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.

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.

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.

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.

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.

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.

Ensuring end-to-end quality of service (QoS) for high-value services in satellite networks is challenging due to dynamic network topologies, varying QoS requirements, and the complex resource allocation across satellite beams and inter-satellite links. To this end, we propose a satellite traffic engineering framework with deterministic QoS (SatTED) by jointly optimizing resource allocation across access and bearer subnets.To tackle the complexity of joint scheduling, SatTED adopts a hierarchical logic-based benders decomposition (LBBD) architecture that coordinates access and bearer subnet resources. The master problem optimizes service admission and satellite selection via binary integer programming, while the subproblem handles routing and bandwidth allocation through linear programming relaxation. Key innovat-ions include scenario-cognizant Benders feasibility cuts to accelerate convergence and a critical constraint link preprocessing (CCLP) mechanism that reduces subproblem complexity by 5.15× in large-scale networks. In simulations on a 220-satellite network with 1 000 flows, SatTED improves total service payoff by 32% and increases high-value flow completion rates by 22%.

We propose a cost-effective multi-carrier generation technique which minimizes the passive optical access network (PON) costs. In this study replacement of laser array with multi-carrier source at optical line terminal (OLT) side in PON is addressed. With 25-GHz frequency spacing, the generated optical multi-carriers exhibit good tone to noise ratio (TNR) i. e. above 20dB, and least amplitude difference i. e. 1.5dB. At the OLT, multi-carriers signal based multiplexed differential phase shift keying (DPSK) data from all the channels each having 10Gbps for downlink is transmitted through 25km single mode fiber. While the transmitted information is retrieved at optical network unit (ONU), part of the downlink signal is re-modulated using intensity modulated (IM) on-off keying(OOK) for upstream transmission at 10-Gbps. Simulation results are in good agreement with the theoretical analysis, showing error free transmission in downlink and uplink with 10Gbps symmetric data rate at each channel. The received power, both for uplink and downlink transmission, is adequate for all channels at BER of 10^-9 with minimum power penalties. Power budget is calculated for different splitting ratios showing excellent system margins for any unseen losses. The proposed setup provides a cost-effective way minimizing transmission losses, and providing greater system’s margin in PON architecture.

Intellectualization has become a new trend for telecom industry, driven by intelligent technology including cloud computing, big data, and Internet of things. In order to satisfy the service demand of intelligent logistics, this paper designed an intelligent logistics platform containing the main applications such as e-commerce, self-service transceiver, big data analysis, path location and distribution optimization. The intelligent logistics service platform has been built based on cloud computing to collect, store and handling multi-source heterogeneous mass data from sensors, RFID electronic tag, vehicle terminals and APP, so that the open-access cloud services including distribution, positioning, navigation, scheduling and other data services can be provided for the logistics distribution applications. And then the architecture of intelligent logistics cloud platform containing software layer (SaaS), platform layer (PaaS) and infrastructure (IaaS) has been constructed accordance with the core technology relative high concurrent processing technique, heterogeneous terminal data access, encapsulation and data mining. Therefore, intelligent logistics cloud platform can be carried out by the service mode for implementation to accelerate the construction of the symbiotic win-win logistics ecological system and the benign development of the ICT industry in the trend of intellectualization in China.

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.

The rapid development of the low-altitude economy has imposed unprecedented demands on wireless infrastructure to accommodate large-scale drone deployments and facilitate intelligent services in dynamic airspace environments. However, unlocking its full potential in practical applications presents significant challenges. Traditional aerial systems predominantly focus on air-ground communication services, often neglecting the integration of sensing, computation, control, and energy-delivering functions, which hinders the ability to meet diverse mission-critical demands. Besides, the absence of systematic low-altitude airspace planning and management exacerbates issues regarding dynamic interference in three-dimensional space, coverage instability, and scalability. To overcome these challenges, a comprehensive framework, termed low-altitude wireless network (LAWN), has emerged to seamlessly integrate communication, sensing, computation, control, and air traffic management into a unified design. This article provides a comprehensive overview of LAWN systems, introducing LAWN system fundamentals and performance evaluation metrics. Subsequently, we delve into the evolution of functional designs and review critical concerns surrounding privacy and security in the open-air network environment. We survey advanced artificial intelligence techniques that enhance LAWN functionality and enable increasingly autonomous operations. Finally, we present the cutting-edge developments in airspace structuring, air traffic management, and path planning, providing insights to facilitate the practical deployment of LAWNs.

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.

