel in-band wireless backhaul scheme is proposed at full-duplex Small cell Base Stations (SBSs). Firstly, a K+1 HetNet is investigated, where the first tier consists of Macro Base Stations (MBSs) that are equipped with massive MIMO antennas and the remainder K tiers consist of the different types of single-antenna SBSs. The base stations of the whole network operate in full-duplex mode. Specially, the downlink transmission of MBSs is considered. Hence, at each SBS the backhaul signal is superposed over the downlink signal. Then, by using the method from stochastic geometry and modeling all network’s elements as independent homogeneous Poisson Point Processes (PPPs) in this HetNet model, the coverage probabilities of up access link and backhaul link of SBSs are investigated as well as the throughput of small cells. Finally, the presented simulations and numerical results show that the coverage probability of small cell backhaul is changing monotonously with the power sharing efficient, but the monotony is not held for the power of mobile users. Compared with the systems without NOMA, it is found that with reasonable power allocation factor, the NOMA-deployed ones achieve the evident throughput gain.

The millimeter-wave hybrid beamforming becomes a widely accepted beamforming method in millimeter-wave systems. However there is almost no hybrid beamforming algorithm based on security. Especially when the eavesdropper has multi-user decoding capability, the system security performance can not be guaranteed. To solve this problem, a security hybrid beamforming algorithm is proposed for millimeter wave downlink multiuser system based on artificial noise. First, the analog part and the digital part of the hybrid beamforming matrix are decoupled. Based on the channel characteristics, the analog and digital beamforming matrices of useful signals are designed by maximizing the user’s received signal energy and Zero-Forcing (ZF). Then, the artificial noise baseband digital precoding matrix is designed by Singular Value Decomposition (SVD), and the artificial noise is placed in null space of the legal users and worsening eavesdropping channel. Simulation results show that the artificial noise-assisted secure hybrid beamforming algorithm solves effectively the security problem of the system when there are multi-user decoding ability eavesdroppers.

To solve the long decoding latency caused by the serial nature of the decoding of polar codes, a pre-decoding based maximum-likelihood simplified successive-cancellation decoding algorithm is proposed. First, the signs of the likelihood values stored in the decoding tree nodes are extracted and grouped to obtain symbol vectors. Then comparing the symbol vectors and the values of some information bits, the distribution rules are found that positive and negative values stored in the vectors are one-to-one corresponding to the value of middle information bits of the node. Based on the above analysis, one or two bits in the middle of the constituent code are pre-decoded. Finally, the maximum likelihood decoding method is used to estimate the remaining information bits in the constituent code, and the final decoding results are obtained. Simulation results show that the proposed algorithm can effectively reduce the decoding delay compared with the existing algorithms without affecting the error performance.

Focusing on the reconnaissance mission of Unmanned Aerial Vehicle (UAV) swarm under complex battlefield environment, the non-uniform energy consumption during the information transmission between UAVs affects the efficient implementation of the reconnaissance mission, thus a cluster-based algorithm of reconnaissance UAV swarm based on wireless ultraviolet secret communication is proposed. Combined the advantages of wireless ultraviolet scattering communication, this algorithm uses cluster topology management mechanism to balance the energy consumption of UAV swarm. Simulation results show that the algorithm can effectively balance the network energy consumption and improve the transmission efficiency of the network when compared with the existing algorithm, and the lifetime of swarm can be extended when selecting the appropriate packet length and node density.

With the development of Network Function Virtualization (NFV), Virtual Network Functions (VNFs) can be deployed in a common platform such as virtual machines in the form of Service Function Chaining (SFC), providing flexibility for management. However for service providers, these come with high OPerational EXpenditure (OPEX), due to the complexity of the network infrastructure and the growing demand for services. To solve this problem, a strategy for OPEX optimization is proposed, which aims to minimize the startup cost, energy consumption, transmission cost and obtain VNF deployment and routing allocation optimization scheme. The VNF deployment problem as a new Mixed Integer Linear Programming (MILP) model is formulated, and three OPEX optimization algorithms are designed including Genetic Algorithm (GA). The OPEX of MILP model and optimization algorithms are compared under different resource allocation constraints. The calculation result shows that the GA can obtain the near-optimal solutions when node resource ratio is more than 60%.

