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.

This paper centres on achieving the maximization of weighted throughput (WTP) in a multiuser cell-free massive multiple-input multiple-output (mMIMO) system with both finite blocklength (FBL) and infinite blocklength (INFBL), which is conducted against the backdrop of constrained time-frequency resources. We aim to ensure quality of service (QoS) for all users, particularly in the FBL scenario, maintaining an acceptable latency and block error rate (BLER). To counteract the impact of reduced DoF of channel matrix due to a large number of users accessing the system, which leads to decreased system performance, we strive to optimize WTP by scheduling multiple users to different resource elements (REs) and applying precoding operations accordingly, subject to the limitations imposed by total power consumption per time slot and requisite QoS parameters. Simulation results demonstrate the superiority of the proposed multiuser processing (MUP) scheme over both single-user processing (SUP) and all-user processing (AUP) alternatives, and the proposed iterative algorithm based on genetic algorithm (GA) achieves up to $49.36\%$ system performance gains compared to the benchmark algorithms. This substantiates the efficacy of our method in enhancing network performance and user satisfaction.

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.

For large-scale multiple-input multiple-output (MIMO) systems, iterative detection algorithms based on belief propagation (BP) have shown near-optimal performance. However, the existing deterministic implementations of BP detection are considerably complex, particularly for MIMO systems with a large scale. %This paper proposes an alternative approach using stochastic computation, offering advantages in both hardware complexity and fault tolerance. This paper proposes an alternative approach using stochastic computation.} This paper introduces a hardware architecture for stochastic message updating of observation nodes and symbol nodes. The signed stochastic real division (SRD) and the stochastic real addition (SRA) are proposed for latency consideration, and multi-bit designs are proposed to improve the performance and reduce the stream length. The look up table (LUT) has been modified for serial input to enhance hardware efficiency. Simulation results demonstrate that for large-scale MIMO systems with either QPSK or 16-QAM, the stochastic BP detector achieves similar performance as the deterministic one. To highlight its implementation advantage, an $8\times 32$ stochastic MIMO detector with QPSK is implemented. Results show that its hardware efficiency is about 10 times greater than existing works and has lower complexity compared with deterministic 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.

Congested link detection (CLD) has attracted more and more attention due to the rapid development of data traffic and services. In this work, we proposed a novel compressive sensing (CS) aided deep learning CLD scheme. Firstly, considering the network tomography structure, we proposed a CS-based preliminary CLD process to estimate the congestion probabilities for each link by utilizing the sparsity of congestions, it enables a decrease in the number of monitors needed, which in turn, enhances the flexibility and practicality of our scheme in various real-world scenarios. Then, based on the CS aided preliminary estimation, a long short-term memory network (LSTMN) is exploited to extract the time relationship of the congested states for each link, which can improve the accuracy of CLD. Moreover, since LSTMN utilizes the CS-aided preliminary estimation results to extract time relationships, our proposed scheme can reduce the monitoring cost and improve CLD accuracy. Ultimately, the simulation results substantiate the efficacy of our proposed scheme.

In this paper, we address the visible region (VR) detection problem in ultra-large-scale (ULS) uniform planar array (UPA) reconfigurable intelligent surface (RIS)-assisted communication systems. We propose a practical VR detection method for each individual RIS element even without channel state information (CSI). Furthermore, we introduce an adaptive binary search method that leverages the block characteristics of obstructed region (OR) in two different scenarios: the rectangular OR and the irregular OR. Numerical results demonstrate that the proposed method significantly reduces the detection overhead while achieving a detection accuracy comparable to, or even surpassing, that of the individual detection method. It can achieve accurate VR detection under sufficient transmitting power, even when the CSI is completely unavailable. % This work ensures precise VR detection in uniform planar array (UPA)-configured RIS systems, which is crucial for subsequent channel estimation and UE localization, ultimately maximizing the performance benefits of RIS deployment.

