To cope with the rapid growth of mobile video, video providers have leveraged cloud technologies to deploy their mobile video service system for more cost-effective and scalable performance. The emergence of Software-Defined Networking (SDN) provides a promising solution to manage the underlying network. In this paper, we introduce an SDN-enabled cloud mobile video distribution architecture and propose a joint video placement, request dispatching and traffic management mechanism to improve user experience and reduce the system operational cost. We use a utility function to capture the two aspects of user experience: the level of satisfaction and average latency, and formulate the joint optimization problem as a mixed integer programming problem. We develop an optimal algorithm based on dual decomposition and prove its optimality. We conduct simulations to evaluate the performance of our algorithm and the results show that our strategy can effectively cut down the total cost and guarantee user experience.

This paper explores the multi-frequency independent channel interference alignment (MFC-IA) system of 3 channels and 4 users, and single data stream transmit, i.e. (3×3,1)⁴ system. We derive the analytic solution for (3×3,1)⁴ MFC-IA system. Based on the analytic solution, an optimization problem is proposed aim at the optimal IA solution. Then based on such a math model, we propose a simulated annealing (SA) algorithm to search optimal IA solution. The simulation results show that the simulated annealing IA algorithm has a better sum rate performance than iterative maximize signal to interference plus noise ratio (Max-SINR) algorithm. This result can be extended to single data stream multi-antenna IA system with 3 antennas and 4 users.

Large intelligent surface (LIS) is considered as a new solution to enhance the performance of wireless networks[1]. LIS comprises low-cost passive elements which can be well controlled. In this paper, a LIS is invoked in the vehicular networks. We analyze the system performance under Weibull fading. We derive a novel exact analytical expression for outage probability in closed form. Based on the analytical result, we discuss three special scenarios including high SNR case, low SNR case, as well as weak interference case. The corresponding approximations for three cases are provided, respectively. In order to gain more insights, we obtain the diversity order of outage probability and it is proved that the outage probability at high SNR depends on the interference, threshold and fading parameters which leads to 0 diversity order. Furthermore, we investigate the ergodic achievable rate of LIS-assisted vehicular networks and present the closed-form tight bounds. Similar to the outage performance, three special cases are studied and the asymptotic expressions are provided in simple forms. A rate ceiling is shown for high SNRs due to the existence of interference which results 0 high SNR slope. Finally, we give the energy efficiency of LIS-assisted vehicular network. Numerical results are presented to verify the accuracy of our analysis. It is evident that the performance of LIS-assisted vehicular networks with optimal phase shift scheme exceeds that of traditional vehicular networks and random phase shift scheme significantly.

In power communication networks, it is a challenge to decrease the risk of different services efficiently to improve operation reliability. One of the important factor in reflecting communication risk is service route distribution. However, existing routing algorithms do not take into account the degree of importance of services, thereby leading to load unbalancing and increasing the risks of services and networks. A routing optimization mechanism based on load balancing for power communication networks is proposed to address the abovementioned problems. First, the mechanism constructs an evaluation model to evaluate the service and network risk degree using combination of devices, service load, and service characteristics. Second, service weights are determined with modified relative entropy TOPSIS method, and a balanced service routing determination algorithm is proposed. Results of simulations on practical network topology show that the mechanism can optimize the network risk degree and load balancing degree efficiently.

Motivated by the objective of pursuing revenue, improvement in coverage and reduction in energy cost for wireless communication networks have been of great significance for mobile operators. Therefore, heterogeneous cellular networks (HCNs) and Coordinated Multipoint (CoMP) transmission are considered as promising solutions to enhance the performances of wireless communication systems. This paper analyzed the K-tier HCNs with a dynamic downlink CoMP scheme, in which the flexible clusters of cooperative stations are determined by a connecting threshold θ. Using stochastic geometry, the coverage probability (CP) and energy efficiency (EE) of a K-tier HCN operating under this scheme are derived, based on which the trade-off between CP and EE is discovered and discussed. Simulation results show the validity of our derivations. The proposed schememay significantly reduce energy consumption sacrificing a small amount of CP, and outperforms the fixed scheme as well. The CP-EE trade-off are also revealed, whichsuggests suitable trade-off points between CP and EE that will deliver the maximum economic profitability. Tendencies discovered in this paper may provide the operators with opportunities for further optimization in pursuit of economic profitability.

