Due to the elimination of cell boundaries, cell-free massive multiple-input multiple-output (MIMO) facilitates seamless handover and ensures consistent service, making it suitable for high-speed railway communications. However, high mobility causes rapid variations in the train-to-ground wireless channel, leading to significant channel aging, which severely undermines channel state information accuracy and degrades system performance, especially in cell-free setups where channel hardening is limited. In this paper, the impact of channel aging caused by high-speed train movement and the performance gains achievable through downlink training are investigated. We derive the downlink spectral efficiency (SE) and compare the outcomes for cases with and without downlink training for cell-free massive MIMO systems. Given the limited coherence time, an algorithm is proposed to mitigate the overhead from downlink training, which enables users to select distributed access points based on large-scale fading coefficients. Orthogonal pilot sequences are allocated to users associated with the same access point, effectively lowering pilot overhead and mitigating pilot contamination. Simulation results demonstrate that the benefits of downlink training offset the performance loss due to limited data transmission time, thereby enhancing system SE. Moreover, in situations characterized by elevated user density, the proposed pilot assignment algorithm markedly improves the system's total SE.

Full-duplex (FD) integrated access and backhaul (IAB) system requires effective self-interference (SI) suppression, relying on accurate channel estimation with high pilot overheads. This paper proposes a cost-effective end-to-end (E2E) hybrid beamforming framework for FD enabled millimeter-wave (mmWave) IAB systems with implicit channel state information (CSI), which unifies the pilot training and data communication to avoid accurate channel estimation. We introduce a Sensing Transformer block to sense spatial channels of the network, where sensing vectors are designed for pilot observation. Specifically, the Sensing Transformer is designed to implement an attention mechanism to effectively leverage implicit CSI from sequential pilot observations with an arbitrary length. Thereafter, we combine graph neural networks and Transformer mechanisms as a novel Graphformer algorithm to design hybrid beamformers with high spectrum efficiency, which can efficiently mitigate multi-user interference and SI via capturing both local and global spatial structure features of the whole network. Numerical results verify that: 1) The proposed E2E framework can significantly improve the spectrum efficiency with lower pilot overheads compared with the conventional method with explicit channel estimation. 2) The proposed E2E framework exhibits superior channel sensing and interference cancellation performance over traditional learning-based schemes across various network configurations, even with modest pilot overheads.

With the rise of the internet of things demanding low latency, the significance of real-time transmission communications is being increasingly emphasized. In real-time transmission systems, there are two ways to do the transmission: without and with channel coding, and therefore, it is worth comparing the performance of these ways. In this paper, the distortions of these two systems are derived. Specifically, a general formula for calculating distortion in wireless communication systems is first provided. Next, variations of the formula are developed for three standard wireless communication systems: single-input, single-output (SISO); multiple-input, multiple-output (MIMO) with a Gaussian channel; and mmWave MIMO. Theoretical derivations, supported by simulation results, demonstrate that systems without channel coding exhibit less distortion than those with channel coding on average.

In this paper, we proposed a novel millimeter wave channel emulation platform for satellite communication scenarios that can support Q/V frequency bands with a maximum bandwidth of 1 GHz. The platform has been calibrated for frequency flatness and frequency offset, and its reliability has been demonstrated through closed-loop verification based on standard testing instruments. At the same time, a method for modeling and analyzing satellite channels and real-time emulation environment was constructed, which can model and analyze satellite channels in different application scenarios. The obtained channel parameters are injected into the channel emulation platform to achieve real-time emulation of satellite channels in various application scenarios (stationary, vehicle mounted, shipborne, airborne, etc.). Finally, we conducted performance verification on the constructed satellite channel hardware-in-the-loop simulation system. This platform can provide support for key technology research and equipment performance evaluation in satellite communication.

