Since the echo characteristics of moving targets are different from that of stationary targets, the traditional reconstruction filter bank algorithm, i.e., the reconstruction filter algorithm, is not applicable. In this paper, a novel reconstruction approach of the moving target for a multichannel in azimuth High-Resolution Wide-Swath (HRWS) Synthetic Aperture Radar (SAR) system is proposed. The approach firstly analyzes the echo characteristics of the moving target for the multi-channel in azimuth SAR system and gives the main reason for the failure of the traditional reconstruction method in contrast to the form of the stationary target echo. By introducing the radial velocity of the moving target, the spectrum reconstruction of the uniform moving target is effectively realized, and the azimuth ambiguities of the uniform moving target for the multi-channel in azimuth SAR system is well suppressed. Space-borne simulated results confirm the effectiveness of the proposed reconstruction approach.

The wide-swath interferometric altimeter working at near-nadir is a newly developed ocean surface topography measurement technology in recent years. Different from land elevation measurement, for the dynamic ocean surface waves, they move randomly all the time and this brings bias in Synthetic Aperture Radar (SAR) imaging and interferometric processes and leads to the final height measurement errors. For the requirement of centimeter-level precision, this error is the main source of measurement errors. The errors due to the characteristics of ocean surface and their impact on near-nadir InSAR’s precision are investigated. The motion error theoretical model is established combining the characteristics of the ocean surface and InSAR working mechanism, and the electromagnetic bias and layover bias are also taken into consideration. The error models in different SAR modes under various sea states are simulated. The error model is validated by the interferometric SAR full-link experimental simulation and the simulation results are consistent with the theoretical values. The results show that the errors are approximately linear changing with the Doppler centroid frequency and are proportional to the radial velocity of targets modulated by scattering. The errors are not only related to the characteristics of the waves, but also related to system parameters. This work can provide the feasible suggestions for future system design, error budget and data processing.

An observer is placed on the airborne in the multistatic passive radar localization system. The error in observer position may seriously affect the localization accuracy. An algebraic closed-form solution is proposed for 3D localization of multistatic passive radar in the presence of sensor position errors. Firstly, the nonlinear Bistatic Range Difference (BRD) measurement equations are linearized by proper additional parameters and a pseudo-linear estimation model is given accordingly. Then a modified Two-Step Weighted Least Squares (TS-WLS) algorithm is developed with considering the statistic characteristics of the observer position measurement noises. Finally the Cramer-Rao Lower Bound (CRLB) and the theoretical error of the algorithm are derived. Simulation results show that the proposed algorithm can achieve the CRLB in a moderate level of noises.

To effectively detect sea surface targets by the passive interferometric microwave technology considered as an important complement for the space-based early warning system of China, a detection algorithm is proposed based on the Passive Interferometric Microwave Images (PIMI). First, the mathematical model of PIMI is established for the sea background and sea surface target. Second, the detection algorithm is introduced in detail, and numerical simulations are performed to demonstrate the feasibility of the proposed algorithm. Finally, the air-borne experiments are also carried out. Both theoretical and experimental results demonstrate that the proposed algorithm is feasible, can effectively detect sea surface targets, and show good performance. That also exhibits that the moving metal vessels on the sea surface show a “hot” and “low” characteristic in PIMI, which can be used to improve the detection probability. The proposed detection algorithm can provide a reference for space-based PIMI to detect sea surface targets.

In order to solve the problem of geomagnetic interference and model nonlinearity in the tracking process of magnetic dipole under geomagnetic background, Monte Carlo Kalman Filter (MCKF) tracking method based on differential magnetic anomaly is proposed in this paper. The new tracking method takes the difference of magnetic field measured by sensor array as the observation signal, and uses Monte Carlo Kalman Filtering (MCKF) algorithm to solve the nonlinear problem of the model to realize the real-time tracking of magnetic dipole targets. The simulation results show that the proposed method is more accurate than the traditional Extended Kalman Filter (EKF) or Untracked Kalman Filter (UKF) in the stable tracking process. The results of real geomagnetic background tracking experiments show that the proposed algorithm has better tracking performance under low SNR.

