Channel state information (CSI) is essential to unlock the potential of reconfigurable intelligent surfaces (RISs) in wireless communication systems. Since massive RIS elements are typically implemented without baseband signal processing capabilities, limited CSI feedback is necessary when designing the reflection/refraction coefficients of the RIS. In this article, the unique RIS-assisted channel features, such as the RIS position-dependent channel fluctuation, the ultra-high dimensional sub-channel matrix, and the structured sparsity, are distilled from recent advances in limited feedback and used as guidelines for designing feedback schemes. We begin by illustrating the use cases and the corresponding challenges associated with RIS feedback. We then discuss how to leverage techniques such as channel customization, structured-sparsity, autoencoders, and others to reduce feedback overhead and complexity when devising feedback schemes. Finally, we identify potential research directions by considering the unresolved challenges, the new RIS architecture, and the integration with multi-modal information and artificial intelligence.

This paper proposes a novel blended hyper-cellular architecture for low-altitude aerial intelligent networks (LAINs) to provide agile coverage tailored to active air routes and takeoff/landing spots. Traditional cellular networks struggle to meet the dynamic demands of low-altitude UAV communications due to their rigid structures. The hyper-cellular network (HCN) architecture separates control and traffic coverage, enabling flexible and energy-efficient operations. The key components include control base stations (CBSs) for wide-area signaling coverage and traffic base stations (TBSs) that can be dynamically activated based on traffic demands. The proposed solution also integrates space information networks (SINs) to enhance the coverage efficiency. Key technologies such as all-G CBS using RISC-V architecture, AI-powered radio maps for low-altitude environments, and agile TBS coverage adaptation are introduced with some preliminary studies. These designs aim to address challenges like mobility management, interference coordination, and the need for real-time spectrum sharing in blended satellite-terrestrial networks. The proposed solution offers a scalable and agile framework to support the rapidly growing demand for reliable, low-latency, and high-capacity UAV communications in urban environments.

Network architectures assisted by Generative Artificial Intelligence (GAI) are envisioned as foundational elements of sixth-generation (6G) communication system. To deliver ubiquitous intelligent services and meet diverse service requirements, 6G network architecture should offer personalized services to various mobile devices. Federated learning (FL) with personalized local training, as a privacy-preserving machine learning (ML) approach, can be applied to address these challenges. In this paper, we propose a meta-learning-based personalized FL (PFL) method that improves both communication and computation efficiency by utilizing over-the-air computations. Its "pretraining-and-fine-tuning" principle makes it particularly suitable for enabling edge nodes to access personalized GAI services while preserving local privacy. Experiment results demonstrate the outperformance and efficacy of the proposed algorithm, and notably indicate enhanced communication efficiency without compromising accuracy.

The large-scale development of the low-altitude economy imposes increasingly stringent requirements on the supporting information infrastructure, necessitating the establishment of a low-altitude intelligent network (LAIN) with wide-area communication, high-precision navigation, and efficient supervision capabilities. Benefiting from its broad coverage, high reliability, and large bandwidth, the 5G cellular network serves as a critical foundation for LAIN construction. However, conventional cellular networks are primarily designed for two-dimensional terrestrial scenarios, and thus face significant limitations in coverage and interference resistance within complex three-dimensional low-altitude environments. To address the unique demands of LAIN applications, key challenges must be tackled, including achieving seamless three-dimensional coverage, mitigating interference in multi-dimensional network deployments, and ensuring stringent requirements for service quality and security supervision. This paper proposes an integrated LAIN architecture characterized by the convergence of communication, navigation, sensing, and management, enhanced with artificial intelligence and security mechanisms to improve overall system intelligence and resilience. Furthermore, this paper conducts an in-depth analysis of the critical challenges in LAIN deployment, explores enabling technologies to address these issues, and offers insights into the future development direction of low-altitude intelligent networks.

Ensuring end-to-end quality of service (QoS) for high-value services in satellite networks is challenging due to dynamic network topologies, varying QoS requirements, and the complex resource allocation across satellite beams and inter-satellite links. To this end, we propose a satellite traffic engineering framework with deterministic QoS (SatTED) by jointly optimizing resource allocation across access and bearer subnets.To tackle the complexity of joint scheduling, SatTED adopts a hierarchical logic-based benders decomposition (LBBD) architecture that coordinates access and bearer subnet resources. The master problem optimizes service admission and satellite selection via binary integer programming, while the subproblem handles routing and bandwidth allocation through linear programming relaxation. Key innovat-ions include scenario-cognizant Benders feasibility cuts to accelerate convergence and a critical constraint link preprocessing (CCLP) mechanism that reduces subproblem complexity by 5.15× in large-scale networks. In simulations on a 220-satellite network with 1 000 flows, SatTED improves total service payoff by 32% and increases high-value flow completion rates by 22%.

