With the rise of low-altitude economy applications, 6G communication systems place stricter demands on optical fiber amplifiers, requiring wider bandwidth, higher gain, and better spectral uniformity. Backbone networks for low-altitude integrated sensing and communication systems, in particular, call for high-performance amplification to support robust data transmission and reliable sensing. However, traditional multi-pump Raman fiber amplifiers (RFAs) are no longer adequate for meeting the performance demands of distributed fiber optic sensing networks in such scenarios. To address this problem, this paper proposes a machine learning-enhanced multi-pump RFA for high-performance optical backbone in low-altitude sensing and communication. The back propagation neural network (BPNN) is employed to accurately model the nonlinear relationship between pump parameters and amplification performance, facilitating adaptive and fine-grained control over signal gain, which is critical for maintaining stable and efficient data transmission across dynamic and heterogeneous communication scenarios. Moreover, the artificial bee colony (ABC) algorithm is integrated to perform global optimization of pump wavelengths and power configurations, thereby improving overall system bandwidth, gain characteristics, and operational robustness under diverse and unpredictable network conditions. The experimental results demonstrate that the proposed method achieves superior prediction accuracy, enhanced stability, and greater adaptability compared to conventional algorithms.

This work proposes two approaches, i.e., Fed-KD and Fed-TKD, to enhance communication efficiency with federated learning (FL) in Industrial Internet of Things (IIoT) radar sensing systems, which face the challenge in transmitting large volumes of sensitive data while still ensuring privacy. Fed-KD leverages knowledge distillation to transfer knowledge from complex teacher networks to simpler student models in order to reduce communication overhead in bandwidth-constrained environments. Fed-TKD improves communication efficiency further by applying tensor decomposition to reduce parameter redundancy. Experimental results using an industrial IoT radar imagery dataset show that both methods can significantly reduce communication costs while maintaining a high model accuracy, making them especially suitable for privacy-preserving industrial IoT sensing applications. Furthermore, experimental results with both IID and non-IID data distributions confirm the robustness of the proposed methods in heterogeneous environments.

With the rapid growth of the low-altitude economy, emerging applications such as urban air mobility and smart logistics demand reliable and low-latency beamforming for unmanned aerial vehicle-to-vehicle (UAV-to-UAV, U2U) communications in millimeter-wave (mmWave) bands under highly dynamic and non-line-of-sight (NLOS) conditions. Traditional beam alignment methods relying on exhaustive search or channel feedback incur heavy training overhead and degraded accuracy in rapidly varying environments. To address these challenges, we propose multi-modal perception-assisted fusion network (MPFusionNet), a multi-modal perception-enhanced Transformer framework for predictive beamforming. Our approach leverages heterogeneous onboard sensing data including global positioning system (GPS), red-green-blue (RGB) cameras, LiDAR, and radar altimeters, incorporates a dynamic time warping (DTW)-based alignment mechanism, and embeds geometry-aware priors within a perceiver input-output (PerceiverIO)-based fusion architecture to achieve robust spatiotemporal representation. Experiments on a simulated U2U dataset show that MPFusionNet attains a top-3 beam prediction accuracy of 97.59%, substantially surpassing conventional models. These results demonstrate the effectiveness of multi-modal learning in improving robustness and generalization of predictive beamforming for future autonomous aerial communication systems.

The conventional machine learning (ML)–based channel state information (CSI) acquisition has overlooked the potential privacy disclosure and estimation overhead problem caused by transmitting pilot datasets during the estimation stage. In this paper, we propose federated edge learning for CSI acquisition to protect the data privacy in the Internet of vehicle network with massive antenna array. To reduce the channel estimation overhead, the joint model pruning and vector quantization algorithm for network gradient parameters is presented to reduce the amount of exchange information between the centralized server and devices. This scheme allows for local fine‐tuning to adapt the global model to the channel characteristics of each device. In addition, we also provide theoretical guarantees of convergence and quantization error bound in closed form, respectively. Simulation results demonstrate that the proposed FL‐based CSI acquisition with model pruning and vector quantization scheme can efficiently improve the performance of channel estimation while reducing the communication overhead.