Linear Tapered Slot Antennas (TSA) have significant advantages over traditional horn antennas, dielectric rod antenna when used as feed elements in Focal Plane Arrays (FPA) of Passive MilliMeter Wave(PMMW) imaging. In this paper, a novel Antipodal Linear Tapered Slot Antenna(ALTSA) is designed and optimized. The proposed antenna, the gain of which is improved by loading metamaterial structure, is fed by the Substrate Integrated Waveguide(SIW). Simulation and measure analysis show that the good impedance characteristics, low sidelobe levels, high and smooth gain are all achieved in a wide frequency band. Meanwhile, the designed antenna has a smaller aperture width and is easier to form a denser feed array in the focal plane to improve the spatial resolution of passive millimeter wave imaging.

Focusing on the problem of rather large estimation error in the traditional Direction Of Arrival (DOA) estimation algorithm induced by finite subsampling, a robust DOA estimation method based on Sparseand Low Rank Decomposition (SLRD)  is proposed in this paper. Following the low-rank matrix decomposition method, the received signal covariance matrix is firstly modeled as the sum of the low-rank noise-free covariance matrix and sparse noise covariance one. After that, the convex optimization problem associated with the signal and noise covariance matrix is constructed on the basis of the low rank recovery theory. Subsequently, a convex model of the estimation error of the sampling covariance matrix can be formulated, and this convex set is explicitly included into the convex optimization problem to improve the estimation performance of signal covariance matrix such that the estimation accuracy and robustness of DOA can be enhanced. Finally, with the obtained optimal noiseless covariance matrix, the DOA estimation can be implemented by employing the Minimum Variance Distortionless Response (MVDR) method. In addition, exploiting the statistical characteristics of the sampling covariance matrix estimation error subjecting to the asymptotic normal distribution, an error parameter factor selection criterion is deduced to reconstruct the noise-free covariance matrix preferably. Compared with the traditional Conventional BeamForming (CBF), Minimum Variance Distortionless Response(MVDR), MUltiple SIgnal Classification (MUSIC) and Sparse and Low-rank Decomposition based Augmented Lagrange Multiplier(SLD-ALM) algorithms, numerical simulations show that the proposed algorithm has higher DOA estimation accuracy and better robustness performance under finite sampling snapshot.

In existing null broadening algorithm, the taper matrix does not contain phase information, and when it is used to against strong directional and large deviation angle interference, the null depth becomes shallow and the interference suppression performance drops seriously. An adaptive null broadening algorithm for sidelobe canceller is proposed based on dense disturbance in virtual airspace. The algorithm reconstructs the self-covariance matrix of the auxiliary array data and the co-covariance matrix of the main and auxiliary array data at the same time to realize the adaptive control of the null region. The taper matrix is only related to the position and width of the array elements, and it can be generated offline without disturbing information and occupying no computing resources of the system. The simulation results show that this method can achieve adaptive broadening of the null region and improve the robustness of non-stationary interference suppression.

In through-the-wall scene, due to the serious attenuation of signal caused by wall, the energy of target reflection signal in the received signal decreases significantly and the received signal is submerged in the direct signal of the transceiver and the reflection signal of indoor furniture, making the target behind wall is hard to be detected. In view of the above problems, a novel Through-the-Wall Multiple human targets Detection (TWMD)  algorithm based on multidimensional signal features fusion is proposed. Firstly, the received Channel State Information(CSI) is preprocessed to eliminate the phase error and amplitude noise, and the multidimensional signal features are fully extracted from the correlation coefficient matrix by using time correlation and subcarrier correlation of CSI. Finally, the mapping between features and detection results is established by BP neural network. The experimental results show that the recognition accuracy of this algorithm in the environment with glass wall, brick wall and concrete wall is above 0.98, 0.90, 0.85, respectively. According to the detection results of 4000 samples, compared with the existing detection algorithms based on single signal feature, the proposed algorithm achieves an average accuracy improvement of 0.45 in the detection of different number of moving targets.

In order to completely remove the spurious-peak side effect in the undersampling based wide-band spectral analysis, this paper proposes a high-performance co-prime spectral analysis method based on paralleled all-phase point-pass filtering. On basis of a deep analysis on the mechanism of the classical co-prime spectral analysis, it is discovered that this spurious-peak side effect arises from those redudant overlapping boundary-bands related to distinct polyphase filtering branches between the up data path and the down data path. Therefore, through replacing the prototype filters in the classical co-prime spectral analysis by the all-phase point-pass filtering banks, a novel co-prime analysis dataflow is derived based on paralleled all-phase point-pass filtering. Both theoretic analysis and numerical simulation show that the proposed spectral analysis method achieves remarkable performance improvement: it can not only completely remove the spurious-peak side effect, but also obtain a much higher spectral resolution than the classical co-prime analysis, thereby possessing another merit of distinguishing dense spectral components. The proposed spectral analysis method possesses vast potentials in the software-defined radio, radar detection, passive positioning and marine wireless communication etc.