In this paper, a method for designing super-massive sparse phased arrays (SMSPAs) known as the unitary modified matrix enhancement and matrix pencil (UMMEMP) method is proposed. In this method, an eigenvalue pairing method, which is inspired by the modified MEMP, effectively pairs the repeated eigenvalues intractable in the unitary matrix pencil method, and it is more effective in determining the locations of elements in the sparse array. Three numerical examples and a full-wave validation are presented to demonstrate the effectiveness of the method, implemented via SMSPA, in achieving low sidelobe level wide-angle scanning radiation patterns, circular flat-top radiation patterns, and ultra wide-angle scanning radiation patterns.

This paper presents an algorithm named the dependency-aware offloading framework (DeAOff), which is designed to optimize the deployment of Gen-AI decoder models in mobile edge computing (MEC) environments. These models, such as decoders, pose significant challenges due to their inter-layer dependencies and high computational demands, especially under edge resource constraints. To address these challenges, we propose a two-phase optimization algorithm that first handles dependency-aware task allocation and subsequently optimizes energy consumption. By modeling the inference process using directed acyclic graphs (DAGs) and applying constraint relaxation techniques, our approach effectively reduces execution latency and energy usage. Experimental results demonstrate that our method achieves a reduction of up to 20% in task completion time and approximately 30% savings in energy consumption compared to traditional methods. These outcomes underscore our solution's robustness in managing complex sequential dependencies and dynamic MEC conditions, enhancing quality of service. Thus, our work presents a practical and efficient resource optimization strategy for deploying models in resource-constrained MEC scenarios.

This paper investigates the uplink spectral efficiency of distributed cell-free (CF) massive multiple-input multiple-output (mMIMO) networks with correlated Rayleigh fading channels based on three different channel estimation schemes. Specifically, each access point (AP) first uses embedded pilots to estimate the channels of all users based on minimum mean-squared error (MMSE) estimation. Given the high computational cost of MMSE estimation, the low-complexity element-wise MMSE (EW-MMSE) channel estimator and the least-squares (LS) channel estimator without prior statistical information are also analyzed. To reduce non-coherent and coherent interference during uplink payload data transmission, simple centralized decoding (SCD) and large-scale fading decoding (LSFD) are examined. Then, the closed-form expressions for uplink spectral efficiency (SE) using MMSE, EW-MMSE, and LS estimators are developed for maximum ratio (MR) combining under LSFD, where each AP may have any number of antennas. The sum SE maximization problem with uplink power control is formulated. Since the maximization problem is non-convex and challenging, a block coordinate descent approach based on the weighted MMSE method is used to get the optimal local solution. Numerical studies demonstrate that LSFD and efficient uplink power control can considerably increase SE in distributed CF mMIMO networks.

High-performance positioning, navigation and timing (PNT) service is critical to the safe flight of low-altitude aircraft and the effective management of low altitude traffic. In low-altitude economic scenarios, the specificity of massive unmanned aerial vehicle (UAV) flights and the complexity of low-altitude airspace traffic management impose stringent demand on the high-continuity, high-accuracy, real-time, and high-security PNT service. However, the current PNT service, which primarily relies on Global Navigation Satellite System (GNSS), Micro-Electro-Mechanical System Inertial Navigation System (MEMS INS), etc., is completely inadequate to support the future needs of low-altitude economic development. In order to bridge the huge gap between existing capability and future demand, a three-layer PNT architecture based on the collaboration of space-based, air-based and ground-based PNT systems is proposed for low-altitude economy. The space-based layer consists of high, medium even possible low orbit GNSS constellations, such as BeiDou Navigation Satellite System (BDS), for high-precision, high-security absolute positioning and timing. The air-based layer leverages inter-aircraft links for high-reliability dynamic relative positioning. The ground-based layer includes pseudolite network, as well as 5G-advanced (5G-A)/6G network, for more comprehensive coverage and real-time positioning. To this end, it is imperative to make breakthroughs in key technologies, from systems to airborne terminal, including but not limited to high-precision anti-jamming GNSS signal processing, high-reliability relative positioning, real-time pseudolite positioning, and high-efficient multi-source information fusion at airborne terminal, etc. Due to the moderate redundancy, heterogeneous mechanism, and multiple coverage from multiple PNT systems, the proposed layered PNT architecture possesses high robustness and resilient. Additionally, the integration of INS, LiDAR and vision etc. perception technologies can significantly enhance the PNT capability. As a result, the proposed three-layer PNT architecture enable greater autonomy for low-altitude aircraft and intelligent traffic management for massive UAV operations, and promoting the safe and efficient development of the low-altitude economy.