Given the rapid advancements in wireless communication and terminal devices, high-speed and convenient Wi-Fi has permeated various aspects of people's lives, and attention has been drawn to the location services that Wi-Fi can provide. Fingerprint-based methods, as an excellent approach for localization, have gradually become a hot research topic. However, in practical localization, fingerprint features of traditional methods suffer from low reliability and lacking robustness in complex indoor environments. To address the identified shortcomings, this paper introduces Secci, an innovative feature-enhanced intelligent localization system underpinned by a squeeze-and-excitation convolutional neural network (SE-CNN) model, utilizing a rich array of channel state information (CSI) data. By modifying the device driver, diversified CSI data are extracted and converted into red, green, and blue (RGB) CSI images, which serve as input to the SE-CNN training in the offline stage, leveraging the SE network's attention mechanism for improved performance. Employing a greedy probabilistic approach, rapid prediction of the estimated location is performed in the online stage using test CSI images. The Secci system is implemented using off-the-shelf Wi-Fi devices, and comprehensive experiments are carried out in two representative indoor environments to showcase the superior performance of Secci compared to four existing algorithms.

Mobile edge computing (MEC) has been envisioned as an important technique of the 5th generation (5G) wireless networks to handle computation tasks from mobile terminal devices (TDs). To accommodate the low-delay requirements of mobile TDs with latency-sensitive and computation-intensive applications, we propose an edge-edge collaborative paradigm for software-defined networking (SDN)-enabled MEC networks by merging adjacent MEC servers with redundant computing resources to execute offloading tasks. Meanwhile, since task data distributed among various edge computation nodes is vulnerable to malicious attacks and eavesdropping, resulting in data and privacy leakage. We take secure offloading service into account and attempt to minimize the total cost of all offloading tasks by the optimal scheme, jointly considering offloading decision, security level and computing resource assignment. To reduce the total cost of all offloading tasks mainly caused by wireless communication, energy consumption and secure computing, we propose an optimization framework which can dynamically adjust the task offloading decision, security level and computing resource allocation policy simultaneously in MEC networks. We formalize the problem as a multi-agent decision based optimization problem, and further address it by using a multi-agent deep reinforcement learning (DRL) based method. Numerical results illustrate that the proposed DRL-based method is superior to the benchmark methods in terms of the total cost and support ratio.

Based on current research, there is a lack of comprehensive review articles that systematically address the security issues of the internet of vehicles (IoV) using blockchain technology. In this study, we thoroughly analyze the threats and challenges within IoV systems and provide a systematic review of blockchain-based IoV security solutions. This paper summarizes blockchain solutions for addressing key challenges in IoV, including network security, communication efficiency, resource optimization, data management, and transaction operational efficiency. Our findings indicate that blockchain technology can enhance V2X communication security, optimize resource utilization, ensure data integrity, and improve transaction security. Additionally, this paper explores future research directions, including advanced security mechanisms, efficient consensus algorithms, the integration of edge computing, and intelligent applications, demonstrating the potential of blockchain in tackling IoV system challenges.

Integrated super-resolution sensing and communication (ISSAC) has emerged as a promising technology to achieve extremely high precision sensing for those key parameters, such as the angles of the sensing targets. In this paper, we propose an efficient channel estimation scheme enabled by ISSAC for millimeter wave (mmWave) and terahertz (THz) systems with a hybrid analog/digital beamforming architecture, where both the pilot overhead and the cost of radio frequency (RF) chains are significantly reduced. The key idea is to exploit the fact that subspace-based super-resolution algorithms such as multiple signal classification (MUSIC) can estimate channel parameters accurately without requiring dedicated a priori known pilots. In particular, the proposed method consists of two stages. First, the angles of the multi-path channel components are estimated in a pilot-free manner during the transmission of data symbols. For hybrid beamforming structure, two approaches are introduced to obtain spatial covariance matrix in this stage. Second, the multi-path channel coefficients are estimated with very few pilots. Compared to conventional channel estimation schemes that rely solely on channel training, our approach requires estimating much fewer parameters in the second stage. Furthermore, with channel multi-path angles obtained, the hybrid beamforming gain can be achieved when pilots are sent to estimate the channel path gains. To comprehensively investigate the performance of the proposed scheme, we consider both the basic line-of-sight (LoS) channels and more general multi-path channels. We compare the performance of the mean square error (MSE) of channel estimation and the resulting hybrid beamforming gains of our proposed scheme with the traditional scheme that rely exclusively on channel training. It is demonstrated that our proposed method significantly outperforms the benchmarking scheme. Simulation results are presented to validate our theoretical findings.