Mobile multihop communication network is an important branch of modern mobile communication system, and is an important technical support for ubiquitous communication. The random movement of the nodes makes the networking be more flexible, but the frequently changing topology will decrease the link duration between nodes significantly, which will increase the packets loss probability and affect the network communication performance. Aiming at the problem of declining link duration caused by nomadic characteristics in mobile multihop communication network, four link duration models for possible moving states are established based on different features in real networking process in this paper, which will provide reliable criterion for the optimal routing selection. Model analysis and simulation results show that the reliable route established by the proposed model will effectively extend the link duration, and can enhance the global stability of the mobile multihop information transmission, so as to provide new option to transmission reliability improvement for the mobile communication network.

Security and privacy issues are magnified by velocity, volume, and variety of big data. User’s privacy is an even more sensitive topic attracting most people’s attention. While XcodeGhost, a malware of iOS emerging in late 2015, leads to the privacy-leakage of a large number of users, only a few studies have examined XcodeGhost based on its source code. In this paper we describe observations by monitoring the network activities for more than 2.59 million iPhone users in a provincial area across 232 days. Our analysis reveals a number of interesting points. For example, we propose a decay model for the prevalence rate of XcodeGhost and we find that the ratio of the infected devices is more than 60%; that a lot of popular applications, such as Wechat, railway 12306, didi taxi, Youku video are also infected; and that the duration as well as the traffic volume of most XcodeGhost-related HTTP-requests is similar with usual HTTP-request which makes it difficult to be found. Besides, we propose a heuristic model based on fingerprint and its web-knowledge to identify the infected applications. The identifying result shows the efficiency of this model.

Network architectures assisted by Generative Artificial Intelligence (GAI) are envisioned as foundational elements of sixth-generation (6G) communication system. To deliver ubiquitous intelligent services and meet diverse service requirements, 6G network architecture should offer personalized services to various mobile devices. Federated learning (FL) with personalized local training, as a privacy-preserving machine learning (ML) approach, can be applied to address these challenges. In this paper, we propose a meta-learning-based personalized FL (PFL) method that improves both communication and computation efficiency by utilizing over-the-air computations. Its "pretraining-and-fine-tuning" principle makes it particularly suitable for enabling edge nodes to access personalized GAI services while preserving local privacy. Experiment results demonstrate the outperformance and efficacy of the proposed algorithm, and notably indicate enhanced communication efficiency without compromising accuracy.

The Internet of things (IoT) as an important application of future communication networks puts a high premium on delay issues. Thus when IoT applications meet heterogeneous networks (HetNets) where macro cells are overlaid with small cells, some traditional problems need rethinking. In this paper, we investigate the delay-addressed association problem in two-tier HetNets considering different backhaul technologies. Specifically, millimeter wave and fiber links are used to provide high-capacity backhaul for small cells. We first formulate the user association problem to minimize the total delay which depends on the probability of successful transmission, the number of user terminals (UTs), and the number of base stations (BSs). And then two algorithms for active mode and mixed mode are proposed to minimize the network delay. Simulation results show that algorithms based on mutual selection between UTs and BSs have better performance than those based on distance. And algorithms for mixed modes have less delay than those for active mode when the number of BSs is large enough, compared to the number of UTs.

Some research work has showed that public mood and stock market price have some relations in some degree. Although it is difficult to clear the relation, the research about the relation between stock market price and public mood is interested by some scientists. This paper tries to find the relationship between Chinese stock market and Chinese local Microblog. First, C-POMS (Chinese Profile of Mood States) was proposed to analyze sentiment of Microblog feeds. Then Granger causality test confirmed the relation between C-POMS analysis and price series. SVM and Probabilistic Neural Network were used to make prediction, and experiments show that SVM is better to predict stock market movements than Probabilistic Neural Network. Experiments also indicate that adding certain dimension of C-POMS as the input data will improve the prediction accuracy to 66.667%. Two dimensions to input data leads to the highest accuracy of 71.429%, which is about 20% higher than using only history stock data as the input data. This paper also compared the proposed method with the ROSTEA scores, and concluded that only the proposed method brings more accurate predicts.

The paper proposes a low power non-volatile baseband processor with wake-up identification (WUI) receiver for LR-WPAN transceiver. It consists of WUI receiver, main receiver, transmitter, non-volatile memory (NVM) and power management module. The main receiver adopts a unified simplified synchronization method and channel codec with proactive Reed-Solomon Bypass technique, which increases the robustness and energy efficiency of receiver. The WUI receiver specifies the communication node and wakes up the transceiver to reduce average power consumption of the transceiver. The embedded NVM can backup/restore the states information of processor that avoids the loss of the state information caused by power failure and reduces the unnecessary power of repetitive computation when the processor is waked up from power down mode. The baseband processor is designed and verified on a FPGA board. The simulated power consumption of processor is 5.1μW for transmitting and 28.2μW for receiving. The WUI receiver technique reduces the average power consumption of transceiver remarkably. If the transceiver operates 30 seconds in every 15 minutes, the average power consumption of the transceiver can be reduced by two orders of magnitude. The NVM avoids the loss of the state information caused by power failure and energy waste caused by repetitive computation.

