LLM-based empathetic dialogue systems enhance agents' emotional support capabilities. Previous approaches primarily relied on Chain-of-Thought (CoT) prompting to extract key dialogue cues and further strengthened the agent's sensitivity to these signals through supervised fine-tuning. However, such methods overly depend on the information extraction capability of LLMs, leading to unstable reasoning and limited interpretability. To simultaneously improve an agent's ability to proactively explore solutions through rational reasoning while attending to users' sensitive emotions via empathetic understanding, we propose Neuro-Sym Supporter, a hybrid decision-making emotional support agent that integrates symbolic reasoning with deep learning. This model combines rational inference with emotional empathy, enabling the agent to generate supportive responses that balance logic and emotion. Specifically, we introduce Sym-Mind, a differentiable logic-based reasoning framework for emotional support strategy selection, which unifies interpretability with stable performance. Experimental results on public datasets demonstrate that our approach consistently outperforms multiple competitive baselines in both automatic and human evaluations, validating its effectiveness.

Intelligent reflecting surfaces (IRS) enable radar sensing in blocked environments by reconfiguring propagation and creating virtual apertures, which is crucial for non-line-of-sight (NLoS) localization. This work addresses high-accuracy direction-of-arrival (DoA) estimation in semi-passive IRS-assisted sensing. We introduce a virtual-domain lifting that vectorizes the received echoes and induces a structured atomic set, leading to an atomic norm minimization (ANM) formulation. The ANM estimator is formulated as a semidefinite program (SDP) via convex relaxation, and we develop an accelerated proximal gradient (APG) solver that leverages the problem structure and avoids interior-point steps, resulting in substantial computational savings. Compared with spatial-domain and grid-based approaches, the transformed-domain estimator delivers an optimal accuracy-complexity tradeoff. It achieves gridless (ANM-level) high resolution while reducing runtime. Extensive simulations across array sizes, transmit power, and IRS configurations confirm accuracy, robustness to off-grid mismatch, and scalability, demonstrating the practicality of the proposed method for IRS-enabled NLoS sensing in complex environments.

Orthogonal time frequency space (OTFS) modulation, combined with massive multiple-input-multiple-output (MIMO) technology, offers robust performance in high-mobility environments and high-user densities by capturing the full diversity of the wireless channel and effectively utilizing spatial multiplexing. This article introduces an adaptive block sparse backtracking (ABSB) algorithm designed to enhance channel estimation in OTFS with massive MIMO (massive MIMO-OTFS) systems. The proposed ABSB algorithm features dynamic block size adjustment based on the residual signal, improving its adaptability to the varying sparsity structure of the channel. Additionally, the algorithm extends the selection range of related block atoms to increase redundancy, reducing the risk of underfitting. Comprehensive simulation results demonstrate that the ABSB algorithm significantly outperforms traditional pilot-based methods in terms of channel estimation accuracy. It also surpasses the block orthogonal matching pursuit (BOMP) method as well as other classical compressed sensing methods. Specifically, the ABSB algorithm achieves up to a 20% reduction in estimation error compared to some of these traditional methods. The enhanced adaptability and robustness of the ABSB algorithm make it a promising solution for channel estimation in massive MIMO-OTFS systems, paving the way for more reliable and efficient next-generation wireless communications.

In the evolving landscape of wireless communications, extremely large-scale multiple-input-multiple-output (XL-MIMO) systems offer promising enhancements in capacity and spectral efficiency, particularly in near-field scenarios. This article investigates polar-domain channel estimation methods for near-field wideband XL-MIMO systems, proposing a novel approach based on the bilinear pattern detection (BPD) method. We introduce the multicandidate BPD (MBPD) algorithm, which improves detection accuracy by incorporating adaptive weight matrix adjustments and evaluating multiple candidate modes per iteration. Comprehensive simulations validate the superiority of MBPD over traditional BPD in terms of estimation accuracy and robustness. Furthermore, a detailed complexity analysis demonstrates the computational feasibility of the proposed algorithm. The MBPD algorithm greatly improves polar-domain channel estimation, facilitating more efficient implementations of near-field wideband XL-MIMO systems.