Satellite-to-ground communications are anticipated to play a crucial role in sixth-generation (6G) mobile systems by providing seamless coverage. Due to the long transmission distances, complex channel characteristics, and diverse terminal environments, satellite-to-ground channels exhibit unique features that distinguish them from terrestrial communication channels. Understanding and accurately modeling the channel is a premise for the design, optimization, and evaluation of satellite-to-ground communication systems. This paper provides a comprehensive review of the challenges and ongoing research in satellite-to-ground channel measurement, characterization, modeling, and standardization. Specifically, the paper discusses simulated and on-orbit satellite channel measurements, introduces large-scale and small-scale characteristics, and analyzes the challenges and progress in four satellite-to-ground channel models. In addition, the paper reviews the relevant standards efforts of key organizations and outlines potential future research directions.

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, the influencing factors that affect few-mode and multi core optical fiber channel are analyzed in a comprehensive way. The theoretical modeling and computer simulation of the information channel are carried out and then the modeling scheme of few-mode multi-core optical fiber channel based on non-uniform mode field distribution is put forward. The proposed modeling scheme can not only exponentially increases the system capacity through few-mode multi-core optical fiber channel, but has better transmission performance compared to the channel of the same type to the uniform channel revealing from the simulation results.

Flying Ad hoc Network (FANET) has drawn significant consideration due to its rapid advancements and extensive use in civil applications. However, the characteristics of FANET including high mobility, limited resources, and distributed nature, have posed a new challenge to develop a secure and efficient routing scheme for FANET. To overcome these challenges, this paper proposes a novel cluster based secure routing scheme, which aims to solve the routing and data security problem of FANET. In this scheme, the optimal cluster head selection is based on residual energy, online time, reputation, blockchain transactions, mobility, and connectivity by using Improved Artificial Bee Colony Optimization (IABC). The proposed IABC utilizes two different search equations for employee bee and onlooker bee to enhance convergence rate and exploitation abilities. Further, a lightweight blockchain consensus algorithm, AI-Proof of Witness Consensus Algorithm (AI-PoWCA) is proposed, which utilizes the optimal cluster head for mining. In AI-PoWCA, the concept of the witness for block verification is also involved to make the proposed scheme resource efficient and highly resilient against 51% attack. Simulation results demonstrate that the proposed scheme outperforms its counterparts and achieves up to 90% packet delivery ratio, lowest end-to-end delay, highest throughput, resilience against security attacks, and superior in block processing time.

The rapid development and continuous updating of the mega satellite constellation (MSC) have brought new visions for the future 6G coverage extension, where the global seamless signal coverage can realize ubiquitous services for user terminals. However, global traffic demands present non-uniform characteristics. Therefore, how to ensure the on-demand service coverage for the specific traffic demand, i.e., the ratio of traffic density to service requirement per unit area, is the core issue of 6G wireless coverage extension exploiting the MSC. To this regard, this paper first discusses the open challenges to reveal the future direction of 6G wireless coverage extension from the perspective of key factors affecting service coverage performance, i.e., the network access capacity, space segment capacity and their matching-relationship. Furthermore, we elaborate on the key factors affecting effective matchings of the aforementioned aspects, thereby improving service coverage capability.

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.

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).

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.

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%.

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.

Software-Defined Networking (SDN) provides flexible and global network management by decoupling control plane from data plane, and multiple controllers are deployed in the network in a logically centralized and physically distributed way. However, the existing approaches generally deploy the controllers with the same type in the network, which easily causes homogeneous controller common-mode fault. To this end, this paper proposes heterogeneous controller deployment in the SDN, considering the different types of controllers and relevant criteria (e.g., delay, control link interruption rate, and controller fault rate). Then, we introduce a Safe and Reliable Heterogeneous Controller Deployment (SRHCD) approach, consisting of two stages. Stage 1 determines the type and the number of heterogeneous controllers required for the SDN network based on the dynamic programming. Stage 2 divides the SDN network into multiple subnets by k-means algorithm and improves the genetic algorithm to optimize the heterogeneous controller deployment in these SDN subnets to ensure reliable switch-controller communications. Finally, the simulation results show that the proposed approach can effectively reduce the control plane fault rate and increase the attack difficulties. Besides, the switch- controller delay has been lowered by 16.5% averagely.

Mobile edge computing (MEC) is an emerging technolohgy that extends cloud computing to the edge of a network. MEC has been applied to a variety of services. Specially, MEC can help to reduce network delay and improve the service quality of recommendation systems. In a MEC-based recommendation system, users' rating data are collected and analyzed by the edge servers. If the servers behave dishonestly or break down, users' privacy may be disclosed. To solve this issue, we design a recommendation framework that applies local differential privacy (LDP) to collaborative filtering. In the proposed framework, users' rating data are perturbed to satisfy LDP and then released to the edge servers. The edge servers perform partial computing task by using the perturbed data. The cloud computing center computes the similarity between items by using the computing results generated by edge servers. We propose a data perturbation method to protect user's original rating values, where the Harmony mechanism is modified so as to preserve the accuracy of similarity computation. And to enhance the protection of privacy, we propose two methods to protect both users' rating values and rating behaviors. Experimental results on real-world data demonstrate that the proposed methods perform better than existing differentially private recommendation methods.