Fluid antenna (FA) is capable of achieving a significant degree of spatial diversity within the limited space of a wireless device by adjusting the radiating elements to optimal positions. In this article, we explore the potential of deploying FAs on the overall performance of wireless powered communication network (WPCN). Specifically, each Internet of Things (IoT) device in WPCN is equipped with a single FA comprising multiple ports. The IoT device (ID) selects the optimal receive port for energy harvesting from the power beacon (PB), followed by choosing the optimal transmit port to send its data to the access point (AP). Our objective is to maximize the sum throughput of IDs by jointly optimizing port activation and time scheduling, subject to constraints on the received signal-to-noise ratio (SNR) of each individual ID and the total transmission time. To tackle this nonconvex problem, we first apply the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to find the optimal solutions for time slots. Then, we introduce an efficient algorithm based on the alternating optimization (AO) method to iteratively achieve a locally optimal solution for port activation. Additionally, a low-complexity scheme is proposed to minimize computational overhead. Simulation results reveal that incorporating FAs into a WPCN markedly improves the overall system performance, and highlights the benefits of port selection for the FA in comparison to baseline methods.

Influence maximization (IM), which aims to identify the most influential k nodes in a network, is fundamental to numerous applications, including viral marketing and recommendation systems. This topic has garnered significant scholarly attention. However, most existing research addresses the IM problem in static networks, neglecting the dynamic and continually evolving nature of social networks. In this article, we introduce a novel problem: influential nodes tracking in future networks (INTFN). The INTFN problem aims to quickly find the most influential k nodes in networks over upcoming time intervals. We formally define the INTFN problem and prove its NP-hardness. To address this challenge, we propose a comprehensive solution that predicts the future structure of social networks using a carefully selected link prediction technique. Subsequently, we identify the most influential k nodes in these future networks by employing classic IM algorithms. Additionally, we design a dictionary structure and propose the compressed subgraphs-based influential nodes tracking (CSINT) algorithm to enhance the efficiency of our solution. Extensive experiments on four real-world datasets demonstrate the effectiveness and efficiency of the proposed CSINT algorithm.

We investigate the problem of maximizing anti-jamming sum throughput in an RIS-assisted Internet of Things (IoT) network. The network’s operation is divided into two stages: 1) IoT terminals first harvest energy from the wireless energy station (WES) and 2) they then transmit their information to the information receiver (IR) using a frequency division multiple access (FDMA) protocol. We consider three different design scenarios: 1) ideal hardware; 2) phase shift error (PSE); and 3) a combination of both PSE and transceiver hardware impairments (THIs). To address the nonconvexities of these designs, we employ novel techniques, such as the Lagrangian dual method, Karush–Kuhn–Tucker (KKT) conditions, quadratic transformation (QT), element-wise block coordinate descent (EBCD), complex circle manifold (CCM), and 1-D search to obtain the optimal solutions. Numerical results are provided to illustrate that the proposed approaches outperform existing benchmarks.

This article focuses on maximizing the anti-jamming sum throughput in a time division multiple access (TDMA)-based reconfigurable intelligent surfaces (RIS)-assisted wireless powered Internet of Things (WP-IoT) network. In this setup, multiple IoT devices harvest energy from wireless energy stations (WES) and then utilize the collected energy to upload their own data to an information receiver (IR). The network also includes a jammer that sends jamming signals to the IR, and a RIS is deployed to mitigate this jamming effect and enhance the sum throughput. This study addresses both an upper bound design and a robust design with fractional nonlinear energy harvesting model. The primary optimization goal is to maximize the anti-jamming sum throughput, with the constraints of RIS phase shifts and time scheduling. For both designs, closed-form expressions for time scheduling are derived using the Lagrangian duality and Karush-Kuhn-Tucker (KKT) conditions. The quadratic transformation (QT) technique is used to handle fractional functions within the optimization. Furthermore, the phase shifts are optimized iteratively using the element-wise block coordinate descent (EBCD) and Riemannian manifold optimization (RMO) algorithms. Simulation results are presented to validate the effectiveness of the proposed approaches.

Vehicle recognition constitutes a foundational technology within intelligent transport systems (ITS), enabling real-time recognition, classification, and tracking of vehicles. With the characteristics of low construction cost, flexible deployment and strong environment adaptability, unmanned aerial vehicle (UAV) is increasingly leveraged for vehicle target recognition, acts as the air part of future intelligent transport systems (ITS) for traffic management, accident handling and vehicle order management, and provides a more efficient, safe and sustainable transport mobility solutions in future ITS. Promoted by the massive number of intelligent vehicles and growing demands of connected vehicles in ITS, continuous and high-fidelity spatio-temporal monitoring of vehicle movement is expected in future ITS, raising the pursuit of higher vehicle recognition performance. As a typical distributed training framework, federated learning (FL) is a desired paradigm to improve sensing performance with the communication of sensing parameters for UAV-enabled ITS. Due to the heterogeneity of sensing data in the cooperative UAV-enabled ITS, the non-independent identically distribution (Non-IID) issue is inevitable. The existing data augmentation works aimed at Non-IID issue in FL utilize single-agent reinforcement learning (SARL), where the local model parameters are input into a central network, resulting in the model privacy leakage problem. To deal with the above issue, a multi-agent reinforcement learning (MARL) algorithm is applied to optimize the training accuracy and data augmentation efficiency for UAV in ITS. Moreover, a decentralized blockchain-based FL (BFL) framework is proposed to avoid the single-point failure in UAV-enabled ITS. The experiments are conducted on the generated vehicle dataset (VRID) and the simulation results indicate that our proposed algorithm exhibits a superior performance than the benchmark algorithms, especially in terms of higher vehicle target recognition accuracy and lower communication overhead, which provides a significant technology support for vehicle identification and tracking in future ITS.

