Accurate positioning provided by Global Navigation Satellite Systems (GNSS) is critical for location-based services. However, in dense urban environments, multipath effects and non-line-of-sight (NLOS) obstructions from tall buildings severely degrade performance. To address these challenges, we propose the Building-Blockage Forecasting Network (BBF-Net), a transformer-based framework that shifts from passive detection to proactive forecasting of satellite visibility. Unlike conventional approaches, BBF-Net jointly models 3D urban structures, vehicle dynamics, and temporal motion patterns, while employing a direction-aware loss to emphasize satellite-relevant azimuths. This design allows the system to predict future obstruction events in advance, thereby actively mitigating multipath and NLOS effects. Comparative experiments across multiple urban scenarios show that BBF-Net reduces blockage prediction error by up to 63.8% and generates more spatially coherent blockage maps. When applied to satellite selection, it further reduces positioning error from 7.395 m to 6.365 m, and standard deviation from 12.906 m to 8.673 m. These results establish a forecasting-based paradigm that enhances GNSS reliability in complex urban environments by directly addressing multipath/NLOS challenges.

With the growing importance of information security, numerous physical-layer encryption algorithms have been developed. However, when deployed in non-reciprocal wireless environments, these schemes often suffer from a high key error rate (KER), elevated key outage probability (KOP), and low encryption efficiency. To overcome these limitations, this paper proposes a Hybrid Sequence Key Generation (HSKG) scheme with targeted design strategies. First, the channel is mapped into a unified fixed-value subspace through channel superposition and discretization of a mapping function. The index of the upper boundary of the fixed-value interval is exchanged to alleviate channel non-reciprocity while preventing leakage of legitimate channel information. This correction significantly enhances the channel consistency, improving the correlation coefficient from 0.5623 to 0.9998. Second, an exchange factor is generated from the corrected channel sequence. The corrected sequence and a pseudo-random sequence are then block-exchanged and combined based on this factor to construct a hybrid sequence. A quadratic key extraction model is subsequently introduced to maximize the divergence between the hybrid sequence and the original sequences, thereby reducing the probability of successful eavesdropping by Eve. Finally, identity legitimacy is verified by evaluating the consistency of the decoded pseudo-random sequence across multiple stages. Extensive experimental results demonstrate the security and effectiveness of the proposed scheme. In real-world environments, the HSKG scheme achieves an outage probability (OP) of 0.0166, a KOP of 0.0486, a KER of 0.0001, and a security rate (SR) of 1.1 bit/s. Benefiting from its targeted design, the HSKG scheme significantly outperforms the BE algorithm in both connection-interruption and security-interruption performance.

For ensuring the integrity of high-resolution remote sensing (HRRS) images, the perceptual hash method offers a dual advantage: it preserves the non-destructive nature of the original image while also ensuring robustness to content-preserving operations. However, current deep learning based HRRS image hashing methods for integrity authentication are notably limited as they terminate at the feature extraction stage and fail to achieve an end-to-end construction from image to hash value. Consequently, there is a looming risk of uncontrollability and unexpected events. To overcome this problem, this paper proposes A Deep Subject-Sensitive Hashing Network (DSSHN), presenting a unified network for end-to-end feature extraction and hash construction. Improved Convolutional Block Attention Module (I-CBAM) helps the network to focus more on subject-sensitive features. A targeted training scheme ensures perceptual hash robustness. Experimental results reveal that the algorithm achieves the best tampering detection performance, with top AUC (0.994) and leading precision and recall rates.