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.

Compared with wired communication, the wireless communication link is more vulnerable to be attacked or eavesdropped because of its broadcast nature. To prevent eavesdropping, many researches on transmission techniques or cryptographic methods are carried out. This paper proposes a new index parameter named as eavesdropping area, to evaluate the anti-eavesdropping performance of wireless system. Given the locations of legitimate transmitter and receiver, eavesdropping area index describes the total area of eavesdropping regions where messages can be wiretapped in the whole evaluating region. This paper gives detailed explanations about its concept and deduces mathematical formulas about performance curves based on region classification. Corresponding key system parameters are analyzed, including the characteristics of eavesdropping region, transmitted beam pattern, beam direction, receiver sensitivity, eavesdropping sensitivity, path loss exponent and so on. The proposed index can give an insight on the confirmation of high-risk eavesdropping region and formulating optimal transmitting scheme for the confidential communications to decrease the eavesdropping probability.

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.

In 5G Ultra-dense Network (UDN), resource allocation is an efficient method to manage inter-small-cell interference. In this paper, a two-stage resource allocation scheme is proposed to supervise interference and resource allocation while establishing a realistic scenario of three-tier heterogeneous network architecture. The scheme consists of two stages: in stage I, a two-level sub-channel allocation algorithm and a power control method based on the logarithmic function are applied to allocate resource for Macrocell and Picocells, guaranteeing the minimum system capacity by considering the power limitation and interference coordination; in stage II, an interference management approach based on K-means clustering is introduced to divide Femtocells into different clusters. Then, a prior sub-channel allocation algorithm is employed for Femtocells in diverse clusters to mitigate the interference and promote system performance. Simulation results show that the proposed scheme contributes to the enhancement of system throughput and spectrum efficiency while ensuring the system energy efficiency.

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.

In recent years, with the growth in Unmanned Aerial Vehicles (UAVs), UAV-based systems have become popular in both military and civil applications.In these scenarios, the lack of reliable communication infrastructure has motivated UAVs to establish a network as flying nodes, also known as Flying Ad Hoc Networks (FANETs).However, in FANETs, the high mobility degree of flying and terrestrial users may be responsible for constant changes in the network topology, making end-to-end connections in FANETs challenging.Mobility estimation and prediction of UAVs can address the challenge mentioned above since it can provide better routing planning and improve overall FANET performance in terms of continuous service availability.We thus develop a Software Defined Network (SDN)-based heterogeneous architecture for reliable communication in FANETs.In this architecture, we apply an Extended Kalman Filter (EKF) for accurate mobility estimation and prediction of UAVs.In particular, we formulate the routing problem in SDN-based Heterogeneous FANETs as a graph decision problem.As the problem is NP-hard, we further propose a Directional Particle Swarming Optimization (DPSO) approach to solve it.The extensive simulation results demonstrate that the proposed DPSO routing can exhibit superior performance in improving the goodput, packet delivery ratio, and delay.

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.

Plenty of multimedia contents such as traffic images, surveillance video, music and movie will flood into vehicular ad hoc networks. However, content distribution over VANETs is not a easy task, due to the high mobility of vehicles and intermittent connectivity. Infrastructure-based scheme can relieve the problem, but with a large amount of investment. In this paper, we propose a mobile content distribution scheme based on roadside parking cloud (RPC), which is formed by the parked car on the roadside, and mobile cloud (MC), which is formed by moving cars on the road. According to a trip history model, a mobile car can estimate its following trajectory. When it wants to download the content, gateway node of the RPC will work out a downloading schedule, which tells it how much chunks it can download from which RPCs. Moreover, the helper of the mobile car in mobile cloud would deliver specified chunks to it when there is lack of RPC in the following trip. Simulation results show that cloud-based scheme performs better than inter-vehicle communication approach and cluster-based scheme.

Cognitive Internet of Vehicles (CIoV) can improve spectrum utilization by accessing the spectrum licensed to primary user (PU) under the premise of not disturbing the PU's transmissions. However, the traditional static spectrum access makes the CIoV unable to adapt to the various spectrum environments. In this paper, a reinforcement learning based dynamic spectrum access scheme is proposed to improve the transmission performance of the CIoV in the licensed spectrum, and avoid causing harmful interference to the PU. The frame structure of the CIoV is separated into sensing period and access period, whereby the CIoV can optimize the transmission parameters in the access period according to the spectrum decisions in the sensing period. Considering both detection probability and false alarm probability, a Q-learning based spectrum access algorithm is proposed for the CIoV to intelligently select the optimal channel, bandwidth and transmit power under the dynamic spectrum states and various spectrum sensing performance. The simulations have shown that compared with the traditional non-learning spectrum access algorithm, the proposed Q-learning algorithm can effectively improve the spectral efficiency and throughput of the CIoV as well as decrease the interference power to the PU.