Space laser communication (SLC) is an emerging technology to support high-throughput data transmissions in space networks. In this paper, to guarantee the reliability of high-speed SLC links, we aim at practical implementation of low-density parity-check (LDPC) decoding under resource-restricted space platforms. Particularly, due to the supply restriction and cost issues of high-speed on-board devices such as analog-to-digital converters (ADCs), the input of LDPC decoding will be usually constrained by hard-decision channel output. To tackle this challenge, density-evolution-based theoretical analysis is firstly performed to identify the cause of performance degradation in the conventional binary-initialized iterative decoding (BIID) algorithm. Then, a computation-efficient decoding algorithm named multiary-initialized iterative decoding with early termination (MIID-ET) is proposed, which improves the error-correcting performance and computation efficiency by using a reliability-based initialization method and a threshold-based decoding termination rule. Finally, numerical simulations are conducted on example codes of rates 7/8 and 1/2 to evaluate the performance of different LDPC decoding algorithms, where the proposed MIID-ET outperforms the BIID with a coding gain of 0.38 dB and variable node calculation saving of 37%. With this advantage, the proposed MIID-ET can notably reduce LDPC decoder's hardware implementation complexity under the same bit error rate performance, which successfully doubles the total throughput to 10 Gbps on a single-chip FPGA.

(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle (UAV) air-to-ground channels are derived for the first time using a novel spatial-vector-based method from a three-dimensional (3-D) arbitrary-elevation one-cylinder model. The derived results include a closed-form expression for the space-time correlation function and some quasi-closed-form ones for the space-Doppler power spectrum density, the level crossing rate, and the average fading duration, which are shown to be the generalizations of those previously obtained from the two-dimensional (2-D) one-ring model and the 3-D low-elevation one-cylinder model for terrestrial mobile-to-mobile channels. The close agreements between the theoretical results and the simulations as well as the measurements validate the utility of the derived channel statistics. Based on the derived expressions, the impacts of some parameters on the channel characteristics are investigated in an effective, efficient, and explicable way, which leads to a general guideline on the manual parameter estimation from the measurement description.

Due to the advantages of high mobility and line-of-sight transmission, unmanned aerial vehicles (UAVs) equipped with mobile edge computing (MEC) servers can effectively reduce the computational burden and task delay of ground users (GUs). However, the offloading data from GU to UAV is vulnerable to be eavesdropped by malicious users in the network. Thus, this paper proposes a secure cooperative offloading scheme in a multi-UAV-assisted MEC network, where each UAV has the capability to partially distributing the tasks to other idle UAVs. Specifically, we first model the task offloading decision process of GUs based on the multi-agent Markov Decision Process (MDP) framework. Then we optimize the offloading decision of GUs by adopting multi-agent deep determined policy gradient (MADDPG) to minimize the overall system latency for task processing and computation offloading. Simulation results verify that the proposed cooperative offloading scheme can effectively reduce the system latency compared with the benchmark.

The advent of the internet-of-everything era has led to the increased use of mobile edge computing. The rise of artificial intelligence has provided many possibilities for the low-latency task-offloading demands of users, but existing technologies rigidly assume that there is only one task to be offloaded in each time slot at the terminal. In practical scenarios, there are often numerous computing tasks to be executed at the terminal, leading to a cumulative delay for subsequent task offloading. Therefore, the efficient processing of multiple computing tasks on the terminal has become highly challenging. To address the low-latency offloading requirements for multiple computational tasks on terminal devices, we propose a terminal multitask parallel offloading algorithm based on deep reinforcement learning. Specifically, we first establish a mobile edge computing system model consisting of a single edge server and multiple terminal users. We then model the task offloading decision problem as a Markov decision process, and solve this problem using the Dueling Deep-Q Network algorithm to obtain the optimal offloading strategy. Experimental results demonstrate that, under the same constraints, our proposed algorithm reduces the average system latency.

In this paper, an index modulation (IM) aided uplink orthogonal time frequency space modulation (OTFS) structure for sparse code multiple access (SCMA) is proposed. To be more specific, the information bits are firstly partitioned for transmit antenna (TA) selection and sparse codeword mapping, respectively. Subsequently, the codewords deployed on the 2-dimensional (2D) delay-Doppler (DD) plane are transmitted by the selected TA, and the superimposed signals are jointly detected at the receiver. Furthermore, a low-complexity zero-embedded expectation propagation (ZE-EP) detector is conceived, where the codebooks are extended with zero vectors to reflect the silent indices. The simulation results demonstrate that the proposed IM-OTFS-SCMA system is capable of providing significant performance gain over the OTFS-SCMA counterpart.