Stepped frequency continuous wave (SFCW) ground penetrating radar (GPR) is a geophysical method to detect the structure and characteristics of underground burial targets by using high-frequency electromagnetic waves. In this article, in order to reconstruct the 3-D structure of underground objects with complex structures, we design a reconfigurable multistate GPR (RM-GPR) and propose a pipelined 3-D back-projection (BP) algorithm. The system generates range profiles by using the phase of the echoes, which can effectively avoid the decline in resolution caused by the attenuation of the high-frequency part. Then, we improve the BP imaging algorithm into 3-D space and improve the computational efficiency of the algorithm by limiting the single imaging area and embedding a part of the calculation process into the signal acquisition process. Compared with the classical GPR system, the unique aspect of the RM-GPR system includes lower complexity for hardware components and lower uncertainty, higher clarity, and less calculation cost for pipelined 3-D BP imaging algorithm, which helps people have an intuitive understanding of unknown targets underground.

Multisource navigation involves using multiple available sensors to achieve high-precision location services, but navigation devices and data are susceptible to various environments and attacks, emphasizing the need for pervasive security measures like data reliability evaluation. Common reliability estimation methods for Internet of Things data are weak for highly dynamic navigation data, which are invalid when sensor motion is considered. We proposed a method combining self-evaluation and mutual evaluation to assess the reliability of real-time navigation sensor data. Our approach introduces an innovative transformer-based model for numerical-based sensors and a feature point prediction consistency test for digit image-based sensors. We also proposed a reconstruction consistency check method for mutual evaluation of multiple digital images. By combining self-assessment and mutual assessment, we determine the final reliability of the sensor data. To enable mutual evaluation of multiple digital images, we proposed a reconstruction consistency check method. By combining the results of self-assessment and mutual assessment, we determined the final reliability of the sensor data. The performance of our method was evaluated using both trustworthy and untrustworthy data. We have observed significant improvements in average absolute errors for UWB positioning and visual odometry positioning through experimentation, utilizing reliability-based weighted fusion, resulting in a reduction of 0.28 m for UWB positioning and 0.044 m for visual odometry positioning.

Ground penetrating radar (GPR) is a non-destructive detection technology. It uses high frequency electromagnetic energy to locate targets underground. In the direction of motion, GPR can produce Bscan with good performance. The lateral image is typically constructed using the results of many detections. By integrating beamforming with stepped frequency continuous wave, we propose a method that increases the lateral detection performance. The performances of two beamforming methods in GPR are compared. We designed a 1 × 4 multi-static GPR and did two experiments. The findings demonstrate that the beamforming improves the lateral detection performance in comparison to single-static GPR. Compared with delay-and-sum methods, MVDR method has better performance in suppressing the sidelobe level and avoiding false targets.

Nowadays, benefiting from its strong capability of non-destructive detection, the ground penetrating radar (GPR) has been applied to detect and reconstruct underground targets and has drawn lots of attention both in military and civilian fields. However, in the processing of GPR imaging, due to the dispersion errors caused by random distribution of various particles in soil, conventional imaging methods have disadvantages of low signal-noise ratio (SNR), low resolution and unbalanced amplitude. In this paper, in order to achieve high resolution and high veracity on underground targets 3-D reconstruction, we improved the conventional reverse time migration (RTM) algorithm in perspective of medium constitutive relationship. Besides, we extended the improved RTM method in 3-D environments and reconstructed the 3-D structure of several underground targets. To make RTM algorithm suitable for stepped frequency continuous wave (SFCW) GPR system, we generated three excitation signal models and analyzed the effect of different excitation signals on imaging performance. Finally, through the quantitative analysis of simulation and on-vehicle experimental results, we found that the 3-D images generated by improved RTM method had higher resolution, smaller measurement error, and higher veracity than those of conventional RTM method.

Coded caching is a technique that promises significant reductions in network traffic by exploiting the multicast opportunities among multiple cache-enabled users. Most works in this area investigate the fundamental performance limits of coded caching from an information-theoretic perspective. In this paper, from a practical perspective, we focus on the design and analysis of the coded caching scheme in large-scale stochastic networks with dynamic traffic. Aiming at the challenges of the large number of users, dynamic packet requests, and distance-dependent interference, the packet caching scheme and packet delivery scheme are jointly designed to ensure the availability of the coded caching gain. To characterize the coded caching performance, the queue dynamic of the packet requests and channel state are first analyzed. Then by combining the successive group decoding theory and stochastic geometry approach, the network interference is characterized and the successful transmission probability of the multicast coded signal is further derived. Moreover, the coded caching scheme is optimized to minimize the average delay of the packet request, which is proved to ensure service fairness and reduce the complexities of network performance analysis. The analytical and numerical results provide useful insights for the provisioning and planning of the coded caching network.

