Aiming at the threat of broad frequency band suppressing jamming and the inherent contradiction between spectrum resources and communication anti-jamming ability, a method was proposed to add the statistics domain dimension on the basis of spread spectrum anti-jamming, which promoted the transformation of communication anti-jamming from hard anti-jamming to accommodate the jamming.The statistical characteristics difference between communication signal and jamming signal was utilized by blind source separation (BSS) communication anti-jamming technology to separate signals, which could improve the communication anti-jamming ability without increasing spectrum resources.To understand and improve the development process of BSS communication anti-jamming (BSSCA) from theory and algorithm to engineering practice, the basic principle of BSSCA was described, the key technologies such as multi-channel BSS and single channel BSS were emphasized.The main characteristics and existing problems of BSSCA were analyzed.Finally, the development priorities of this field were pointed out.

In view of the concern that the traditional anti-jamming method based on jamming recognition is challenging to defend against both the unknown and the intelligent jamming attacks, a dynamic, heterogeneous, and redundant channel-space anti-jamming technique was proposed by adopting multiple reconfigurable intelligent surface (RIS), which facilitated the exploitation of channels’ resources for combating with the unknown jamming attacks.To elaborate, a channel-space endogenous anti-jamming method was proposed by leveraging the optimization of both the RIS’ coefficients and the multiple RIS’ on-off status.Firstly, after decoupling the tightly coupled variables, the optimal closed-form solutions of transmit precoder, RIS’ coefficients, and receive decoder could be derived under the alternative optimization framework.Then, the greedy algorithm was adopted to optimize the multiple RIS’ on-off status for obtaining better anti-jamming communications, and the convergence and the computation complexity of the proposed method was analyzed.Theoretical analysis and simulation results show that the proposed method can effectively defend against the uncertain jamming attacks.

Despite the excellent error correction performance and burst error resistance capability of non-binary LDPC codes, the complexity of the decoding algorithms hinders their broader application.In the classic FHT-QSPA decoding, the fast Hadamard transform (FHT) and its inverse transform (IFHT) have become the main bottleneck for updating the check nodes.Therefore, a coded distributed FHT scheme based on systematic MDS codes was proposed.In the scheme, the channel probability was modeled by the master node as a matrix and it was segmented, and those sub-matrices were encoded into redundant ones.Then, all the sub-matrices were offloaded to worker nodes to perform parallel FHT and IFHT, and the results were sent back to the master node for the final decoding.By embedding redundant information, the proposed scheme resolved the straggling problem, improving the efficiency, and accelerating the entire decoding process.Comparisons with other coded matrix multiplication schemes, the proposed scheme provides lower encoding complexity, higher numerical accuracy in decoding recovery, and maintains an efficient butterfly operation, effectively reducing the computational complexity of worker nodes.The time comparison and decoding performance analysis reveal that the proposed scheme achieves up to approximately 3.8 times acceleration compared to the traditional single-node FHT scheme, significantly enhances the FHT-QSPA decoding efficiency without affecting the decoding performance.

To solve the problems of data leakage and identity impersonation caused by traditional single factor authentication, a multi-factor identity authentication scheme based on blockchain was proposed for the trusted sharing of cultural resources.Considering a variety of identity factors, a heterogeneous digital identity model was constructed using the tamper resistance and distribution of the blockchain distributed ledger.Through asymmetric encryption algorithm and exclusive OR (XOR) operation, the trusted reuse of heterogeneous digital identity and fast authentication of multi-agent access were realized.Security analysis and simulation results show that the proposed scheme outperforms existing multi-factor authentication schemes in terms of security and efficiency, and can effectively reduce the cost of identity authentication.

