A new image quality metric is proposed, it can be used to predict the no-reference image quality. Based on the Singular Value Decomposition (SVD) of the local structure tensor of the image, the noise and blur level is measured using the characteristic of singular value. The performance of the method is evaluated with a publicly available database of images and their quality score. The results show that the proposed no-reference method for the quality prediction of noise and blur images has a comparable performance to the leading metrics available in literature, and also that the method is easier to implement.

Through analyzing the image structure direction characteristics and the stereoscopic perception of human visual system, and combining semi-fragile digital watermarking and Support Vector Regression (SVR), a reduced-reference stereoscopic image quality assessment model based on visual perception and zero-watermark is proposed. In this model, the view zero-watermark is constructed by judging the relation of the horizontal and vertical wavelet coefficients, which can reflect the image structure information. Meanwhile, the disparity zero-watermark that reflects the stereoscopic perception quality is constructed by using the disparity between the left and right views. And then the relativity of two watermark-recovering rates (watermark-recovering rates of the view and disparity zero-watermarks) and subjective quality scores can be learned by the training procedure of SVR. Finally, stereoscopic image quality is predicted by trained SVR. Experimental results show that the proposed reduced-reference model is in accordance with human visual characteristics, and consistent with the result of subjective evaluation value preferably.

Symmetric Raised Cosine Keying (SRCK) modulation is a new binary digital modulation method. The information is sent by the different polar of the frequency change waveform. For continuous phase and smoothing frequency change, the SRCK signals have characteristics about high energy concentration, high bandwidth efficiency and good resistance to amplitude fading. In order to analyze the communication performance of the SRCK modulated signals, the correlation of the SRCK modulated sample waveforms is deduced, and an analytic formula with the Bessel function is obtained. The main factors of effecting the correlation value changes are analyzed. Orthogonal symmetric raised cosine keying modulation method is proposed, and the characteristics of the signals are analyzed. An efficient modulation communication system is provided for next generation wireless communication.

The hybrid macromodels of high-speed digital systems fitted by the Modal Vector Fitting (MVF) approach have the advantages of accurate fitness and easy to be simulated with equivalent circuits, but the initial-built models may violate the system passivity in partial-band. To solve this problem, a method of passivity enforcement of hybrid macromodels based on modal components is presented. Firstly, it proposes a new formulation for the objective function, wherein the eigenpairs of the macromodel are included. The relative accuracy of the model is controlled with weighting by the eigenpairs in the process of the parameter perturbation. Then the relevant passivity constrain function is also discussed and united with the above objective function. According to the analysis of the equations set, it is essentially that the passivity compensation is equivalent to the solution of a least-square-based optimization problem, which is able to be solved using the analysis method. This proposed method can effectively enforce the passivity of hybrid macromodel and be satisfactory with arbitrary terminal conditions. Compared with the reported methods such as Residue Perturbation (RP) and Modal Perbation (MP), experimental results demonstrate that the presented method has a certain advantage on the model accuracy or computational efficiency.

To solve sequential acquisition problems with long pseudo-noise code, such as large computation cost and poor capture performance in the environment of few received sequences, a fast sequential acquisition method based on frequency sampling is proposed. Firstly, the general sequential acquisition model based on frequency subset is designed according to the sequential relationship between frequency and time domain. And then the frequency subsets are composed by sampling the complete frequency set of entire local sequences. The optimal subsets are used to do cyclic correlation operation with received sequences to accomplish capture at the end. In the perspective of time domain, frequency sampling is equal to sequences folding, and some phase weighting factors are imported simultaneously, which can improve the probability distribution of nonpeak values. Therefore, the acquisition computation cost is reduced in higher degree and the background noise is compressed at the same time. The simulation results show that frequency sampling method can reduce computation cost by 2⁷ times, and its average capture success rate is probably 50% larger than XFAST, when the period of local sequences is 2¹⁷.