Meteor-burst communications (MBC) use ionized meteor trails to enable long-distance, beyond-line-of-sight links. Most existing models address long-range forward-scatter configurations, whereas back-scatter scenarios for short-range communications are less well studied. This paper presents a statistical, comparative analysis of meteor-trail parameters using observations from two back-scatter meteor radars operating at different frequencies (53.1 MHz at Qujing, 25.6°N, 103.8°E; and 39.0 MHz at Lincang, 23.63°N, 99.83°E) in Yunnan, Southwest China. The analysis is based on data collected over a 46-day period from 13th December 2024 to 27th January 2025, during which the 39.0 MHz system detected approximately 360 000 meteor events and the 53.1 MHz system detected approximately 280 000 events. We focus on spatiotemporal characteristics, decay times, and inter-arrival times of meteor trails. Detection rates exhibit strong diurnal variation, with higher counts in the local morning. Operating frequency strongly affects the detection of specular height: the peak of the meteor specular height distribution occurs near 85 km for the 53.1 MHz system and near 88 km for the 39.0 MHz system. While the lower-frequency radar generally provides higher echo occurrence rate and shorter inter-arrival times, this advantage reverses during local afternoon hours, when the higher-frequency system achieves a higher echo occurrence rate coupled with shorter inter-arrival times. These results provide quantitative constraints and prior information useful for optimizing short-range MBC systems, particularly with respect to frequency selection, temporal scheduling, and antenna configuration.

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.

Modulation signals are likely to experience channel fading when transmitted over wireless channels, and channel blind equalization is a powerful method to combat fading and guarantee high transmission quality and efficiency because no pilot information is required. In this paper, we propose a dual-branch blind equalization autoencoder with hybrid attention mechanisms, referred to as DBeA-HA, to restore various modulation signal waveforms. In the main branch, we employ multi-layer depthwise separable convolutions (DSC) with rich residual connections and squeeze-and-excitation (SE) mechanism in our encoder to extract channel features of faded signals at different resolutions, while reducing complexity. The auxiliary branch is constructed by incorporating a lightweight residual temporal dilated convolution to capture temporal correlations of faded signals. Additionally, the convolutional block attention module (CBAM) and multi-head self-attention (MHSA) mechanisms are applied within and between branches, respectively, to further enhance feature extraction and fusion capabilities. By integrating two branches effectively, the proposed method achieves high equalization performance and keeps low complexity. Experimental results reveal that in signal-to-noise ratio (SNR) ranging from -12-8 dB with six-path fading, our DBeA-HA effectively compensates for the effects of fading channels and surpasses existing equalization methods. For demodulation, compared with “ResNet+De” and the least mean square (LMS) methods, our DBeA-HA reduces the BER of QPSK by an average of 32.66% and 72.98%, and reduces the BER of 16-QAM by an average of 19.41% and 55.33%, respectively, with a moderate level of complexity.

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.

The security of maritime communication for transmitting sea clutter signals is of critical importance from both information-theoretic and electromagnetic signal processing perspectives. Existing prediction models face significant challenges in accurately capturing the non-stationary and chaotic characteristics of sea clutter, a typical electromagnetic scattering signal influenced by complex maritime environments. To address this, we propose a novel maritime covert communication scheme based on information-theoretic secrecy metrics, leveraging communication relay unmanned aerial vehicles to minimize the monitor's detection probability. A hybrid neural network model is developed for sea clutter prediction by integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and an attention mechanism. Initially, phase space reconstruction is applied to sea clutter signals measured by IPIX radar, exploiting their spatiotemporal correlation—a key property in electromagnetic signal analysis. The CNN extracts spatial features from the reconstructed signals, while the BiLSTM models temporal dependencies, effectively mitigating overfitting in long-sequence prediction. The attention mechanism further enhances performance by dynamically weighting salient features. Experimental results demonstrate superior prediction accuracy and robust target detection capability based on prediction errors. This work bridges deep learning-based electromagnetic signal processing and covert communication design, providing insights for the design of secure maritime information systems.

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.

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.

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.

Human body posture recognition has attracted considerable attention in recent years in wireless body area networks (WBAN). In order to precisely recognize human body posture, many recognition algorithms have been proposed. However, the recognition rate is relatively low. In this paper, we apply back propagation (BP) neural network as a classifier to recognizing human body posture, where signals are collected from VG350 acceleration sensor and a posture signal collection system based on WBAN is designed. Human body signal vector magnitude (SVM) and tri-axial acceleration sensor data are used to describe the human body postures. We are able to recognize 4 postures: Walk, Run, Squat and Sit. Our posture recognition rate is up to 91.67%. Furthermore, we find an implied relationship between hidden layer neurons and the posture recognition rate. The proposed human body posture recognition algorithm lays the foundation for the subsequent applications.

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.

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.

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 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.

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.

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.