As one of the key 5G technologies, Non-Orthogonal Multiple Access (NOMA) can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner. In the uplink grant-free NOMA system, the Compressive Sensing (CS) and generalized Orthogonal Matching Pursuit (gOMP) algorithm are introduced in active user and data detection, to enhance the system performance. The gOMP algorithm is literally generalized version of the Orthogonal Matching Pursuit (OMP) algorithm, in the sense that multiple indices are identified per iteration. Meanwhile, the optimal number of indices selected per iteration in the gOMP algorithm is addressed to obtain the optimal performance. Simulations verify that the gOMP algorithm with optimal number of indices has better recovery performance, compared with the greedy pursuit algorithms and the Gradient Projection Sparse Reconstruction (GPSR) algorithm. In addition, given different system configurations in terms of the number of active users and subcarriers, the proposed gOMP with optimal number of indices also exhibits better performance than that of the other algorithms mentioned in this paper.

In the heterogeneous wireless networks, the parameter weight is difficult to determine for the vertical handover algorithm considering the parameters of the network and the user, at the same time, the vertical handover algorithm based on fuzzy logic has high complexity. Considering this problem, a hierarchical vertical handover algorithm based on fuzzy logic is proposed. Firstly, the Received Signal Strength (RSS), bandwidth and delay are input into the first-level fuzzy logic system. Combining with the rule adaptive matching, the QoS fuzzy value is inferred, and the network is initially filtered by the QoS fuzzy value to obtain the candidate network set; Then, the second-level fuzzy logic system is triggered by the trigger mechanism, and the QoS fuzzy value, network load rate and user access cost of the candidate network are input into the second-level fuzzy logic system. At the same time, the output decision value is obtained by combining the adaptive rule matching, so as to select the best access network. Finally, the experimental results show that the algorithm can guarantee the network performance while reducing the time cost of the system.

Considering the interference problem of overlapping areas of cells, the service continuity of mobile users and the utilization of spectrum resources in the 5G network, an Energy Efficient resource allocation scheme for the Inactive user(EEI) is proposed. Firstly, a user-centered virtual cell is generated based on the notification area of the inactive users, and the intra-cell next-generation NodeBs (gNBs) could cooperatively provide communication services for users to improve the communication quality, lower the inter-cell interference, and reduce the handover signaling overhead. Secondly, Lyapunov optimization method is used to maximize the energy efficiency of the network, while ensuring the stability of the data queue. To make the optimization problem tractable, the scheme is decomposed into two sub-problems: the optimal transmission resource allocation and optimal transmission power allocation. Notice that, the optimal solutions are local optimal, which are based on the relaxed optimization problem. The simulation results show that the proposed energy efficiency resource allocation scheme based on the inactive users could achieve a better performance than the comparison algorithms,in the price of higher computational complexity.

In the emerging vehicular networks, the task of the car terminal requesting offloading has more stringent requirements for network bandwidth and offload delay, and the proposed Mobile Edge Computing (MEC) in the new communication network research solves better this challenge. This paper focuses on matching the offloaded objects when the car terminal performs the task offloading. By introducing the Software-Defined in-Vehicle Network (SDN-V) to schedule uniformly global variables, which realizes resource control management, device information collection and task information analysis. Based on the differentiated nature of user tasks, a model of importance is defined. On this basis, task priority is divided by designing the task to offload the priority mechanism. For the multi-objective optimization model, the non-convex optimization model is solved by the multiplier method. The simulation results show that compared with other offloading strategies, the proposed offloading mechanism has obvious effects on delay and energy consumption optimization, which can guarantee the benefit of users to the greatest extent.

Link prediction considers to discover the unknown or missing links of complex networks by using the existing topology or other information. Resource Allocation index can achieve a good performance with low complexity. However, it ignores the path effectiveness of resource transmission process. The resource transmission process is an important internal driving force for the evolution of the network. By analyzing the effectiveness of the topology around the resource transmission path between nodes, a link prediction method based on topological effectiveness of resource transmission paths is proposed. Firstly, the influence of potential resource transmission paths between nodes on resource transmission is analyzed, and a quantitative method for resource transmission path effectiveness is proposed. Then, based on the effectiveness of the resource transmission path, after studying the two-way resource transmission amount between two nodes, the transmission path effectiveness index is proposed. The experimental results of 12 real networks show that compared with other link prediction methods, the proposed method can achieve higher prediction accuracy under the AUC and Precision metrics.