Protocol Reverse Engineering (PRE) is of great practical importance in Internet security-related fields such as intrusion detection, vulnerability mining, and protocol fuzzing. For unknown binary protocols having fixed-length fields, and the accurate identification of field boundaries has a great impact on the subsequent analysis and final performance. Hence, this paper proposes a new protocol segmentation method based on Information-theoretic statistical analysis for binary protocols by formulating the field segmentation of unsupervised binary protocols as a probabilistic inference problem and modeling its uncertainty. Specifically, we design four related constructions between entropy changes and protocol field segmentation, introduce random variables, and construct joint probability distributions with traffic sample observations. Probabilistic inference is then performed to identify the possible protocol segmentation points. Extensive trials on nine common public and industrial control protocols show that the proposed method yields higher-quality protocol segmentation results.

Network virtualization is an enabling technology of running multiple virtual networks on a shared substrate network. It aims to deal with the ossification of current network architecture. As a crucial component of network virtualization, virtual network embedding (VNE) can efficiently and effectively allocates the substrate resource to proposed virtual network requests. According to the optimization strategy, VNE approaches can be classified into three categories: exact, heuristic and meta-heuristic solution. The VNE exact solution is the foundation of its corresponding heuristic and meta-heuristic solutions. This paper presents a survey of existing typical VNE exact solutions, and open problems for the future research of VNE exact solutions are proposed.

With rapid advances of solar blind ultraviolet LED and ultraviolet detecting technology in recent years, ultraviolet communication gradually becomes a research hotspot due to its inherent advantages: low solar background noise, non-line-of-sight(NLOS) and good secrecy. The strong scattering characteristics in atmospheric render ultraviolet waveband the ideal choice for achieving NLOS optical communication. This paper reviews the research history and status of ultraviolet communication both in China and abroad, and especially introduces three main issues of ultraviolet communication: channel model, system analysis and design, light sources and detectors. For each aspect, current open issues and prospective research directions are analyzed.

Due to limited volume, weight and power consumption, micro-satellite has to reduce data transmission and storage capacity by image compression when performs earth observation missions. However, the quality of images may be unsatisfied. This paper considers the problem of recovering sparse signals by exploiting their unknown sparsity pattern. To model structured sparsity, the prior correlation of the support is encoded by imposing a transformed Gaussian process on the spike and slab probabilities. Then, an efficient approximate message-passing algorithm with structured spike and slab prior is derived for posterior inference, which, combined with a fast direct method, reduces the computational complexity significantly. Further, a unified scheme is developed to learn the hyperparameters using expectation maximization (EM) and Bethe free energy optimization. Simulation results on both synthetic and real data demonstrate the superiority of the proposed algorithm.

This paper presents an algorithm named the dependency-aware offloading framework (DeAOff), which is designed to optimize the deployment of Gen-AI decoder models in mobile edge computing (MEC) environments. These models, such as decoders, pose significant challenges due to their inter-layer dependencies and high computational demands, especially under edge resource constraints. To address these challenges, we propose a two-phase optimization algorithm that first handles dependency-aware task allocation and subsequently optimizes energy consumption. By modeling the inference process using directed acyclic graphs (DAGs) and applying constraint relaxation techniques, our approach effectively reduces execution latency and energy usage. Experimental results demonstrate that our method achieves a reduction of up to 20% in task completion time and approximately 30% savings in energy consumption compared to traditional methods. These outcomes underscore our solution's robustness in managing complex sequential dependencies and dynamic MEC conditions, enhancing quality of service. Thus, our work presents a practical and efficient resource optimization strategy for deploying models in resource-constrained MEC scenarios.

There are a lot of security issues in block cipher algorithm. Security analysis and enhanced design of a dynamic block cipher was proposed. Firstly, the safety of ciphertext was enhanced based on confusion substitution of S-box, thus disordering the internal structure of data blocks by four steps of matrix transformation. Then, the diffusivity of ciphertext was obtained by cyclic displacement of bytes using column ambiguity function. The dynamic key was finally generated by using LFSR, which improved the stochastic characters of secret key in each of round of iteration. The safety performance of proposed algorithm was analyzed by simulation test. The results showed the proposed algorithm has a little effect on the speed of encryption and decryption while enhancing the security. Meanwhile, the proposed algorithm has highly scalability, the dimension of S-box and the number of register can be dynamically extended according to the security requirement.

