The Service-based Architecture (SBA) is one of the key innovations of 5G architecture that leverage modularized, self-contained and independent services to provide flexible and cloud-native 5G network. In this paper, SBA for Space-Air-Ground Integrated Network (SAGIN) is investigated to enable the 5G integration deployment. This paper proposes a novel Holistic Service-based Architecture (H-SBA) for SAGIN of 5G-Advanced and beyond, i.e., 6G. The H-SBA introduces the concept of end-to-end service-based architecture design. The “Network Function Service”, introduced in 5G SBA, is extended from Control Plane to User Plane, from core network to access network. Based on H-SBA, the new generation of protocol design is proposed, which proposes to use IETF QUIC and SRv6 to substitute 5G HTTP/2.0 and GTP-U. Testing results show that new protocols can achieve low latency and high throughput, making them promising candidate for H-SBA.

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.

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.

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 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.

To deal with the dynamic and imbalanced traffic requirements in Low Earth Orbit satellite networks, several distributed load balancing routing schemes have been proposed. However, because of the lack of global view, these schemes may lead to cascading congestion in regions with high volume of traffic. To solve this problem, a Hybrid-Traffic-Detour based Load Balancing Routing (HLBR) scheme is proposed, where a Long-Distance Traffic Detour (LTD) method is devised and coordinates with distributed traffic detour method to perform self-adaptive load balancing. The forwarding path of LTD is acquired by the Circuitous Multipath Calculation (CMC) based on prior geographical information, and activated by the LTD- Shift-Trigger (LST) through real-time congestion perception. Simulation results show that the HLBR can mitigate cascading congestion and achieve efficient traffic distribution.

In Heterogeneous Wireless Sensor Networks, the mobility of the sensor nodes becomes essential in various applications. During node mobility, there are possibilities for the malicious node to become the cluster head or cluster member. This causes the cluster or the whole network to be controlled by the malicious nodes. To offer high level of security, the mobile sensor nodes need to be authenticated. Further, clustering of nodes improves scalability, energy efficient routing and data delivery. In this paper, we propose a cluster based secure dynamic keying technique to authenticate the nodes during mobility. The nodes with high configuration are chosen as cluster heads based on the weight value which is estimated using parameters such as the node degree, average distance, node’s average speed, and virtual battery power. The keys are dynamically generated and used for providing security. Even the keys are compromised by the attackers, they are not able to use the previous keys to cheat or disuse the authenticated nodes. In addition, a bidirectional malicious node detection technique is employed which eliminates the malicious node from the network. By simulation, it is proved that the proposed technique provides efficient security with reduced energy consumption during node mobility.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Emerging wireless community cloud enables usergenerated video content to be shared and consumed in a social context. However, the nature of shared wireless medium and timevarying channels seriously limits the quality of service (QoS), partially owing to the lack of mechanisms for effectively utilizing multi-rate channel resources. In this paper, the joint optimization of admission control and rate adaptation is proposed, resulting in a bandwidth-aware rate-adaptive admission control (BRAC) scheme to provide bandwidth guarantee for sharing social multimedia contents. The analytical approach leads to the following major contributions: (1) a bandwidth-aware rate selection (BRS) algorithm to optimally meet the bandwidth requirement of the data session and channel conditions at the physical layer; (2) a routing-coupled rate adaption and admission control algorithm to admit data sessions with bandwidth guarantee. Moreover, extensive numerical simulations suggest that BRAC is efficient and effective in meeting the bandwidth requirements for sharing social multimedia contents. These insights will shed light on communication system implementation for multimedia content sharing over multirate wireless community cloud.

For the sake of meeting the demand of data rates at terabit (Tbit) per second scale in future networks, the terahertz (THz) band is widely accepted as one of the potential key enabling technologies for next generation wireless communication systemsWith the progressive development of THz devices, regrading THz communications at system level is increasing crucial and captured the interest of plenty of researchersWithin this scope, THz channel modeling serves as an indispensable and fundamental elementBy surveying the latest literature findings, this paper reviews the problem of channel modeling in the THz band, with an emphasis on molecular absorption loss, misalignment fading and multipath fading, which are major influence factors in the THz channel modelingThen, we focus on simulators and experiments in the THz band, after which we give a brief introduction on applications of THz channel models with respects to capacity, security, and sensing as examplesFinally, we discuss some key issues in the future THz channel modeling.