Request acceptance rate and energy saving are the two most important indicators in the virtual network mapping process. However, the current virtual network embedding problem considers only a single index, ignoring the correlation and constraints between the two, resulting in a decrease in the overall performance of the virtual network embedding. This paper proposes a Multi-Objective Virtual Network Embedding algorithm based on Nash Bargaining (MOVNE-NB). Firstly negotiating the virtual network embedding problem in the framework of Nash bargaining by using game theory technology. Then a fair bargaining mechanism is put forward to avoid selfish decisions by players and lead to bargaining failures. Experiments show that the MOVNE-NB algorithm can not only produce a Pareto efficient solution, but also achieve a fair tradeoff between request acceptance rate and energy saving.

With the rapid growth of Data Center Network (DCN) traffic, how to improve the performance and service quality of data center network become a research hotspot. However, when the network load increases, the existing traffic scheduling algorithm on the one hand may cause bandwidth fragmentation results in the network throughput decrease, on the other hand, it neglects the traffic application requirements to lead to poor QoS. Therefore, a dynamic traffic scheduling algorithm for bandwidth fragmentation minimization and QoS guarantee is proposed. The algorithm takes into account the different requirements of the bandwidth-sensitive large flows, and delay sensitive and packet-loss sensitive small flows. Firstly, the shortest path set is established according to the source address and destination address of the to-be-scheduled flow. Secondly, all the paths that satisfy the bandwidth requirement of the to-be-scheduled flow are selected. Then, the weight function is established for each path according to the free bandwidth of the path and the application requirements of the small flow. Finally, the forwarding path is selected based on the weight function value by roulette algorithm. The network simulation results show that when the network load increases, the proposed algorithm reduces the packet loss rate and delay of small flows, and improves the network throughput compared with other algorithms.

The existing virtual network reconfiguration algorithms do not consider the fragment resources generated in the physical network, which results in the improvement of the performance of the online virtual network embedding algorithms is not obvious. To solve this problem, a definition of network resource fragmentation is given, and a Fragment-Aware Secure Virtual Network Reconfiguration (FA-SVNR) algorithm is proposed. In the process of reconfiguration, the virtual node set to be migrated is selected by considering the fragmentation of nodes in the physical network periodically, and the best virtual node migration scheme is selected by considering the reduction of the fragmentation of the physical network and the reduction of the embedding cost of the virtual network. Simulation results show that the proposed algorithm has the higher acceptance ratio and revenue to cost ratio compared with the existing virtual network reconfiguration algorithm, especially in the metric of revenue to cost ratio.

Trust based access control is a research hotspot in  open network that access control is one of the importation technology of information security. For the interactive access behaviors of non-honest cooperation between network interactive entities in open network, the dynamic game access control model is established based on trust, and interactive entities are encouraged to rationally choose strategies expected by the system (the designer) driven by its own benefits through the designed mechanism. Taking benefits as the driven force, the mechanism rewards the honest nodes and punishes and restrains the non-honest nodes, and then reaches the general state of equalization between entities which meets the goal. The simulation experiment and result analysis show that the incentive and restraint mechanism is valid and necessary on the issue of non-honest access between network interactive entities.

The misuse of signcryption ciphertext means that the malicious recipient uses the received signcryption ciphertext to forge a new ciphertext that has a different recipient. It is found that the Existential UnForgeability against adaptive Chosen Message Attack (EUF-CMA) model can not simulate misuse attacks on signcryption schemes, and many of the existing signcryption schemes, claimed provable secure, can not resist the misuse attack. By enhancing the capabilities of adversaries in the EUF-CMA model, an extended EUF-CMA model is defined which captures the security associated with the resistance to misuse attacks on signcryption schemes. This paper describes the misuse attack instances in several newly proposed heterogeneous signcryption schemes, analyzes the reasons for the attacks and proposes improvement approaches. Finally, using the enhanced EUF-CMA model, the unforgeability of an improved heterogeneous signcryption scheme is analyzed, and the procedure of simulating the misuse attack is demonstrated. The results indicate that the enhanced EUF-CMA model and the improvement approaches for signcryption schemes are reasonable and effective.