To meet the demands for the explosive growth of mobile data rates and scarcity of spectrum resources in the near future, the terahertz (THz) band has widely been regarded as a key enabler for the upcoming beyond fifth-generation (B5G) wireless communications. An accurate THz channel model is crucial for the design and deployment of the THz wireless communication systems. In this paper, a three-dimensional (3-D) dynamic indoor THz channel model is proposed by means of combining deterministic and stochastic modeling approaches. Clusters are randomly distributed in the indoor environment and each ray is characterized with consideration of molecular absorption and diffuse scattering. Moreover, we present the dynamic generation procedure of the channel impulse responses (CIRs). Statistical properties are investigated to indicate the non-stationarity and feasibility of the proposed model. Finally, by comparing with delay spread and K-factor results from the measurements, the utility of the proposed channel model is verified.

Most of previous video recording devices in mobile vehicles commonly store captured video contents locally. With the rapid development of 4G/WiFi networks, there emerges a new trend to equip video recording devices with wireless interfaces to enable video uploading to the cloud for video playback in a later time point. In this paper, we propose a QoE-aware mobile cloud video recording scheme in the roadside vehicular networks, which can adaptively select the proper wireless interface and video bitrate for video uploading to the cloud. To maximize the total utility, we need to design a control strategy to carefully balance the transmission cost and the achieved QoE for users. To this purpose, we investigate the tradeoff between cost incurred by uploading through cellular networks and the achieved QoE of users. We apply the optimization framework to solve the formulated problem and design an online scheduling algorithm. We also conduct extensive trace-driven simulations and our results show that our algorithm achieves a good balance between the transmission cost and user QoE.

A brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) was developed by four-class phase-coded stimuli. SSVEPs elicited by flickers at 60Hz, which is higher than the critical fusion frequency (CFF), were compared with those at 15Hz and 30Hz. SSVEP components in electroencephalogram (EEG) were detected using task related component analysis (TRCA) method. Offline analysis with 17 subjects indicated that the highest information transfer rate (ITR) was 29.80±4.65bpm with 0.5s data length for 60Hz and the classification accuracy was 70.07±4.15%. The online BCI system reached an averaged classification accuracy of 87.75±3.50% at 60Hz with 4s, resulting in an ITR of 16.73±1.63bpm. In particular, the maximum ITR for a subject was 80bpm with 0.5s at 60Hz. Although the BCI performance of 60Hz was lower than that of 15Hz and 30Hz, the results of the behavioral test indicated that, with no perception of flicker, the BCI system with 60Hz was more comfortable to use than 15Hz and 30Hz. Correlation analysis revealed that SSVEP with higher signal-to-noise ratio (SNR) corresponded to better classification performance and the improvement in comfortableness was accompanied by a decrease in performance. This study demonstrates the feasibility and potential of a user-friendly SSVEP-based BCI using imperceptible flickers.

Many websites use verification codes to prevent users from using the machine automatically to register, login, malicious vote or irrigate but it brought great burden to the enterprises involved in internet marketing as entering the verification code manually. Improving the verification code security system needs the identification method as the corresponding testing system. We propose an anisotropic heat kernel equation group which can generate a heat source scale space during the kernel evolution based on infinite heat source axiom, design a multi-step anisotropic verification code identification algorithm which includes core procedure ofbuilding anisotropic heat kernel, settingwave energy information parameters, combing outverification codecharacters and corresponding peripheral procedure of gray scaling, binarizing, denoising, normalizing, segmenting and identifying, give out the detail criterion and parameter set. Actual test show the anisotropic heat kernel identification algorithm can be used on many kinds of verification code including text characters, mathematical, chinese, voice, 3D, programming, video, advertising, it has a higher rate of 25% and 50% than neural network and context matching algorithm separately for Yahoo site, 49% and 60% for Captcha site, 20% and 52% for Baidu site, 60% and 65% for 3DTakers site, 40% and 51% for MDP site.