Abstract Satellite communications (SatCom) have been viewed as a promising technique to achieve ubiquitous global coverage in next‐generation wireless systems. This article investigates the secure energy efficient downlink transmission for SatCom where an intelligent transmissive surface‐aided transmitter is deployed at the satellite to perform energy efficient beamforming and an intelligent reflecting surface as well as a cooperative jammer are deployed on the ground to enhance the secure performance. The aim is to maximize the secure energy efficiency by jointly designing ITS beamforming, IRS phase shift, jammer precoding vector, and transmit power allocation. To develop a low‐complexity solution,first, an approximated concave lower bound of the non‐concave objective function is derived by utilizing Dinkelbach's method and a novel successive convex approximation technique. Then the reformulated problem is decoupled using alternating optimization algorithm and the subproblems are solved by adopting Riemannian manifold optimization, element‐wise block coordinate descent, Lagrange dual method and Karush–Kuhn–Tucker conditions. At last, simulation results demonstrate the effectiveness of the proposed scheme.

In this paper, we present a double‐intelligent reflecting surfaces (IRS)‐assisted multiuser secure system where the inter‐IRS channel is considered. In particular, we maximize the weighted sum secrecy rate of the system by jointly optimizing the beamforming vector for transmitted signal and artificial noise at the base station (BS) and the cooperative phase shifts of two IRSs, under the constraints of transmission power at the BS and the unit‐modulus phase shift of IRSs. To tackle the nonconvexity of the optimization problem, we first convert the objective function to its concave lower bound by utilizing a novel successive convex approximation technique, then solve the transformed problem iteratively by applying an alternating optimization method. The Lagrange dual method, Karush–Kuhn–Tucker conditions, and alternating direction method of multipliers are applied to develop a low‐complexity solution for each subproblem. Finally, simulation results are provided to verify the advantages of the cooperative double‐IRS scheme in comparison with the benchmark schemes.

The Industrial Internet of Things (IIoT) enables machines to communicate robustly. High reliability, high throughput, and low latency are the critical capabilities of IIoT, which have posed great challenges to existing wireless solutions for industrial applications. Due to the vast available bandwidth, the emerging millimeter-wave (mmWave) technology is promising to address this bottleneck. However, the propagation behaviors at such high frequencies in the harsh industrial environment have yet been well understood. In this work, extensive measurements have been conducted in a representative industrial application scenario using a 2-GHz wideband directional channel sounder in the 28-GHz mmWave band. By exploiting the measurement with excellent resolution, the multipath components’ (MPCs) delay-angular space is transformed onto the scatter points (SPs) in the propagation environment. An effective clustering algorithm is then proposed to cluster the SPs without prior knowledge and iterations. Through a geometrical optics analysis, the SP clusters are classified corresponding to the reflectors. By doing this, the cluster-generating reflectors are reduced to a quasi-deterministic (QD) channel model that ensures spatial consistency and MPCs’ stochastic dispersion. Finally, it is shown that the measurement data agrees well with the proposed QD model, indicating the high fidelity of the proposed model.

Aiming to solve the problem of interruption of normal communication service caused by the damage of ground communication facilities after disaster, an Air-Ground Integrated Mobile Edge Network (AWMEN) offloading model was established under the constraints of communication security, energy consumption and coverage. The traditional method needs to be re-iterated every time the preset environmental state changes, which will waste a lot of communication resources and computing resources, greatly reduce the efficiency, and face the risk of data privacy disclosure. However, the deep reinforcement learning method under the federated learning framework will be more flexible and applicable to dynamic scenarios. A Markov decision process model is constructed based on the unmanned aerial vehicles (UAV) and the environment. The experience trajectory is designed by interacting with the external environment, and the optimal offloading strategy is obtained. The Twin Delayed Deep Deterministic Policy Gradient of behavior cloning (TD3-BC-R) is compared with baseline method (0-1 mode), Actor-Critic (AC-R), Deep Deterministic Policy Gradient (DDPG-R) and Twin Delayed Deep Deterministic Policy Gradient (TD3-R), the experiment shows that, The total time cost of TD3-BC-R is reduced by more than 1/3, and low latency transmission is also achieved.