Cognitive Internet of Things (IoT) has attracted much attention due to its high spectrum utilization. However, potential security of the shortpacket communications in cognitive IoT becomes an important issue. This paper proposes a relay-assisted maximum ratio combining/zero forcing beamforming (MRC/ZFB) scheme to guarantee the secrecy performance of dual-hop short-packet communications in cognitive IoT. This paper analyzes the average secrecy throughput of the system and further investigates two asymptotic scenarios with the high signal-to-noise ratio (SNR) regime and the infinite blocklength. In addition, the Fibonacci-based alternating optimization method is adopted to jointly optimize the spectrum sensing blocklength and transmission blocklength to maximize the average secrecy throughput. The numerical results verify the impact of the system parameters on the tradeoff between the spectrum sensing blocklength and transmission blocklength under a secrecy constraint. It is shown that the proposed scheme achieves better secrecy performance than other benchmark schemes.

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.

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.

Although small cell offloading technology can alleviate the congestion in macrocell, aggressively offloading data traffic from macrocell to small cell can also degrade the performance of small cell due to the heavy load. Because of collision and backoff, the degradation is significant especially in network with contention-based channel access, and finally decreases throughput of the whole network. To find an optimal fraction of traffic to be offloaded in heterogeneous network, we combine Markov chain with the Poisson point process model to analyze contention-based throughput in irregularly deployment networks. Then we derive the close-form solution of the throughput and find that it is a function of the transmit power and density of base stations. Based on this, we propose the load-aware offloading strategies via power control and base station density adjustment. The numerical results verify our analysis and show a great performance gain compared with non-load-aware offloading.

The varying trajectory of Doppler frequency under changing speed motion conditionsare investigated in HighSpeed Railway (HSR) scenarios. Based on the geometrical physical parameters, instantaneous Doppler trajectories and expression forms of the change rate arededuced, including acceleration and deceleration cases.These modified models provide more accurate and realisticapproximations in modeling rapidly fading channels.

In cellular network, users with same demand and in proximity to each other form the mobile cloud, in which the short-range D2D technology is employed by users to improve the data dissemination efficiency. In view of the fact that the D2D links with the poor channel conditions are likely to be the bottleneck of resource utilization improvement, aiming at the differentiation of link quality, this paper proposes a intra-cloud D2D multicast retransmission algorithm based on SINR constraint to meet the minimum requirement of D2D retransmission for QoS. In the proposed algorithm, the model of system link cost is built, the number of multicast retransmission times is restricted and each link quality matrix is traversed to reasonably select the multicast transmitter as well as its routing, which further reduces the link cost consumption, and in turn improves the bandwidth efficiency. Simulation results show that the proposed algorithm is more efficient to improve the bandwidth utilization when the ratio between normal user and non-normal user is small in mobile cloud.

In this paper, we propose a novel speed and service-sensitive handoff algorithm and analytical model for hierarchical cellular networks. First, we use the Gauss-Markov mobility model to predict the speeds of mobile stations, and divide mobile stations into three classes based on the predicted speeds: fast, medium-speed, and slow. Then, according to the mobility classification, network conditions, and service types, mobile stations will be handoff to the proper target networks prior to the deterioration of the currently operating channel. We further develop an analytical model to evaluate the performance of such a hierarchical system with different speed classes and service types. Simulations and analytical results show that the proposed handoff algorithm can significantly improve the network performance in terms of the handoff failure probability, unnecessary handoff probability, and network throughput, comparing with the traditional algorithms.

Network virtualization is known as a promising technology to tackle the ossification of current Internet and will play an important role in the future network area. Virtual network embedding(VNE) is a key issue in network virtualization. VNE is NP-hard and former VNE algorithms are mostly heuristic in the literature. VNE exact algorithms have been developed in recent years. However, the constraints of exact VNE are only node capacity and link bandwidth. Based on these, this paper presents an exact VNE algorithm, ILP-LC, which is based on Integer Linear Programming(ILP), for embedding virtual network request with location constraints. This novel algorithm is aiming at mapping virtual network request(VNR) successfully as many as possible and consuming less substrate resources. The topology of each VNR is randomly generated by Waxman model. Simulation results show that the proposed ILP-LC algorithm outperforms the typical heuristic algorithms in terms of the VNR acceptance ratio, at least 15%.

This paper presents an efficient image feature representation method, namely angle structure descriptor (ASD), which is built based on the angle structures of images. According to the diversity in directions, angle structures are defined in local blocks. Combining color information in HSV color space, we use angle structures to detect images. The internal correlations between neighboring pixels in angle structures are explored to form a feature vector. With angle structures as bridges, ASD extracts image features by integrating multiple information as a whole, such as color, texture, shape and spatial layout information. In addition, the proposed algorithm is efficient for image retrieval without any clustering implementation or model training. Experimental results demonstrate that ASD outperforms the other related algorithms.