In hybrid beamforming design using the conventional gradient projection (GP) algorithm, it is common to use a fixed step size, which results in a slow convergence rate and unsatisfactory achievable rate performance. This paper employs a deep unfolding algorithm within a small fixed number of iterations to tackle the hybrid beamforming optimization problem. The optimal step size is obtained by combining the conventional GP algorithm with the deep learning technique, and every step in deep learning is explainable. Simulation results show that the proposed deep unfolding algorithm demonstrates a lower computational time and superior achievable rate performance than the conventional GP algorithm.

The rapid development of the low-altitude economy has imposed unprecedented demands on wireless infrastructure to accommodate large-scale drone deployments and facilitate intelligent services in dynamic airspace environments. However, unlocking its full potential in practical applications presents significant challenges. Traditional aerial systems predominantly focus on air-ground communication services, often neglecting the integration of sensing, computation, control, and energy-delivering functions, which hinders the ability to meet diverse mission-critical demands. Besides, the absence of systematic low-altitude airspace planning and management exacerbates issues regarding dynamic interference in three-dimensional space, coverage instability, and scalability. To overcome these challenges, a comprehensive framework, termed low-altitude wireless network (LAWN), has emerged to seamlessly integrate communication, sensing, computation, control, and air traffic management into a unified design. This article provides a comprehensive overview of LAWN systems, introducing LAWN system fundamentals and performance evaluation metrics. Subsequently, we delve into the evolution of functional designs and review critical concerns surrounding privacy and security in the open-air network environment. We survey advanced artificial intelligence techniques that enhance LAWN functionality and enable increasingly autonomous operations. Finally, we present the cutting-edge developments in airspace structuring, air traffic management, and path planning, providing insights to facilitate the practical deployment of LAWNs.

With the rapid growth of the low-altitude economy, the demand for typical low-altitude applications has accelerated the advancement of integrated sensing and communications (ISAC) networks. This paper begins by analyzing representative application scenarios to clarify the core requirements of the low-altitude economy for modern ISAC networks. By investigating the distinctive characteristics of ISAC networks in low-altitude environments, it presents a comprehensive analysis of key challenges and identifies four major issues: challenges in precise target detection, interference management, inconsistent sensing and communication coverage, and the complexity of air-ground coordination and handover. Based on fundamental theories and principles, the paper proposes corresponding solutions, encompassing advanced technologies for precise target detection and recognition, high-reliability networked detection, robust interference management, and seamless air-ground collaboration. These solutions aim to establish a solid foundation for the future development of intelligent low-altitude networks and ensure effective support for emerging applications.

Federated semi-supervised learning (FSSL) faces two major challenges: the scarcity of labeled data across clients and the non-independent and identically distributed (Non-IID) nature of data among clients. To address these issues, we propose diffusion model-based data synthesis aided FSSL (DDSA-FSSL), a novel approach that leverages diffusion model (DM) to generate synthetic data, thereby bridging the gap between heterogeneous local data distributions and the global data distribution. In the proposed DDSA-FSSL, each client addresses the scarcity of labeled data by utilizing a federated learning-trained classifier to perform pseudo labeling for unlabeled data. The DM is then collaboratively trained using both labeled and precision-optimized pseudo-labeled data, enabling clients to generate synthetic samples for classes that are absent in their labeled datasets. As a result, the disparity between local and global distributions is reduced and clients can create enriched synthetic datasets that better align with the global data distribution. Extensive experiments on various datasets and Non-IID scenarios demonstrate the effectiveness of DDSA-FSSL, achieving significant performance improvements, such as increasing accuracy from 38.46% to 52.14% on CIFAR-10 datasets with 10% labeled data.

New communication systems require high spectral and energy efficiencies to meet the growing demand for services in future networks. In this paper, an efficient multiple parallel reconfigurable intelligent surfaces (RIS)-assisted multiuser (MU) multiple input-multiple output (MIMO) double quadrature spatial modulation (DQSM) downlink transmission system is presented. In the transmitter, the proposed N-RIS-MU-MIMO-DQSM system uses a modified block diagonalization technique and a genetic algorithm (GA) to jointly design the precoding signals required at the base station (BS) and the optimal phase changes required at multiple RISs. A reduced detection complexity and improved bit error rate (BER) performance are achieved by incorporating spatial modulation. The proposed system is compared under the same conditions and parameters with two reference systems, considering blind and optimized RISs approaches over correlated Rayleigh fading channels. Results show that compared with a similar system that does not use RISs, the proposed system has up to 30 dB gain in BER performance. Compared with a similar system based on conventional quadrature amplitude modulation (QAM), the proposed system has gains of up to 2-3 dB in BER performance and up to 55.8% lower detection complexity for the analyzed cases.