The integration of beyond diagonal reconfigurable intelligent surfaces (BD-RIS) with non-orthogonal multiple access (NOMA) in the Internet of Vehicles (IoV) networks presents the transformative potential for 6G vehicular communications, promising unprecedented gains in energy and spectral efficiency. However, existing research fails to address the joint optimization of BD-RIS configuration, NOMA power allocation, and dynamic IoV association–a critical oversight given their inherent interdependence in practical deployment scenarios. Current literature predominantly examines these components in isolation, with conventional RIS architectures and orthogonal multiple access schemes, leading to suboptimal performance in high-mobility vehicular environments. This work bridges this gap by proposing a novel hybrid optimization framework for NOMA-aided BD-RIS assisted IoV networks. The proposed framework simultaneously optimizes the IoV association with the base station (BS), NOMA power allocation, and BD-RIS phase shift design to maximize the energy efficiency of the system while ensuring the minimum signal-to-interference plus noise ratios (SINRs) of IoVs. The proposed framework is formulated as non-linear optimization due to joint decision variables and rate expressions of IoVs, resulting in an NP-hard problem where achieving a joint optimal solution is computationally complex. To handle this complexity, the original joint formulation is decomposed into three subproblems: IoV association with BS, BS power allocation, and BD-RIS phase shift design. An efficient iterative solution is then developed through the synergistic combination of deep reinforcement learning, first-order Taylor expansion, and manifold optimization methods. For comprehensive evaluation, we introduce a NOMA-aided conventional RIS assisted IoV framework as a benchmark. Extensive numerical results based on Monte Carlo simulations demonstrate that the proposed NOMA-aided BD-RIS assisted IoV network achieves significant improvements of 32% in energy efficiency and 28% in spectral efficiency compared to conventional RIS-assisted architectures, validating its potential for next-generation vehicular networks.

The Internet-of-Things (IoT) has greatly facilitated our daily lives. Nevertheless, how to achieve higher spectral efficiency, large-scale device access, and lower latency for the next-generation IoT is still a challenge. Inspired by this, a non-orthogonal multiple access (NOMA) assisted cognitive radio (CR) IoT network is proposed in this paper, where the communication between the indoor secondary transmitter and secondary receivers is performed in the presence of an eavesdropper and under the constraint of secondary transmit power. In particular, we introduce simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) into the secondary network to assist the secondary transmitter to communicate with its receivers in different rooms. To characterize the reliability and security of the proposed system, we derive analytical approximate expressions for the outage probabilitys (OPs) and intercept probabilitys (IPs) by using Gaussian-Chebyshev quadrature. With the aim of providing a deeper understanding, we also explore the impacts of transmission signal-to-noise ratios (SNRs), power allocation coefficient and the number of STAR-RIS elements on the system performance. Presented numerical results show that: 1) the OPs of near and far users gradually decrease with SNRs until floors appear at high SNR, and the floors of near user is always lower than that of far user; 2) IPs increasing with SNRs and near user is always less than far user, which proves that near user has better security; 3) under appropriate parameters, the trade-off between reliability and security of the considered system can be arisen.

The existing medical image privacy solutions cannot completely solve the security problems created by applying the metaverse healthcare system. A robust zero-watermarking scheme based on the Swin Transformer is proposed in this article to improve the security of medical images in the metaverse healthcare system. This scheme uses a pretrained Swin Transformer to extract deep features from the original medical images with a good generalization performance and multiscale, and binary feature vectors are generated by using the mean hashing algorithm. Then, the logistic chaotic encryption algorithm boosts the security of the watermarking image by encrypting it. Finally, an encrypted watermarking image is XORed with the binary feature vector to create a zero-watermarking, and the validity of the proposed scheme is verified through experimentation. According to the results of the experiments, the proposed scheme has excellent robustness to common attacks and geometric attacks, and implements privacy protections for medical image security transmissions in the metaverse. The research results provide a reference for the data security and privacy protection of the metaverse healthcare system.

In frequency selective channel environment, channel estimation in hybrid precoding millimeter-wave (mmWave) massive multiple input multiple output (MIMO) system is a challenge issue. To solve this problem, we propose an effective channel estimation scheme for frequency selective channel, which is based on the training channel model in urban traffic environment. Considering that the practical mmWave MIMO channel is sparsity and the subcarrier multi-channels have the same sparse structure, we regard the channel estimation problem as the sparse channel recovery, and propose a multipath simultaneous matching tracking estimation method. It is assumed that the noise between the practical channels has a certain correlation, and the noise correlation has an impact on the selection of the optimal atomic support set in the process of channel recovery. Therefore, noise weighting is introduced in our proposed method. The simulation results prove the validity of this proposed method in frequency selective mmWave MIMO channel. Without increasing the complexity of the algorithm, the proposed method can achieve better local performance than the traditional classical methods.

