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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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%.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

Re-routing system has become an important technology to improve traffic efficiency. The traditional re-routing schemes do not consider the dynamic characteristics of urban traffic, making the planned routes unable to cope with the changing traffic conditions. Based on real-time traffic information, it is challenging to dynamically re-route connected vehicles to alleviate traffic congestion. Moreover, how to obtain global traffic information while reducing communication costs and improving travel efficiency poses a challenge to the re-routing system. To deal with these challenges, this paper proposes CHRT, a clustering-based hybrid re-routing system for traffic congestion avoidance. CHRT develops a multi-layer hybrid architecture. The central server accesses the global view of traffic, and the distributed part is composed of vehicles divided into clusters to reduce latency and communication overhead. Then, a clustering-based priority mechanism is proposed, which sets priorities for clusters based on real-time traffic information to avoid secondary congestion. Furthermore, to plan the optimal routes for vehicles while alleviating global traffic congestion, this paper presents a multi-metric re-routing algorithm. Through extensive simulations based on the SUMO traffic simulator, CHRT reduces vehicle traveling time, fuel consumption, and CO₂ emissions compared to other systems. In addition, CHRT globally alleviates traffic congestion and improves traffic efficiency.

Multi-access Edge Computing (MEC) is one of the key technologies of the future 5G network. By deploying edge computing centers at the edge of wireless access network, the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios. Meanwhile, with the development of IOV (Internet of Vehicles) technology, various delay-sensitive and compute-intensive in-vehicle applications continue to appear. Compared with traditional Internet business, these computation tasks have higher processing priority and lower delay requirements. In this paper, we design a 5G-based vehicle-aware Multi-access Edge Computing network (VAMECN) and propose a joint optimization problem of minimizing total system cost. In view of the problem, a deep reinforcement learning-based joint computation offloading and task migration optimization (JCOTM) algorithm is proposed, considering the influences of multiple factors such as concurrent multiple computation tasks, system computing resources distribution, and network communication bandwidth. And, the mixed integer nonlinear programming problem is described as a Markov Decision Process. Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption, optimize computing offloading and resource allocation schemes, and improve system resource utilization, compared with other computing offloading policies.

Sixth Generation (6G) wireless communication network has been expected to provide global coverage, enhanced spectral efficiency, and AI(Artificial Intelligence)-native intelligence, etc. To meet these requirements, the computational concept of Decision-Making of cognition intelligence, its implementation framework adapting to foreseen innovations on networks and services, and its empirical evaluations are key techniques to guarantee the generation-agnostic intelligence evolution of wireless communication networks. In this paper, we propose an Intelligent Decision Making (IDM) framework, acting as the role of network brain, based on Reinforcement Learning modelling philosophy to empower autonomous intelligence evolution capability to 6G network. Besides, usage scenarios and simulation demonstrate the generality and efficiency of IDM. We hope that some of the ideas of IDM will assist the research of 6G network in a new or different light.

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.

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.

This paper establishes a new layered flying ad hoc networks (FANETs) system of mobile edge computing (MEC) supported by multiple UAVs, where the first layer of user UAVs can perform tasks such as area coverage, and the second layer of MEC UAVs are deployed as flying MEC sever for user UAVs with computing-intensive tasks. In this system, we first divide the user UAVs into multiple clusters, and transmit the tasks of the cluster members (CMs) within a cluster to its cluster head (CH). Then, we need to determine whether each CH' tasks are executed locally or offloaded to one of the MEC UAVs for remote execution (i.e., task scheduling), and how much resources should be allocated to each CH (i.e., resource allocation), as well as the trajectories of all MEC UAVs. We formulate an optimization problem with the aim of minimizing the overall energy consumption of all user UAVs, under the constraints of task completion deadline and computing resource, which is a mixed integer non-convex problem and hard to solve. We propose an iterative algorithm by applying block coordinate descent methods. To be specific, the task scheduling between CH UAVs and MEC UAVs, computing resource allocation, and MEC UAV trajectory are alternately optimized in each iteration. For the joint task scheduling and computing resource allocation subproblem and MEC UAV trajectory subproblem, we employ branch and bound method and continuous convex approximation technique to solve them, respectively. Extensive simulation results validate the superiority of our proposed approach to several benchmarks.

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.

The continuous change of communication frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication networks. Since the frequency-hopping (FH) sequence is usually generated by a certain model with certain regularity, the FH frequency is thus predictable. In this paper, we investigate the FH frequency reconnaissance and prediction of a non-cooperative communication network by effective FH signal detection, time-frequency (TF) analysis, wavelet detection and frequency estimation. With the intercepted massive FH signal data, long short-term memory (LSTM) neural network model is constructed for FH frequency prediction. Simulation results show that our parameter estimation methods could estimate frequency accurately in the presence of certain noise. Moreover, the LSTM-based scheme can effectively predict FH frequency and frequency interval.

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.

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.