Aiming at the problem that it is difficult to achieve an optimal trade-off between wireless communication and model accuracy in FEEL training caused by the heterogeneity of wireless links and data distribution, a reconfigurable intelligent surface (RIS) enabled federated edge learning system was proposed, which exploited the channel reconfigurability of RIS to adaptively manipulate the signal propagation environment, and utilized the over-the-air computation (Aircomp) to achieve fast model aggregation.Specifically, the convergence behavior of the FEEL algorithm under the influence of wireless channels and data heterogeneity was rigorously derived, and accordingly, an unified wireless resources optimization problem was constructed with the goal of minimizing the learning loss by jointly designing the transceiver design and the RIS phase shift.Simulation results demonstrate that the proposed scheme achieves substantial performance improvement compared against several baselines, and prove that RIS can play an important role in improving the accuracy of Aircomp enabled FEEL systems under data heterogeneity.Finally, the probability of applying it into Internet of vehicles is discussed.

The planar coding distortion, which affects objective quality, and spherical distortion, which affects subjective quality, as well as existing independent rate-distortion optimization techniques that fail to consider the temporal propagation of spherical distortion and its impact on coding performance, result in coding performance degradation.To address these issues, a spatio-temporal dependent spherical distortion model was proposed for optimizing panoramic video coding.Firstly, a spatial mapping model was proposed to map spherical distortions to coding distortions, aiming to align subjective and objective quality assessments.Secondly, a temporal propagation model for spherical distortions was introduced to enhance the overall coding performance of all coding units in the propagation chain.Finally, the coding parameters were adjusted by computing the weights for spatial mapping and temporal propagation of spherical distortions.Experimental results demonstrate that, under low-delay encoding configurations, compared to the VTM14.0, a state-of-the-art video coding benchmark, the proposed algorithm achieves an average bitrate savings of 7.4% (up to 22.1%) and reduction in coding time of 9%.

To enhance the feature representation of region of interest (RoI), which incorporated a spatial context encoding module and soft regression loss, a grid self-attention mechanism 3D object detection method based on raw point cloud, named GT3D, was proposed.The spatial context encoding module was designed to effectively weight the local and spatial features of points through the attention mechanism, considering the contribution of different point cloud features for a more accurate feature representation.The soft regression loss was introduced to address label ambiguity arising during the data annotation phase.Experiments conducted on the public KITTI 3D object detection dataset demonstrate that the proposed method achieves significant improvements in detection accuracy compared to other publicly available point cloud-based 3D object detection methods.The detection results of the test set are submitted to the official KITTI server for public evaluation, achieving detection accuracies of 91.45%, 82.76%, and 79.74% for easy, moderate, and hard difficulty levels in car detection, respectively.

Generalized frequency division multiplexing (GFDM) system always faces challenges related to out-of-band (OOB) power leakage and a high peak-to-average power ratio (PAPR).Through an in-depth analysis of power spectrum characteristics of spectrally precoded GFDM systems, a universal spectral precoding (SP) framework based on the weighted fractional Fourier transform (WFRFT) was established.A two-dimensional parameter-adjustable WFRFT-SP-GFDM system was introduced.By flexibly configuring the parameters, the WFRFT spectral precoder could be made to approximate the weighted fractional Fourier transform matrix, achieve precise control over OOB power and PAPR.Simulation results indicate that, when compared to conventional GFDM and WFRFT-GFDM waveforms, the WFRFT-SP-GFDM waveform effectively suppresses OOB power leakage, while maintaining favorable PAPR and bit error rate (BER) performance.

Aiming at the problem of frequent entry space occupation and resource scheduling caused by passive response insertion of flow entries by software defined network (SDN) devices, a operational function-based multi-granularity data flow management algorithm (MgdFlow) was proposed.Through delayed sorting and multiple aggregation and splitting, the number of flow entry insertions and scheduling instructions were reduced.Experimental data shows that the load balancing performance is 18% higher than the adaptive two-stage flow, and the average entry occupation is 9% lower than the traditional OpenFlow scheme.There are also 24% and 12% improvements in the defined controller scheduling resource ratio ImproveQoS compared to other schemes.