Intelligent Reflecting Surface (IRS) represents a significant breakthrough in wireless communications, allowing the reconstruction of wireless channels even for occluded users to the base station (BS). Estimating the Direction-of-Arrival (DOA) of a source oriented toward Non-Line-of-Sight (NLOS) propagation is an intriguing topic in an IRS-aided wireless communication scenario. However, the existing optimization-based approaches are overly complex to be practically implemented. In this paper, we propose a polarized IRS architecture, in which both IRS and BS are equipped with arbitrarily placed Electromagnetic Vector Sensor (EMVS) arrays. A Normalized Vector-Cross Product (NVCP) estimator is developed for DOA estimation, which avoids the need for complicated data recovery or exhaustive grid search. The proposed framework enables Two-Dimensional (2D) DOA estimation for NLOS signals without requiring prior knowledge of the BS-IRS channel. Numerical simulations have been conducted to verify its effectiveness.

Layout generation is important in the field of graphic design and has attracted intensive research attention recently. To further prompt human-computer interactions, we construct the ALDA to assist users throughout the design process, which achieves adaptive and diverse content expansions upon only one element for beginners and generates high-quality posters subsequently. Specifically, to obtain diverse contents, we propose aesthetic-aware design graphs (AGs) for effective poster representation and propose a self-constrained blending strategy upon related examples. In addition, we build a novel layout generator to better arrange elements conditioned on our AGs. Finally, we implement ALDA as an online tool with a set of controllable factors to enhance its practicality.

In recent years, more and more attention has been paid to the unmanned aerial vehicle (UAV) cooperative task assignment. In order to complete the task with the lowest cost, some researchers use multi-objective optimization to solve the assignment problem. But few of them consider the complex dynamic scenarios. In this article, the time-varying resource supply and demands are provided by established digital twins (DTs) of UAVs and targets, thereby enabling accurate decision guidance for dynamic task assignment. It takes the scheduling cost, path cost, risk cost and total task time cost as the optimization objectives. To solve this model, an improved dynamic multi-objective adaptive weighted particle swarm Optimization algorithm (DMOAWPSO) is proposed. In the initialization stage, a heuristic method is used to increase the effectiveness of the solution. Besides, the adaptive mutation and subgroup methods are adopted to improve the diversity of the solution. Then, effective environment change detection and response strategies are designed to adapt to dynamic scenarios. Finally, the evaluation metrics are calculated in different instances. Compared with the popular and classic dynamic multi-objective algorithms, the simulation results verify that the proposed algorithm is effective and can cope with the environment changes better in solving the task assignment problem.

Sustainable connectivity between an augmented intelligence of things system and terminal devices can provide a strong guarantee for an enterprise's continuity of production. To ensure sustainable data interaction in emergency situations, such as damage to communication infrastructure facilities in a postdisaster period, a pyramidal air–space–ground network model is proposed in this article, and its transmission performance is investigated. The space network, constructed of geostationary satellites and medium and low earth orbit (MEO and LEO) satellites, is heterogeneously fused with the terrestrial AIoT network to ensure around-the-clock network service for enterprise production. Then, bandwidth–delay product analysis is performed for this heterogeneous network. The proposed scheme improves the data transmission efficiency of the network model by mitigating the impact of a long round-trip time and improving the congestion window optimization in the case of data loss. The proposed scheme increases the transmission rate in the network model by a factor of 3.72 compared to the traditional transmission scheme in the case of a 5-hop terrestrial AIoT network and has a 3.94-fold advantage in download time.