The rapid development and continuous innovation of wireless services have led to a boom in the number and types of smart terminals. The huge amount of data that deep learning needs to calculate relies on the continuous improvement of hardware devices to solve. Therefore, the realization of intelligent transportation system(ITS) has become the general trend. The increase in the number of Internet of Things devices and the changes in the location of vehicles in the Internet of Vehicles(IOV) system have put forward higher requirements for the reliability and effectiveness of information transmission and the effective use of spectrum resources. Aiming at the influence and elimination of aggregated interference in intelligent transportation system, an anti-fragile communication algorithm is proposed to improve the reliability of signal transmission. At the same time, the outage probability of energy harvesting and cognitive radio technology enhanced with relay cooperative transmission under cognitive wireless network system with the aggregate interference can be deduced in detail. Finally, the correctness of theoretical analysis and the reliability of the proposed anti-fragile communication algorithms are verified by simulation experiments.

Abstract This paper investigates a three‐node proactive eavesdropping system with the aid of intelligent reflecting surface (IRS), where a monitor tries to wiretap information and transmit jamming signals simultaneously to interfere with the suspicious link. The IRS is deployed around the suspicious users to reconstruct propagation channel. Meanwhile, with large low‐cost passive reflecting elements, it can provide more spatial degrees of freedom to moderate the link quality. In order to degrade exposure risk of eavesdropping behavior, the authors' objective is to minimize the jamming power by carefully designing jamming beamforming vectors and the phase shifts under the constraint of reliable intercepted information. The formulated problem is non‐trivial to solve due to the coupling variables and unit‐modulus constraints. Fortunately, by using alternating optimization and successive convex approximation techniques, the original problem is transformed into convex form and a sub‐optimal solution is achieved. Numerical results show that the proposed scheme can effectively reduce the jamming power and the number of the jamming antennas over benchmark schemes, which is quite suitable for covertly overhearing signal.

In this paper, secure transmission in a simultaneous wireless information and power transfer technology-enabled heterogeneous network with the aid of multiple IRSs is investigated. As a potential technology for 6G, intelligent reflecting surface (IRS) brings more spatial degrees of freedom to enhance physical layer security. Our goal is to maximize the secrecy rate by carefully designing the transmit beamforming vector, artificial noise vector, and reflecting coefficients under the constraint of quality-of-service. The formulated problem is hard to solve due to the nonconcave objective function as well as the coupling variables and unit-modulus constraints. Fortunately, by using alternating optimization, successive convex approximation, and sequential Rank-1 constraint relaxation approach, the original problem is transformed into convex form and a suboptimal solution is achieved. Numerical results show that the proposed scheme outperforms other existing benchmark schemes without IRS and can maintain promising security performance as the number of terminals increases with lower energy consumption.

This study investigates a novel unmanned aerial vehicle (UAV)‐based wireless sensor network, where the UAV acts as a flying base station to serve multiple wireless sensor nodes (SNs). The authors goal is to maximise the system energy efficiency of the UAV while satisfying the fairness among SNs by jointly optimising the UAV trajectory and UAV time allocation. The formulated problem is shown to be a non‐convex fractional optimisation problem, which is hard to tackle. To this end, they decompose the original problem into two sub‐problems, and the block coordinate descent method and successive convex optimisation technique are employed to solve these two sub‐problems iteratively. Specifically, in the first sub‐problem, the optimal UAV time allocation is obtained by maximising the minimum achievable rate of SNs with given UAV trajectory constraints. In the second sub‐problem, the UAV trajectory is achieved by minimising the energy consumption of the UAV with the given UAV time allocation. Subsequently, an iterative algorithm is proposed to optimise the time allocation and UAV trajectory alternately. Furthermore, the convergence and complexity of their proposed algorithm are provided. Numerical results show that the proposed scheme outperforms the existing benchmark strategies in terms of energy efficiency.

Caching popular contents in the storage of base stations (BSs) has emerged as a promising solution for reducing the transmission latency and providing extra‐high throughput. This study tackles the optimal trade‐off problem between the sum of effective rates and backhaul saving through joint service improvement and content placement in cache‐enabled heterogeneous cellular networks while guaranteeing the quality‐of‐service requirements of all user terminals (UTs) and backhaul traffic constraints of all BSs. However, there exists an intractable issue of mixing the integer nature into the feasible region in the nonlinear optimisation problem. To this end, the authors decompose the optimisation problem into three subproblems by alternately fixing two of three classes of variables (i.e. UT association, power control, and content placement). Aiming at these subproblems, they, respectively, convert them into the tractable forms and propose the corresponding algorithms. By combining them, they propose a three‐tier iterative algorithm for jointly optimising UT association and cache placement. Finally, numerical results have verified the effectiveness of proposed schemes.