For sparse recovery of underdetermined linear systems where noise perturbations exist in both the measurements and sensing matrix, based on FOCal Underdetermined System Solver (FOCUSS) algorithm, an improved algorithm, named Synchronous Descending (SD) –FOCUSS, is proposed. The objective function of system model is deduced through a Maximum A Posteriori (MAP) estimation; then approximate optimum sparse-solution can be found while optimizing objective function using iterative relaxation algorithm. Another breakthrough of SD-FOCUSS is that the new algorithm can be applied to Multiple Measurement Vector (MMV) models. The convergence of SD-FOCUSS algorithm can be established with mathematical proof. The simulation results illustrate advantages of the new algorithm on accuracy and stability compared with other algorithms.

Firstly, the Non-negative Matrix Factorization with Sparseness Constraints on Parts of the Basis Matrix　(NMFSCPBM) method is proposed in this paper. Secondly, the encrypted watermark is embedded into the big coefficients of the basis matrix by NMFSCPBM. At the same time, the watermark embedding strength is adaptively adjusted by the video motion characteristics extracted by NMFSCPBM. Finally, when detecting the watermark, as long as the residual video contains the numbers of least remaining sub-blocks, the complete basis matrix can be completely recovered, and then the complete watermark can be extracted. The experimental results show that the performance of resisting the strong cropping attacks of this paper is improved greatly compared with other similar methods.

For the issue of multi-object robust tracking, a type of watershed segmentation and Scale-Invariant Feature Transform (SIFT) feature points based full-automatic tracking algorithm is presented. To avoid flat area while do watershed segmentation on the image, a regular gradient image is added to the source image. After the Gaussian blurred process is done on the added image in float field, field minimal points are selected as object feature points as well as seed points to do watershed segmentation. Moving object is detected through short time points trajectories derived from watershed region mapping relationship between current and backward image. SIFT feature pool is built and updated based on object occlusion occurred or not and watershed segmentation. With the help of watershed region mapping and feature matching with the SIFT feature pool, object is robustly tracked. Actual tests show that the algorithm can track multi-object well and with a better performance of mutual occlusion robustness.

The potential application of OFDM waveforms in SAR is considered in this paper, especially for the SAR imaging based on OFDM waveforms. The OFDM waveforms destroy the flat spectrum from the conventional Linear Frequency Modulation (LFM) waveform, which bring difficulties to high resolution imaging. This paper first analyzes the characteristics of OFDM waveforms in detail, derives its two-dimensional spectral, then proposes a novel algorithm for the high resolution imaging based on OFDM waveforms. This algorithm is based on the compensation of coding term. It manages Range Cell Migration Correction (RCMC), suppresses the sidelobe from coding, and improves the imaging performance greatly. The simulation results prove the validity of this algorithm. 

A novel method to obtain the formulations of the return signals in the two dimensional (2–D) frequency domain for translational variant Bistatic Synthetic Aperture Radar (BiSAR) is proposed. In this study, the instantaneous Doppler frequency is used and two individual azimuth frequencies are introduced, so that the 2–D spectrum can be derived by these two azimuth frequencies. Using the imaging geometry of the general bistatic SAR, the expressions of the two azimuth frequencies can be obtained, as well as the 2–D spectrum. Then, the range Doppler algorithm based on the two 2–D spectrums is developed to process the spotlight general BiSAR data. Finally, the proposed algorithms are applied to the simulated data and the results confirm not only the accuracy of the derived 2–D spectrums for general BiSAR but also the effectiveness of the proposed algorithm.

The imaging method of the mid-course missile is one  of the key techniques of the missile defense system. Cause of the special precession character of the ballistic missile warhead, the small rotation angle assumption is not  satisfied, which leads to blurring of the image obtained by Range Doppler (RD) algorithm. To solve this problem, a novel wideband imaging method for precession targets with  rotational symmetry is proposed in this paper. Using
Back Projection Transform (BPT), the echoes of the target in the range-time domain are transformed to reconstruct the scattering centers position in the para meter domain by means of coherent accumulation. The algorithm is verified by the experiment results of the anechoic chamber data, which demonstrates that the algorithm is more efficient than the Generalized Radon Transform (GRT) method.

