Endogenous security in next-generation wireless communication systems attracts increasing attentions in recent years. A typical solution to endogenous security problems is the quantum key distribution (QKD), where unconditional security can be achieved thanks to the inherent properties of quantum mechanics. Continuous variable-quantum key distribution (CV-QKD) enjoys high secret key rate (SKR) and good compatibility with existing optical communication infrastructure. Traditional CV-QKD usually employ coherent receivers to detect coherent states, whose detection performance is restricted to the standard quantum limit. In this paper, we employ a generalized Kennedy receiver called CD-Kennedy receiver to enhance the detection performance of coherent states in turbulent channels, where equal-gain combining (EGC) method is used to combine the output of CD-Kennedy receivers. Besides, we derive the SKR of a post-selection based CV-QKD protocol using both CD-Kennedy receiver and homodyne receiver with EGC in turbulent channels. We further propose an equivalent transmittance method to facilitate the calculation of both the bit-error rate (BER) and SKR. Numerical results show that the CD-Kennedy receiver can outperform the homodyne receiver in turbulent channels in terms of both BER and SKR performance. We find that BER and SKR performance advantage of CD-Kennedy receiver over homodyne receiver demonstrate opposite trends as the average transmittance increases, which indicates that two separate system settings should be employed for communication and key distribution purposes. Besides, we also demonstrate that the SKR performance of a CD-Kennedy receiver is much robust than that of a homodyne receiver in turbulent channels.

Specific Emitter Identification (SEI) has been widely studied, aiming to distinguish signals from different emitters given training samples from those emitters. However, real-world scenarios often require identifying signals from novel emitters previously unseen. Since these novel emitters only have a few or no prior samples, existing models struggle to identify signals from novel emitters online and tend to bias toward the distribution of seen emitters. To address these challenges, we propose the Online Specific Emitter Identification (OSEI) task, comprising both online few-shot and generalized zero-shot learning tasks. It requires constructing models using signal samples from seen emitters and then identifying new samples from seen and novel emitters online during inference. We propose a novel hash-based model, Collision-Alleviated Signal Hash (CASH), providing a unified approach for addressing the OSEI task. The CASH operates in two steps: in the seen emitters identifying step, a signal encoder and a seen emitters identifier determine whether the signal sample is from seen emitters, mitigating the model from biasing toward seen emitters distribution. In the signal hash coding step, an online signal hasher assigns a hash code to each signal sample, identifying its specific emitter. Experimental results on real-world signal datasets (i.e., ADSB and ORACLE) demonstrate that our method accurately identifies signals from both seen and novel emitters online. This model outperforms existing methods by a minimum of 6.08% and 8.55% in accuracy for the few-shot and generalized zero-shot learning tasks, respectively.

With the rapid growth of the low-altitude economy, demand for real-time data collection via UAV-assisted wireless sensor networks (WSNs) is increasing. This paper studies the problem of minimizing the age of information (AoI) in UAV-assisted WSNs by optimizing the UAV flight routing. We formulate the AoI minimization task and propose a large language model (LLM)-assisted UAV routing algorithm (LAURA). LAURA employs an LLM as the intelligent crossover operators within an evolutionary optimization framework to efficiently explore the solution space. Simulation results show that LAURA outperforms benchmark methods in reducing the maximum AoI, especially in scenarios with many sensor nodes.

Distributed machine learning has attracted significant attention for the future vehicular networks. However, the inadequate computation capacity of multi-vehicles and the dynamic wireless communication environment degrade the training effectiveness of distributed models. Against these backdrops, a novel end-edge-cloud collaborative computation offloading for distributed learning paradigm is proposed in this paper. In order to minimize the weighted sum cost of latency and learning loss, we jointly optimize the offloading decisions for access selection and the proportion of data offloading, as well as the resource allocation strategy for bandwidth and transmit power. Wherein, the straggler effect is addressed by formulating a dynamic short timescale problem based on service fairness and a combinatorial iterative approach is suggested to resolve this min-max problem. Then, over the long timescale, the modified multi-agent reinforcement learning (MARL) algorithm based on multiple base stations and users is developed to overcome the dilemma of the missing explicit relationship between optimization variables and learning loss function. Moreover, a fractional programming (FP) based MARL algorithm is designed to solve this NP-hard issue. Simulation results verify the superiority of the proposed FP-MARL algorithm in comparison to the benchmark schemes.