In this paper, we employ the unmanned aerial vehicle (UAV) as a flying base station (BS) to offload the data computing tasks from mobile terminal (MT) for saving mobile energy consumption. Our goal is to minimize consumption of the computational tasks at MT by jointly designing the resource partitioning scheme and bit allocation strategy. Specifically, the portion of total bits for local computation at MT is optimized, and the other portion of bits is computed by jointly optimizing the number of bits transmitted in the uplink, the number of bits computed locally at UAV and the number of bits transmitted in the downlink. The formulated problem has been shown in a convex form, which has optimal solutions. Instead of solving original problem using standard convex optimization techniques, we propose a resource partitioning scheme and bit allocation strategy based on dual decomposition, which has been shown in a low computational complexity. Furthermore, the numerical results are provided to demonstrate the superiority of our proposed scheme over the compared benchmarks.

When massive MIMO technology is combined with dense heterogeneous networks (HetNets), the user association and power allocation problems are fundamentally different although the energy- efficiency benefits can be intensified. This paper aims to investigate the energy efficient joint user association and power allocation problem in downlink massive MIMO empowered dense HetNets under proportional fairness criterion. The joint optimization problem is a non-convex mixed-integer nonlinear program (MINLP) which is NP-hard, and hence it is difficult to efficiently obtain exact solution. In order to obtain the highquality suboptimal solution, the joint optimization problem is first decomposed into two subproblems with alternating iterative method. Then a two-layer iterative suboptimal algorithm is proposed to solve the joint optimization problem with guaranteed convergence. The involved association subproblem adopts dual decomposition to achieve the optimal association index, whilst the power allocation subproblem allocates the transmit power of each BS with Newton's method. Numerical results verify the effectiveness of our proposed algorithm and show that our proposed algorithm outperforms conventional association schemes in the enhancement of energy efficiency performance. Furthermore, it can be seen that the energy efficiency performance is enhanced by increasing the number of antennas at macro base station (MBS).

This paper considers a downlink massive MIMO frequency division duplexing (FDD) system. Due to the large number of antennas, the required length of training sequence for downlink training significantly increases in FDD mode, which leads to prohibitive overhead in real system. Thus, in this work we investigate how the length of training sequence affects the system performance. For this purpose, we derive an analytical expression of the ergodic achievable rate from a worst case viewpoint with the the training sequence length as a parameter in it. It is revealed from the analytical results that i.) the length of training sequence divided by the number of base station antennas approaches to zero yet the achievable rate can increase to infinity as long as the antenna number is sufficient large; ii.) there is a ceiling effect on the achievable rate if the antenna number grows large with any fixed training length. Furthermore, we propose a guideline for the selection of the training length. Numerical results validate the derivations and analysis.

In this paper, a massive MIMO downlink frequency division duplexing (FDD) system over correlated Rayleigh fading channel is considered. It is well known that the length of training sequence not only affects the accuracy of channel estimation but also accounts for the rate loss resulting from training overhead. However, as the number of the base station antennas becomes large, the required length of training sequence cannot increase unlimitedly. Thus, in this work we derive the analytical expression of achievable rate and investigate the impacts of the training sequence length on system asymptotic performance. It is discovered from the analytical results in two-fold that (1) the length of training sequence normalized by the antenna number approaches to zero yet the system capacity is guaranteed to positive infinity as long as the antenna number is large enough; (2) the transmission capability saturates to a certain level if the antenna number grows to very large with any given training length. Simulation results verify the theoretical derivations and demonstrate the performance limit.

This paper investigates the resource allocation issue between downlink training stage and data transmission stage for the frequency division duplexing (FDD) massive multiple-input multiple-output system from the viewpoint of energy efficiency (EE). For a given total energy budget during a coherence period, how to jointly select the training duration, training power and data power is of great significance for the system EE. Thus, an optimization problem of energy-efficient resource allocation is put forward. Since the analytical expression of the involved average spectral efficiency (SE) is intractable, a closed-form approximation of the SE is deduced using deterministic equivalent. Based on the simplified expression, the original non-convex fractional optimization problem is transformed into an equivalent problem in subtractive form by the means of fraction programming, which includes an achievable solution. Then, an iterative algorithm is proposed. Numerical results validates the benefits of the proposed resource allocation scheme.