This work explores signal transmission for serving multiple communication users (CUs) while simultaneously estimating multiple targets. In this context, the multi-stream signals intended for downlink CUs equipped with multiple antennas are also employed as probing signals for target estimation. We consider precoding design to ensure quality of service, quantified by the CUs’ individual rates and the mean squared error in target estimation. We develop path-following computational procedures that generate a sequence of improved feasible points by iterating closed-form expressions, ensuring convergence. As a byproduct, a computational solution for estimating the targets’ response vectors or their reflection coefficients and angles manifests, addressing long-standing open problems in estimation theory. Computational experiments not only demonstrate their consistency but also reveal that the obtained precoders produce highly directionally selective beampatterns toward the targets, even though this is not a primary objective.

The integration of machine learning (ML) in cyber physical systems (CPS) is a complex task due to the challenges that arise in terms of real-time decision making, safety, reliability, device heterogeneity, and data privacy. There are also open research questions that must be addressed in order to fully realize the potential of ML in CPS. Federated learning (FL), a distributed approach to ML, has become increasingly popular in recent years. It allows models to be trained using data from decentralized sources. This approach has been gaining popularity in the CPS field, as it integrates computer, communication, and physical processes. Therefore, the purpose of this work is to provide a comprehensive analysis of the most recent developments of FL-CPS, including the numerous application areas, system topologies, and algorithms developed in recent years. The paper starts by discussing recent advances in both FL and CPS, followed by their integration. Then, the paper compares the application of FL in CPS with its applications in the internet of things (IoT) in further depth to show their connections and distinctions. Furthermore, the article scrutinizes how FL is utilized in critical CPS applications, e.g., intelligent transportation systems, cybersecurity services, smart cities, and smart healthcare solutions. The study also includes critical insights and lessons learned from various FL-CPS implementations. The paper's concluding section delves into significant concerns and suggests avenues for further research in this fast-paced and dynamic era.

Reconfigurable intelligent surfaces (RISs) have been recognized as a revolutionary technology for future wireless networks. However, RIS-assisted communications have to continuously tune phase-shifts relying on accurate channel state information (CSI) that is generally difficult to obtain due to the large number of RIS channels. The joint design of CSI acquisition and subsection RIS phase-shifts remains a significant challenge in dynamic environments. In this paper, we propose a diffusion-enhanced decision Transformer (DEDT) framework consisting of a diffusion model (DM) designed for efficient CSI acquisition and a decision Transformer (DT) utilized for phase-shift optimizations. Specifically, we first propose a novel DM mechanism, i.e., conditional imputation based on denoising diffusion probabilistic model, for rapidly acquiring real-time full CSI by exploiting the spatial correlations inherent in wireless channels. Then, we optimize beamforming schemes based on the DT architecture, which pre-trains on historical environments to establish a robust policy model. Next, we incorporate a fine-tuning mechanism to ensure rapid beamforming adaptation to new environments, eliminating the retraining process that is imperative in conventional reinforcement learning (RL) methods. Simulation results demonstrate that DEDT can enhance efficiency and adaptability of RIS-aided communications with fluctuating channel conditions compared to state-of-the-art RL methods.

With the rapid growth of intelligent aerial-terrestrial applications, enabling collaborative multimodal learning (CML) across heterogeneous data sources, such as aerial images from unmanned aerial vehicles (UAVs) and time-series signals from ground edge devices (EDs), has become essential for achieving reliable intelligence beyond unimodal approaches. However, aerial-terrestrial CML systems face stringent latency requirements, limited energy and computation resources, and vulnerability to adversarial attacks, which are not jointly addressed in existing studies. This paper proposes a wireless aerial-terrestrial CML framework that integrates distributed UAVs and terrestrial EDs with modality-specific encoder training and multimodal fusion at a ground base station (BS). We formulate a latency minimization problem under energy, and security-aware constraints by jointly optimizing UAV trajectories and resource allocation, ED resource allocation, as well as resource allocation of the BS. The framework explicitly incorporates both passive eavesdropping and active interference attacks to ensure secure and robust aerial-terrestrial CML operation. To solve the resulting non-convex latency minimization problem, we develop a simple yet efficient iterative optimization algorithm to find a high-quality optimal solution based on successive convex approximation. Extensive simulation results with real-world datasets demonstrate that the proposed framework significantly outperforms existing training methods in terms of accuracy, loss, and convergence. Moreover, our joint optimization framework achieves up to 94.05% lower latency and stronger robustness against aerial adversaries compared with baseline schemes.