An Efficient and flexible implementation of block ciphers is critical to achieve information security processing. Existing implementation methods such as GPP, FPGA and cryptographic application-specific ASIC provide the broad range of support. However, these methods could not achieve a good tradeoff between high-speed processing and flexibility. In this paper, we present a reconfigurable VLIW processor architecture targeted at block cipher processing, analyze basic operations and storage characteristics, and propose the multi-cluster register-file structure for block ciphers. As for the same operation element of block ciphers, we adopt reconfigurable technology for multiple cryptographic processing units and interconnection scheme. The proposed processor not only flexibly accomplishes the combination of multiple basic cryptographic operations, but also realizes dynamic configuration for cryptographic processing units. It has been implemented with 0.18µmCMOS technology, the test results show that the frequency can reach 350MHz, and power consumption is 420mw. Ten kinds of block and hash ciphers were realized in the processor. The encryption throughput of AES, DES, IDEA, and SHA-1 algorithm is 1554Mbps, 448Mbps, 785Mbps, and 424Mbps respectively, the test result shows that our processor’s encryption performance is significantly higher than other designs.

Cloud-based satellite and terrestrial spectrum shared networks (CB-STSSN) combines the triple advantages of efficient and flexible network management of heterogeneous cloud access (H-CRAN), vast coverage of satellite networks, and good communication quality of terrestrial networks. Thanks to the complementary coverage characteristics, anytime and anywhere high-speed communications can be achieved to meet the various needs of users. The scarcity of spectrum resources is a common problem in both satellite and terrestrial networks. In order to improve resource utilization, the spectrum is shared not only within each component but also between satellite beams and terrestrial cells, which introduces inter-component interferences. To this end, this paper first proposes an analytical framework which considers the inter-component interferences induced by spectrum sharing (SS). An intelligent SS scheme based on radio map (RM) consisting of LSTM-based beam prediction (BP), transfer learning-based spectrum prediction (SP) and joint non-preemptive priority and preemptive priority (J-NPAP)-based proportional fair spectrum allocation is than proposed. The simulation result shows that the spectrum utilization rate of CB-STSSN is improved and user blocking rate and waiting probability are decreased by the proposed scheme.

Spectrum sharing for efficient reuse of licensed spectrum is an important concept for cognitive radio technologies. In a spectrum-sharing system (SSS), deploying the antennas in a distributed manner can offer a new spatial dimension for the efficient reuse of licensed frequency bands. To improve the whole performance of multiple secondary users (SUs), this paper addresses the problem of coordinated multi-SU spectrum sharing in a distributed antenna-based SSS. By adopting the Hungarian method, the primal decomposition method and pricing policy, we propose a coordinated multi-user transmission scheme, so as to maximize the sum-rate of SUs. Simulation results show that the proposed method can significantly enhance the system performance, and the computational complexity is low.

With the development of automated driving vehicles, more and more vehicles will be fitted with more than one automotive radars, and the radar mutual interference will become very significant. Vehicle to everything (V2X) communication is a potential way for coordinating automotive radars and reduce the mutual interference. In this paper, we analyze the positional relation of the two radars that interfere with each other, and evaluate the mutual interference for different types of automotive radars based on Poisson point process (PPP). We also propose a centralized framework and the corresponding algorithm, which relies on V2X communication systems to allocate the spectrum resources for automotive radars to minimize the interference. The minimum spectrum resources required for zero-interference are analyzed for different cases. Simulation results validate the analysis and show that the proposed framework can achieve near-zero-interference with the minimum spectrum resources.

With the successful demonstration of terahertz (THz) high-speed wireless data transmission, the THz frequencies are now becoming a worth candidate for post-5G wireless communications. On the other hand, to bring THz communications a step closer to real scenario application, solving high data rate real-time transmission is also an important issue. This paper describes a 220-GHz solid-state dual-carrier wireless link whose maximum transmission real-time data rates are 20.8 Gbps (10.4 Gbps per channel). By aggregating two carrier signals in the THz band, the contradiction between high real-time data rate communication and low sampling rate analog-to-digital (ADC) and digital-to-analog converter (DAC) is alleviated. The transmitting and receiving front-ends consist of 220-GHz diplexers, 220-GHz sub-harmonic mixers based on anti-parallel Schottky barrier diodes, G-band low-noise amplifiers (LNA), WR-4.3 band high-gain Cassegrain antennas, high data rates dual-DAC and -ADC baseband platform and other components. The low-density parity-check (LDPC) encoding is also realized to improve the bit error rate (BER) of the received signal. Modulated signals are centered at 214.4 GHz and 220.6 GHz with -11.9 dBm and -13.4 dBm output power for channel 1 and 2, respectively. This link is demonstrated to achieve 20.8-Gbps real-time data transmission using 16-QAM modulation over a distance of 1030 m. The measured signal to noise ratio (SNR) is 17.3 dB and 16.5 dB, the corresponding BER is 8.6e-7 and 3.8e-7, respectively. Furthermore, 4K video transmission is also carried out which is clear and free of stutter. The successful transmission of aggregated channels in this wireless link shows the great potential of THz communication for future wireless high-rate real-time data transmission applications.