In Software-Defined Networking (SDN) with distributed control plane, network expansion problems arise due to network domain management. To address this issue, a Traffic Engineering-based control Resource Optimization (TERO) mechanism of SDN is proposed. It analyzes the control resource consumption of flow requests processing with different path characteristics, and points out that the control resource consumption can be reduced by changing the association relationship between controllers and switches. The controller association mechanism is divided into two phases: firstly, a minimum set cover algorithm is designed to solve the controller association problem efficiently in large-scale network. Then, a coalitional game strategy is introduced to optimize the controller association relationship to reduce both control resource consumption and control traffic overhead. The simulation results demonstrate that while keeping control traffic overhead low, mechanism which in this paper can reduce control resource consumption by about 28% in comparison with the controller proximity mechanism.

In order to solve the Virtual Network Function (VNF) migration optimization problem caused by the dynamicity of service requests on the 5G network slicing architecture, firstly, a stochastic optimization model based on Constrained Markov Decision Process (CMDP) is established to realize the dynamic deployment of multi-type Service Function Chaining (SFC). This model aims to minimize the average sum operating energy consumption of general servers, and is subject to the average delay constraint for each slicing as well as the average cache, bandwidth resource consumption constraints. Secondly, in order to overcome the issue of having difficulties in acquiring the accurate transition probabilities of the system states and the excessive state space in the optimization model, a VNF intelligent migration learning algorithm based on reinforcement learning framework is proposed. The algorithm approximates the behavior value function by Convolutional Neural Network (CNN), so as to formulate a suitable VNF migration strategy and CPU resource allocation scheme for each network slicing according to the current system state in each discrete time slot. The simulation results show that the proposed algorithm can effectively meet the QoS requirements of each slice while reducing the average energy consumption of the infrastructure.

Existing Point-Of-Interest (POI) recommendation algorithms lack adaptability for users with different check-in features. To solve this problem, an adaptive POI recommendation method UCA-TS based on User Check-in Activity (UCA) feature and Temporal-Spatial (TS) probabilistic models is proposed. The user check-in activity is extracted using a probabilistic statistical analysis method, and a calculation method of user's inactive and active membership is given. On this basis, one-dimensional power law function and two-dimensional Gaussian kernel density estimation combined with time factor are used to calculate the probability for inactive and active features respectively, and the popularity of POI is incorporated to recommend. This method can adapt to the users' check-in features and reflect the users' check-in temporal-spatial preferences more accurately. The experiments show that the proposed method can effectively improve the recommendation precision and recall.

Orthogonal Frequency Division Multiplexing(OFDM) is widely used in wireless communication systems, and its data transmission security has certain practical significance. A double encryption scheme is proposed which enhances the confidentiality of the OFDM communication system and can prevent brute force attacks significantly. Specifically, the first encryption is achieved by using neural network to generate the scrambling matrix, and the second encryption is implemented by chaotic sequence generating by composite discrete chaotic system based on Logistic mapping and Sine mapping. Moreover, it has larger secret key space compared with the single one-dimensional Logistic mapping chaotic system. The performance of double encryption is measured by verifying its chaotic characteristics and randomness (Lyapunov exponent and NIST) as well as its security performance in simulation. The results show that Lyapunov index is increased to 0.9850, and the maximum P-value in the NIST test is 0.9995 by using the proposed double encryption in this paper. It indicates such double encryption significantly improve the confidentiality of the OFDM communication system without affecting the transmission performance.

In view of the problems of low attack detection rate and high false positive rate caused by poor accuracy and robustness of the extracted traffic features in network traffic anomaly detection, a network traffic anomaly detection method based on deep features learning is proposed, which is combined with Stacked Denoising Autoencoders (SDA) and softmax. Firstly, a two-stage optimization algorithm is designed based on particle swarm optimization algorithm to optimize the structure of SDA, the number of hidden layers and nodes in each layer is optimized successively based on the traffic detection accuracy, and the optimal structure of SDA in the search space is determined, improving the accuracy of traffic features extracted by SDA. Secondly, the optimized SDA is trained by the mini-batch gradient descent algorithm, and the traffic features with strong robustness are extracted by minimizing the difference between the reconstruction vector of the corrupted data and the original input vector. Finally, softmax is trained by the extracted traffic features to construct an anomaly detection classifier for detecting traffic attacks with high performance. The experimental results show that the proposed method can adjust the structure of SDA based on the experimental data and its classification tasks, extract traffic features with a higher accuracy and robustness, and detect traffic attacks with high detection rate and low false positive rate.