To realize the vision of the Internet of Things (IoT), mobile-edge computing (MEC) has recently emerged as a promising paradigm to meet the computation demand from mobile users (MUs). In this article, we study the network performance in large-scale stochastic MEC wireless networks, where the tasks can be computed locally by the local computation capabilities (LCCs) or be offloaded to MEC servers for edge computing. To this end, a MEC network is modeled featuring random node distribution, dynamic task requests, orthogonal frequency-division multiple access, task retransmission, and parallel computing in MEC servers. Given the model, a 2-D discrete-time Markov chain is first adopted to characterize the task execution process, including local computing and task offloading. Based on the coupling between communication and computing, the average outage probability of the task transmission and the average MEC computation load are derived by integrating the stochastic geometry and queuing theory. Furthermore, by jointly analyzing the local computation latency, transmission latency, and edge computation latency, we derive the average end-to-end latency of the task execution. Our results show that the LCCs in MUs can improve the network performance, including communication and computation performance, in stochastic MEC networks. In addition, useful guidelines for MEC network provisioning and planning are provided to avoid either the local computing or the task offloading being the latency performance bottleneck.

In this paper, we analyze the system performance of a stochastic network, which is in the stationary state. Different from previous full-loaded assumptions that base stations (BSs) are always transmitting signals, which overestimates the inter-cell interference (ICI), dynamic traffic is considered in our model and BSs can be silent in some time slots. Packet retransmission is adopted, which brings a coupled relationship between the outage probability and the buffer empty probability. That is, as the outage probability increases, packets that are not successfully received will remain at the head of the buffer and be retransmitted until they are successfully received, which increases the proportion of active BSs and aggravates the ICI in turn. To better analyze this coupled relationship, the ICI is analyzed with the aid of the probability generating function and the Laplace transform. Then a queuing model is established to depict the packet arrival and departure processes. Some important system metrics, including the outage probability, the buffer empty probability, and the stable throughput region, are derived. Numerical results verify our theoretical analysis, and it is revealed that packet retransmission can enhance the system performance when the packet request rate is within the stable throughput region.

Mobile edge computing (MEC) is an emerging architecture to enable variety of innovative applications and services with ultra low latency at the resource-limited mobile devices. In this paper, we investigate how the communication resources and the computing resources, including mobile users and MEC servers, interact with each other in multi-cell MEC-enabled stochastic wireless networks. To this end, the MEC-enabled network model including mobile users with limited storage capacity and computing capabilities is considered, which is characterized in random node distribution, dynamic traffic, orthogonal frequency division multiple access and task retransmission mechanism. Based on the model, the two-dimensional discrete Markov chain is employed to characterize the task execution process. We derive the stationary distribution of the buffer length and outage probability by combining the queuing theory and stochastic geometry, based on which the radio access network throughput is calculated to measure the network performance. Extensive simulations have been conducted to verify the effectiveness of the proposed offloading strategy and to provide valuable insight.

Multi-User scheduling is a challenging problem under the relaying scenarios. Traditional schemes, which are based on the instantaneous signal-to- interference-plus-noises ratios (SINRs), cannot solve the inherent disparities of the qualities between different links. Hence, the system performance is always limited by the weaker links. In this paper, from the whole system throughput view, we propose an optimal multi-user scheduling scheme for the multi-user full-duplex (FD) buffer aided relay systems. We first formulate the throughput maximization problem. Then, according to the characteristics of the Karush-Kuhn-Tucker conditions, we obtain the optimal decision functions and the optimal weighted factors of different links of the proposed scheme. Simulation results show that the proposed scheme not only solves the disparities of the qualities between Si- R and R-Dilinks, but also that between different Si-R or Di-R links, which can be used as guidance in the design of the practical systems.