In order to alleviate the spectrum scarcity and capacity limitation of the current wireless system, the terahertz frequency band was introduced and the reconfigurable intelligent surface (RIS) was used for auxiliary communication to construct a downlink vehicle network.Considering the constraints of limited total system power, QoS constraints of cellular mobile users, and randomness constraints of vehicle and user locations, a hybrid optimization model was established for optimal power allocation and optimal deployment of RIS with the aim of maximizing the total rate of vehicle users.Based on the balance method, linear transformation method, and element elimination method, the original NP-hard problem was transformed into a convex optimization problem with complex correlation constraints and multivariate coupling.The inner layer iteration was based on the Lagrange multiplier method to solve the optimal power allocation, and the outer layer iteratively solved the optimal deployment of RIS based on a improved genetic algorithm.The simulation results show that rationally deploying the number and distribution density of RIS nodes based on optimal power allocation can save costs while achieving a higher target total capacity.

To solve the problem that how users choose access points in cell-free massive multiple-input multiple-output (CF-mMIMO) network, a prioritized access strategy for poorer users based on channel coefficient ranking was proposed.First, users were evaluated and ranked for their channel quality and stability after channel sensing, and suitable access points were selected in sequence according to the order of the channel state information.Second, considering issues such as users' energy consumption and data security, a federal learning framework was used to enhance user's data privacy and security.Meanwhile, an alternating optimization variables algorithm based on energy consumption optimization was proposed to optimize the multi-dimensional variables, for the purpose of minimizing the total energy consumption of the system.Simulation results show that compared with the traditional user-centric in massive MIMO, the proposed access strategy can improve the average uplink reachable rate of users by 20%, and the uplink rate of users with poor channels can be double improved; in terms of energy consumption optimization, the total energy consumption can be reduced by much more than 50% after optimization.

A two-stage adaptive non-orthogonal/orthogonal multiple access transmission scheme was designed for content delivery scenarios in wireless caching networks.For the scheme, a high-reliability user scheduling strategy was proposed, and the analytical expression of the outage probability and its asymptotic expression in high signal-to-noise ratio regime were derived by theoretical analysis.The analysis results indicate that the proposed strategy can achieve full spatial diversity gain.Simulation results demonstrate that compared with the instantaneous channel state information-based scheduling strategy, the random scheduling strategy, and the conventional orthogonal multiple access transmission strategy, the proposed strategy can achieve better content delivery reliability than existing strategies by obtaining the spatial diversity gain.

Although satellite formation flying could effectively support deterministic transmission due to its stable formation, but its formation control required significant real-time computational overhead and did not support flexible deployment for multi-missions.Thus, based on the extensively deployed low-earth orbit (LEO) constellations, a virtual satellite formation scheme was proposed, which achieved deterministic transmission of time-sensitive mission by selecting appropriate satellites to form a formation.Firstly, the concept of virtual satellite formation was elaborated, and the challenges and performance requirements of virtual satellite formation based on LEO constellations were analyzed.Then, by characterizing the survival time and distance variation rate of inter satellite links (ISL), formation node selection mode was constructed, and a virtual satellite formation selection algorithm was designed to meet the requirements of time-sensitive missions.Finally, satellite network scenario was built to verify the proposed scheme by STK and Exata.Experimental results show that the proposed scheme can provide the same performance indicators as satellite formation flying for time-sensitive missions, while offering more flexibility in deployment.

To solve the problem of huge pressure on network operation caused by the surge of 5G mobile data traffic and the continuous emergence of new network applications, a traffic scheduling system of the 5G core network user plane was designed and implemented, including network state information perception subsystem and routing decision subsystem.In the network state information perception subsystem, traditional in-band network telemetry methods incurred high bandwidth overhead and were not specifically designed for wireless network systems.Furthermore, the application of in-band network telemetry in the 5G core network faced challenges such as low measurement accuracy and the inability to guarantee QoS.A 5G core network user plane state information perception scheme based on in-band network telemetry was proposed.The telemetry information was inserted into the extended header of GTP-U message realize the measurement of network state along the path.The routing decision subsystem implemented the traffic scheduling algorithm based on the improved ant colony algorithm.The update method of the pheromone function was promoted by the perceived status information to complete the routing decision based on the real-time network state.The deployment test results showed that the network state information perception subsystem can perceive network information normally, and the routing decisions were superior to traditional routing algorithms in terms of delay, throughput and packet loss rate.

