Non-Orthogonal Multiple Access (NOMA)-Intelligent Reflection Surface (IRS) systems can improve user access capability through joint "transmit-reflect" beamforming between multiple antenna transmitters and IRS.For the user fairness problem of multi-user NOMA-IRS systems,the minimum received signal-to-Interference plus Noise Ratio (SINR) of users should be maximized in a practical scenario with restricted RF links,so as to guarantee the communication quality of each user without any difference.To this end,a maximum-minimum SINR fractional planning problem is constructed by clustering users according to the number of RF links,and the transmit beam vector of this problem is highly coupled with the reflected array element matrix at SINR.Therefore,a semi-definite relaxation and arithmetic-geometric mean-based algorithm is proposed to maximize the minimum channel capacity in each cluster by alternately optimizing the transmit beam vector and the reflected array element matrix in each iteration.Furthermore,a dichotomous search algorithm is used to solve the intra-cluster power allocation problem to enhance the user minimum SINR.Simulation results show that,compared with the zero-forcing scheme,this scheme can improve the minimum SINR among users with lower computational complexity,thus improving the communication quality of each user.Different from the maximum ratio transmission scheme,the minimum SINR of users in this scheme does not saturate with the increase of transmit power at base stations,thus enabling the steady growth of communication quality of each user.

In order to improve the efficiency of network transmission and obtain a better low-latency performance,caching technology appeared.Unlike traditional caching technology,coded caching enables a single broadcast transmission from the server to simultaneously satisfy different demands of users by creating multicast opportunities with a global caching gain obtained.A coded caching network with parallel transmission is considered in which the server can broadcast messages to all users and users can also send messages to each other.An uncoded prefetching coded caching scheme is proposed which is composed of three phases:the pre-caching phase,the allocation phase and the delivery phase,where the optimal delivery time is obtained by pre-allocating different workloads to the server and users.It is shown that the proposed scheme with parallel transmission has a better performance compared with either server-multicast transmission alone or transmission within a D2D network alone.Also,after considering the channel capability gap between the two different channels,the proposed scheme obtains a better performance than when channel transmission capability is ignored.Finally,the proposed cache and delivery scheme with parallel transmission in the case of uncoded prefetching is proved to be optimal when the users’ cache resources are sufficient and the server broadcast channel and the D2D network transmission channel have the same channel capacity.

Existing low-light dehazing algorithms are affected by the low and uneven illumination of the hazy images with their dehazed images often suffering from loss of details and color distortion.To address the above problems,a low-light image dehazing network with aggregated context-aware attention (ACANet) is proposed.First,an intra-layer context-aware attention module is introduced to identify and highlight significant features at the same scale from the channel dimension and the spatial dimension,respectively,so that the network can break through the constraints of the local field of view,and extract image texture information more efficiently.Second,an inter-layer context-aware attention module is introduced to efficiently fuse multi-scale features and the advanced features are mapped to the signal subspace through projection operations in order to further enhance the reconstruction of image details.Finally,the CIEDE2000 color shift loss function is adopted to constrain the image hue by CIELAB color space and jointly optimize the network together with L2 loss so as to enable the network to learn image colors accurately and solve the severe color shift problem.Both quantitative and qualitative experimental results on several datasets demonstrate that the proposed ACANet outperforms existing dehazing methods.Specifically,the ACANet improves the PSNR of dehazed images by 8.8% compared to the baseline network,and enhances the image visibility with richer details and more natural color.

In order to verify the influence of different water quality conditions on underwater optical communication,the Monte Carlo simulation algorithm is used to simulate the discrete impulse response of the underwater optical channel.Combined with the Orthogonal Frequency Division (OFDM) and strong anti-fading ability,the underwater optical communication system is built based on the OFDM.With known channel state information at the receiver and the transmitter,the 16 Quadrature Amplitude Modulation is used,and the Monte Carlo construction algorithm is applied to construct the polar codes.The CRC-Aided Successive Cancellation List polarization decoding algorithm is used to design the polar codes in OFDM-based underwater optical communication.The influence of parameters such as code length on the performance of polar codes under different water quality conditions is verified by experiments.It is also proved that compared with the low-density parity check(LDPC) codes of the same code length,the polar codes get a performance gain of 0.2dB~0.6dB at the high signal noise ratio.As the water quality environment worsens,its progressive performance becomes more obvious,and there will be no error leveling problem.In addition,the polar codes have a simpler coding structure,the decoding complexity is not much different from that of LDPC codes,and multiple iterations are not required in decoding.Since polar codes have a lower coding and decoding complexity than other coding schemes,polar codes have strong competitiveness and application potential in underwater optical communication scenarios.

