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.

Meteor radars are widely used to study the dynamics of the mesosphere and lower thermosphere, where the accuracy of atmospheric observations depends critically on the quality of meteor echo signals. This study focuses on improving echo signal quality, as meteor trail echoes are transient, low-SNR, and highly susceptible to interference. Four filtering algorithms-wavelet denoising-bilateral filtering (WD-BF), wavelet denoising-guided filtering (WD-GF), extended Kalman-guided filtering (EK-GF), and an improved convolutional neural network (ICNN)-based method are examined through theoretical analysis and numerical simulations. The optimal algorithm is further integrated into the digital acquisition and processing unit of a meteor radar system. The WD-GF method shows superior denoising performance and robustness, yielding an average SNR improvement of 9.3 dB relative to the raw signal. Long-term field observations verify its effectiveness, demonstrating a 15.26 % increase in detected meteors. The proposed WD-GF filtering algorithm significantly improves meteor radar detection capability and measurement accuracy, providing a practical and efficient solution for high-precision, real-time atmospheric observations.

Meteor burst channel demonstrates special fading characteristics, in which the statistics of channel envelope along the time domain explicitly violate the traditional iid (independent and identically distributed) assumption. Because this kind of channel will rapidly disappear after its occurrence. In order to adapt to this particular characteristic of meteor burst channel, a non-iid assumption based Gaussian approximation (GA) algorithm is employed to rebuild the state-of-the-art polarization adjusted convolutional (PAC) code. Then, the polarization effect under non-iid condition is analyzed and an interleaving method is employed to enhance the polarization efficiency. Compared with the widely used low density parity check (LDPC) codes and turbo product codes (TPC), our interleaved PAC codes are capable of decreasing the BLER level by one order of magnitude.

Meteor burst communications (MBC) offers a resilient method for long-range data transfer, whose performance could be enhanced during meteor showers. However, the potential improvements in channel parameters attributable to meteor showers have not been systematically evaluated. This study presents a detailed analysis of key MBC metrics including the spatial distribution, decay time, and interval time of usable meteor trails in the case of Geminids meteor shower in 2024, leveraging the University of Science and Technology of China (USTC) multi-static meteor radar network. By synergistically combining forward-scatter (FS) and backscatter (BS) observations, we overcome the limitations of single-technique studies and can compare the different echoes simultaneously detected in this network. The spatial distribution of usable trails is mapped, showing significant regional variability related to the shower radiant. Compared with sporadic background conditions, the Geminids (GEM) shower increases the mean and median trail decay time ($\tau$) by up to 20% and decreases the mean and median interval (waiting) time by up to 70% (maximum relative changes across the investigated links). This work translates raw meteor observations into practical communications insights, providing a predictive framework for optimizing MBC system scheduling, data throughput, and receiver design during shower periods.

Meteor-burst communication provides low probability of interception and strong resilience to jamming, making it attractive for maritime and terrestrial emergency links. In practice, however, short and rapidly decaying trails prevent conventional modulation and coding schemes (MCS) from exploiting high-SNR opportunities. To address this, we propose a joint adaptation strategy that adjusts transmit power, symbol rate, modulation, and coding within each burst. The threshold-tracking power control and the geometrically quantized symbol rate ladder stabilize the symbol SNR. On top of this, bit-interleaved coded modulation, with adaptive modulation and code-family selection (LDPC for longer trails and Polar for shorter ones), enables higher-order constellations while satisfying block error rate (BLER) targets. Incremental redundancy (IR) coding and IR hybrid automatic repeat request (HARQ) further improve reliability. The joint configuration covers the symbol rate, modulation order, and IR coding, enabling efficient utilization of the channel. The numerical results show consistent gains in average throughput and success probability over baselines, with robustness against fading and short trails.

The meteor burst communication (MBC) system exploits short-lived ionized meteor trails to establish long-distance but intermittent radio links. However, the feasibility of such data transmission strongly depends on meteor trail properties. We utilize observation data from the University of Science and Technology of China (USTC) multistatic meteor radar network to statistically analyze the key parameters of meteor trail echoes. The decay times, waiting time, and signal-to-noise ratio (SNR) are investigated in detail. Results show that most trails are only suitable for instantaneous data transmission, and longer-lived trails occur at lower altitudes. More distant forward-scatter links detect shorter decay times, leading to longer waiting times and higher latency. The SNR statistics further reveal diurnal, seasonal, and spatial patterns consistent with meteor occurrence. These findings offer quantitative guidance for the design and optimization of MBC systems, informing decisions on packet sizing, probing intervals, and rate adaptation based on link geometry.

