This paper addresses the challenge of energy-constrained maritime monitoring networks by proposing an unmanned aerial vehicle (UAV)-enabled integrated sensing, communication, powering and backhaul transmission scheme with a tailored time-division duplex frame structure. Within each time slot, the UAV sequentially implements sensing, wireless charging and uplink receiving with buoys, and lastly forwards part of collected data to the central ship via backhaul links. Considering the tight coupling among these functions, we jointly optimize time allocation, UAV trajectory, UAV-buoy association, and power scheduling to maximize the performance of data collection, with the practical consideration of sea clutter effects during UAV sensing. A novel optimization framework combining alternating optimization, quadratic transform and augmented first-order Taylor approximation is developed, which demonstrates good convergence behavior and robustness. Simulation results show that under sensing quality-of-service constraint, buoys are able to achieve an average data rate over 22 bps/Hz using around 2 mW harvested power per active time slot, validating the scheme’s effectiveness for open-sea monitoring. Additionally, it is found that under the influence of sea clutters, the optimal UAV trajectory always keeps a certain distance with buoys to strike a balance between sensing and other multi-functional transmissions.

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.

Semantic communication (SemCom) is a crucial direction for future wireless communication, aiming to achieve efficient and low-redundancy communication by extracting and transmitting semantic information. It has significant application value in emerging scenarios with limited bandwidth and intensive data, such as the Low-Altitude Internet of Things (LA-IoT). However, existing SemCom frameworks face limitations in semantic compression and generalization. To address these challenges, this paper proposes a knowledge graph-enhanced SemCom system. The proposed system leverages a shared knowledge graph (KG) and deep learning models to extract semantic information from the input text, represented in the form of entity–relation–entity triples. By aligning these triples with existing knowledge in the KG, the system distinguishes between common semantics and unique semantics. Common semantics are reconstructed at the receiver using the KG, whereas unique semantics are compressed and transmitted through the channel and then fused with the shared knowledge to reconstruct the original text. Furthermore, considering the structured nature of triple-based information, we propose a structured attention mechanism to enhance transmission accuracy. Experimental results demonstrate that the proposed system achieves a BLEU score exceeding 0.9 and a METEOR score exceeding 0.93 under high SNR conditions, outperforming baseline methods in semantic fidelity while significantly reducing redundancy.

Integrated sensing and communication (ISAC) emerges as an advanced technology to improve the spectrum efficiency by sharing the same spectrum for both communication and sensing. However, the open nature and the shared spectrum make the privacy a critical issue. Fortunately, covert communication can tackle this issue and provide an additional privacy protection for ISAC. In this paper, we propose a novel multi-user covert ISAC scheme against collusive wardens. Specifically, a dual-functional transmitter senses the wardens while communicating with multiple legitimate users covertly, where the more practical Rician fading is considered. First, we analyze the global detection performance of collusive wardens, where we employ the moment matching to handle the intractable theoretical analysis and computation introduced by Rician fading. Then, we optimize each warden’s detection threshold to achieve the greatest detection, creating the worst scenario for legitimate communication. Under this threat, we maximize the average covert transmission rate through jointly optimizing the power allocation and beamforming. To solve this non-convex optimization problem, semidefinite relaxation and successive convex approximation are adopted to transform it into a convex problem, and a convergence-guaranteed iteration algorithm is developed to obtain the optimal solutions. Simulation results show the superiority of the proposed multi-user covert ISAC scheme while revealing the inherent trade-off among covertness, sensing, and communication.

Integrated sensing and communication (ISAC) enables numerous innovative wireless applications. Communication-centric design is a practical choice for the construction of the sixth generation (6G) ISAC networks. Continuous-wave-based ISAC systems, with orthogonal frequency-division multiplexing (OFDM) being a representative example, suffer from the self-interference (SI) problem, and hence are less suitable for long-range sensing. On the other hand, pulse-based half-duplex ISAC systems are free of SI, but are also less favourable for high-throughput communication scenarios. In this treatise, we propose MASked Modulation (MASM), a half-duplex ISAC waveform design scheme, which minimises a range blindness metric, termed as “mainlobe fluctuation”, given a duty cycle (proportional to communication throughput) constraint. In particular, MASM is capable of supporting high-throughput communication (∼50% duty cycle) under mild mainlobe fluctuation. Moreover, MASM can be flexibly adapted to frame-level waveform designs by operating on the slow-time scale. In terms of optimal transmit mask design, a set of masks is shown to beidealin the sense of sidelobe level and mainlobe fluctuation intensity.

As uncrewed aerial vehicles (UAVs) are widely used in many communication scenarios, the issue of security has also caused much concern due to the line-of-sight propagation. Therefore, in this paper, we study an air-to-ground covert communication system, where a UAV transmitter Alice transmits messages covertly to a ground receiver Bob under the communication behavior detection of a ground warden Willie with location uncertainty and concurrent interference from other ground environmental nodes whose locations follow a two-dimensional Poisson point process. Under this setup, we first provide the approximate probability distribution of the aggregated interference power from all environmental co-channel nodes to facilitate the covert analysis. Then, we derive the average covert probability separately for two cases: case 1 assumes that Willie knows the received power from Alice; case 2 assumes that Willie knows the probability distribution of the received power from Alice. Next, we derive the connection probability and the covert throughput which is the maximal transmission rate under the covertness and reliability constraints. Numerical results demonstrate the feasibility of air-to-ground covert communication with location and interference uncertainty. The results also show the average covert probability and covert throughput in case 2 are higher than that in case 1, especially for a sparse deployment of interfering nodes.