The rapid advancement toward sixth-generation (6G) wireless networks has significantly intensified the complexity and scale of optimization problems, including resource allocation and trajectory design, often formulated as combinatorial problems in large discrete decision spaces. However, traditional optimization methods, such as heuristics and deep reinforcement learning (DRL), face practical challenges in meeting stringent latency and scalability requirements, especially in large-scale, highly dynamic, and reconfiguration-sensitive deployments in increasingly heterogeneous and resource-constrained network environments. Large language models (LLMs) present a transformative paradigm by enabling natural language-driven problem formulation, context-aware reasoning, and adaptive solution refinement through advanced semantic understanding and structured reasoning capabilities. This paper provides a systematic and comprehensive survey of LLM-enabled optimization frameworks tailored for wireless networks. We first introduce foundational design concepts and distinguish LLM-enabled methods from conventional optimization paradigms. Subsequently, we critically analyze key enabling methodologies, including natural language modeling, solver collaboration, and solution verification processes. Moreover, we explore representative case studies to demonstrate LLMs’ transformative potential in practical scenarios such as optimization formulation, low-altitude economy networking, and intent networking. Finally, we discuss current research challenges, examine prominent open-source frameworks and datasets, and identify promising future directions to facilitate robust, scalable, and trustworthy LLM-enabled optimization solutions for next-generation wireless networks.

Collaborative vision-based environment perception is envisioned as a critical component of autonomous driving in intelligent transportation systems. In collaborative vision-based environment perception, vehicular nodes interact and transfer their local visual information to overcome occlusions induced by surrounding obstacles. Despite significant algorithmic advances in collaborative vision-based environment perception, the development of a comprehensive probabilistic framework to study its performance remains insufficiently explored. Inspired by this, this paper develops a novel analytical framework for collaborative vision-based environment perception in vehicular networks by applying tools from stochastic geometry. In particular, we model the road system as a Poisson line process (PLP). Further, by leveraging concepts from random shape theory, the building blockages and vehicle blockages are modeled as independent random rectangle processes, while the irregularly shaped blockages are characterized as stochastic polygonal blockages based on Voronoi tessellations. For this setup, we derive analytical expressions for the single-vehicle perception probability, the coverage probability, and the mean area fraction of the typical vehicle. We perform Monte-Carlo simulations to validate the theoretical framework and provide system-design insights through numerical analysis. To the best of the authors' knowledge, this is the first work to analyze the system-level performance of collaborative vision-based environment perception in vehicular networks with random visual blockages.

Terahertz (THz) communications, with their substantial bandwidth, are essential for meeting the ultra-high data rate demands of emerging high-mobility scenarios such as vehicular-to-everything (V2X) networks. In these contexts, beamwidth adaptation has been explored to address the problem that high-mobility targets frequently move out of the narrow THz beam range. However, existing approaches cannot effectively track targets due to a lack of real-time motion awareness. Consequently, we propose a sensing-assisted beam tracking scheme with real-time beamwidth adaptation. Specifically, the base station (BS) periodically collects prior sensing information to predict the target's motion path by applying a particular motion model. Then, we build a pre-calculated codebook by optimising precoders to align the beamwidth with various predicted target paths, thereby maximising the average achievable data rates within each sensing period. Finally, the BS selects the optimal precoder from the codebook to maintain stable and continuous connectivity. Simulation results show that the proposed scheme significantly improves the rate performance and reduces outage probability compared to existing approaches under various target mobility.

Modeling of multiple-scattering channels in atmospheric turbulence is essential for the performance analysis of long-distance non-line-of-sight (NLOS) ultraviolet (UV) communications. Existing works on the turbulent channel modeling for NLOS UV communications either focused on single-scattering cases or estimate the turbulent fluctuation effect in an unreliable way based on Monte-Carlo simulation (MCS) approach. In this paper, we establish a comprehensive turbulent multiple-scattering channel model by using a more efficient Monte-Carlo integration (MCI) approach for NLOS UV communications, where both the scattering, absorption, and turbulence effects are considered. Compared with the MCS approach, the MCI approach is more interpretable for estimating the turbulent fluctuation. To achieve this, we first introduce the scattering, absorption, and turbulence effects for NLOS UV communications in turbulent channels. Then we propose the estimation methods based on MCI approach for estimating both the turbulent fluctuation and the distribution of turbulent fading coefficient. Numerical results demonstrate that the turbulence-induced scattering effect can always be ignored for typical UV communication scenarios. Besides, the turbulent fluctuation will increase as either the communication distance increases or the zenith angle decreases, which is compatible with existing experimental results and also with our experimental results. Moreover, we demonstrate numerically that the distribution of the turbulent fading coefficient for UV multiple-scattering channels under all turbulent conditions can be approximated as log-normal distribution; and we also demonstrate both numerically and experimentally that the turbulent fading can be approximated as a Gaussian distribution under weak turbulence.