The decision on computation offloading to Mobile Edge Computing (MEC) may expose user’s characteristics and cause the user to be locked. A privacy-aware computation offloading method based on Lyapunov optimization is proposed in this paper. Firstly, the privacy of task is defined, and privacy restrictions are introduced to minimize the cumulative privacy of each MEC node; Then, the fake task mechanism is proposed to balance the terminal energy consumption and privacy protection, reducing the cumulative privacy of MEC node by generating a fake task non-feature task when offloading is not performed due to privacy restrictions; Finally, the privacy-aware computing offloading decision is modeled and solved based on the Lyapunov optimization. Simulation results validate that the Lyapunov optimization-based Privacy-aware Offloading Algorithm (LPOA) can stabilize user’s privacy near zero, and the total offloading frequency is consistent with the decision that don’t consider privacy, effectively protecting user’s privacy while maintaining a low average energy consumption.

Live migration of Virtual Machines(VMs) across WANs is an important support for multi-datacenter cloud computing environments. The current live migration of VMs across WANs faces many technical challenges due to the limitations of small bandwidth and no shared storage, such as ensuring the security and consistency of image data migration. Therefore, a method for VM live migration across data centers based on HashGraph is proposed in this paper, decentralized ideas are used to achieve reliable and efficient image distribution between data centers. The Merkle DAG of HashGraph improves the deficiencies of deduplication when migrating images across data centers. Compared with existing methods, it can reduce total migration time.

The SIMON block cipher receives extensive attention since its proposed. With respect to integral attacks, some integral attacks on SIMON32 and SIMON48 are presented by Wang, Fu and Chu et al. In this paper, on the basis of the existing analysis results, the integral attacks on SIMON64 are further studied. Based on known 18-round integral distinguisher presented by Xiang et al., the integral attacks on 25-round SIMON64/128 are presented using meet-in-the-middle and partial-sum techniques. Then the amount of subkeys that need to be guessed during the attack is further reduced by equivalent-subkey technique, and the improved integral attacks on 26-round SIMON64/128 are also presented. Through further analysis, it is found that the higher version of SIMON algorithm has better resistance to integral analysis.

The application of Dynamic Voltage Scaling (DVS) technique in real-time system energy management will result in the decrease of system reliability. A dynamic energy management method based on Improved Bird Swarm Algorithm (IoBSA) is proposed in this paper. Firstly, the population is initialized uniformly with the principle of good point set, so as to improve the quality of initial solution and increase the diversity of population effectively. Secondly, in order to balance better the global and local search ability of BSA algorithm, the nonlinear dynamic adjustment factor is proposed. Then, a power consumption model with time and reliability constraints is established for the dynamic adjustment of processor frequency in embedded real-time systems. On the premise of ensuring real-time performance and stability, the proposed IoBSA algorithm is used to find the solution with minimum energy consumption. The experimental results show that compared with the traditional BSA algorithm and other common algorithms, the improved bird swarm algorithm has a strong advantage in solving the minimum energy consumption and a fast processing speed energy management.

It is of great significance to optimize emergency resource schedule for earthquake emergency rescue. The conflicting multiple schedule goals, such as time, fairness, and cost, should be taken into consideration together in an earthquake emergency resource schedule. A three-objective optimization model with constraints is constructed according to earthquake emergency resource schedule problems. An Adaptive MultiObjective Particle Swarm Optimization (PSO) based on Evolutionary State Evaluation (AMOPSO/ESE) is proposed to optimize this model for obtaining the Pareto optimal set. At the same time, based on the decision behavior pattern of "macro first and micro later", the two-level optimal solution sets consisting of an interest optimal solution set and their neighborhood optimal solution sets are proposed to represent the Pareto front roughly, which can simplify the decision-making process. The simulation results show that the multiobjective resource schedules can be effectively obtained by the AMOPSO/ESE algorithm, and the performance of the proposed algorithm is better than that of the chosen competed algorithms in terms of convergence and diversity.