Modified and developed from the original Geometrical Theory of Diffraction (GTD) model, attributed scattering center model takes the advantages of both frequency and angle dependent properties for the model parameters, providing much richer information of the target. However, it also brings such problem as parameter dimension increasing. Research on the independent scattering centers a novel algorithm of parameter estimation is proposed, where the linking parameter dimension could be reduced effectively and the parameters could be estimated in precision. In the scheme, the object function is constructed to seek parameters and they can be estimated by solving the optimization. In this method, precision initial values of the parameters are not required. Thus the computational complexity is reduced. Numeric simulation results confirm the validation of the proposed algorithm.

The Generalized Gamma Distribution  is one of the most important distributions for modeling of Synthetic Aperture Radar (SAR) images, of which the main limiting factor for application is the parameters estimation. In this paper, a new parameters estimation foris proposed. At first, the weakness of Method-of-Log-Cumulants (MoLC) for is analyzed. Moreover, the estimation expression for based on Scale-Independent-Shape-Estimation (SISE) equation is derived, and the concrete solving method is presented too. Finally, the Monte Carlo simulation results demonstrate that the proposed method is more stable and effective than MoLC.

Radon-Fourier Transform (RFT) is a kind of method which integrates radar target energy by integrating the target track in range-slow time domain according to the target motion parameters. Considering the two issues of high computation cost of RFT and the energy lose caused by quantization error without interpolation, a fast RFT method based on Chirp-Z Transform (CZT) is proposed, which implements in frequency domain, and combines its implement process and CZT algorithm together to solve the above issues successfully. In addition, the method can also eliminate the loss of match filtering by compensating the Doppler frequency of target without increasing the computation cost. The experiment results show that the integration performance of this method can almost achieve the optimal one under ideal condition.

For conventional Space-Time Adaptive Processing (STAP), sufficient Independent and Identically Distributed (IID) samples are hard to obtain by the clutter within heterogeneity. To mitigate this problem, a Compressive Sensing (CS) based Ground Moving Target Indication (GMTI) method is proposed. In the method, firstly the space-time data of the interested range bin are transformed to two-dimensional frequency fields to accumulate the signal energy and the corresponding redundant dictionary is constructed; Then several primary clutter spectrum peaks are extracted by Bayesian compressive sensing technique to estimate the clutter ridge; Finally, the weighted l₁ minimization optimization model is constructed to realize the ground moving target indication. Simulation results and theory analysis demonstrate that the proposed method attains good resolution meanwhile it takes on excellent performance in low SNR.

For Frequency Modulated Continuous Wave Synthetic Aperture Radar (FMCW-SAR) at sliding spotlight mode, only time domain algorithm is available. That is because an azimuth window function suitable for each point target echo in the scene can not be obtained, which yields numerous operation. A novel fast echo simulating algorithm in range-Doppler domain is presented to solve the problem. Under some approximations, the algorithm successfully forms the azimuth window function by adding an azimuth linear frequency modulated signal and the phase information of the echo is obtained in range-doppler domain, which increases greatly the calculation speed while the simulating accuracy remains relatively high. The more the point targets are, the better performance in speed compared to the time-domain algorithm is achieved. S他和rmesfully                                                                                                                     SSsssimulations verify the effectiveness of the presented algorithm.

The Compressed Sensing (CS) technique is an effective approach for sparse imaging and extraction of scattering structure of targets, which can be applied to target discrimination and recognition. Based on the experimental data from the Forward Looking Virtual Aperture Radar (FLVAR) system, the scattering structures of landmines can be acquired by CS sparse imaging algorithm. Then the sparse scattering structures are parameterized to form the features exploited by classifiers later. In this paper, a novel approach to target discrimination is proposed, which transforms the scattering of landmines to geometrical features, which have strong relationship with its physical characteristics. This new approach not only broadens the methodology for landmine discrimination, but also explores a new way of applying sparse scattering structures to target discrimination.