The low Earth orbit (LEO) satellite networks play an important role in the future communication networks. However, under the end-to-end (E2E) transmission background, inter-satellite routing has been widely studied, but the influence of ground-satellite links (GSL) on routing has received less attention. In this paper, a fast E2E satellite routing algorithm based on access node selection is proposed. Firstly, the delay of four path modes generated by users accessing the network from different satellites is analyzed, and the influence of delay on E2E routing performance is presented. Then, jointly considering routing delay and node load, an access node selection strategy is proposed by using the shortest E2E delay to determine the access source and destination node within satellites. Finally, an optimization domain is divided from the network topology by using the shortest delay path based on hops constraints. And a routing optimization algorithm based on Q-learning has been proposed in the optimization domain, realizing high computational speed and stable results. The simulation results show that the access node selection strategy can decrease E2E delay by up to $10$~ms and enhance the performance of node load balancing. And the routing optimization algorithm can reduce the average computation time.

Orthogonal time frequency space (OTFS) has been broadly studied and applied in various fields due to its robustness in high dynamic scenes. In this paper, we investigate the application of OTFS in radar-embedded communication systems, and propose an OTFS-based radar-embedded communication model suitable for high-speed mobile scenarios. By comparing the communication reliability at different velocities, the benefits of OTFS-based radar-embedded communication systems over conventional linear frequency modulation (LFM) based systems are verified. The proposed algorithm also shows excellent radar detection performance, and compensates the channel state information according to the motion parameters of the target to achieve more accurate communication reception. Besides that, our algorithm exhibits superior covertness performance to that of LFM.

Utilizing unmanned aerial vehicles (UAVs) for flexible and efficient mobile data dissemination offers a promising solution for infrastructure-less internet of things applications. This paper investigates a UAV-enabled data dissemination system in which a multiantenna UAV is deployed to disseminate data to ground terminals (GTs). Under the constraints of UAV speed, communication outage probability (OP), UAV transmission power, and task data size, we aim to minimize the system mission completion time (MCT) by optimizing the UAV trajectory, UAV-GT transmission scheduling, and bandwidth allocation, while considering the imperfect channel state information (CSI). To address this challenging problem, we introduce matched-filter precoding and orthogonal frequency division multiplexing (OFDM) techniques and propose a two-stage optimization algorithm. Initially, an offline optimization algorithm is developed to determine the optimum UAV trajectory and the MCT. Subsequently, an online optimization algorithm is proposed to enhance the subchannel and transmission power allocation. Finally, the numerical results confirm the effectiveness of the proposed scheme, demonstrating a substantial improvement in the time efficiency of UAV data dissemination.

In the process of minimizing the training loss of machine learning model pursuit, it is very easy to inadvertently remember sensitive private data, which leads to data reconstruction, member reasoning attacks and other security problems. In order to mitigate these risks, differentiated privacy has become a key standard for privacy preserving machine learning. The classical differential privacy depth learning algorithm, differential privacy stochastic gradient descent (DP-SGD), that adapts the standard stochastic gradient descent (SGD) algorithm to incorporate differential privacy, ensuring that the trained model doesn't reveal sensitive information about individual training data points. However, DP-SGD has the problems of slow convergence speed and large utility loss. We propose an effective solution that is the cooperative combination of selective updating and early vertical gradient disturbance. Selective updating ensures that the training track of the model is aligned with the optimal direction, significantly accelerating convergence. Subsequently, the application of vertical gradient perturbation ensures that the model with significantly improved accuracy can be achieved even under strict privacy constraints (small privacy budget). Through theoretical analysis and a large number of experiments, this paper proves that DP-VGPSU has superior performance in convergence speed and accuracy.