The privacy protection and data security problems existing in the healthcare framework based on the Internet of Medical Things (IoMT) have always attracted much attention and need to be solved urgently. In the teledermatology healthcare framework, the smartphone can acquire dermatology medical images for remote diagnosis. The dermatology medical image is vulnerable to attacks during transmission, resulting in malicious tampering or privacy data disclosure. Therefore, there is an urgent need for a watermarking scheme that doesn't tamper with the dermatology medical image and doesn't disclose the dermatology healthcare data. Federated learning is a distributed machine learning framework with privacy protection and secure encryption technology. Therefore, this paper presents a robust zero-watermarking scheme based on federated learning to solve the privacy and security issues of the teledermatology healthcare framework. This scheme trains the sparse autoencoder network by federated learning. The trained sparse autoencoder network is applied to extract image features from the dermatology medical image. Image features are undergone to two-dimensional Discrete Cosine Transform (2D-DCT) in order to select low-frequency transform coefficients for creating zero-watermarking. Experimental results show that the proposed scheme has more robustness to the conventional attack and geometric attack and achieves superior performance when compared with other zero-watermarking schemes. The proposed scheme is suitable for the specific requirements of medical images, which neither changes the important information contained in medical images nor divulges privacy data.

Data transmission for the industrial Internet of Things (IoT) is crucial for industrial production, especially in the Industry 5.0 era, where human–machine collaboration is increasingly intensive. To ensure the continuity and robustness of industrial IoT communications in the case of damaged infrastructure communication facilities postdisaster, the industrial IoT can be connected with satellite networks in emergencies. This article presents a cross-regional, end-to-end, transmission control scheme for satellite-supported, multihop industrial IoT. The proposed scheme adjusts the window of data transmission from two phases, slow start and congestion avoidance, to accommodate the low-transmission performance caused by a long delay and high bit error rate in converged networks. The window of data transmission is also adjusted to increase the amount of data transmission for the slow start to fill the high bandwidth-delay product of the converged network, while adjusting the threshold of data transmission based on feedback information to distinguish different data losses during congestion avoidance. The feasibility of the heterogeneous network transmission model is experimentally verified. The results show that the scheme can achieve good performance in heterogeneous networks of industrial IoT and satellite networks. The scheme is effective in ensuring the continuity and stability of intelligent machine production in Industry 5.0 in emergency communication cases.

Vehicular ad hoc networks (VANETs) have attracted increasing attention in cross-regional communication. The interconnection between VANETs and satellite networks expands their communication range. To improve the transmission performance of heterogeneous networks composed of VANETs and satellite networks, this paper proposes an end-to-end transmission control scheme for satellite network-assisted VANETs, analyzes the bandwidth delay product of multihop VANETs through heterogeneous networks, and establishes a transmission control model applicable to cross-regional environments. To address the long delay and high bit error rate in heterogeneous networks, the model designs the congestion window in the slow start and congestion avoidance of the transmission scheme. The data transmission volume is increased in the slow start, and various packet losses are distinguished in congestion avoidance. The experimental results show that the scheme can achieve good transmission performance in heterogeneous networks composed of VANETs and satellite networks. The results show that VANETs data can be effectively transmitted in heterogeneous networks. Compared with the traditional transmission scheme, this simple and practical end-to-end transmission control scheme can improve speed by 83.49 kbps and reduce transmission duration time of FTP server by$4.72\times 10^{3}$s. The results of this study provide a reference for massive data transmission in heterogeneous networks of VANETs and satellite networks.

The transmission of huge amounts of data generated from various sensors is the basis and prerequisite for the analysis and prediction of the marine ecosystem. The special geographical conditions of the ocean determine that its data transmission is different from the common terrestrial data transmission. In this study, a marine environmental monitoring network architecture is proposed to collect environmental data from marine ecosystems using a heterogeneous network constructed by a multi-hop network and a satellite network. Considering the characteristics of satellite networks, such as high bandwidth delay product (BDP), long latency and high bit error rate, this work proposes a transmission scheme that improves the transmission performance of Transmission Control Protocol/Internet Protocol (TCP/IP)-based Internet networks over satellite networks. The improved scheme increases the monitoring data transmission rate, distinguishes between random data loss and congested data loss, rationalizes the threshold in fast recovery, and improves the overall transmission performance of the marine environmental monitoring network. The experimental results show that the proposed scheme can greatly improve the transmission performance of the monitoring network compared with traditional transmission protocols. The network model proposed in this study makes full use of current advanced network technologies, including multi-hop networks, wireless sensor networks, and satellite networks to expand the monitoring area at sea. This study provides a useful reference for the network model and data transmission for ocean monitoring.