In this paper, based on the principle theory of angle tracking and the deterministic propagation modeling, a method of predicting the errors in the measured elevation angle is presented, which adopt the effective earth's radius to represent the atmospheric refraction effects. The errors of elevation angle are simulated in different refraction environments. The results of the study prove that the change of atmospheric refraction can affect the errors of elevation angle notablely associated with the multipath effects, indicating the atmospheric refraction is a key factor to be considered in low-angle tracking.

To investigate the problem of classical two-scale sea surface model, which can not effectively analyze the non-Bragg scattering at high sea state and low grazing angle, a more practical scattering model for one-dimension (1-D) time-varying sea surface is adopted, in which a chirp parameter is employed to describe the changes of Doppler frequency. Then, the angular scattering characteristic of the improved model is investigated based on the mean scattered power. Next, the Fractional Power Spectrum (FPS) of the improved model is analyzed, which shows that the FPS of the scattered field is found to be distributed as sum of impulse signals and the peaks are relative to the frequency parameters of the improved model. Finally, X-band real sea data is used for verification and the influences of incident wavelength and transform angle on the FPS are discussed. The results prove that the improved model is suitable for the analysis and extraction of the frequency changes and Doppler shift for the time-varying sea surface.

Hyperspectral image classification by the Relevance Vector Machine (RVM) is a relatively new hyperspectral image classification method, however this method exists some shortcomings such as when the sample data is large and high dimension, the training time will be quit long and the classification accuracy is not so good. To solve these problems, this paper proposes a RVM classification method based on the new Kernel Principal Component Analysis (KPCA). This method uses the kernel function into the PCA and replaced the traditional kernel function with the wavelet kernel function. By using the feature of multiresolution analysis, the new method improves the nonlinear mapping capability of KPCA and the experiment completes the RVM hyperspectral image classification based on the wavelet kernel function PCA, And then the different effects of the hyperspectral image classification between the traditional PCA and the wavelet kernel PCA are analyzed and compared. The results show that by using the WKPCA method, the Euclidean distance of AVIRIS hyperspectral image data between the different categories and the same categories is lower 20% and the variance has been sharp rised. The classification accuracy, by using the RVM, improves the 3%~5%.

GSM is the most popular world-wide standard for mobile communication system. GSM uses A5/1 algorithms to protect the users’ information. It is very important to research the attack of A5/1. In this paper, by analyzing the property of the state space’ reduction, a model of Time-memory-data trade-off attack to A5/1 algorithms is described, that model based on variable distinguished point and thin rainbow tables. The formulas are derived and the parameters are determined. By using FPGA and parameters determining, the attack success rate exceeds 99% in 1 second on average that improve the practicability of the attack. Based on this model, the method to determine the parameters has reference value to other platform and constraints.

To improve the security of quantum secure direct communication protocols and reduce their cost, this paper presents a new means after analyzing the process of the eavesdropping detection, and offers a quantum secure direct communication scheme and a quantum secret sharing protocol to argue for it. Players employ the travel particles carrying secret messages to detect eavesdropping, and leak noting about secret messages. Investigation shows that players can make the quantum cryptography protocol not only with qubit efficiency of 100% but also secure in case of none checking particles made for eavesdropping detection only.

The linear complexity and the k-error linear complexity of a sequence are used as important measures of keystream strength. By studying linear complexity of binary sequences with period 2ⁿ, it is proposed that the computation of k-error linear complexity can be converted to finding error sequences with minimal Hamming weight. In order to study sequence randomness, the k-error sequences distribution that corresponds with the k-error linear complexity of sequence is discussed. Based on Games-Chan algorithm, for k=3, the counting functions on the k-error sequences of  2ⁿ-periodic binary sequences with linear complexity 2ⁿ- are derived and the effectiveness is proved with computer programming.