In order to verify the mechanism of GEO SAR imaging with a long synthetic aperture time,the equivalence verification is executed by satellite-to-ground bistatic configuration,which is composed of the Beidou 3 IGSO navigation satellite and the ground-stationary receiver.According to the signal characteristics of navigation satellites and the time-frequency synchronization problem of the satellite-to-ground bistatic SAR,based on the echo data a time-frequency synchronization error estimation method of satellite-ground bistatic SAR echo data is proposed to estimate the delay error of navigation satellite ranging code and correct the error.First,the one-dimensional direct wave signal and the reflected wave signal are divided into two dimensions according to the pulse repetition frequency of the navigation satellite,which preserves the integrity of the whole collected signal.The correct peak position sequence under non-ideal sampling environment is obtained by the matching filter on the range of the direct wave signal,and then the peak position sequence is used to complete the time synchronization error compensation of the direct wave signal and the reflected wave signal,and so with the correction of the local ranging code,which solves the problem of the mismatch between the envelope distribution of the distance pulse pressure signal after time synchronization and the theorectical model in the satellite-ground bistatic SAR imaging of the Beidou-3 IGSO navigation satellite.Second,the corrected local ranging code is used to obtain the direct wave peak phase vector by matching filtering the direct wave signal.Finally,the peak phase vector is used to perform frequency error compensation,followed by the bistatic SAR imaging processing of the reflected wave signal.The processing results of the measured data verify the effectiveness of the proposed method.

In this paper,we investigate the covert communications in uplink nonorthogonal multiple access(NOMA) systems with the random power allocation and weighted fractional Fourier transform(WFRFT) scheme to confront a two-phase detector.In the NOMA system,a covert user(CU) and a reliable user(RU) transmit messages to Bob in the presence of a warden (Willie) who tries to detect the CU's transmission behavior.A two-phase detector,i.e.,energy detection and similarity detection phases,is designed to improve the detection performance.The similarity detection phase provides a priori probability for the energy detection phase and reduces the detection error probability.In addition,corresponding to the RU and CU,a random power allocation and WFRFT are proposed to cover the CU's transmissions.The expected minimum detection error probability(EMDEP) and connection outage probabilities(COPs) at the RU and CU are derived in closed-form expressions for the proposed scheme.To optimize the power allocation of the RU,the maximum expected covert rate(ECR) is analyzed under covertness and reliability constraints.Numerical results show that the proposed two-phase detector has a lower EMDEP,and that the random power allocation and WFRFT scheme improve the covertness performance.

Aiming at the problem of physical layer security transmission when there is no direct transmission link between transmitter and desired user,a physical layer transmission scheme based on the reconfigurable intelligent surface (RIS) is proposed.This dissertation first establishes an RIS array antenna system model.The secure communication of the wireless system is completed through the array antenna direct transmission link and the RIS reflection link.Furthermore,in view of the situation that eavesdropers passively receive information and cannot determine the specific location of eavesdropers,the dissertation optimizes the transmitting beamforming vector and the RIS coefficient matrix to maximize artificial noise interference power while ensuring reliable signal reception for the desired user.The non-convex quadratic problem is transformed into an equivalent convex problem by using auxiliary variables and semidefinite relaxation methods.The transmitting beamforming vector and reflection coefficient matrix of the intelligent reflector are optimized jointly,which can restrain the eavesdropper from receiving information and ensure the reliable and secure communication of expected users.Finally,simulation results show that the secure transmission scheme based on the RIS improves the reliability and security of information transmission.

In order to solve the problem of energy shortage,energy harvesting technology has been proposed and widely used in several typical communication systems.The author studies the problem of how to effectively select remote radio frequency units to realize downlink communication in a distributed base-station system with energy harvesting capability.First,an energy harvesting distributed base-station system model is established,which does not rely on external energy sources such as power grids,and only consists of a baseband processing subsystem,energy subsystem,and remote radio frequency subsystem.Second,based on the model,a joint optimization problem covering beamforming,energy sharing,and power allocation is formed with the goal of maximizing the information transmission rate.Since the energy harvesting situation is uncontrollable,two different energy-sharing strategies are proposed,on the basis of which the problem is analyzed mathematically,the optimal power allocation strategy of the system remote radio frequency end is deduced,and then the remote radio frequency end selection algorithm for the system model is summarized.Finally,Monte Carlo simulation is carried out based on the model and algorithm in this paper,and compared with the literature algorithm.Simulation results show that the algorithm proposed in this paper has a good performance in terms of average channel capacity and energy efficiency,and helps to save system power consumption and resources.