Dynamic spectrum sharing (DSS) between satellite and terrestrial networks has increasingly engaged the academic and industrial sectors. Nevertheless, facilitating secure, efficient and scalable sharing continues to pose a pivotal challenge. Emerging as a promising technology to bridge the trust gap among multiple participants, blockchain has been envisioned to enable DSS in a decentralized manner. However, satellites with limited resources may struggle to support the frequent interactions required by blockchain networks. Additionally, given the extensive coverage of satellites, spectrum sharing needs vary by regions, challenging traditional blockchain approaches to accommodate differences. In this work, a partitioned, self-governed, and customized dynamic spectrum sharing approach (PSC-DSS) is proposed for spectrum sharing between satellite access networks and terrestrial access networks. This approach establishes a sharded and tiered architecture which allows various regions to manage spectrum autonomously while jointly maintaining a single blockchain ledger. Moreover, a spectrum-consensus integrated mechanism, which decouples DSS process and couples it with blockchain consensus protocol, is designed to enable regions to conduct DSS transactions in parallel and dynamically innovate spectrum sharing schemes without affecting others. Furthermore, a theoretical framework is derived to justify the stability performance of PSC-DSS. Finally, simulations and experiments are conducted to validate the advantageous performance of PSC-DSS, which achieves up to a 89% reduction in consensus latency and a 375% improvement in throughput compared to baseline approaches.

The frame structure is regarded as one of the implementation concerns for integrated sensing and communications (ISAC). Unlike other solutions, the shared use of new radio (NR) pilots is particularly promising due to their adaptability to existing standards and infrastructure. However, the integration of sensing capabilities introduces new challenges for the pilots and frame structure designs. Generally, the sensing requirements are diverse and dynamically changing, calling for adaptive adjustments to the frame structure. The intricate interplay of various trade-offs between sensing and communication metrics complicates the design of the adaptive frame structure. To address this issue, we introduced a two-timescale frame adjustment and task scheduling (FATS) mechanism leveraging the NR position reference signal (PRS). The prime objective is to maximize the ISAC synergistic rate, a unified metric defined by sensing mutual information (SMI) and the queued transmission rate. Then, a twin agent based FATS (Twin-FATS) algorithm was proposed that synergizes deep Q-network (DQN) and soft actor-critic (SAC) to simultaneously enable the adaptive adjustment of configurable parameters and efficient resource utilization within the frames. Comprehensive simulations were conducted to validate its superiority over other strategies, with ISAC synergistic rate increased by up to 21.95%, and the task completion rate improved by up to 16.3%.

Residual dispersion breaks temporal-mode matching in photon-starved coherent links. For equiprobable $M$-ary PSK coherent states in a known spectral mode, with unknown symbols and carrier phase, we establish the quantum limit for blind joint estimation of group delay and second-order dispersion: after eliminating the common phase, it is $4N_s\mathbf{C}$, set by the covariance of the centered generators alone. A multi-output quantum pulse gate with photon-number-resolving detection locally attains it and supports reception below the standard quantum limit under turbulent fading.

Accurate cascaded channel state information is pivotal for extremely large-scale intelligent reflecting surfaces (XL-IRS) in next-generation wireless networks. However, the large XL-IRS aperture induces spherical wavefront propagation due to near-field (NF) effects, complicating cascaded channel estimation. Conventional dictionary-based methods suffer from cumulative quantization errors and high complexity, especially in uniform planar array (UPA) systems. To address these issues, we first propose a tensor modelization method for NF cascaded channels by exploiting the tensor product among the horizontal and vertical response vectors of the UPA-structured base station (BS) and the incident-reflective array response vector of the IRS. This structure leverages spatial characteristics, enabling independent estimation of factor matrices to improve efficiency. Meanwhile, to avoid quantization errors, we propose an off-grid cascaded channel estimation framework based on sparse Tucker decomposition. Specifically, we model the received signal as a Tucker tensor, where the sparse core tensor captures path gain-delay terms and three factor matrices are spanned by BS and NF IRS array responses. We then formulate a sparse core tensor minimization problem with tri-modal log-sum sparsity constraints to tackle the NP-hard challenge. Finally, the method is accelerated via higher-order singular value decomposition preprocessing, combined with majorization-minimization and a tailored tensor over-relaxation fast iterative shrinkage-thresholding technique. We derive the Cramér-Rao lower bound and conduct convergence analysis. Simulations show the proposed scheme achieves a 13.6 dB improvement in normalized mean square error over benchmarks with significantly reduced runtime.