To accommodate the ever-increasing wireless capacity, the terahertz (THz) orbital angular momentum (OAM) beam that combines THz radiation and OAM technologies has attracted much attention recently, with contributing efforts to explore new dimensions in the THz region. In this paper, we provide an overview of the generation and detection techniques of THz-OAM beams, as well as their applications in communications. The principle and research status of typical generation and detection methods are surveyed, and the advantages and disadvantages of each method are summarized from a viewpoint of wireless communication. It is shown that developing novel THz components in generating, detecting and (de)multiplexing THz-OAM beams has become the key engine to drive this direction forward. Anyway, beneficial from the combination of infinite orthogonal modes and large bandwidth, THz-OAM beams will have great potential in delivering very large capacity in next generation wireless communications.

Micro-UAV swarms usually generate massive data when performing tasks. These data can be harnessed with various machine learning (ML) algorithms to improve the swarm's intelligence. To achieve this goal while protecting swarm data privacy, federated learning (FL) has been proposed as a promising enabling technology. During the model training process of FL, the UAV may face an energy scarcity issue due to the limited battery capacity. Fortunately, this issue is potential to be tackled via simultaneous wireless information and power transfer (SWIPT). However, the integration of SWIPT and FL brings new challenges to the system design that have yet to be addressed, which motivates our work. Specifically, in this paper, we consider a micro-UAV swarm network consisting of one base station (BS) and multiple UAVs, where the BS uses FL to train an ML model over the data collected by the swarm. During training, the BS broadcasts the model and energy simultaneously to the UAVs via SWIPT, and each UAV relies on its harvested and battery-stored energy to train the received model and then upload it to the BS for model aggregation.To improve the learning performance, we formulate a problem of maximizing the percentage of scheduled UAVs by jointly optimizing UAV scheduling and wireless resource allocation.The problem is a challenging mixed integer nonlinear programming problem and is NP-hard in general. By exploiting its special structure property, we develop two algorithms to achieve the optimal and suboptimal solutions, respectively. Numerical results show that the suboptimal algorithm achieves a near-optimal performance under various network setups, and significantly outperforms the existing representative baselines.considered.

Top-k ranking of websites according to traffic volume is important for Internet Service Providers (ISPs) to understand network status and optimize network resources. However, the ranking result always has a big deviation with actual rank for the existence of unknown web traffic, which cannot be identified accurately under current techniques. In this paper, we introduce a novel method to approximate the actual rank. This method associates unknown web traffic with websites according to statistical probabilities. Then, we construct a probabilistic top-k query model to rank websites. We conduct several experiments by using real HTTP traffic traces collected from a commercial ISP covering an entire city in northern China. Experimental results show that the proposed techniques can reduce the deviation existing between the ground truth and the ranking results vastly. In addition, we find that the websites providing video service have higher ratio of unknown IP as well as higher ratio of unknown traffic than the websites providing text web page service. Specifically, we find that the top-3 video websites have more than 90% of unknown web traffic. All these findings are helpful for ISPs understanding network status and deploying Content Distributed Network (CDN).

In the past two decades, software aging has been studied by both academic and industry communities. Many scholars focused on analytical methods or time series to model software aging process. While machine learning has been shown as a very promising technique in application to forecast software state: normal or aging. In this paper, we proposed a method which can give practice guide to forecast software aging using machine learning algorithm. Firstly, we collected data from a running commercial web server and preprocessed these data. Secondly, feature selection algorithm was applied to find a subset of model parameters set. Thirdly, time series model was used to predict values of selected parameters in advance. Fourthly, some machine learning algorithms were used to model software aging process and to predict software aging. Fifthly, we used sensitivity analysis to analyze how heavily outcomes changed following input variables change. In the last, we applied our method to an IIS web server. Through analysis of the experiment results, we find that our proposed method can predict software aging in the early stage of system development life cycle.

Cache-enabled small cell networks have been regarded as a promising approach for network operators to cope with the explosive data traffic growth in future 5G networks. However, the user association and resource allocation mechanism has not been thoroughly studied under given content placement situation. In this paper, we formulate the joint optimization problem of user association and resource allocation as a mixed integer nonlinear programming (MINLP) problem aiming at deriving a balance between the total utility of data rates and the total data rates retrieved from caches. To solve this problem, we propose a distributed relaxing-rounding method. Simulation results demonstrate that the distributed relaxing-rounding method outperforms traditional max-SINR method and range-expansion method in terms of both total utility of data rates and total data rates retrieved from caches in practical scenarios. In addition, effects of storage and backhaul capacities on the performance are also studied.