The research on steganalysis has mainly focused on hidden information detection, and there are few methods about active steganalysis. From the view of hidden information detection, the pixels are classified into different kinds. Based on the analysis of the effects on the frequencies of different kinds of pixels by message embedding and Least Significant Bit (LSB) plane flipping, an active steganalysis approach is proposed to recover the stego key of LSB replacement steganography in spatial domain of images. This method has remarkable physical significance and can be implemented conveniently. Experimental results show that this method can recover the stego key successfully in certain range of embedding ratio.

Most traditional security risk assessment methods have the shortcomings of subjectivity and one-sidedness. Considering the risk analysis demand of vulnerabilities and attacks of network nodes, this paper proposes the concept of vulnerability reliability and attack reliability, and designs a reliability vector orthogonal projection decomposition method of network security risk assessment. First, this method associates vulnerability information which attacks relying on with vulnerability information of the node itself, and quantifies the security risk analysis from the node to the whole network, with the own weight of each node in the network. Second, in order to exclude the own uncertainties of vulnerability scanning tools and the unity of the data source, this method fuses several test results of scan tool, and constitutes the data source when calculating the vulnerability reliability. Finally, based on the idea of Euclidean space vector projection, the method puts forward an algorithm of reliability vector projection decomposition. The result of the experiment of the network security risk evaluation procedure is given to verify the proposed evaluate method.

The existing physical layer encryption algorithm, which is based on artificial noise, could affect legitimate receivers negatively when the number of users is no less than sending antennas in the multiuser MIMO system. In order to improve the multiuser MIMO system security under this scenario, this paper proposes a new multiuser MIMO system physical layer encryption algorithm based on joint channel state matrix. Firstly, multiple users are processed together, thus a multiuser joint channel state matrix is established. After Singular Value Decomposition (SVD) of the joint channel state matrix, the minimum singular value is obtained and can be utilized for pre-coding, so as to eliminate the interference of artificial noise to legitimate receivers. Further, the paper also presented an approach to optimize the power allocation. The simulation results show that the proposed algorithm can increase the secrecy capacity by  0.1 bit/(s?Hz) averagely, and improve the multiuser MIMO system security.

This paper investigates continued-time two-dimensional birth-and-death process with priority in cognitive radio system. To simplify analysis, one-dimensional queuing model without priority is derived to equalize the complicated process. And a step-queuing scheme based on channel reward in multichannel scene is proposed, whose block probability and force termination probability performance determined by the Queuing-Structure Coefficient (QSC). Analytical results and simulation confirm the equivalent queuing model without priority valid. Also some diverse access scheme are compared in performance, step-queuing scheme presents a lower block probability and an adaptive force termination probability.

An algorithm is proposed for energy-efficient user scheduling and resource allocation scheme in multi- user OFDMA mobile communication downlink system. A mathematical formulation of optimization issue is provided with the objective of maximizing system energy-efficient under QoS constraint. The proposed method can use system diversity to implement user selection and rate allocation with perfect Channel State Information (CSI) at the base station, it also enhanced the usage of energy effectively. Simulation results demonstrate that the performance of proposed algorithm is close to the optimum, it also has low complexity.

In order to improve the system throughput when the wireless resource are shared by real and no-real services, a new type proportional fairness scheme for real time services occupying no-real time services is proposed, which first carries out union proportional fair scheduling, and then followes the procedure of real-time service occupying no-real-time service. The packet scheduling not only can guarantee the resource allocation of real time services, but also acquire better multi-user diversity gain. Theoretical analysis and simulation show that the suggested scheme can effectively improve the system throughput and spectrum efficiency in comparison with the existing approaches.

Network coding is a promising technique for reliable multicast in wireless networks. In this paper, the characteristics of network coding opportunities are analyzed in multicast networks. The effect of the network coded packets, which can not be decoded, on the performance of network coding is studied. Based on these analyses, a Network Coding Loss Recovery (NCLR) scheme is proposed. NCLR allows receivers to store the packets, which they can not decode, and report the reception status to the sender. According to the feedback, NCLR prioritizes the packets that can contribute more to the overall transmission performance. Moreover in NCLR, not only original packets, but also encoded packets are scheduled and coded together to fully exploit coding opportunities. Simulation results show that NCLR improves significantly the multicast performance in terms of the number of retransmissions and recovery latency compared with existing schemes.