To address the problems of difficult identification of zone attribution of fingerprint points and misjudgment of neighboring zone matching accompanying the traditional spatial division method in fingerprint localization,a spatial fuzzy division method applicable to zone center identification and transition dual domain discrimination is proposed.By using the difference degree between inter-class distance and intra-class distance of reference points to measure the ambiguity of sub-region boundaries,we ensure the optimization of the localization cost of experimental scenes while taking into account the advantage of spatial overlap division,so as to alleviate the negative effect of absolute discrimination between sub-regions and improve the generalization ability of localization matching.In the position estimation stage,the distance metric in the signal domain between the reference point and the point to be located is transformed into a dimensionless ranking under the same source difference by considering the received signal fluctuation difference between the neighborhoods of the reference point,and the similarity between the point to be located and the reference point is indirectly mapped with the corrected multi-source ranking equalization result;in addition,the introduction of the spatial density reachable search strong correlation reference point set,combined with the signal domain and spatial domain iterative constraint reference points,to achieve dynamic selection and clustering effect of the target nearest neighbor set,so as to effectively overcome the interference of environmental changes and signal fluctuations,and improve the environmental adaptability of the localization method.After the evaluation of the localization performance by the measured data under the road it is shown that the proposed algorithm outperforms similar zoning algorithms in localization accuracy by 4.7%~11.8%,and that the average localization error can be best reduced by 0.422m in comparison with the global matching method.

As the core computing platform of the convolution neural network,general-purpose graphics processor(GPGPU),its performance of processing two-dimensional and three-dimensional convolution determines the application of the neural network in real-time target recognition and detection.However,limited by inherent cache system design,the current GPGPU architecture cannot achieve efficient acceleration of 2D and 3D convolution computing.Aiming at this problem,a dynamic L1Dcache bypassing design for this problem is proposed.First,we define a new data structure that can dynamically reflect the cache access characteristics of an instruction,and then defines a memory-access-feature record table based on this information,in order to record the execution status of different memory accesses.Second,the warp scheduling strategy with the priority thread block is adopted,which can speed up the sampling of the memory access state.Next,the L1Dcache bypassing decision of memory accesses under different PCs is obtained due to the sampling results.Finally,the L1Dcache bypassing of some low-locality data accesses is completed.As a result,the L1Dcache space is reserved for data with high locality and the memory access stall cycle of 2D and 3D convolution is reduced.In addition,the memory access efficiency of 2D and 3D convolution has been improved.Compared with the original design,experimental results show that the L1Dcache bypassing design brings 2.16% performance improvements in 2D convolution and 19.79% in 3D convolution.Experiments prove the effectiveness and practicality of this design.

In target tracking,the realization of 3D extended target tracking usually requires a large number of measurement data from multiple angles,and the measurement obtained by a single sensor can not meet the requirements of 3D shape estimation in either quantity or integrity.Aiming at the problem of poor shape tracking performance of existing 3D extended target tracking algorithms under a low measurement rate,a three-dimensional extended target algorithm based on the B-spline Poisson Multi-Bernoulli Mixture (B-Spline-PMBM) filter is proposed.First,wavelet clustering is used to process the 3D spatial measurement data obtained by multiple sensors to obtain the measurement cluster,which can extract effective information and ensure the efficiency of the algorithm.Then,the control matrix is obtained by dividing the measurement cluster.The control matrix is realized based on the B-spline control point principle,so it can represent the parameters of the complex three-dimensional shape.The shape of the 3D extended target is obtained by fitting the B-spline surface with the control matrix.Finally,the B-spline is integrated into the PMBM filter,which is extended to 3D target tracking to predict and update the motion state and shape parameters of the extended target.Simulation and real point cloud data set verify that the proposed algorithm can achieve a good tracking effect on the motion state and the extended shape of the three-dimensional extended target,and can realize the estimation of the irregular three-dimensional shape.

Block selection is a key strategy to improve the performance of pixel value ordering based reversible data hiding.By preferentially embedding data into blocks with a smaller fluctuation at first,better imperceptibility is able to be achieved.However,the accuracy of existing methods to calculate the fluctuation value is limited by the block size,shape and the selected predictor,so it is particularly important to design a calculation method that can be used under different block sizes,shapes and types of predictors.For large blocks or those irregular in shape,if the spatial position correlation of pixels used in the expansion is weak,even though the corresponding block is small in fluctuation,invalid shifting is still able to be introduced in the actual embedding process.To deal with this problem,we first propose an improved definition of fluctuation to consider the consistency of context pixels in horizontal,vertical,and bi-diagonal directions simultaneously.Once the consistency of adjacent pixels in the local neighborhood is determined,we further calculate the overall consistency of neighboring pixels in each direction,which improves the accuracy of block selection.Second,we consider the Chebyshev distance between the maximum value and the second maximum value,and that between the minimum value and the second minimum value,and reduce the invalid shift by subtracting the maximum or minimum pixel expansion.Experimental results show that our proposed scheme is able to achieve a better imperceptibility.