Aimed at the defects of existing test methods, a vulnerability detection method for blockchain smart contracts based on metamorphic testing was proposed, which could generate test cases for specific functions in blockchain smart contracts to detect possible vulnerabilities.According to the possible security vulnerabilities, different metamorphosis relationships were designed and then metamorphic testing was performed.Through verifying whether the metamorphic relationship between the source test case and the subsequent test case was satisfied, whether the smart contract had related security vulnerabilities was judged.The experimental results show that the proposed method can effectively detect the security vulnerabilities in the smart contracts.

To solve the problem of limited number of sequences in the mutually orthogonal complementary code set and high peak-to-average power ratio (PAPR) in MC-CDMA system, a construction of asymptotically optimal a periodic binary QCSS with low column vector PAPR was proposed.The obtained QCSS with asymptotically optimal parameters had more sequences than the existing QCSS due to the designed mapping set.The QCSS based on the orthogonal Golay sequence set had column vector PAPR of at most 2.Experimental simulation results show that the constructed sequence sets can effectively reduce the time-domain MC-CDMA signal PAPR to 3 dB and have good bit error rate performance.

Aiming at the problem that most existing algorithms were based on the Gaussian inverse gamma prior model (GIG-SBL), which ignored the sparsity of the support set vector within the sparse solution, a sparse Bayesian learning framework based on the Bernoulli Gaussian inverse gamma prior model (BGIG-SBL) was proposed.By introducing a binary vector of Bernoulli prior, the BGIG-SBL-SMV algorithm based on single measurement vector (SMV) was designed, which utilized the sparsity of the support set vector to improve reconstruction performance.Then the proposed algorithm was extended to multiple measurement vector (MMV) models by sharing the same hyperparameters.The BGIG-SBL-MMV algorithm was developed based on the joint sparsity of MMV.The experimental results show that the proposed BGIG-SBL-SMV can achieve a performance gain of 2 dB in mMTC over the traditional GIG-SBL-SMV.Moreover, BGIG-SBL-MMV has a performance gain of 4 dB as compared with its SMV counterparts, which demonstrates the advantages of the proposed schemes.

Blockchain provides a trusted channel for information sharing in an untrusted network.However, blockchain ledgers are public and transparent.Hence, the sensitive data stored in plaintext on the blockchain will compromise user privacy.Considering privacy preservation, users are more inclined to provide noisy data.And in different application scenarios, the level of data noise that users need to provide also varies.To this end, a secure, efficient, and searchable noisy data sharing control protocol was proposed based on the blockchain and smart contract technology with the ciphertext policy attribute-based encryption (CP-ABE) algorithm.First, an outsourced CP-ABE algorithm was introduced to reduce the computing burden on the user side.Secondly, smart contract was implemented to perform data search on ciphertext, which could prevent malicious operations by malicious servers.Third, the proposed protocol reduced the data update complexity to O(1), which is friendly to the scenarios with real-time data update.Finally, security analysis and simulation experiments demonstrate the security and feasibility of the proposed protocol.

Aiming at the problems of discontinuous segmentation of thin strip objects that were easy to blend into the surrounding background and a large number of model parameters in the semantic segmentation algorithm of traffic scenes, a lightweight Transformer traffic scene semantic segmentation algorithm integrating multi-scale depth convolution was proposed.First, a multi-scale strip feature extraction module (MSEM) was constructed based on deep convolution to enhance the representation ability of thin strip target features at different scales.Secondly, a spatial detail auxiliary module (SDAM) was designed using the convolutional inductive bias feature in the shallow network to compensate for the loss of deep spatial detail information to optimize object edge segmentation.Finally, an asymmetric encoding-decoding network based on the Transformer-CNN framework (TC-AEDNet) was proposed.The encoder combined Transformer and CNN to alleviate the loss of detail information and reduce the amount of model parameters; while the decoder adopted a lightweight multi-level feature fusion design to further model the global context.The proposed algorithm achieves the mean intersection over union (mIoU) of 78.63% and 81.06% respectively on the Cityscapes and CamVid traffic scene public datasets.It can achieve a trade-off between segmentation accuracy and model size in traffic scene semantic segmentation and has a good application prospect.