Stacked intelligent metasurfaces (SIMs), which integrate multiple programmable metasurface layers, have recently emerged as a promising technology for advanced wave-domain signal processing. SIMs benefit from flexible spatial degree-of-freedom (DoF) while reducing the requirement for costly radio-frequency (RF) chains. However, current state-of-the-art SIM designs face challenges such as complex phase shift optimization and energy attenuation from multiple layers. To address these aspects, we propose incorporating meta-fibers into SIMs, with the aim of reducing the number of layers and enhancing the energy efficiency. First, we introduce a meta-fiber-connected 2-layer SIM that exhibits the same flexible signal processing capabilities as conventional multi-layer structures, and explains the operating principle. Subsequently, we formulate and solve the optimization problem of minimizing the mean square error (MSE) between the SIM channel and the desired channel matrices. Specifically, by designing the phase shifts of the meta-atoms associated with the transmitting-SIM and receiving-SIM, a non-interference system with parallel subchannels is established. In order to reduce the computational complexity, a closed-form expression for each phase shift at each iteration of an alternating optimization (AO) algorithm is proposed. We show that the proposed algorithm is applicable to conventional multi-layer SIMs. The channel capacity bound and computational complexity are analyzed to provide design insights. Finally, numerical results are illustrated, demonstrating that the proposed two-layer SIM with meta-fiber achieves over a 25% improvement in channel capacity while reducing the total number of meta-atoms by 59% as compared with a conventional seven-layer SIM.

Pinching-antenna systems (PASSs) are a recent flexible-antenna technology that is realized by attaching simple components, referred to as pinching elements, to dielectric waveguides. This work explores the potential of deploying PASS for uplink and downlink transmission in multiuser MIMO settings. For downlink PASS-aided communication, we formulate the optimal hybrid beamforming, in which the digital precoding matrix at the access point and the pinching locations on the waveguides are jointly optimized to maximize the achievable weighted sum-rate. We discuss the key challenges in this design problem and propose two low-complexity algorithms to iteratively update the precoding matrix and activated pinching locations. We further formulate the design problem for uplink transmission in a PASS and develop an iterative scheme for the underlying hybrid multiuser detection problem. We validate the proposed schemes through extensive numerical experiments. The results demonstrate that using a PASS, the throughput in both uplink and downlink is significantly enhanced compared to baseline MIMO architectures, such as massive MIMO and classical hybrid analog-digital designs. This highlights the great potential of the PASS, making it a promising reconfigurable antenna technology for next-generation wireless systems.

Pinching-antenna systems have emerged as a novel and transformative flexible-antenna architecture for next-generation wireless networks. They offer unprecedented flexibility and spatial reconfigurability by enabling dynamic positioning and activation of radiating elements along a signal-guiding medium (e.g., dielectric waveguide), which is not possible with conventional fixed antenna systems. In this paper, we introduce the concept of generalized pinching antenna systems, which retain the core principle of creating localized radiation points on demand, but can be physically realized with a variety of technologies. These include implementations based on dielectric waveguides, leaky coaxial cables, surface-wave guiding structures, and other types of media, employing different feeding methods and activation mechanisms (e.g., mechanical, electronic, or hybrid). Despite differences in their physical realizations, they all share the same inherent ability to form, reposition, or deactivate radiation sites as needed, enabling user-centric and dynamic coverage. We first describe the underlying physical mechanisms of representative generalized pinching-antenna realizations and their associated wireless channel models, highlighting their unique propagation and reconfigurability characteristics compared with conventional antennas. Then, using dielectric waveguide-based pinching antennas as primary examples, we review several representative pinching-antenna system architectures, ranging from single- to multiple-waveguide configurations, and discuss advanced design strategies tailored to these flexible deployments. Furthermore, we examine their integration with emerging wireless technologies to enable synergistic, user-centric solutions. Finally, we identify key open research challenges and outline future directions, charting a pathway toward the deployment of generalized pinching antennas in next-generation wireless networks.