A provable secure consensus mechanism is proposed.The consensus mechanism consists of a committee agreement protocol and a transaction confirmation algorithm.Nodes with a strong initiative and more stakes are elected from consensus nodes through the committee agreement protocol,which form a dynamic and iterative committee to parallelly handle Normal Net Transactions (NNTs) generated by users in the blockchain.The transaction confirmation algorithm is based on a Directed Acyclic Graph (DAG) which is constructed from Chain Transactions (CTs) sent by committee members.The number of times that an NNT is confirmed by committee members is counted based on the direct and indirect references of a DAG.When an NNT is confirmed by at least two-thirds of the committee members,the transaction is welded in the chain.Under some accepted assumptions,the consensus mechanism is proven to have properties of consistency and termination.Further,a preliminarily blockchain system is built based on the consensus mechanism with the performance of the system tested.The test result is consistent with the theoretical estimation.When 16 committee members are deployed and the transaction batch is 10⁶,the transaction throughput of the system is about 17000 transactions per second;Compared with the current HoneyBadger BFT consensus protocol,under the same configuration,the transaction throughput of the HoneyBadger BFT consensus protocol is about 2600 transactions per second,which is about 1/6 that of the system.

An App traffic identification scheme based on machine learning under ShadowSocksR (SSR) proxy is proposed with the purpose being to identify from which APP the ShadowSocksR proxy traffic generated by the smartphone originates.The scheme consists of three steps:traffic preprocessing,feature extraction and model construction.First,the packet set corresponding to the ShadowSocksR traffic generated by smartphones is divided into fine-grained stream data groups according to the arrival time interval,source and destination IP address and port,and then the stream data groups containing fewer packets are further filtered with the purpose being to filter out noise traffic generated by the background App or smart phone operating system that interferes with traffic identification.Then,from the filtered flow data grouping set,the statistical features and distribution features of packet length,time statistical features,packet frequency features,packet filtering ratio features,and the combined features of the front and rear streams are extracted to form a feature matrix,which is input into the machine learning algorithm.An app traffic identification model for the ShadowSocksR traffic that needs to be identified is obtained,and after the feature matrix is obtained through the same processing steps,the flow identification results can be obtained by inputting the App traffic identification model.Experimental results show that the traffic identification method can reach an accuracy rate of more than 97% for App traffic identification under ShadowSocksR proxy.

Various location-based big data services not only provide users with convenience but also lead to privacy leakage risks.The local differential privacy model avoids the dependence on trusted third-party data collection platforms and enables users to process and protect sensitive information according to their personal needs.Therefore,it is more suitable for location privacy protection scenarios.In view of the complex encoding mechanism and low availability of the current local differential privacy location protection methods,a local differential privacy location protection method based on the Hilbert encoding is proposed.The user side performs random response perturbation on the Hilbert code of the grid where he is located according to the local differential privacy model,so as to realize the privacy protection of his original location.The server side collects a large number of users’ disturbed location codes and performs the Hilbert decoding,in order to determine the grid location of users and realize the statistical analysis of distribution density of users.Experiments on actual location datasets prove that the proposed method can provide a better location data availability and operational efficiency on the basis of realizing local differential privacy protection of users’ location.

The ring signature is a special digital signature that can provide unconditional anonymous protection for signers,and a traceable ring signature is a variant of the ring signature,which aims to prevent signers from abusing the anonymity of the ring signature,that is,the anonymity provided by the traceable ring signature for signers is not unconditional,which will lead to the identity of signers being disclosed under certain behaviors of the signer.The traceable ring signature plays an important role in an electronic voting system and an electronic cash system.Aiming at the present situation that traceable ring signature schemes on lattice are based on the PKI system and have a complex burden of digital certificate management,this paper combines identity-based cryptography with the traceable ring signature on lattice and proposes the first identity-based traceable ring signature scheme on the lattice.Different from the previous traceable ring signature schemes,the proposed scheme is constructed according to the framework of Baum et al.’s linkable ring signature scheme on lattice and based on the techniques of preimage sampling and reject sampling,etc.,thus avoiding the use of cumbersome zero-knowledge proofs.Under the random oracle model,it is proved that the proposed scheme can meet the tag-linkability,anonymity and exculpability,and that the security can be reduced to SIS and ISIS problems.In addition,compared with the related schemes,the proposed scheme also has some advantages in time overhead and storage overhead.