As the fifth-generation (5G) mobile communication network may not meet the requirements of emerging technologies and applications, including ubiquitous coverage, industrial internet of things (IIoT), ubiquitous artificial intelligence (AI), digital twins (DT), etc., this paper aims to explore a novel space-air-ground integrated network (SAGIN) architecture to support these new requirements for the sixth-generation (6G) mobile communication network in a flexible, low-latency and efficient manner. Specifically, we first review the evolution of the mobile communication network, followed by the application and technology requirements of 6G. Then the current 5G non-terrestrial network (NTN) architecture in supporting the new requirements is deeply analyzed. After that, we proposes a new flexible, low-latency and flat SAGIN architecture, and presents corresponding use cases. Finally, the future research directions are discussed.

The satellite-ground integrated Networks (SGIN) emerge as a promising paradigm to extend the coverage and resilience of terrestrial networks. However, the high mobility and intermittent connectivity of satellites lead to inevitable ground-satellite handovers. Existing handover algorithms often overlook the inherent interdependence between ground-satellite handover and inter-satellite routing, resulting in suboptimal performance and degraded quality of service (QoS). To address these issues, we propose a heterogeneous graph neural networks-enhanced deep reinforcement learning (HGRL) algorithm for joint handover and routing optimization. First, we propose the semantic-based heterogeneous graph neural networks (SHGNN) to model SGIN as a heterogeneous graph, capturing the intricate relationships between handover and routing through diverse representations of nodes and edges. Then, we embed the SHGNN into a deep reinforcement learning (DRL) framework, enabling QoS-aware decisions for both ground-satellite handover and inter-satellite routing. Additionally, a non-dominated crowding sorting (NCS) mechanism is proposed to prune alternative paths while balancing multiple QoS objectives. Finally, extensive simulations in NS3 show that HGRL outperforms state-of-the-art algorithms, reducing the handover times and average delay by 63.63% and 36.85%, and improving the average throughput by 26.53%.

In recent years, with the advancement of computational hardware performance, machine learning algorithms have achieved significant development and widespread application across various fields, and have become deeply embedded in smart grids and communication systems. However, it is important to note that despite the widespread deployment of smart meters in the power system, the lack of reliable intelligent diagnostic, a large number of such electricity meters experiencing communication failures caused by internal topological defects every year. To address this issue, we propose a machine learning-based monitoring and early warning model using multidimensional feature fusion. By integrating more than twenty key features in four categories, including attribute features, operational load features, communication behavior features, and derived combined features—an XGBoost classification algorithm framework is constructed to implement risk early warning for electricity meter communication faults. Validated with data from millions of users, the proposed model achieves an accuracy of approximately 90%, the annual average reduction in power outages caused by communication faults is more than 10,000 hours, and significantly enhances the grid’s safety and operational stability.

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 5G networks, optimization of antenna beam weights of base stations has become the key application of AI for network optimization. For 6G, higher frequency bands and much denser cells are expected, and the importance of automatic and accurate beamforming assisted by AI will become more prominent. In existing network, servers are “patched” to network equipment to act as a centralized brain for model training and inference leading to high transmission overhead, large inference latency and potential risks of data security. Decentralized architectures have been proposed to achieve flexible parameter configuration and fast local response, but it is inefficient in collecting and sharing global information among base stations. In this paper, we propose a novel solution based on a collaborative cloud edge architecture for multi-cell joint beamforming optimization. We analyze the performance and costs of the proposed solution with two other architectural solutions by simulation. Compared with the centralized solution, our solution improves prediction accuracy by 24.66%, and reduces storage cost by 83.82%. Compared with the decentralized solution, our solution improves prediction accuracy by 68.26%, and improves coverage performance by 0.4dB. At last, the future research work is prospected.

In this paper, we propose a Transformer-OP co-optimized routing framework for UAV optical communication networks to address routing challenges. The framework features an encoder that employs multi-head attention mechanisms to extract spatiotemporal correlation features among nodes. The de-coder incorporates triple dynamic masking for path generation while dynamically evaluating link quality using Gamma-Gamma turbulence modeling. The REINFORCE reinforcement learning algorithm is applied to optimize reward functions, achieving an optimal trade-off between mission performance and turbulence-induced losses. Experimental results demonstrate that our method significantly improves routing stability in dynamic turbulence environments, enables millisecond-level routing decisions, and enhances overall system efficiency by more than 30%, providing valuable theoretical foundations and technical solutions for space-air-ground integrated communications.