Orthogonal time frequency space (OTFS) modulation has emerged as a promising solution to mitigate the severe Doppler shift in low Earth orbit (LEO) satellite communications. However, the frequency-dependent Doppler shift induced by the high mobility of LEO satellites leads to the Doppler squint effect (DSE). This effect compromises the channel sparsity in the delay-Doppler (DD) domain, rendering existing channel estimation methods ineffective. To overcome this challenge, this paper proposes a DSE-resilient transmission scheme for cyclic prefix OTFS (CP-OTFS)-based LEO satellite systems. Specifically, we analyze the input-output relationship of the CPOTFS- based LEO satellite communication system and derive a DSE-aware representation of the satellite-terrestrial channel in the DD domain. To efficiently capture DSE-aware channel characteristics, we propose a novel OTFS frame structure that allows the energy distribution of the received signal to serve as prior information for channel estimation. Meanwhile, this frame structure strategically allocates pilot symbols to achieve uniform energy distribution and reduce the peak-to-average power ratio (PAPR), while imposing a time-domain waveform continuity constraint to suppress out-of-band emission (OOBE) caused by rectangular pulses. Based on the frame structure, we propose a prior-aided iterative channel reconstruction (PAICR) algorithm to mitigate the severe power leakage induced by DSE. The proposed algorithm iteratively extracts and removes dominant channel components using Doppler-domain received signal energy observations, with a convergence criterion ensuring reliable termination. Furthermore, a Cramer-Rao lower bound is derived to provide a theoretical benchmark for evaluating the algorithm's performance.

The unprecedented advancements in multimodal large language models (MLLMs) have demonstrated strong potential for language-vision interaction in remote sensing tasks such as visual question answering and scene understanding. However, these models are largely constrained to basic instruction-following or descriptive tasks, and struggle with real-world remote sensing scenarios where multi-source data, fine-grained spatial semantics, and expert knowledge are indispensable. To address these limitations, we propose RS-Agent, a domain-adapted intelligent agent that bridges user intent and professional remote sensing workflows through structured task planning and tool orchestration. RS-Agent is built upon four key components that explicitly follow the typical workflow of remote sensing applications: an LLM-based Central Controller for user intent understanding and analytical process planning, a dynamic toolkit for tool execution, a Solution Space for task-specific expert guidance, and a Knowledge Space for domain-level knowledge support. To further enhance the performance of RS-Agent, we introduce two novel mechanisms: Task-Aware Retrieval, which improves task planning by explicitly inferring task types and retrieving expert-defined procedural solutions, rather than relying on query-level similarity or ad-hoc tool chaining; and DualRAG, a weighted dual-path retrieval-augmented generation method, which enhances the relevance and completeness of retrieved domain knowledge. RS-Agent natively supports multiple imaging modalities, including optical and SAR imagery. For SAR tasks in particular, the agent plans and orchestrates dedicated SAR processing and analysis tools into executable workflows, improving reliability and automation under underspecified requests. Extensive experiments across 9 datasets and 18 remote sensing tasks demonstrate that RS-Agent significantly outperforms state-of-the-art MLLMs, achieving over 95 https://github.com/IntelliSensing/RS-Agent .

This letter proposes an active reconfigurable intelligent surface (ARIS) assisted rate-splitting multiple access (RSMA) integrated sensing and communication (ISAC) system to overcome the fairness bottleneck in multi-target sensing under obstructed line-of-sight environments. Beamforming at the transceiver and ARIS, along with rate splitting, are optimized to maximize the minimum multi-target echo signal-to-interference-plus-noise ratio under multi-user rate and power constraints. The intricate non-convex problem is decoupled into three subproblems and solved iteratively by majorization-minimization (MM) and sequential rank-one constraint relaxation (SROCR) algorithms. Simulations show our scheme outperforms nonorthogonal multiple access, space-division multiple access, and passive RIS baselines, approaching sensing-only upper bounds.

Channel modeling for the satellite-to-underwater laser communication (StULC) link remains challenging due to long distances and the diversity of the channel constituents. The StULC channel is typically segmented into three isolated channels: the atmospheric channel, the air-water interface channel, and the underwater channel. Previous studies involving StULC channel modeling either focused on separated channels or neglected the combined effects of particles and turbulence on laser propagation. In this paper, we established a comprehensive StULC channel model by an analytical-Monte Carlo hybrid approach, taking into account the effects of particles, bubbles, and turbulence. We first obtained the intensity distribution of the transmitted laser beam after passing through the turbulent atmosphere based on the extended Huygens-Fresnel principle. Then we derived a closed-form probability density function of angular fluctuations of the beam refracted by the random sea surface. These analytical results are then mapped to initialize photon states for the Monte Carlo simulation of the underwater channel. Based on the proposed StULC channel model, we analyzed the bit error rate and the outage probability under different environmental conditions and system parameters. Numerical results demonstrated that the influence of underwater particle concentration on the communication performance is more pronounced than that of both the atmospheric turbulence and the underwater turbulence. In addition, enlarging the receiver aperture provides a more effective performance improvement than increasing the receiver field-of-view angle.