The Space-Air-Ground Integrated Network (SAGIN) realizes the integration of space, air, and ground networks, obtaining the global communication coverage. Software-Defined Networking (SDN) architecture in SAGIN has become a promising solution to guarantee the Quality of Service (QoS). However, the current routing algorithms mainly focus on the QoS of the service, rarely considering the security requirement of flow. To realize the secure transmission of flows in SAGIN, we propose an intelligent flow forwarding scheme with endogenous security based on Mimic Defense (ESMD-Flow). In this scheme, SDN controller will evaluate the reliability of nodes and links, isolate malicious nodes based on the reliability evaluation value, and adapt multipath routing strategy to ensure that flows are always forwarded along the most reliable multiple paths. In addition, in order to meet the security requirement of flows, we introduce the programming data plane to design a multi-protocol forwarding strategy for realizing the multi-protocol dynamic forwarding of flows. ESMD-Flow can reduce the network attack surface and improve the secure transmission capability of flows by implementing multipath routing and multi-protocol hybrid forwarding mechanism. The extensive simulations demonstrate that ESMD-Flow can significantly improve the average path reliability for routing and increase the difficulty of network eavesdropping while improving the network throughput and reducing the average packet delay.

With the advent of the era of cloud computing, the high energy consumption of cloud computing data centers has become a prominent problem, and how to reduce the energy consumption of cloud computing data center and improve the efficiency of data center has become the research focus of researchers all the world. In a cloud environment, virtual machine consolidation (VMC) is an effective strategy that can improve the energy efficiency. However, at the same time, in the process of virtual machine consolidation, we need to deal with the tradeoff between energy consumption and excellent service performance to meet service level agreement (SLA). In this paper, we propose a new virtual machine consolidation framework for achieving better energy efficiency-Improved Underloaded Decision (IUD) algorithm and Minimum Average Utilization Difference (MAUD) algorithm. Finally, based on real workload data on PlanetLab, experiments have been done with the cloud simulation platform CloudSim. The experimental result shows that the proposed algorithm can reduce the energy consumption and SLA violation of data centers compared with existing algorithms, improving the energy efficiency of data centers.

In this paper, we propose a new method to derive a family of regular rate-compatible low-density parity-check (RC-LDPC) convolutional codes from RC-LDPC block codes. In the RC-LDPC convolutional family, each extended sub-matrix of each extended code is obtained by choosing specified elements from two fixed matrices H_E1^k and H_E2^k, which are derived by modifying the extended matrices H_E1 and H_E2 of a systematic RC-LDPC block code. The proposed method which is based on graph extension simplifies the design, and prevent the defects caused by the puncturing method. It can be used to generate both regular and irregular RC-LDPC convolutional codes. All resulted codes in the family are systematic which simplify the encoder structure and have maximum encoding memories which ensure the property. Simulation results show the family collectively offer a steady improvement in performance with code compatibility over binary-input additive white Gaussian noise channel (BI-AWGNC).

Terahertz (THz) communication has been envisioned as a key enabling technology for sixth-generation (6G). In this paper, we present an extensive THz channel measurement campaign for 6G wireless communications from 220 GHz to 330 GHz. Furthermore, the path loss is analyzed and modeled by using two single-frequency path loss models and a multiple-frequencies path loss model. It is found that at most frequency points, the measured path loss is larger than that in the free space. But at around 310 GHz, the propagation attenuation is relatively weaker compared to that in the free space. Also, the frequency dependence of path loss is observed and the frequency exponent of the multiple-frequencies path loss model is 2.1. Moreover, the cellular performance of THz communication systems is investigated by using the obtained path loss model. Simulation results indicate that the current inter-site distance (ISD) for the indoor scenario is too small for THz communications. Furthermore, the tremendous capacity gain can be obtained by using THz bands compared to using microwave bands and millimeter wave bands. Generally, this work can give an insight into the design and optimization of THz communication systems for 6G.

With the advent of the era of big data, cloud computing, Internet of things, and other information industries continue to develop. There is an increasing amount of unstructured data such as pictures, audio, and video on the Internet. And the distributed object storage system has become the mainstream cloud storage solution. With the increasing number of distributed applications, data security in the distributed object storage system has become the focus. For the distributed object storage system, traditional defenses are means that fix discovered system vulnerabilities and backdoors by patching, or means to modify the corresponding structure and upgrade. However, these two kinds of means are hysteretic and hardly deal with unknown security threats. Based on mimic defense theory, this paper constructs the principle framework of the distributed object storage system and introduces the dynamic redundancy and heterogeneous function in the distributed object storage system architecture, which increases the attack cost, and greatly improves the security and availability of data.