Communications system has a significant impact on both operational safety and logistical efficiency within low-altitude drone logistics networks. Aiming at providing a systematic investigation of real-world communication requirements and challenges encountered in Meituan UAV's daily operations, this article first introduces the operational scenarios within current drone logistics networks and analyzes the related communication requirements. Then, the current communication solution and its inherent bottlenecks are elaborated. Finally, this paper explores emerging technologies and examines their application prospects in drone logistics networks.

As the development of 6G accelerates, non-terrestrial networks (NTN) are emerging as a critical component to ensure seamless global coverage communication. This paper systematically reviews the evolution of the third-generation partnership project (3GPP) NTN standardization, from initial discussions in Release 15 to the in-depth optimizations in Release 19 and beyond. Through a detailed gap analysis, we identify key technical and industrial challenges across the wireless, network, and terminal domains that hinder the realization of a fully integrated space-terrestrial network. To address these challenges, we propose a 6G-oriented holistic architecture composed of three segments and three functional layers. We further outline essential enabling technologies, including networking technologies and intelligent resource management technologies. To validate the feasibility and effectiveness of the proposed architecture, we present a case study on integrated sensing and communication in high-speed mobility scenarios. Simulation results demonstrate significant performance gains in robustness, sensing accuracy, and adaptability compared to conventional approaches. Our findings establish a solid foundation for future research and standardization of 6G integrated networks, aiming to achieve intelligent, ubiquitous, and resilient communication infrastructures across space, air, and ground domains.

Spectrum map construction, which is crucial in cognitive radio (CR) system, visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation. Traditional reconstruction methods are generally for two-dimensional (2D) spectrum map and driven by abundant sampling data. In this paper, we propose a data-model-knowledge-driven reconstruction scheme to construct the three-dimensional (3D) spectrum map under multi-radiation source scenarios. We firstly design a maximum and minimum path loss difference (MMPLD) clustering algorithm to detect the number of radiation sources in a 3D space. Then, we develop a joint location-power estimation method based on the heuristic population evolutionary optimization algorithm. Considering the variation of electromagnetic environment, we self-learn the path loss (PL) model based on the sampling data. Finally, the 3D spectrum is reconstructed according to the self-learned PL model and the extracted knowledge of radiation sources. Simulations show that the proposed 3D spectrum map reconstruction scheme not only has splendid adaptability to the environment, but also achieves high spectrum construction accuracy even when the sampling rate is very low.

In response to the current gaps in effective proactive defense methods within application security and the limited integration of security components with applications, this paper proposes a biomimetic security model, called NeuroShield, specifically designed for web applications. Inspired by the "perception-strategy-effect-feedback" mechanism of the human nervous control system, the model integrates biomimetic elements akin of neural receptors and effectors into applications. This integration facilitates a multifaceted approach to security: enabling data introspection for detailed perception and regulation of application behavior, providing proactive defense capabilities to detect and block security risks in real-time, and incorporating feedback optimization to continuously adjust and enhance security strategies based on prevailing conditions. Experimental results affirm the efficacy of this neural control mechanism-based biomimetic security model, demonstrating a proactive defense success rate exceeding 95%, thereby offering a theoretical and structural foundation for biomimetic immunity in web applications.

As emerging services continue to be explored, indoor communications geared towards different user requirements will face severe challenges such as larger penetration losses and more critical multipath issues, leading to difficulties in achieving flexible coverage. In this paper, we introduce transmissive reconfigurable intelligent surfaces (RISs) as intelligent passive auxiliary devices into indoor scenes, replacing conventional ultra-dense small cell and relay forwarding approaches to address these issues at low deployment and operation costs. Specifically, we study the optimization design of active and passive beamforming for the transmissive RISs-aided indoor multi-user downlink communication systems. This involves considering more realistic indoor congestion modeling and near-field propagation characteristics. The goal of our optimization is to minimize the total transmit power at the access point (AP) for different user service requirements, including quality-of-service (QoS) and wireless power transfer (WPT). Due to the non-convex nature of the optimization problem, adaptive penalty coefficients are imported to solve it alternatively with closed-form solutions for both active and passive beamforming. Simulation results demonstrate that the use of transmissive RISs is indeed an efficient way to achieve flexible coverage in indoor scenarios. Furthermore, the proposed optimization algorithm has been proven to be effective and robust in achieving energy-saving transmission.

This paper proposes a genetic optimization method for the construction of non-binary quasi-cyclic low-density parity-check (NB-QC-LDPC) codes with short block lengths. In our scheme, the initial template base matrices and the corresponding non-binary replacement matrices are constructed by the progressive edge growth algorithm and randomly generated, respectively. The genetic algorithm is then utilized to optimize the base matrices and the replacement ones. The simulation results show that the NB-QC-LDPC codes constructed by the proposed method achieve better decoding performance and lower implementation complexity compared to the existing NB-LDPC codes such as consultative committee for space data system and BeiDou satellite navigation system.