In extremely high-mobility scenarios, the severe wideband Doppler effect significantly deteriorates the performance of traditional acquisition methods and increases computational complexity. Using the generalized wideband ambiguity function (GWAF), this paper derives the analytical and approximate closed-form expressions of the GWAF for the linear frequency modulated (LFM) signal and further clarifies the mechanism of correlation acquisition performance degradation for LFM signals under extremely high-mobility conditions. To overcome the limitation of performance degradation, an optimization method for the superposed LFM signal, based on its GWAF, using an alternating iterative genetic algorithm (GA) is proposed to improve the time-domain correlation performance of the superposed LFM signal, satisfying the requirements for time-domain correlation acquisition in low SNR and extreme high mobility scenarios. Based on the optimized signal, a low-complexity iterative superposition matched filtering (ISMF) acquisition method is designed for extremely high-mobility signals. Simulation results demonstrate the validity of the theoretical expressions of the GWAF and the proposed ISMF acquisition method based on optimized waveforms, achieving performance comparable to traditional methods while significantly reducing computational complexity.

Meteor burst channels (MBC) exhibit significant randomness and non-stationarity, limiting the effectiveness of traditional fixed-rate transmission strategies. This paper proposes an adaptive coding and modulation (ACM) scheme based on real-time channel state perception using enhanced Turbo codes. By leveraging the interleaving structure of Turbo codes to mitigate channel fading and incorporating multi-rate adaptation, the scheme dynamically adjusts symbol rate, modulation, and coding methods to improve spectral efficiency. Meanwhile, a joint evaluation mechanism integrating physical-layer signal to noise ratio (SNR) estimation and media access control (MAC)-layer frame error rate (FER) statistics is introduced to achieve dynamic optimization of switching thresholds in multipath channels. Simulation and experimental results demonstrate that the proposed scheme significantly enhances system adaptability to MBC, and increases data transmission success rates, offering a more efficient and reliable solution for MBC systems.

Steady-state visual evoked potential (SSVEP) has become a powerful tool for Brain Computer Interface (BCI) because of its high signal-tonoise ratio, high information transmission rate,and minimal user training.At present, the edge information of each region cannot be identified in spatial coding based on SSVEP-BCI technology, and the user experience is poor. To solve this problem, this paper designed a new paradigm to explore the relationship between the fixation point position of continuous sliding and the correlation coefficient ratio in the dualfrequency case. Firstly, the standard sinusoidal signal was employed to simulate the Electroencephalogram (EEG) signal, which verified the reliability of characterizing the amplitude variation of test signal by correlation coefficient.Then, the relationship between the amplitude response of SSVEP and the distance between the fixation point and the stimulus in the horizontal direction was tested by Canonical Correlation Analysis (CCA) and Filter bank CCA (FBCCA). Finally, the experimental data were offline analyzed under the condition of continuous sliding of the fixation point. It is feasible and reasonable to detect the amplitude change of frequency component in SSVEP by utilizing the spatial coding method in this paper to improve the extraction accuracy of spatial information.

Conventional path protection algorithms in all-optical data center networks (DCNs) need a relatively long calculation time but with low efficiency. In this article, we propose a preset path recovery algorithm by combining machine learning and priority execution method. The former is to predict areas of frequent optical path failures in data centers, and the latter is to ensure accurate protection in those areas while reducing computation time and improving performance. Firstly, we construct a dataset using rules composed of topological edge relationships, failure time, failure frequency, packet loss rate, latency and service type. Then, based on the above dataset, SVM (support vector machine) algorithm is used to predict frequent failure areas. Finally, based on different prediction frequencies, priority construction is carried out to cluster the entire topology, and p-cycle (preconfigured protection cycle) is applied to each topology. Experiments show that the combination of SVM prediction and classification protection can significantly reduce the protection range of potential failure areas to reduce computation time while ensuring high accuracy, as compared with traditional optical path protection algorithms.

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.

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.

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.

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.

Processors have been playing important roles in both communication infrastructure systems and terminals. In this paper, both application specific and general purpose processors for communications are discussed including the roles, the history, the current situations, and the trends. One trend is that ASIPs (Application Specific Instruction-set Processors) are taking over ASICs (Application Specific Integrated Circuits) because of the increasing needs both on performance and compatibility of multi-modes. The trend opened opportunities for researchers crossing the boundary between communications and computer architecture. Another trend is the serverlization, i.e., more infrastructure equipments are replaced by servers. The trend opened opportunities for researchers working towards high performance computing for communication, such as research on communication algorithm kernels and real time programming methods on servers.

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.

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.