The large-scale development of the low-altitude economy imposes increasingly stringent requirements on the supporting information infrastructure, necessitating the establishment of a low-altitude intelligent network (LAIN) with wide-area communication, high-precision navigation, and efficient supervision capabilities. Benefiting from its broad coverage, high reliability, and large bandwidth, the 5G cellular network serves as a critical foundation for LAIN construction. However, conventional cellular networks are primarily designed for two-dimensional terrestrial scenarios, and thus face significant limitations in coverage and interference resistance within complex three-dimensional low-altitude environments. To address the unique demands of LAIN applications, key challenges must be tackled, including achieving seamless three-dimensional coverage, mitigating interference in multi-dimensional network deployments, and ensuring stringent requirements for service quality and security supervision. This paper proposes an integrated LAIN architecture characterized by the convergence of communication, navigation, sensing, and management, enhanced with artificial intelligence and security mechanisms to improve overall system intelligence and resilience. Furthermore, this paper conducts an in-depth analysis of the critical challenges in LAIN deployment, explores enabling technologies to address these issues, and offers insights into the future development direction of low-altitude intelligent networks.

Decoding by alternating direction method of multipliers (ADMM) is a promising linear programming decoder for low-density parity-check (LDPC) codes. In this paper, we propose a two-step scheme to lower the error floor of LDPC codes with ADMM penalized decoder. For the undetected errors that cannot be avoided at the decoder side, we modify the code structure slightly to eliminate low-weight code words. For the detected errors induced by small error-prone structures, we propose a post-processing method for the ADMM penalized decoder. Simulation results show that the error floor can be reduced significantly over three illustrated LDPC codes by the proposed two-step scheme.

Reconfigurable intelligent surface (RIS) can manipulate the wireless propagation environment by smartly adjusting the amplitude/phase in a programmable panel, enjoying the improved performance. The accurate acquisition of the instantaneous channel state information (CSI) in the cascaded RIS chain makes an indispensable contribution to the performance gains. However, it is quite challenging to estimate the CSI in a time-variant scenario due to the limited signal processing capability of the passive elements embedded in a RIS pannel. In this work, a channel estimation scheme for the RIS-assisted wireless communication system is proposed, which is demonstrated to perform well in a time-variant scenario. The cascaded RIS channel is modeled as a state-space model based upon the mobility situations. In addition, to fully exploit the time correlation of channel, Kalman filter is employed by taking the prior information of channels into account. Further, the optimal reflection coefficients are derived according to the minimum mean square error (MMSE) criterion. Numerical results show that the proposed methods exhibit superior performance if compared with a conventional channel estimation scheme.

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.

Remaining time prediction of business processes plays an important role in resource scheduling and plan making. The structural features of single process instance and the concurrent running of multiple process instances are the main factors that affect the accuracy of the remaining time prediction. Existing prediction methods does not take full advantage of these two aspects into consideration. To address this issue, a new prediction method based on trace representation is proposed. More specifically, we first associate the prefix set generated by the event log to different states of the transition system, and encode the structural features of the prefixes in the state. Then, an annotation containing the feature representation for the prefix and the corresponding remaining time are added to each state to obtain an extended transition system. Next, states in the extended transition system are partitioned by the different lengths of the states, which considers concurrency among multiple process instances. Finally, the long short-term memory (LSTM) deep recurrent neural networks are applied to each partition for predicting the remaining time of new running instances. By extensive experimental evaluation using synthetic event logs and real-life event logs, we show that the proposed method outperforms existing baseline methods.

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.

Due to the rapid development of mobile Internet services, such as Skype and WeChat, traditional telecom services have suffered a large decline in the business volumes in recent years. Thus, telecom operators pay much attention to analyzing the changes of the enterprise performance, in order to adjust market strategies in time. In this paper, we propose a new methodology to analyze the operation data of telecom operators dynamically, which can characterize the changing process of the operating states and predict the developing trends. In particular, the proposed methodology contains two steps: migration pattern analysis and trajectory pattern analysis. Firstly, migration analysis is based on the changing of operating states between two points-in-time, which is referred to as the migration paths. Applying the clustering analysis to the migration paths can obtain the migration patterns, which characterize the changing feature of telecom operators during a short period, e.g. one month. Secondly, we combine a sequence of consecutive migration paths to obtain the migration trajectory. Through analyzing different trajectories based on the hierarchical cluster method and the Markov chain model, we obtain the trajectory patterns, which describe the changing progress during a relatively long period, e.g. one year. Based on the trajectory patterns, we can predict the possible performance changes. Finally, we apply the proposed method to a Chinese telecom operator for an empirical research, and has obtained a lot of development rules which provides insights into current telecom.