The intelligent operation management of distribution services is crucial for the stability of power systems. Integrating the large language model (LLM) with 6G edge intelligence provides customized management solutions. However, the adverse effects of false data injection (FDI) attacks on the performance of LLMs cannot be overlooked. Therefore, we propose an FDI attack detection and LLM-assisted resource allocation algorithm for 6G edge intelligence-empowered distribution power grids. First, we formulate a resource allocation optimization problem. The objective is to minimize the weighted sum of the global loss function and total LLM fine-tuning delay under constraints of long-term privacy entropy and energy consumption. Then, we decouple it based on virtual queues. We utilize an LLM-assisted deep Q network (DQN) to learn the resource allocation strategy and design an FDI attack detection mechanism to ensure that fine-tuning remains on the correct path. Simulations demonstrate that the proposed algorithm has excellent performance in convergence, delay, and security.

Space-terrestrial integrated networks (ST-IN) require a trustworthy and auditable environment for multi-party spectrum sharing under dynamic and heterogeneous conditions. Blockchain, as a decentralized ledger, exhibits promising properties such as transparency and tamper resistance, making it a potential enabler for such scenarios. However, conventional blockchain-based approaches tightly couple strategy execution with transaction consensus, resulting in excessive overhead and poor adaptability to fast-changing spectrum semantics. To address these issues, this paper presents a spectrum-semantics-driven meta-consensus framework built upon a directed acyclic graph (DAG) mainchain architecture. By decoupling policy optimization from on-chain coordination and leveraging semantic representations of spectrum states for meta-level consensus and policy migration, the framework enables agile and scalable spectrum sharing across dynamically clustered network agents. Simulation results verify that the proposed design significantly enhances spectrum utilization and adaptability while maintaining decentralized transparency and auditability in large-scale STIN environments.

The deployment of the low earth orbit (LEO) satellites provides a large number of signals of opportunity (SOPs), unmanned aerial vehicle (UAV) positioning and navigation via LEO-SOPs have received much attention. Current research is focused on Doppler positioning techniques, which require the collaboration of multiple satellites ($\geq 3$). However, the dynamic changes of LEO satellites weaken the generalization ability of Doppler positioning. In this paper, a direct position determination (DPD) method with uniform circular array (UCA) is proposed for UAV positioning from the perspective of the spatial spectrum estimation of LEO-SOPs. The proposed method employs the orthogonality between the signal and noise subspaces of the covariance matrix of the different received SOPs to establish the cost function for UAV's coordinate. Instead of the multiple dimensional search, a root mean square propagation (RMSProp) gradient optimizer with an adaptive learning rate is developed to find the coordinate of UAV. The effectiveness and robustness of the proposed method are verified using numerical data generated from the systems tool kit (STK).

With more and more IoT terminals being deployed in various power grid business scenarios, terminal reliability has become a practical challenge that threatens the current security protection architecture. Most IoT terminals have security risks and vulnerabilities, and limited resources make it impossible to deploy costly security protection methods on the terminal. In order to cope with these problems, this paper proposes a lightweight trust evaluation model TCL, which combines three network models, TCN, CNN, and LSTM, with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device, and the trust evaluation of the terminal's continuous behavior can be achieved by combining the scores of different periods. After experiments, it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763, 94.456, 99.923, and 99.195 on HDFS, BGL, N-BaIoT, and KDD99 datasets, respectively, and the size of TCL is only 91KB, which can achieve similar or better performance than CNN-LSTM, RobustLog and other methods with less computational resources and storage space.

In this paper, we propose a random access scheme termed sign-compute diversity slotted ALO-HA (SCDSA). The SCDSA scheme combines diversity transmission with compute-and-forward. Without considering the capture effect and multiple user detection techniques, our scheme can reach a high throughput of 0.98 without feedback under finite frame size settings, where the upper bound on performance is 1. Moreover, a lower bound on throughput performance is derived, which is tight in some parameter settings and can be used to approximate theoretical performance. Simulation results validate our analysis and confirm the advantages of our proposed scheme.