This paper analyzes the threat of TCG Software Stack (TSS)/TCM Service Module (TSM) deadlock in multi-user environment such as cloud and discusses its causes and mechanism. In addition, this paper puts forward a dynamic priority task scheduling strategy based on value evaluation to handle this threat. The strategy is based on the implementation features of trusted hardware and establishes a multi-level ready queue. In this strategy, an algorithm for real-time value computing is also designed, and it can adjust the production curves of the real time value by setting parameters in different environment, thus enhancing its adaptability, which is followed by scheduling and algorithm description. This paper also implements the algorithm and carries out its performance optimization. Due to the experiment result from Intel NUC, it is shown that TSS based on advanced DPTSV is able to solve the problem of deadlock with no negative influence on performance and security in multi-user environment.

Spatial division multiplexing enabled elastic optical networks (SDM-EONs) are the potential implementation form of future optical transport networks, because it can curve the physical limitation of achievable transmission capacity in single-mode fiber and single-core fiber. However, spectrum fragmentation issue becomes more serious in SDM-EONs compared with simple elastic optical networks (EONs) with single mode fiber or single core fiber. In this paper, multi-core virtual concatenation (MCVC) scheme is first proposed considering inter-core crosstalk to solve the spectrum fragmentation issue in SDM-EONs. Simulation results show that the proposed MCVC scheme can achieve better performance compared with the baseline scheme, i.e., single-core virtual concatenation (SCVC) scheme, in terms of blocking probability and spectrum utilization.

Federated semi-supervised learning (FSSL) faces two major challenges: the scarcity of labeled data across clients and the non-independent and identically distributed (Non-IID) nature of data among clients. To address these issues, we propose diffusion model-based data synthesis aided FSSL (DDSA-FSSL), a novel approach that leverages diffusion model (DM) to generate synthetic data, thereby bridging the gap between heterogeneous local data distributions and the global data distribution. In the proposed DDSA-FSSL, each client addresses the scarcity of labeled data by utilizing a federated learning-trained classifier to perform pseudo labeling for unlabeled data. The DM is then collaboratively trained using both labeled and precision-optimized pseudo-labeled data, enabling clients to generate synthetic samples for classes that are absent in their labeled datasets. As a result, the disparity between local and global distributions is reduced and clients can create enriched synthetic datasets that better align with the global data distribution. Extensive experiments on various datasets and Non-IID scenarios demonstrate the effectiveness of DDSA-FSSL, achieving significant performance improvements, such as increasing accuracy from 38.46% to 52.14% on CIFAR-10 datasets with 10% labeled data.

In order to provide ultra low-latency and high energy-efficient communication for intelligences, the sixth generation (6G) wireless communication networks need to break out of the dilemma of the depleting gain of the separated optimization paradigm. In this context, this paper provides a comprehensive tutorial that overview how joint source-channel coding (JSCC) can be employed for improving overall system performance. For the purpose, we first introduce the communication requirements and performance metrics for 6G. Then, we provide an overview of the source-channel separation theorem and why it may not hold in practical applications. In addition, we focus on two new JSCC schemes called the double low-density parity-check (LDPC) codes and the double polar codes, respectively, giving their detailed coding and decoding processes and corresponding performance simulations. In a nutshell, this paper constitutes a tutorial on the JSCC scheme tailored to the needs of future 6G communications.

In recent years, autonomous driving technology has made good progress, but the non-cooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving challenges. V2I (Vehicle-to-Infrastructure) communication is a potential solution to enable cooperative intelligence of vehicles and roads. In this paper, the RGB-PVRCNN, an environment perception framework, is proposed to improve the environmental awareness of autonomous vehicles at intersections by leveraging V2I communication technology. This framework integrates vision feature based on PVRCNN. The normal distributions transform(NDT) point cloud registration algorithm is deployed both on onboard and roadside to obtain the position of the autonomous vehicles and to build the local map objects detected by roadside multi-sensor system are sent back to autonomous vehicles to enhance the perception ability of autonomous vehicles for benefiting path planning and traffic efficiency at the intersection. The field-testing results show that our method can effectively extend the environmental perception ability and range of autonomous vehicles at the intersection and outperform the PointPillar algorithm and the VoxelRCNN algorithm in detection accuracy.