A new ensemble TSK fuzzy classifier (i,e. IK-D-TSK) is proposed. First, all zero-order TSK fuzzy sub-classifiers are organized in a parallel way, then the output of each sub-classifier is augmented to the original (validation) input space, finally, the proposed Iterative Fuzzy C-Means  (IFCM) clustering algorithm generates dictionary data on augmented validation dataset, and then KNN is used to predict the result for test data. IK-D-TSK has the following advantages: the output of each zero-order TSK subclassifier is augmented to the original input space to open the manifold structure in parallel, according to the principle of stack generalization, the classification accuracy can be improved; Compared with traditional TSK fuzzy classifiers which trains sequentially, IK-D-TSK trains all the sub-classifiers in parallel, so the running speed can be effectively guaranteed; Because IK-D-TSK works based on dictionary data obtained by IFCM & KNN, it has strong robustness. The theoretical and experimental results show that IK-D-TSK has high classification performance, strong robustness and high interpretability.

In view of shortcomings of dark channel prior dehazing methods, such as transmission in sky areas is small and halo effects are prone to occur in the edges, this paper proposes a novel and efficient dehazing algorithm. Firstly, the fan-shaped model with dark channel map of haze-free image is established by geometric analysis. Then a new Gaussian mean function is set to estimate the boundary values of the model and its standard deviation is adaptive processing. Mean-value unequal relationship is also introduced to approximate the two-sided boundary, which is used to fit the most excellent dark channel map of haze-free, further obtains the best transmission. At the same time the local atmospheric light is improved to recover the final result. Experimental results show that the proposed method can be widely applied to all kinds of images compared with other classical algorithms. The degree of dehazing is thorough, final result is clear and natural. More importantly, it is favorable for real-time processing that has low time complexity.

To improve performance of denoising and edge preservation of the total variational image denoising model, a curvature difference driven minimal surface filter is proposed. Firstly, the presented filter model is constructed by adding an adaptive edge detection function of curvature difference to the mean curvature filter model. After that, from the perspective of differential geometry theory, the physical meaning of the energy functional model and the method of reducing the average curvature energy are elaborated. Finally, in the discrete image domain, the surface in the neighborhood of each pixel of the image is iteratively evolved to the minimal surface to minimize the average curvature energy of the energy functional, so that the total energy of the energy functional is also minimized. Experiments show that the filter can not only remove Gauss noise and salt and pepper noise, but also remove the mixed noise composed of these two kinds of noise. Its performance of noise reduction and edge preservation is better than the other five total variational algorithms of the same kind.

Existing few-shot methods have problems that feature extraction scale is single, the learned class representations are inaccurate, the similarity calculation still relies on standard metrics. In order to solve the above problems, multi-level attention feature network is proposed. Firstly, the multiple scale images are obtained by scale processing, the features of multiple scale images are extracted and the image-level attention features are obtained by the image-level attention mechanism to fusion them. Then, class-level attention features are learned by using the class-level attention mechanism. Finally, the classification is performed by using the network to compute the similarity scores between features. The proposed method is evaluated on the Omniglot dataset and the MiniImagenet dataset. The experimental results show that multi-level attention feature network can further improve the classification accuracy under small sample conditions compared to the single-scale image features and average prototypes.

Based on the in-depth research on the S-box constitution arithmetic of composite, an area optimized generic low-entropy higher-order masking scheme is proposed in this paper. The low entropy masking method is introduced on GF(2⁴), and the partial module reusing design is adopted, which reduces effectively the number of multiplications based on the S-box inversion operation of the composite. The algorithm can be applied to any order masking scheme of arbitrary S-box composed of inversion operation. This scheme is applied  to AES, gives detailed simulation results and optimizes the layout area, compared with the traditional masking scheme, reduces effectively the use of logical resources. In addition, the security is theoretically proved.

To improve bandwidth, efficiency and linearity of Envelope Tracking (ET) architecture, it is necessary to optimize the performance of envelope supply modulator and linearize nonlinear behavior of the ET system. The optimization procedure of the supply modulator is proposed based on the equivalent circuit model. The frequency compensation network is used to improve the bandwidth and linearity of the modulator circuit. An envelope enhanced memory polynomial digital pre-distortion model is introduced to address the nonlinear distortion of the ET system. The practical circuit mentioned above is fabricated and the overall experimental system is set up. Measurement results show that the ET PA at S-band obtains measured efficiency 61%, 54%, 44% and Error Vector Magnitude (EVM) 1% for 6.7 dB PAPR signals with 5 MHz/10 MHz/20 MHz modulation bandwidths, respectively. The ET system exhibits competitive bandwidth, efficiency and linearity, which verifies the proposed optimization and linearization methodology.