Smart containers have been extensively applied in the maritime industry by embracing the Internet of Things to realize container status monitoring and data offloading without human intervention. However, the offloading rate and delay in the offshore region are limited by the coverage of the onshore base station (BS). In this paper, we investigate the unmanned aerial vehicle (UAV)-assisted data offloading for smart containers in offshore maritime communications where the UAV is as a relay node between smart containers and onshore BS. We first consider the mobility of container vessel in the offshore region and establish a UAV-assisted data offloading model. Based on this model, a data offloading algorithm is proposed to reduce the average offloading delay under data-size requirements and available energy constraints of smart containers. Specifically, the convex-concave procedure is used to update time-slot assignment, offloading approach selection, and power allocation in an iterative manner. Simulation results show that the proposed algorithm can efficiently reduce average offloading delay and increase offloading success ratio. Moreover, it is shown that the UAV relay cannot always bring the performance gain on offloading delay especially in the close-to-shore area, which could give an insight on the deployment of UAV relay in offshore communications.

Unsourced multiple access (UMA) is a multi-access technology for massive, low-power, uncoordinated, and unsourced Machine Type Communication (MTC) networks. It ensures transmission reliability under the premise of high energy efficiency. Based on the analysis of the 6G MTC key performance indicators (KPIs) and scenario characteristics, this paper summarizes its requirements for radio access networks. Following this, the existing multiple access models are analyzed under these standards to determine UMA's advantages for 6G MTC according to its design characteristics. The critical technology of UMA is the design of its multiple-access coding scheme. Therefore, the existing UMA coding schemes from different coding paradigms are further summarized and compared. In particular, this paper comprehensively considers the energy efficiency and computational complexity of these schemes, studies the changes of the above two indexes with the increase of access scale, and considers the trade-off between the two. It is revealed by the above analysis that some guiding rules of UMA coding design. Finally, the open problems and potentials in this field are given for future research.

The highly dynamic channel (HDC) in an extremely dynamic environment mainly has fast time-varying nonstationary characteristics. In this article, we focus on the most difficult HDC case, where the channel coherence time is less than the symbol period. To this end, we propose a symbol detector based on a long short-term memory (LSTM) neural network. Taking the sampling sequence of each received symbol as the LSTM unit's input data has the advantage of making full use of received information to obtain better performance. In addition, using the basic expansion model (BEM) as the preprocessing unit significantly reduces the number of neural network parameters. Finally, the simulation part uses the highly dynamic plasma sheath channel (HDPSC) data measured from shock tube experiments. The results show that the proposed BEM-LSTM-based detector has better performance and does not require channel estimation or channel model information.

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 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.

Heterogeneous cellular networks (HCNs), by introducing caching capability, has been considered as a promising technique in 5G era, which can bring contents closer to users to reduce the transmission delay, save scarce bandwidth resource. Although many works have been done for caching in HCNs, from an energy perspective, there still exists much space to develop a more energy-efficient system when considering the fact that the majority of base stations are under-utilized in the most of the time. Therefore, in this paper, by taking the activation mechanism for the base stations into account, we study a joint caching and activation mechanism design to further improve the energy efficiency, then we formulate the optimization problem as an Integer Linear Programming problem (ILP) to maximize the system energy saving. Due to the enormous computation complexity for finding the optimal solution, we introduced a Quantum-inspired Evolutionary Algorithm (QEA) to iteratively provide the global best solution. Numerical results show that our proposed algorithm presents an excellent performance, which is far better than the strategy of only considering caching without deactivation mechanism in the actual, normal situation. We also provide performance comparison among our QEA, random sleeping algorithm and greedy algorithm, numerical results illustrate our introduced QEA performs best in accuracy and global optimality.

With the explosion of wireless data rates, the terahertz (THz) band (0.1-10 THz) is envisioned as a promising candidate to break the existing bandwidth bottleneck and satisfy the ever-increasing capacity demand. The THz wireless communications feature a number of attractive properties, such as potential terabit-per-second capacity and high energy efficiency. In this paper, an overview on the state-of-the-art THz communications is studied, with a special focus on key technologies of THz transceivers and THz communication systems. The recent progress on both electronic and photonic THz transmitters are presented, and then the THz receivers operating in direct- and heterodyne reception modes are individually surveyed. Based on the THz transceiver schemes, three kinds of THz wireless communication systems are reviewed, including solid-state electronic systems, photonics-assisted systems and all-photonics systems. The prospective key enabling technologies, corresponding challenges and research directions for lighting up high-speed THz communication systems are discussed as well.