Based on the diversified technology and the cross-validation mechanism, the N-variant system provides a secure service architecture for cloud providers to protect the cloud applications from attacks by executing multiple variants of a single software in parallel and then checking their behaviors' consistency. However, it is complex to upgrade current Software as a Service (SaaS) applications to adapt N-variant system architecture. Challenges arise from the inability of tenants to adjust the application architecture in the cloud environment, and the difficulty for cloud service providers to implement N-variant systems using existing API gateways. This paper proposes SecIngress, an API gateway framework, to overcome the challenge that it is hard in the cloud environment to upgrade the applications based on N-variants system. We design a two-stage timeout processing method to lessen the service latency and an Analytic Hierarchy Process Voting under the Metadata mechanism (AHPVM) to enhance voting accuracy. We implement a prototype in a testbed environment and analyze the security and performance metrics before and after deploying the prototype to show the effectiveness of SecIngress. The results reveal that SecIngress enhances the reliability of cloud applications with acceptable performance degradation.

In this paper, we consider a wireless ad hoc network consisting of multiple source nodes transmitting to their respective destinations, where an eavesdropper attempts to intercept their transmissions. We propose an optimal transmission scheduling scheme to defend against the eavesdropper, where a source node having the highest secrecy rate is scheduled to access the wireless medium for transmitting to its destination in an opportunistic manner. To be specific, the secrecy rate between a pair of the source and destination in the presence of an eavesdropper varies temporally due to the wireless fading effect. The proposed optimal transmission scheduling scheme opportunistically selects a source node with the highest secrecy rate to transmit its data for the sake of maximizing the security of the ad hoc network against eavesdropping attacks. For comparison purposes, we also consider the conventional round-robin scheduling as a benchmark, where multiple source nodes take turns in accessing their shared wireless medium for transmitting to their respective destinations. We derive closed-form secrecy outage probability expressions of both the round-robin scheduling and the proposed optimal scheduling schemes over Rayleigh fading environments. Numerical results show that the proposed transmission scheduling scheme outperforms the conventional round-robin method in terms of its secrecy outage probability. Additionally, upon increasing the number of source-destination pairs, the secrecy outage probability of the round-robin scheme keeps unchanged, whereas the secrecy outage performance of the proposed transmission scheduling significantly improves, showing the security benefits of exploiting transmission scheduling for protecting wireless ad hoc networks against eavesdropping.

Terahertz (THz) communications have been widely envisioned as a promising enabler to provide adequate bandwidth and achieve ultra-high data rates for sixth generation (6G) wireless networks. In order to mitigate blockage vulnerability caused by serious propagation attenuation and poor diffraction of THz waves, an intelligent reflecting surface (IRS), which manipulates the propagation of incident electromagnetic waves in a programmable manner by adjusting the phase shifts of passive reflecting elements, is proposed to create smart radio environments, improve spectrum efficiency and enhance coverage capability. Firstly, some prospective application scenarios driven by the IRS empowered THz communications are introduced, including wireless mobile communications, secure communications, unmanned aerial vehicle (UAV) scenario, mobile edge computing (MEC) scenario and THz localization scenario. Then, we discuss the enabling technologies employed by the IRS empowered THz system, involving hardware design, channel estimation, capacity optimization, beam control, resource allocation and robustness design. Moreover, the arising challenges and open problems encountered in the future IRS empowered THz communications are also highlighted. Concretely, these emerging problems possibly originate from channel modeling, new material exploration, experimental IRS testbeds and intensive deployment. Ultimately, the combination of THz communications and IRS is capable of accelerating the development of 6G wireless networks.

The spectrum sensing model based on deep learning has achieved satisfying detection performence, but its robustness has not been verified. In this paper, we propose primary user adversarial attack (PUAA) to verify the robustness of the deep learning based spectrum sensing model. PUAA adds a carefully manufactured perturbation to the benign primary user signal, which greatly reduces the probability of detection of the spectrum sensing model. We design three PUAA methods in black box scenario. In order to defend against PUAA, we propose a defense method based on autoencoder named DeepFilter. We apply the long short-term memory network and the convolutional neural network together to DeepFilter, so that it can extract the temporal and local features of the input signal at the same time to achieve effective defense. Extensive experiments are conducted to evaluate the attack effect of the designed PUAA method and the defense effect of DeepFilter. Results show that the three PUAA methods designed can greatly reduce the probability of detection of the deep learning-based spectrum sensing model. In addition, the experimental results of the defense effect of DeepFilter show that DeepFilter can effectively defend against PUAA without affecting the detection performance of the model.