Vehicular ad-hoc networks (VANETs) have received substantial attention on account of great convenience to modern transportation systems.In VANETs,the authentication of the vehicular access control and the privacy of the messages are two crucial criteria.At the same time,verification efficiency is still critical due to the limited bandwidth and high mobility characteristics of vehicles.Aggregate signcryption schemes can effectively solve the above issues.However,some of the state-of-art schemes based on the Schnorr signature are unable to resist two types of signature forgery attacks due to incorrect hash binding.In addition,two vehicles can maliciously exchange their signcryption information which can be verified successfully.A new certificateless aggregate signcryption scheme for VANETs is presented.Secret key preimage protection technology is used to prevent signature forgery attacks and hash collision resistance is utilized to resist coalition attack.The confidentiality and unforgeability of the scheme are proved under the random oracle model.Furthermore,in comparison with the state-of-art schemes,the proposed scheme which requires 6n+1 point multiplication operations during the whole authentication process enhances security without increasing computational overhead.Performance analysis shows that the scheme is suitable for VANETs.

Most of the existing distributed learning schemes solve the problem of malicious nodes by adding a disciplinary mechanism to the protocol.This method is based on two assumptions:1.Participants give up the assumption of malicious behavior to maximize their own interests,and the calculation results can be verified only after the event occurs,which is not suitable for some scenarios requiring immediate verification;2.It is based on the assumption of a trusted third party.However,in practice,the credibility of the third party cannot be fully guaranteed.Using the trust mechanism of the blockchain,this paper proposes an anti malicious node scheme based on the smart contract,which realizes the whole process of model training in machine learning through the smart contract to ensure that the machine learning model is not damaged by malicious nodes.This scheme takes the distributed machine learning model based on secure multi-party computing as the research model,and uses the smart contract of the blockchain to realize the data sharing,verification and training process.All participants can only execute according to the specified protocol,converting all participants into semi sincere participants;At the same time,in order to solve the privacy problems brought by the open and transparent characteristics of the blockchain,ring signature is used to hide the data address of participants and protect the identity of participants.Experiments show that this scheme has great advantages in resisting malicious nodes compared with the traditional distributed machine learning model based on secure multi-party computing.

Aiming at the problem that the echo signals of moving targets on the sea surface are difficult to detect in the background of a strong sea clutter,a sea clutter suppression algorithm (SVD-MTI algorithm for short) is proposed,which combines singular value decomposition (SVD) and dual delay line pair elimination algorithm.First,the pulse-compressed target echo signals are periodically rearranged into a matrix of fast and slow time dimensions,and the periodic singular value decomposition is performed;Then,it is proposed that the threshold value of the singular value corresponding to the signal and the input signal to clutter plus noise ratio obey Gaussian distribution,and that the threshold value is used to judge the energy singular value exponential ratio,so as to realize the adaptive distinction between sea clutter and target signal.Finally,dual delay line pair elimination is performed on the reconstructed target signal to suppress the clutter and minimize the loss of the target signal.The performance of the algorithm is experimentally verified by using the measured data.Compared with the existing sea clutter suppression algorithm,the proposed method can adapt to the change of the signal to clutter plus noise ratio of the target echo sequence,and can still suppress most of the clutter when the input SCNR is -30dB,and accurately detect the signal,which verifies that the new algorithm has a better suppression effect and a better detection performance.

The blind area of a driver's vision is a key problem in the frequent occurrence of traffic accidents.The existing cameras,corner mirrors and sensors are vulnerable to weather or insufficient light when sensing the environment,resulting in accidents.Therefore,based on the low power consumption,low cost and all-day ubiquitous perception capability of the Wi-Fi network,this paper proposes a blind area dynamic object monitoring system based on intelligent perception of wireless channel status.In scenes similar to corners or turns,wireless sensing technology and pattern recognition are used to monitor whether pedestrians are about to enter the blind area of vehicle vision perpendicular to the travel direction in advance,so as to realize "visual turning",warn passing vehicles and avoid accidents.Experimental results show that the proposed blind area monitoring system can achieve an average accuracy of 96% in the blind area within 5 meters,and has the advantages of robustness and universality.Compared with traditional monitoring methods,the system can work well in the dark environment without violating people's privacy so that it has application potential in the complex urban traffic network and is of great significance to improving traffic conditions and ensuring pedestrian travel safety.