In this paper, we mainly analyze the inter-cell interference (ICI) and its impacts on system performance for cellular networks with dynamic traffic, where base stations (BSs) do not remain active all the time. This is quite different from the assumption in previous works that base stations (BSs) are full-loaded, which overestimates the interference. To ensure the packet transmission, automatic repeat request (ARQ) protocol is adopted and packets will remain at the head of the buffer until they are successfully transmitted. Retransmissions bring a coupled relationship between the ICI and the buffer states, i.e., the packet transmission is impaired by the ICI, and packets will be blocked at the buffer to be retransmitted, which aggravates the interference in turn. To better reveal this coupled relationship, a queuing model is established to analyze the packet arrival and departure processes. With the aid of the probability generating function (PGF) and the Laplace transform, both the outage probability and the buffer empty probability are derived.

In this paper, we focus on the efficient deployment of high altitude platforms (HAPs). As the accuracy of the channel model highly affects the deployment scheme, we first propose a geometry-based HAP channel model, which considers the statistical and geometry properties of the terrestrial environments comprehensively. Based on the proposed channel model, we derive the Line-of-Sight (LoS) transmission probability of air-to-ground communication, and characterize the path loss analytically. We further propose an algorithm to maximize the deployment efficiency in terms of the coverage ratio, i.e., the ratio of the radius of the HAP to the inter-HAP distance. The accuracy of our theoretical analysis is validated by simulations.

In this paper, we investigate fundamental throughput and delay tradeoffs in cognitive radio networks (CRN) with a primary user (PU) and a cooperative secondary user (SU). Considering the user fairness, we propose a novel cooperation scheme, where the PU transmits packets with a probability of α instead of always occupying the spectrum, in exchange for the SU's cooperation. The unauthorized SU can sense the environment periodically and get access to the licensed spectrum when the PU is idle. The SU will either transmit its own packets or forward the PU's packets based on a priority factor β. Considering the bursty nature of the sources, Markov chain model is used to characterize the evolution of buffer states. Based on the steady state probabilities, the average buffer length and the system's stable throughput region are obtained. The impacts of the cooperation scheme (α,β) on the throughput tradeoff between the PU and the SU are theoretically analyzed. We also derive the average end-to-end transmission delay of the two users. The throughput-delay tradeoff and the impacts of the link qualities on the system performance are analyzed. The accuracy of the analytical results are validated by simulations.

The Gaussian noise assumption may result in a major decline in state estimation accuracy when the measurements are with the presence of outliers. In this letter, we endow the unknown measurement noise with the Student-t distribution to model the underlying non-Gaussian dynamics of a real physical system. Thereafter a robust particle filtering algorithm is developed. First, we employ variational Bayesian (VB) approach to robustly infer the unknown noise parameters recursively. Second, in order to decrease the computational complexity resulted by the unknown noise parameters, those parameters are marginalized out to allow each particle to be updated by using sufficient statistics estimated by VB approach. The proposed algorithm is tested with a typical non-linear model and the robustness of our algorithm has been borne out.

A variable tap-length LMS algorithm based on adaptive parameters for tapped-delay-line (TDL) structure adaption is proposed in this paper. The objective of this work is to overcome some drawbacks of previous variable tap-length adaptive filter algorithms, including slow convergence speed and high sensitivity to noise. Novel tap-length iteration equation based on adaptive parameters and arctangent-limited value is proposed. The values of adaptive parameters can automatically change according to the filter state, which not only improves the convergence speed but also reduces the steady-state error. The arctangent-limited value can effectively reduce the instantaneous estimated error, which enhances the robustness to noise. Computer simulations are conducted to verify the performance of proposed algorithm.