In 6G networks, integrated sensing and communication (ISAC) is envisioned as a key technology that enables wireless systems to perform joint sensing and communication using shared hardware, antenna(s) and spectrum. ISAC designs facilitate emerging applications such as digital twins, smart cities and autonomous driving. Such applications also demand ultra-reliable and low-latency communication (URLLC), a feature that was first introduced in 5G and is expected to be further enhanced in 6G. Thus, an ISAC-enabled URLLC system can prioritize critical and time-sensitive targets and ensure information delivery under strict latency and reliability constraints. We propose a bi-static multiple-input multiple-output (MIMO) ISAC system to detect the arrival of URLLC messages and prioritize their delivery. In this system, a dual-function base station (BS) communicates with a user equipment (UE) and a sensing receiver (SR) is deployed to collect echo signals reflected from a target of interest. The BS regularly transmits messages of enhanced mobile broadband (eMBB) services to the UE. During each eMBB transmission, if the SR senses the presence of a target of interest, it immediately triggers the transmission of an additional URLLC message. To reinforce URLLC transmissions, we propose a dirty-paper coding (DPC)-based technique that mitigates the interference of both eMBB and sensing signals. To decode the eMBB message, we consider two approaches for handling the URLLC interference: treating interference as noise (TIN) and successive interference cancellation (SIC). For this system, we formulate the rate-reliability-detection trade-off in the finite blocklength (FBL) regime by evaluating the communication rate of the eMBB transmissions, the reliability of the URLLC transmissions and the probability of the target detection. Our numerical analysis show that our proposed DPC-based ISAC scheme significantly outperforms power-sharing based ISAC and traditional time-sharing schemes. In particular, it achieves higher eMBB transmission rate while satisfying both URLLC and sensing constraints.

A key challenge in integrated sensing and communications (ISAC) is the synthesis of waveforms that can modulate communication messages and achieve good sensing performance simultaneously. In ISAC systems, standard communication waveforms can be adapted for sensing, as the sensing receiver (co-located with the transmitter) has knowledge of the communication message and consequently the waveform. However, the randomness of communications may result in waveforms that have high sidelobes masking weak targets. Thus, it is desirable to refine communication waveforms to improve the sensing performance by reducing the integrated sidelobe levels (ISL). This is similar to the peak-to-average power ratio (PAPR) mitigation in orthogonal frequency division multiplexing (OFDM), in which the OFDM-modulated waveform needs to be refined to reduce the PAPR. In this paper, inspired by PAPR reduction algorithms in OFDM, we employ trellis shaping in OFDM-based ISAC systems to refine waveforms for specific sensing metrics using convolutional codes and Viterbi decoding. In such a scheme, the communication data is encoded and then mapped to the signaling constellation in different subcarriers, such that the time-domain sidelobes are reduced. An interesting observation is that sidelobe reduction in OFDM-based ISAC is dual to PAPR reduction in OFDM, thereby sharing a similar signaling structure. Numerical simulations and hardware software defined radio USRP experiments are carried out to demonstrate the effectiveness of the proposed trellis shaping approach.