The fog radio access network (F-RAN) is regarded as a promising architecture allowing distributed signal processing and edge intelligent, offering a synergistic paradigm of communication and computing. However, emerging services transcend this framework, necessitating additional support for advanced generalized computing and environmental sensing. In response, this paper presents an upgraded F-RAN enabled by integrated sensing, communication and computing (ISCC). Specifically, integrated sensing and communication is introduced for extended radio capabilities, with computing-first design concurrently employed as an advanced computational solution. The updated F-RAN architecture harmonizes these two emerging technical trends, underscoring their synergistic benefits. Then, key challenges in realizing this vision are identified, with corresponding solutions discussed as key technologies. Some simulation results are provided to demonstrate that incorporating ISCC design into F-RANs enhances the comprehensive performance encompassing communication, sensing, and computing. Finally, open issues are outlined to stimulate innovation in related fields.

Discrete-time Poisson channels with finite intersymbol interference provide a natural model for direct-detection optical links in which multipath memory and signal-dependent shot noise appear simultaneously. Under peak and average optical-intensity constraints, we study the independent and identically distributed (i.i.d.)-constrained capacity problem of such channels. We prove that every input distribution maximizing the stationary mutual information rate within the i.i.d. input class has finite support. The proof is carried out directly on the entropy rate of the continuous-state hidden Markov output process induced by the finite-memory channel. We first establish a filtering-forgetting estimate whose constants are uniform over all admissible i.i.d. input laws. We then derive the entropy-rate first variation, construct a holomorphic extension of the corresponding influence function, and combine the Karush-Kuhn-Tucker condition with a supralinear growth argument.

Continuous variable-quantum key distribution (CV-QKD) protocols attract increasing attentions in recent years because they enjoy high secret key rate (SKR) and good compatibility with existing optical communication infrastructure. Classical coherent receivers are widely employed in coherent states based CV-QKD protocols, whose detection performance is bounded by the standard quantum limit (SQL). Recently, quantum receivers based on displacement operators are experimentally demonstrated with detection performance outperforming the SQL in various practical conditions. However, potential applications of quantum receivers in CV-QKD protocols under turbulent channels are still not well explored, while practical CV-QKD protocols must survive from the atmospheric turbulence in satellite-to-ground optical communication links. In this paper, we consider the possibility of using a quantum receiver called multi-stage CD-Kennedy receiver to enhance the SKR performance of a quadrature phase shift keying (QPSK) modulated CV-QKD protocol in turbulent channels. We first derive the error probability of the multi-stage CD-Kennedy receiver for detecting QPSK signals in turbulent channels and further propose three types of multi-stage CD-Kennedy receiver with different displacement choices, i.e., the Type-I, Type-II, and Type-III receivers. Then we derive the SKR of a QPSK modulated CV-QKD protocol using the multi-stage CD-Kennedy receiver and post-selection strategy in turbulent channels. Numerical results show that the multi-stage CD-Kennedy receiver can outperform the classical coherent receiver in turbulent channels in terms of both error probability and SKR performance and the Type-II receiver can tolerate worse channel conditions compared with Type-I and Type-III receivers in terms of error probability performance.

Terahertz (THz) band is acknowledged as a candidate spectrum in future networks. However, THz communications demand the assistance of high-accuracy parameter estimations to achieve narrow beam-alignment. Despite existing works have intensively explored sensing-assisted THz communications, they fail in mobile and near-field scenarios without concerning the benefits brought from velocity and distance estimations. To this end, a closed-form framework toward sensing-assisted terahertz near-field mobile communications (SA-TNMC) is introduced in this paper. Four-dimensional parameters containing distance, azimuth angle, radial velocity, and tangential angular velocity are extracted from the echoes, all of which are utilized for beamfocusing. For performance characterizations, both Cramer-Rao Bound and Ergodic Shannon Capacity are derived in closed-form expressions. Besides, a novel critical point is proposed to demarcate the minimized resource division factor to activate SA-TNMC system. At last, numerical results are proceeded to validate the framework.