To reduce the delay of Quadrature tRiangle (QR)-decomposition in the multi-carrier systems, a Distributed Systolic Array Processing (DSAP) algorithm for MIMO-OFDM system is proposed. The algorithm includes two processing mechanism: the first is the interleaved pre-processing, which groups and interleaves the row vectors of different channel matrix, and then the matrix elements are sent into the systolic array in columns according to the delay increasing pattern; the other is the signal processing of the distributed systolic array, which uses the CORDIC computation in the boundary and internal cells of systolic array and the synchronization operation, can distribute the QR-decomposition of different sub-carriers into the different stages of the pipelining operation of CORDIC in systolic array. Compared to Serial Systolic Array Processing (SSAP) algorithm, the clock periods can be put to great use in the DSAP algorithm, the delay is reduced by ninety-two percent with the same complexity, and it reduces the delay of subsequent data processing effectively.

A duality relation between the downlink max-min Signal-to-Interference-Noise Ratio (SINR) optimization issue and the virtual uplink min-max SINR optimization issue is revealed by using the Lagrange duality theory for coordinated multiple point joint transmission multiple input single output interference downlink system subject to per-base station power constraints. Based on the duality relation and subgradient theory, a two layer alternating iterative optimization beamforming method is proposed to solve the virtual uplink min-max SINR optimization issue. And the convergence of the proposed algorithm is proven. The complexity of the proposed method is analyzed by using real floating-point operation theory. Numerical simulation verifies the validity of the proposed method.

To solve the problem of limited convergence in Least Square (LS) estimation method, an improved scheme is proposed. At first, the scheme uses the idea of matrix diagonalization to improve the Minimum Mean Square Error (MMSE) estimation method, making it to reduce greatly the complexity while maintain the estimate convergence. Then, a new scheme is proposed according to the adaptive theory, the estimate scheme can select the corresponding algorithm to estimate adaptively according to the state of current channel, which shows its high flexibility. The simulation result shows that using this scheme can further improve the Bit Error Rate (BER) and Mean Square Error (MSE) with low computational complexity, which is promising for practical applications.

A construction scheme of reversible Quasi Cyclic-Low Density Parity Check (QC-LDPC) codes is proposed by setting rationally zero matrices. This solves the problems of singular check matrix and high encoding complexity in conventional QC-LDPC codes. With circulant matrix corresponding to polynomial in finite fields, the scheme exploits the extended version of Euclid's algorithm to conquer the problem of QC-LDPC construction rate lager than design rate. Moreover, in the encoding process, first dividing the check matrix into blocks, and then the extended version of Euclid's algorithm is used to invert a circulant matrix, it results in dynamic complexity decrease. EXtrinsic Information Transfer (EXIT) chart implies the convergence of decoder. More simulations illustrate that the performance of the proposed construction structure is better than random LDPC when the code length is short, which is suitable for UnderWater Acoustic Communication (UWAC). Finally, applying QC-LDPC to Zero Padding-Orthogonal Frequency Division Multiplexing (ZP-OFDM) for evaluating the performance in UWAC, extended simulation shows that the reversible QC-LDPC codes can dynamically improve the system robustness.

Botnet is a novel attack strategy evolved from traditional malware forms; It provides the attackers stealthy, flexible and efficient one to many Command and Control (C&C) mechanisms, which can be used to order an army of zombies to achieve the goals including information theft, launching Distributed Denial of Service (DDoS), and sending spam. This paper proposed a botnet detecting method which independent of botnet C&C protocol and structure, and not analysis payload of packets. At first this method use pre-filter rules to filter flow which have irrelevant with botnet; Second, the flow attributes are analyzed; Third, two-steps clustering algorithm which based on X-means clustering is used to analyze and cluster flow attributes of C&C channel, and the botnet detection is implemented. The experiment shows that this method can differentiate traffic of botnet and normal network with high accuracy, low false positive, achieve the goal that detects botnet under real network environment.