This paper investigates signaling by a base station equipped with a dynamic metasurface antenna (DMA) to support the transmission of multi-stream information to multiple near-field users. We consider the joint design of the low-resolution DMA elements’ frequency responses and the baseband precoder to ensure the quality-of-service (QoS) for all users in terms of their rates. First, we develop convex quadratic solver-based iterations of cubic complexity to address the computationally challenging max-min rate optimization problem involving nonsmooth large-scale mixed discrete-continuous optimization. We then opt for the soft max-min rate optimization problem, which involves smooth mixed discrete-continuous optimization, and develop closed-form expression-based iterations of scalable complexity for its computation. The latter approach is not only computationally efficient but also achieves both a high minimum user rate and sum-rate, thereby guaranteeing QoS and high overall network throughput.

In this paper, we analyze the performance of the estimation of Laplacian matrices under general observation models. Laplacian matrix estimation involves structural constraints, including symmetry and null-space properties, along with matrix sparsity. By exploiting a linear reparametrization that enforces the structural constraints, we derive closed-form matrix expressions for the Cramer-Rao Bound (CRB) specifically tailored to Laplacian matrix estimation. We further extend the derivation to the sparsity-constrained case, introducing two oracle CRBs that incorporate prior information of the support set, i.e. the locations of the nonzero entries in the Laplacian matrix. We examine the properties and order relations between the bounds, and provide the associated Slepian-Bangs formula for the Gaussian case. We demonstrate the use of the new CRBs in three representative applications: (i) topology identification in power systems, (ii) graph filter identification in diffused models, and (iii) precision matrix estimation in Gaussian Markov random fields under Laplacian constraints. The CRBs are evaluated and compared with the mean-squared-errors (MSEs) of the constrained maximum likelihood estimator (CMLE), which integrates both equality and inequality constraints along with sparsity constraints, and of the oracle CMLE, which knows the locations of the nonzero entries of the Laplacian matrix. We perform this analysis for the applications of power system topology identification and graphical LASSO, and demonstrate that the MSEs of the estimators converge to the CRB and oracle CRB, given a sufficient number of measurements.

Semantic communication (SemCom) has emerged as a promising paradigm for 6G wireless systems by transmitting task-relevant information rather than raw bits, yet existing approaches remain vulnerable to dual sources of uncertainty: semantic misinterpretation arising from imperfect feature extraction and transmission-level perturbations from channel noise. Current deep learning based SemCom systems typically employ domain-specific architectures that lack robustness guarantees and fail to generalize across diverse noise conditions, adversarial attacks, and out-of-distribution data. In this paper, a novel and generalized semantic communication framework called WaSeCom is proposed to systematically address uncertainty and enhance robustness. In particular, Wasserstein distributionally robust optimization is employed to provide resilience against semantic misinterpretation and channel perturbations. A rigorous theoretical analysis is performed to establish the robust generalization guarantees of the proposed framework. Experimental results on image and text transmission demonstrate that WaSeCom achieves improved robustness under noise and adversarial perturbations. These results highlight its effectiveness in preserving semantic fidelity across varying wireless conditions.

Massive random access is an important technology for achieving ultra-massive connectivity in next-generation wireless communication systems. It aims to address key challenges during the initial access phase, including active user detection (AUD), channel estimation (CE), and data detection (DD). This paper examines massive access in massive multiple-input multiple-output (MIMO) systems, where deep learning is used to tackle the challenging AUD, CE, and DD functions. First, we introduce a Transformer-AUD scheme tailored for variable pilot-length access. This approach integrates pilot length information and a spatial correlation module into a Transformer-based detector, enabling a single model to generalize across various pilot lengths and antenna numbers. Next, we propose a generative diffusion model (GDM)-driven iterative CE and DD framework. The GDM employs a score function to capture the posterior distributions of massive MIMO channels and data symbols. Part of the score function is learned from the channel dataset via neural networks, while the remaining score component is derived in a closed form by applying the symbol prior constellation distribution and known transmission model. Utilizing these posterior scores, we design an asynchronous alternating CE and DD framework that employs a predictor-corrector sampling technique to iteratively generate channel estimation and data detection results during the reverse diffusion process. Simulation results demonstrate that our proposed approaches significantly outperform baseline methods with respect to AUD, CE, and DD.