With the development of internet of things (IoT) technology, sensor networks are used in a wide variety of fields. However, the energy replenishment of sensor nodes (SNs) is still a challenge. In this paper, we propose an unmanned aerial vehicle (UAV) wireless charging strategy (WCS), which can jointly maximize the system profit and the number of survival nodes through an efficient charging order. To effectively reduce the data transmission delay, we group the nodes to realize the data collection solely by cluster head (CH). In the proposed charging order algorithm, we particularly design a CH replacement scheme when the original head runs out of energy before the UAV arrives. Compared with other UAV charging schemes, the proposed WCS shows a better performance in terms of system profit and time delay. Furthermore, it ensures a rather satisfying node survival rate.

This work presents a concept of a multiple-input multiple-output (MIMO) antenna with wideband, high isolation, and high gain characteristics. Wideband performance is achieved using a patch antenna with a substrate suspended from an air gap. Unlike the conventional MIMO designs, in which only one element is excited for each port, the proposed antenna excites two MIMO elements simultaneously with the aid of a wideband hybrid coupler. Consequently, high gain radiation without requiring additional MIMO element and high isolation can be simultaneously achieved. A prototype designing at the central frequency of 2.45 GHz has been fabricated and measured, showing an impedance bandwidth of 25.7% (2.11--2.74~GHz) with a minimum isolation between two ports of 15 dB, and a peak gain of about 10.35 dBi.

Cognitive radio enables efficient utilization of spectrum resources and adapts to changing environments through intelligent sensing and adaptability. However, centralized spectrum allocation limits their efficiency. This paper proposes a blockchain-based priority auction consensus mechanism for dynamic spectrum allocation in cognitive radio. The distributed spectrum allocation improves efficiency, the use of blockchain ensures transparent and secure transactions, while the priority auction consensus ensures the communication quality of users. Simulation results validate that primary users achieve near-optimal performance, while secondary and other users guarantee communication performance, resulting in an overall spectrum utilization close to 100%. These highlight the potential of the priority auction consensus mechanism in enhancing the spectrum management of cognitive radio networks.

This paper presents an 8-port multiple-input multiple-output (MIMO) array with single-layer and compact size characteristics for 5G internet of things (IoT) devices. The proposed array employs four dual-polarized radiators arranged in a $2 \times 2$ configuration to realize 8 uncorrelated streams. Each radiator is formed by combining a crossed patch with four parasitic elements for bandwidth enhancement. For mutual coupling mitigation, multiple shorting pins are inserted between the radiators. Accordingly, high isolation can be obtained without requiring large spacing between the MIMO elements. The final array has overall dimensions of $57.2 \text{ mm} \times 57.2 \text{ mm} \times 1.6 \text{ mm}$, or about $0.82\lambda \times 0.82\lambda \times 0.02\lambda$ at the lower edge frequency of the operating band from $4.32$ to $4.68 \text{ GHz}$, corresponding to about 8%. Across this band, the inter-port isolations are always better than $17 \text{ dB}$.

In this paper, we investigate the secure communication in internet-of-things (IoT) network. Specifically, we propose a novel dual-IRS-UAV assisted secure transmission scheme, wherein two unmanned aerial vehicles (UAVs), each carriying an intelligent reflecting surface (IRS), can adjust their horizontal positions to efficiently assist the secure transmission from the IoT data sender to the receivers. We aim to maximize the minimum system secrecy rate by jointly optimizing the horizontal locations of UAVs, the transmit beamforming of IoT data sender and the reflection phase shifts of IRSs. The considered problem is non-convex. We first divide it into several sub-problems which are then transformed using the first-order Taylor expansion approach, which can be solved by standard optimization tools. Numerical results show the performance gain in minimum system secrecy rate achieved by the proposed dual-IRS-UAV assisted secure transmission scheme over the benchmark schemes.