In the field of transportation, the Internet of Vehicles (IoV) is an important component of the Internet of Things. The vehicle-to-vehicle communication is particularly challenging in mobile IoV networks because they are operated in complex and highly variable environments. The mobile IoV transmission interruption level can be evaluated by the outage probability (OP) performance. If the OP performance can be analyzed and predicted accurately, the Quality of Service (QoS) in the mobile IoV networks can be improved. However, the analysis and prediction of mobile IoV transmission channels is very challenging because they are highly dynamic. In this article, the analysis and prediction of the OP performance for mobile IoV networks are investigated. A hybrid decode-amplify-forward (HDAF) relaying scheme with transmit antenna selection (TAS) is considered. The exact OP expressions are derived in a closed form, and the analytical results are verified. To realize the real-time analysis of the OP performance, an intelligent OP prediction algorithm based on the improved cuckoo search (ICS) is presented. The proposed algorithm is compared with different methods and the results show that it has a better OP prediction performance. The prediction accuracy of ICS-BP can be increased by 51.8% compared with the existing algorithms.

Industrial applications can produce significant amounts of data that require low delay and high data rate communications. Multiple-input-multiple-output filter bank multicarrier (MIMO-FBMC) communications employing offset quadrature amplitude modulation has been proposed for industrial big data due to its reliability and high spectrum efficiency. One of the difficulties in implementing a MIMO-FBMC system is accurate channel estimation (CE). The main factor affecting the CE performance is intrinsic imaginary interference, and the conventional preamble-based CE is not effective in this case. Thus, in this article, a low-complexity sparse adaptive CE scheme is proposed that is based on a dynamic threshold. This reduces the number of inner product calculations by considering only the columns of the measurement matrix greater than the threshold. Simulation results are presented that show that the proposed scheme is better than other well-known methods in terms of computational complexity and CE accuracy.

In sensing systems, nodes must be able to rapidly detect whether a signal from a primary transmitter is present in a certain spectrum. However, traditional energy‐detection algorithms are poorly adapted to treating noisy signals. In this paper, we investigate how rapid energy detection and detection sensitivity are related to detection duration and average power fluctuation in noise. The results indicate that detection performance and detection sensitivity decrease quickly with increasing average power fluctuation in noise and are worse in situations with low signal‐to‐noise ratio. First, we present a dynamic threshold algorithm based on energy detection to suppress the influence of noise fluctuation and improve the sensing sensitivity. Then, we present a new energy‐detection algorithm based on cooperation between nodes. Simulations show that the proposed scheme improves the resistance to average power fluctuation in noise for short detection timescales and provides sensitive detection that improves with increasing numbers of cooperative detectors. In other words, the proposed scheme enhances the ability to overcome noise and improves spectrum sensing performance.

In direct position determination (DPD), a large number of observation data need to be integrated and transmitted, which creates higher requirements for the transmission performance of the network. To alleviate the problem of performance degradation in a large number of data transmissions, this paper proposes a heterogeneous network architecture and transmission control algorithm for cross‐regional heterogeneous networks. Through the heterogeneous integration of satellite and multihop networks, a transmission control model suitable for the long delay and high bit error rate environments is established, the congestion window of each stage of network transmission is analyzed, and the efficiency and accuracy of the algorithm are verified by experiments. The results show that a large amount of data can be transmitted in a heterogeneous network. When dealing with direct location, the algorithm can effectively transmit a large number of observation data for cross‐regional heterogeneous networks. This simple and applicable transmission control algorithm can improve the satellite link throughput and reduce the download response time compared with the traditional transmission algorithm. These studies provide a reference for a large number of data transmissions in direct position determination.

A filter bank multicarrier (FBMC) with offset quadrature amplitude modulation (OQAM) (FBMC/OQAM) is considered to be one of the physical layer technologies in future communication systems, and it is also a wireless transmission technology that supports the applications of Internet of Things (IoT). However, efficient channel parameter estimation is one of the difficulties in realization of highly available FBMC systems. In this paper, the Bayesian compressive sensing (BCS) channel estimation approach for FBMC/OQAM systems is investigated and the performance in a multiple-input multiple-output (MIMO) scenario is also analyzed. An iterative fast Bayesian matching pursuit algorithm is proposed for high channel estimation. Bayesian channel estimation is first presented by exploring the prior statistical information of a sparse channel model. It is indicated that the BCS channel estimation scheme can effectively estimate the channel impulse response. Then, a modified FBMP algorithm is proposed by optimizing the iterative termination conditions. The simulation results indicate that the proposed method provides better mean square error (MSE) and bit error rate (BER) performance than conventional compressive sensing methods.