Pinching antennas have emerged as a promising technology for reconfiguring wireless propagation environments, particularly in high-frequency communication systems operating in the millimeter-wave and terahertz bands. By enabling dynamic activation at arbitrary positions along a dielectric waveguide, pinching antennas offer unprecedented channel reconfigurability and the ability to provide line-of-sight (LoS) links in scenarios with severe LoS blockages. The performance of pinching-antenna systems is highly dependent on the optimized placement of the pinching antennas, which must be jointly considered with traditional resource allocation (RA) variables -- including transmission power, time slots, and subcarriers. The resulting joint RA problems are typically non-convex with complex variable coupling, necessitating sophisticated optimization techniques. This article provides a comprehensive survey of existing RA algorithms designed for pinching-antenna systems, supported by numerical case studies that demonstrate their potential performance gains. Key challenges and open research problems are also identified to guide future developments in this emerging field.

This work addresses the problem of integrated sensing and communications (ISAC) involving a massive number of unsourced and uncoordinated users. In the proposed model, known as the unsourced ISAC system (UNISAC), all active communication and sensing users simultaneously share a short frame to transmit their signals without requiring scheduling by the base station or the need to announce their identities. Consequently, the received signal from each user is heavily affected by interference from numerous other users, making it challenging to extract individual transmissions. UNISAC is designed to decode the message sequences from communication users while simultaneously detecting active sensing users and estimating their angles of arrival, regardless of the senders’ identities. We establish a second-order achievable bound for UNISAC that explicitly quantifies performance deviations due to finite resources, and we show that it outperforms ISAC approaches built on traditional multiple access methods, including ALOHA, time-division multiple access (TDMA), treating interference as noise (TIN), and a TDMA-based scheme combined with multiple signal classification for sensing. Additionally, we propose a practical model that validates the feasibility of the achievable result, showing comparable or even superior performance in scenarios with a small number of users. Through numerical simulations, we demonstrate the effectiveness of both the practical UNISAC model and the achievable result.

As intelligent network services continue to diversify, ensuring efficient and adaptive resource allocation in edge networks has become increasingly critical. Yet the wide functional variations across services often give rise to new and unforeseen optimization problems, rendering traditional manual modeling and solver design both time-consuming and inflexible. This limitation reveals a key gap between current methods and human solving - the inability to recognize and understand problem characteristics. It raises the question of whether problem-aware learning can bridge this gap and support effective cross-problem generalization. To answer this question, we propose a problem-aware diffusion (PAD) model, which leverages a problem-aware learning framework to enable cross-problem generalization. By explicitly encoding the mathematical formulations of optimization problems into token-level embeddings, PAD empowers the model to understand and adapt to problem structures. Extensive experiments across ten representative network optimization problems show that PAD generalizes well to unseen problems while avoiding the inefficiency of building new solvers from scratch, yet still delivering competitive solution quality. Meanwhile, an auxiliary constraint-aware module is designed to enforce solution validity further. The experiments indicate that problem-aware learning opens a promising direction toward general-purpose solvers for intelligent network operation and resource management. Our code is open source at https://github.com/qiyu3816/PAD.

In this paper, deceptive signal-assisted private split learning is investigated. In our model, several edge devices jointly perform collaborative training, and some eavesdroppers aim to collect model and data information from devices. To prevent the eavesdroppers from collecting model and data information, a subset of devices can transmit deceptive signals. Therefore, it is necessary to determine the subset of devices used for deceptive signal transmission, the subset of model training devices, and the models assigned to each model training device. This problem is formulated as an optimization problem whose goal is to minimize the information leaked to eavesdroppers while meeting the model training energy consumption and delay constraints. To solve this problem, we propose a soft actor-critic deep reinforcement learning framework with intrinsic curiosity module and cross-attention (ICM-CA) that enables a centralized agent to determine the model training devices, the deceptive signal transmission devices, the transmit power, and the sub-models assigned to each model training device without knowing the position and monitoring probability of eavesdroppers. The proposed method uses an ICM module to encourage the server to explore novel actions and states and a CA module to determine the importance of each historical state-action pair, thus improving training efficiency. Simulation results demonstrate that the proposed method improves the convergence rate by up to 3 & times; and reduces the information leaked to eavesdroppers by up to 13% compared to the traditional SAC algorithm.