As a novel signaling technology, the power splitting receiver (PSR) simultaneously employs both the coherent and non-coherent signal processing. In order to improve its communication performance, an intelligent reflecting surface (IRS) is introduced into its signal propagation path. Consequently, an IRS-aided PSR is concerned for a point-to-point (P2P) data link, where both the single-antenna and multi-antenna deployments on the receiver are discussed. We aim at maximizing the capacity of the concerned P2P data-link by jointly optimizing the passive beamforming of IRS and the splitting ratio of PSR, either in single-antenna or multi-antenna case. However, owing to the coupling of multiple variables, the optimization problems are non-convex and challenging, especially in the later multi-antenna case. The proposed alternating-approximating algorithm (A-A), aided by semi-definite relaxation (SDR) and successive convex approximation (SCA) methods, etc., successfully overcomes these challenges. We compare the IRS-aided PSR system that optimized by our proposed algorithm to the systems without IRS or PSR, and the systems without joint optimization. The simulation results show that our proposal has a better performance.

Text semantic extraction has been envisio-ned as a promising solution to improve the data transmission efficiency with the limited radio resources for the autonomous interactions among machines and things in the future sixth-generation (6G) wireless networks. In this paper, we propose a Chinese text semantic extraction model, namely T-Pointer, to improve the quality of semantic extraction by integrating the Transformer with the pointer-generator network. The proposed T-Pointer model consists of a semantic encoder and a semantic decoder. In the encoding stage, we use the multi-head attention mechanism of the Transformer to extract semantic features from the input Chinese text. In the decoding stage, we first use the Transformer to extract multi-level global text features. Then, we introduce the pointer-generator network model to directly copy the keyword information from the source text. The simulation results demonstrate that the T-Pointer model can improve the bilingual evaluation understudy (BLEU) and recall-oriented understudy for gisting evaluation (ROUGE) by 14.69% and 14.87% on average in comparison with the state-of-the-art models, respectively. Also, we implement the T-Pointer model on a semantic communication system based on the universal software radio peripheral (USRP) platform. The result shows that the packet delay of semantic transmission can be reduced by $52.05\%$ on average, compared to traditional information transmission.

As the types of traffic requests increase, the elastic optical network (EON) is considered as a promising architecture to carry multiple types of traffic requests simultaneously, including immediate reservation (IR) and advance reservation (AR). Various resource allocation schemes for IR/AR requests have been designed in EON to reduce bandwidth blocking probability (BBP). However, these schemes do not consider different transmission requirements of IR requests and cannot maintain a low BBP for high-priority requests. In this paper, multi-priority is considered in the hybrid IR/AR request scenario. We modify the asynchronous advantage actor critic (A3C) model and propose an A3C-assisted priority resource allocation (APRA) algorithm. The APRA integrates priority and transmission quality of IR requests to design the A3C reward function, then dynamically allocates dedicated resources for different IR requests according to the time-varying requirements. By maximizing the reward, the transmission quality of IR requests can be matched with the priority, and lower BBP for high-priority IR requests can be ensured. Simulation results show that the APRA reduces the BBP of high-priority IR requests from 0.0341 to 0.0138, and the overall network operation gain is improved by 883 compared to the scheme without considering the priority.

Satellite and terrestrial cellular networks can be integrated together to achieve extended broadband coverage for, e.g., maritime communication scenarios, in the upcoming sixth-generation (6G) era. To counter spectrum scarcity, collaborative spectrum sharing is considered for hybrid satellite-terrestrial networks (HSTNs) in this paper. With only slowly-varying large-scale channel state information (CSI), joint power and channel allocation is implemented for terrestrial mobile terminals (MTs) which share the same frequency band with the satellite MTs opportunistically. Specially, strict quality service assurance is adopted for terrestrial MTs under the constraint of leakage interference to satellite MTs. With the target of maximizing both the number of served terrestrial MTs and the average sum transmission rate, a double-target spectrum sharing problem is formulated. To solve the complicated mixed integer programming (MIP) problem efficiently, user-centric channel pools are introduced. Simulations demonstrate that the proposed spectrum sharing scheme could achieve a significant performance gain for the HSTN.

Reconfigurable intelligent surfaces (RISs) not only assist communication but also help the localization of user equipment (UE). This study focuses on indoor localization of UE with a single access point (AP) and multiple RISs. First, we propose a two-stage channel estimation scheme where RIS phase shifts are tuned to obtain multiple channel soundings. In the first stage, the newtonized orthogonal matching pursuit algorithm extracts the parameters of multiple paths from the received signals. Then, the LOS path and RIS-reflected paths are identified. In the second stage, the estimated path gains of RIS-reflected paths with different phase shifts are utilized to determine the angle of arrival (AOA) at the RIS by obtaining the angular pseudo spectrum. Consequently, by taking the AP and RISs as reference points, the linear least squares estimator can locate UE with the estimated AOAs. Simulation results show that the proposed algorithm can realize centimeter-level localization accuracy in the discussed scenarios. Moreover, the higher accuracy of pseudo spectrum, a larger number of channel soundings, and a larger number of reference points can realize higher localization accuracy of UE.