IEEE 802.11ax introduces orthogonal frequency division multiple access (OFDMA) technology to achieve parallel access for multiple users. However, when a large number of users access, the probability of collision will be greatly increased, resulting in greatly reduced throughput. In high-density wireless networks, in order to reduce collisions between users, a hybrid contention/designation-based uplink OFDMA random access (HCDRA) scheme is proposed. The proposed scheme adopts a hybrid of contention and transmission of designation, where the access point (AP) arranges one of the collided stations (STA) on the resource unit (RU) for transmission of designation in the next transmission, and other STAs still follow the traditional contention mechanism. The addition of designated transmission scheme can effectively reduce the intensity of contention and improve the utilization rate of RU without knowing the prior STA and without increasing the overhead of the sensing time. The proposed scheme can greatly improve throughput and latency performance without introducing additional overhead. Moreover, our proposed hybrid scheme has almost no changes to the IEEE 802.11ax standard protocol, and is suitable for IEEE 802.11ax and beyond. The feasibility of the proposed scheme has been demonstrated through numerical simulation results.

A covert communication approach in time-frequency domain is presented. By deliberately embedding covert signal into the primary transmitted signal, the covert signal is completely hidden, rendering conventional signal detection methods ineffective. However, the synchrosqueezing transform (SST) maps the signal into the time-frequency domain, resulting in improved time-frequency localization and more effective separation of its components. Based on the proposed instantaneous frequency (IF) curve extraction and filtered reconstruction scheme, the waveform of the covert signal can be automatically extracted. Experimental results from synthetic signal testing validate the efficiency of the proposed method, demonstrating its potential for covert communication applications.

Large language models (LLMs) are rapidly transforming the design and operation of communication systems, while the advent of 6G networks provides the infrastructure necessary to sustain their unprecedented scale. This survey investigates the bidirectional relationship between LLMs and 6G networks from two complementary perspectives. From the perspective of LLM for Network, we illustrate how LLMs can enhance network management, strengthen security, optimize resource allocation, and act as intelligent agents. By leveraging their natural language understanding and reasoning capabilities, LLMs offer new opportunities for intent-driven orchestration, anomaly detection, and adaptive optimization beyond the scope of conventional AI models. From the Network for LLM perspective, we discuss how 6G-native features support scalable, efficient, and sustainable LLM training and inference across the edge and cloud. Building on these two perspectives, we identify key challenges related to scalability and efficiency, robustness and security, as well as trustworthiness and sustainability. We further highlight open research directions as well. We envision that this work serves as a roadmap for cross-disciplinary research, fostering the integration of LLMs and 6G toward trustworthy and intelligent next-generation communication systems.

Accurate radar-cross-section (RCS) characterization of unmanned aerial vehicles (UAVs), human heads, and combined human head-hand targets is essential for integrated sensing and communication (ISAC) channel modeling. We therefore conduct monostatic RCS measurements over 0-360° azimuth in an anechoic chamber and derive statistical models for these targets. The measured RCS data are fitted using Rician, Gamma, and LogNormal distributions. Our analysis reveals that the Rician distribution is suitable for modeling small UAV RCS, while the Gamma distribution shows superiority for human head and combined human head-hand targets. Detailed distribution parameters are provided, offering valuable insights for predicting and evaluating the sensing performance of ISAC systems. These findings contribute to advancing ISAC channel research and practical applications.

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 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.

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

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

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.

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.

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.