This paper introduces a deep reinforcement learning-based block coordinate descent (DRL-based BCD) algorithm to address the nonconvex weighted sum-rate maximization (WSRM) problem with a total power constraint. Firstly, we present an efficient block coordinate descent (BCD) method to solve the problem. While this method may not always achieve globally optimal solutions, it provides a pathway for integrating machine learning and domain-specific techniques with theoretical analysis of the underlying convexity of the subproblems. We then integrate deep reinforcement learning (DRL) techniques into the BCD method and propose the DRL-based BCD algorithm. This approach combines the data-driven learning capability of machine learning techniques with the navigational and decision-making characteristics of the optimization-theoretic-based BCD method. This combination significantly improves the algorithm’s performance by reducing its sensitivity to initial points and mitigating the risk of entrapment in local optima. The primary advantages of the proposed DRL-based BCD algorithm lie in its ability to adhere to the constraints of the WSRM problem and significantly enhance accuracy, potentially achieving the exact optimal solution. Moreover, unlike many pure machine-learning approaches, the DRL-based BCD algorithm capitalizes on the underlying theoretical analysis of the WSRM problem’s structure. This enables it to be easily trained and computationally efficient while maintaining a level of interpretability. Moreover, the DRL-based BCD framework demonstrates strong extensibility and can effectively be applied to other scenarios, such as joint beamforming for sum rate maximization, as demonstrated in this paper. Through numerical experiments, the DRL-based BCD algorithm demonstrates substantial advantages in effectiveness, efficiency, robustness, and interpretability for maximizing sum rates, which also provides valuable potential for designing resource-constrained AI-native wireless optimization strategies in next-generation wireless networks.

Generative artificial intelligence (GenAI) models have innovated content creation but raise concerns about privacy, security, and regulatory compliance such as General Data Protection Regulation. In response, unlearning techniques have emerged to selectively remove data while preserving the utility of the model. This article reviews unlearning methods in centralized and decentralized settings. These strategies mitigate risks, such as data leakage, membership inference, and bias amplification. By integrating unlearning with continuous or lifelong learning paradigms, GenAI models can adapt dynamically while honoring the "right to be forgotten." In existing unlearning methods, we explore key tradeoffs involving computational overhead, accuracy retention, generative quality, and thorough data deletion. Our review covers technical and ethical considerations and future directions, highlighting a balanced path toward responsible GenAI systems.

Integrated sensing and communication (ISAC) is envisioned as a key technology in 6G networks, owing to its potential for high spectral and cost efficiency. As a promising solution for extremely large-scale arrays, reconfigurable holographic surfaces (RHSs) can be integrated with ISAC to form the holographic ISAC paradigm, where enlarged radiation apertures of RHSs can achieve significant beamforming gains, thereby improving both communication and sensing performance. In this paper, we investigate holographic beamforming designs for ISAC systems. However, unlike existing holographic beamforming schemes developed for RHS-aided communications, such designs require explicit consideration of mutual coupling effects within the RHS. This is because, unlike prior works only considering communication performance, ISAC systems incorporate sensing functionality, which is sensitive to sidelobe levels. Ignoring mutual coupling in holographic beamforming can lead to notable undesired sidelobes, thus degrading sensing performance. The consideration of mutual coupling introduces new challenges, i.e., it induces non-linearity in beamforming problems, rendering them inherently non-convex. To address this issue, we propose a tractable electromagnetic-compliant holographic ISAC model that characterizes mutual coupling in a closed form using coupled dipole approximations. We then develop an efficient holographic beamforming algorithm to suppress sidelobes and enhance ISAC performance. Numerical results validate the effectiveness of the proposed algorithm.