With miscellaneous applications generated in vehicular networks, the computing performance cannot be satisfied owing to vehicles' limited processing capabilities. Besides, the low-frequency (LF) band cannot further improve network performance due to its limited spectrum resources. High-frequency (HF) band has plentiful spectrum resources which is adopted as one of the operating bands in 5G. To achieve low latency and sustainable development, a task processing scheme is proposed in dual-band cooperation-based vehicular network where tasks are processed at local side, or at macro-cell base station or at road side unit through LF or HF band to achieve stable and high-speed task offloading. Moreover, a utility function including latency and energy consumption is minimized by optimizing computing and spectrum resources, transmission power and task scheduling. Owing to its non-convexity, an iterative optimization algorithm is proposed to solve it. Numerical results evaluate the performance and superiority of the scheme, proving that it can achieve efficient edge computing in vehicular networks.

In this paper, we analyze the physical layer security (PLS) performance of a free-space optical (FSO) communication system composed of a transmitting satellite and ground users. Specifically, the FSO fading channels follow the Málaga distribution. Further, we scrutinize the influence of non-zero boresight pointing errors and angle-of-arrival fluctuations on the PLS performance for the first time. We derived the probability density function and cumulative density function of the FSO link, followed by the closed-form expressions of the secrecy outage probability (SOP) and the probability of strictly positive secrecy capacity (SPSC). The asymptotic SOP expression at the high signal-to-noise ratio regime and diversity order are also provided to reveal the physical mechanism of the PLS of the considered system. Finally, Monte Carlo simulation results are presented to verify the correctness of the analytical expressions. The results afford helpful insights for the future design of satellite FSO communication systems.

This paper proposes a linear companding transform (CT) using either a single inflection point or two inflection points to reduce the peak-to-average power ratio (PAPR) in orthogonal time-frequency space (OTFS) signals. The CT strategically compresses higher amplitudes and enhances lower amplitudes based on carefully chosen scaling factors and points of inflection. With these selected parameters, the CT effectively reduces peak power while maintaining average power, leading to a substantial decrease in PAPR. We analyze noise changes in the inverse companding transform (ICT) process. The analysis reveals that the ICT amplifies less than $20 \%$ of the total noise. A convolutional encoder and soft decision Viterbi decoding algorithm are utilized in the OTFS system to improve the detection performance. We present simulation results focusing on PAPR reduction and bit error rate (BER) performance. These results demonstrate that the CT with two inflection points outperforms both the single inflection point case and the existing $\mu$-law companding, clipping, peak windowing, unique OTFS frame structure, selected mapping, and partial transmit sequence methods, achieving significant PAPR reduction and BER performance.

This paper considers a multi-antenna access point (AP) transmitting secrecy message to a single-antenna user in the presence of a single-antenna illegal eavesdropper (Eve) and proposes a double active reconfigurable intelligent surfaces (DARISs) assisted physical layer security (PLS) scheme denoted by DARISs-PLS to protect the secrecy message transmission. We formulate a secrecy rate maximization problem for the proposed DARISs-PLS scheme by considering a power budget constraint for the two active reconfigurable intelligent surfaces (ARISs) and AP. To address the formulated optimization problem, we jointly optimize the reflecting coefficients for the two ARISs and the beamforming at the AP in an iterative manner by applying Dinkelbach based alternating optimization (AO) algorithm and a customized iterative algorithm together with the semidefinite relaxation (SDR). Numerical results reveal that the proposed DARISs-PLS scheme outperforms the double passive reconfigurable intelligent surfaces-assisted PLS method (DPRISs-PLS) and single ARIS-assisted PLS method (SARIS-PLS) in terms of the secrecy rate.

The quality of spectrum sensing plays a significant role in determining the outage probability during the data transmission phase in an interweave cognitive radio network. If the secondary user (SU) fails to detect the primary user (PU) activity, it can result in interference that limits the system performance. Additionally, since the wireless medium is broadcast in nature, there is a risk of eavesdroppers intercepting the cognitive users' data. Therefore, it is crucial to consider secrecy in the system analysis. In this paper, we analyze the secrecy outage probability (SOP) at the secondary receiver and derive the secret diversity gain for an interweave cognitive multiple-input multiple-output (MIMO) fading channel in the presence of an eavesdropper. Our study takes into account the effects of the fading channel, the PU interference, and the eavesdropper on both spectrum sensing and data transmission phases. We demonstrate that utilizing all the antennas for sensing eliminates the limiting effects of missed detection probability and PU interference on the secret diversity gain. As a result, the cognitive user can achieve the same level of secret diversity gain as a conventional non-cognitive system (CNCS). Our analytical results are further validated through simulations.