In order to avoid frequent manual replacement of underground sensor node batteries, many researches have been devoted to the realization of wireless powered underground sensor networks (WPUSNs). However, existing schemes mainly focus on the design of routing protocols and network topologies, failing to address the challenge of activating energy harvesting circuits. To this end, we propose a backscatter-assisted distributed beamforming-based WPUSN (B$^2$-WPUSN). The key insight of B$^2$-WPUSN is utilizing backscatter to acquire the accurate channel state information (CSI) and designing the corresponding beamforming vector to concentrate the energy until it exceeds the startup threshold of the node. In particular, since backscatter causes additional attenuation, we use a LoRa signal, whose high sensitivity ensures correct channel estimation. We prototype B$^2$-WPUSN on universal software radio peripheral (USRP) radios and evaluate its charging performance in a sandbox. The experimental results show that the average charging time is less than 40 seconds even at a soil moisture of 15%.

Neural receivers have demonstrated strong performance in wireless communication systems. However, their effectiveness typically depends on access to large-scale, scenario-specific channel data for training, which is often difficult to obtain in practice. Recently, generative artificial intelligence (AI) models, particularly diffusion models (DMs), have emerged as effective tools for synthesizing high-dimensional data. This paper presents a scenario-specific channel generation method based on conditional DMs, which accurately model channel distributions conditioned on user location and velocity information. The generated synthetic channel data are then employed for data augmentation to improve the training of a neural receiver designed for superimposed pilot-based transmission. Experimental results show that the proposed method generates high-fidelity channel samples and significantly enhances neural receiver performance in the target scenarios, outperforming conventional data augmentation and generative adversarial network-based techniques.

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.

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.

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.

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.

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.

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.

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.

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.

The air traffic management (ATM) system is one of the important national information infrastructures, and its information security is crucial. According to the requirements of the aviation cybersecurity strategy released by the international civil aviation organization (ICAO) in October 2019, it is necessary to achieve security situational awareness of ATM systems. This paper proposes an ATM multiple regression situation assessment model based on the sparrow search algorithm bidirectional long short-term memory (SSA-BiLSTM), considering the characteristics of wide area distribution and multi-source heterogeneous data in ATM systems. This model utilizes sparse autoencoder (SAE) to extract multivariate and time series features of ATM system data, and uses sparrow search algorithm (SSA) to optimize the hyperparameters of BiLSTM model, reducing the influence of subjective factors on security situation assessment results and solving the problem of subjective interference in existing situation assessment methods. This paper compares the SSA-BiLSTM model with other situational assessment methods. The mean absolute error (MAE) on the standard dataset is 0.0027, outperforming other methods and demonstrating advantages. In tests on the ATM dataset, due to its high nonlinearity, noise interference, and temporal dependencies, the model’s MAE reached 0.36744, which outperforms other approaches and meets the practical requirements of the ATM environment.

In this paper, we propose a robust coherent joint transmission scheme for user-centric cell-free orbital angular momentum (OAM) networks. We formulate a coherent joint transmission (JT) problem to maximize the weighted sum capacity. The formulation enforces per-access point (AP) power budgets and per-mode exclusivity. To address the coupling between mode selection and beamforming, a lightweight block coordinate descent based fractional programming (BCD-FP) algorithm is developed, which alternates between mode assignment, AP scheduling, and beamforming. Extensive simulations validate that the proposed method improves sum capacity by approximately 1.4 to 1.9 times over stochastic gradient descent (SGD) and greedy baselines, reaching 85-90% of the perfect channel bound. Results also indicate an additional 8-10% gain over non-OAM counterparts. Furthermore, simulations under high mobility demonstrate the algorithm's robustness against channel aging, validating its efficacy in dynamic environments.

Low earth orbit satellite communication systems (LEO-SCS) have gained significant attention as a crucial component of future space-terrestrial integrated networks. This paper explores the application of non-orthogonal multiple access (NOMA) in downlink transmission for LEO-SCS, addressing a joint problem of user grouping, beamforming, and power allocation. This problem can be formulated as a system sum rate (SSR) maximization problem, subject to constraints as the minimum transmission rate requirement for each user equipment (UE), the satellite’s total power budget, and the positions of the UEs. To tackle this non-convex problem, we propose a multiview-aided multibeam multiuser grouping (MVA-MBMUG) scheme. Specifically, we first define the grouping problem as a multiview grouping problem, in which all UEs are divided into multiple groups. These groups are designed to achieve optimal performance with respect to both the downlink channel vectors and the locations of each UE simultaneously. Next, we introduce a low-complexity multicast beamforming technique that effectively mitigates inter-group interference. Finally, by employing a fast water-filling algorithm for power allocation, we optimize the SSR by determining the optimal power distribution for each UE. Simulation results demonstrate that the proposed MVA-MBMUG scheme in NOMA-based LEO-SCS significantly outperforms other benchmark algorithms in terms of SSR.

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.