Meteor burst communication exploits transient meteor trails to enable beyond-line-of-sight transmission, where the inherently short-lived channel conditions impose stringent requirements on rapid and reliable signal-to-noise ratio estimation. This study proposes a novel pilotless SNR estimator founded on the two-sample order statistics method, which quantifies the distributional deviation between received signal amplitudes and pre-generated reference distributions without assuming a specific noise model. Comprehensive Monte Carlo simulations under BPSK, QPSK, and 16-QAM modulations in additive white Gaussian noise environments demonstrate that the proposed OS-based approach consistently surpasses the M2M4, M8, and Kolmogorov-Smirnov estimators in terms of estimation accuracy, normalized mean squared error, and success rate, with particularly notable gains in limited-sample scenarios. These results underscore the method’s robustness and adaptability for SNR estimation in MBC applications.

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.

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.

The characteristics of the meteor burst channel are defined by a number of random parameters, such as trail ionization level, its direction and localization relative to the receiver and transmitter, height of radio reflection, time of day, among others. Such randomness significantly increases the experimental cost of evaluating performance of the meteor radio link. Thus, a multi-dimensional joint modeling approach based on the spatio-temporal evolution of meteor trails, which takes into account interactions among random parameters of meteor burst channel, is proposed. The link performance sensitivity to localization, link length, and power margin are obtained by meteor radio reflections modeled for the typical meteor radio link. A comparison with simulated and experimental data shows good accuracy of forecasts made by this model.

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

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.

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.

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.

The proliferation of phone communications has escalated the risk of call security, prompting extensive research into phone tapping. Existing optical and trojan-based techniques encounter challenges in conditions such as darkness and resistance from antivirus software. In this paper, we present mmwTapper, a system leveraging mmWave signals to measure the subtle vibrations of a phone caused by its speaker, enabling the tapping of the speech and identity of a remote anonymous caller. Due to the extremely weak vibration caused by the speaker, the signal-to-noise ratio of the detected caller speech is very low. To enhance caller speech clarity, we propose a multi-stage learning based caller speech enhancement network to sequentially denoise the amplitude and phase of the speech. Moreover, collecting samples is extremely challenging in tapping applications. To ensure accurate identification with a limited number of samples, we design a contrastive learning based caller identification network, which can be pre-trained with a substantial number of public samples to extract invariant speech features. We extensively evaluate the performance of our proposed mmwTapper under various conditions and the results demonstrate its effectiveness in tapping caller speech and identity, even with a limited number of samples.

Amid intensifying global cyberspace confrontation, traditional security systems face non-systematized defenses, asymmetric cost challenges, and quantum computing threats, highlighting the urgent need for a new security paradigm with proactive defense capabilities. In response, this paper proposes the "quantum-native security" theoretical framework, which integrates cutting-edge quantum technologies such as quantum random number generation, quantum keys, quantum identification, quantum genes, and quantum vaccines. Based on quantum-native security protocols, a multi-dimensional quantum security architecture is constructed, covering terminals, networks, applications, and data. Research demonstrates that quantum random numbers, with their inherent physical unpredictability, can effectively resolve the vulnerabilities of traditional pseudo-random algorithms. Additionally, this paper introduces the "network-protection-network" quantum guardian paradigm, leveraging quantum backbone networks as a trusted foundation to upgrade from point-based protection to comprehensive collaborative defense. This study presents innovative solutions to counter the emerging threats posed by quantum computing and lays the foundation for developing an autonomous, controllable next-generation cybersecurity system.

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.

With the dramatic increase in the demand for computing power across various services, the emergence of the computing power network (CPN) becomes inevitable. This paper studies the task scheduling to minimize energy consumption under delay constraints considering the heterogeneity of computing resources. Specifically, we decompose the original problem and alternatively optimize the scheduling strategy and server parameters until convergence. Dynamic Voltage and frequency scaling (DVFS) technology is leveraged to allocate the optimal voltage and frequency for each server based on their task loads. An enhanced projection gradient descent method is utilized to update the scheduling strategy under the given server parameters. Simulation results show that our algorithm achieves significant performance gains compared to the baselines across various CPN scenarios.