Topology construction based on virtual backbone communication is an effective way to shut down redundant nodes and save energy of whole network. In this paper, an issue of finding optimal virtual backbone through fully connected network is abstracted and converted into Minimum Connected Dominating Set (MCDS) issue while a new mathematical modeling approach which is called (NMIP)-MCDS based on Mixed Integer Programming is proposed. Furthermore, with the help of analyzing MCDS solution, the product of token distribution number and energy consumption from nodes are built as the goal of optimization function. Simultaneously, the optimal MCDS is constructed via manner of token distribution and network energy load balancing. Finally, simulation results show the effectiveness of NMIP-MCDS. It could be further applied to practical general WSNs.

Physical Unclonable Functions (PUF) is a new method for the safety of Integrated Circuit (IC) products. Nowadays, the Ring Oscillator (RO) based puf is under the shadow of being attacked because of RO's constant frequency. A new construct(Multiple Frequency Slots based PUF,MFS-PUF) is proposed to solve this problem. All the ROs are configurable, each RO's frequency changes from one to another after generating one response bit. In each frequency, ROs differ from each other because of the uncontrollable difference in manufacture and there exists a frequency slot. In the whole system, many frequency slots exist and the transfer between them are unpredictable, this makes us get more Challenge-Response Pairs (CRPs for short), what's more, the system is more unpredictable. Compared with the traditional RO based PUF, it is more difficult for the attacker to model this system. This architecture not only ensures the uniqueness, but also increases the safety of itself.

In this paper, the outage performance and diversity gain of amplify-and-forward opportunistic relaying systems under the constraint of limited feedback are studied. With limited feedback, the quantization error of Channel State Information (CSI) will impact the distinguishing of the best relay and worsen the system performance. Considering this issue, the outage performance is studied and its approximate analytical expression is derived for opportunistic relaying systems with limited feedback. Further, the following two propositions are analyzed and proved: with limited feedback, no matter how many potential relays in cooperation, the diversity order can and only can be 2; with the increasing accuracy of feedback, the outage probability approaches to that of the ideal opportunistic relaying systems. Simulation results verify the analytical expression of the outage probability and the proposed propositions, which provide a theoretical support for the design of relaying protocols under the constraint of limited feedback.

A novel multi-modulus method of Bussgang-type blind equalization algorithm in higher-order Quadrature Amplitude Modulation (QAM) systems is proposed. To reduce the steady-state error effectively, an instantaneous switch-mode hybrid algorithm whose associative cost function is formulated by the novel cost function and the Constellation Matched Error (CME) is proposed, and a criterion of circular mode switching decision is designed. Finally, 256-QAM signal is used to simulate, all of these results verify the proposed blind equalization algorithm can achieve good performance in high-order QAM systems.

Based on the pass-band characters of defected coaxial Bragg waveguides, a novel topology of narrow-band, band-pass millimeter-wave filter at 60 GHz is designed and analyzed by making use of the multi-wave interaction formulation as well as software Ansoft HFSS. It is found that, if a defected Bragg filter with a large transverse dimensional size operates at high frequency, the spurious coupling between the operating mode and the competing modes will spoil the pass-band structure; but fortunately, this negative effect can be suppressed by applying Hamming-window-function distribution to the ripples amplitude of the filter. Results show the filter has a narrow fractional bandwidth less than 0.1% with a low insertion loss below 1.5 dB and large dimensional size, which favors to select the specified narrow-band signal in high power applications.

This paper presents a novel 3-D finite-element analysis method for arbitrary lossy Slow-Wave Structure (SWS) of the traveling-wave tube. In this analysis method, a new Frequency-Specified Eigenmode Analysis (FSEA) for SWSs is proposed and utilized. Unlike the traditional phase-advance-specified eigenmode analysis for SWSs which has to solve a nonlinear Generalized Eigenvalue Problem (GEP), the new FSEA approach only needs solving a linear GEP and is capable of obtaining the attenuation constant more accurately and directly without any post-processing when simulating the lossy SWSs.