In low-altitude Internet of Things (IoT) scenarios, achieving integrated sensing and communication (ISAC) with multi-base station (BS) cooperation is essential for building intelligent air–ground networks. In such system, user fairness becomes increasingly important, especially in heterogeneous environments with diverse channel conditions and service demands. However, incorporating rate-splitting multiple access (RSMA) into multi-user MIMO system presents new challenges for fairness-aware resource allocation, due to the coupling between communication and sensing signals, the high dimensionality of optimization variables, and the non-convexity of the joint design. This paper proposes a fairness-oriented alternating optimization framework for coordinated precoding in multi-BS RSMA-enabled ISAC system. The objective is to maximize the minimum common stream rate across users to enhance fairness, while meeting private stream quality of service (QoS) and sensing performance constraints. The precoding subproblem is convexified using semidefinite relaxation (SDR) and the Schur complement, while the reconfigurable intelligent surface (RIS) phase optimization is performed on the complex unit-modulus manifold via the Riemannian conjugate gradient (RCG) method. Numerical results demonstrate that the proposed method effectively ensures fairness, achieves a well-balanced trade-off between sensing and communication, and improves the overall user rate compared to conventional multiple access schemes.

With the advancement of technologies in Internet of Vehicles (IoV), traditional IoV architectures face challenges in latency and energy consumption due to inefficient resource scheduling, especially as data grows and computational tasks become more complex. Current optimization methods have not addressed the joint optimization of latency and energy consumption oriented to the Integration of Communication, Sensing, and Computation (ICSC) in IoV, which limits practical applications and cannot adapt well to dynamic IoV environments. Therefore, in this paper, we first construct an IoV communication-sensing-computation integration architecture. Then we propose a Deep Reinforcement Learning-based method for the joint optimization of latency and energy consumption for the ICSC in IoV, incorporating weighted decomposition and neighborhood parameter transfer strategy. Simulation results demonstrate that our method has a better ability of convergence and diversity and lower running time than the traditional method.

Unmanned aerial vehicles (UAVs) have been widely employed for emergency communications in Internet of Things (IoT) networks. The temporal freshness of data in disaster rescue, represented by the Age of Information (AoI), holds critical significance. Due to UAVs' limited energy capacity, it is crucial to optimize their energy consumption while ensuring effective assistance in the IoT system's fresh data collection tasks. Therefore, our work sets out to minimize the AoI and energy consumption to meet time-sensitive and priority-aware demands in resource-constrained environments. We formulate a multi-objective optimization problem by jointly optimizing UAV flight trajectory, IoT device transmitting power, and UAV radio frequency (RF) power, with constraints such as IoT devices' priority levels and UAVs' locations. This problem is modeled as a Markov Decision Process (MDP), and we propose a novel reinforcement learning (RL)-based method, termed the MultiAgent Data Collection Energy Transmitting Scheme (MADCET), to solve it. Extensive simulations demonstrate that the proposed scheme significantly outperforms benchmarks by reducing AoI, satisfying priority demands, minimizing energy consumption, and achieving good convergence.

With the explosive growth of user data in 6G networks, existing infrastructures face significant scalability and latency challenges. Mobile Edge Computing (MEC) partially alleviates these issues by deploying computational resources closer to users, but still struggles to fully meet the growing demands. Re-configurable Intelligent Surfaces (RIS) enhance communication performance by improving channel quality. However, optimizing resource allocation in RIS-aided MEC systems remains a critical challenge due to the complexity of real-time optimization of multiple parameters. Although Deep Reinforcement Learning (DRL) methods like Deep Deterministic Policy Gradient (DDPG) have been applied, they often suffer from Q-value overestimation and instability, resulting in suboptimal performance in dynamic environments. This paper focuses on a single-cell RIS-aided MEC network where multiple user devices offload computational tasks to an edge server. We propose an optimized resource allocation scheme using an improved Distributed Soft Actor-Critic (DSAC-T) algorithm. This approach jointly optimizes power control, computation offloading, edge computing resource allocation, and RIS phase shifts, aiming to minimize total offloading delay to ensure real-time service requirements. Simulation results demonstrate that DSAC-T outperforms multiple baseline methods (e.g., DDPG and SAC) in reducing offloading delay by 46.39% and 16.70%, respectively, while significantly enhancing system stability and convergence speed.

We propose a robust beamforming design methodology for integrated sensing and communications (ISACs) beamform, where the beamforming design is investigated under the sensing optimal beamforming designed to overcome the channel uncertainty that arises from the communication system. Under the assumption that the channel state information (CSI) error is elliptically bounded, we study the robust ISAC beamforming design problem with the minimization of the Cramér-Rao bound (CRB) under the signal-to-noise ratio (SINR) threshold constraint. We consider the long-range and near-range cases separately and categorize them into point-target and extended-target for processing. In the point target scenario, we address the problem through distributed optimization using the S-procedure and solve it with the semidefinite relaxation (SDR) method. Meanwhile, in the extended target scenario, we transform the infinite constraints of the robust ISAC design problem into a finite set, employing linear matrix inequalities (LMIs) for equivalent representation. Under specific conditions, we illustrate that the SDR problem in this scenario can yield a rank-1 solution. Simulation results verify the effectiveness of the proposed CRB optimizationmin method and prove its application value in the next generation of Internet of Things devices.

With line-of-sight mode deployment and fast response, unmanned aerial vehicle (UAV), equipped with the cutting-edge integrated sensing and communication (ISAC) technique, is poised to deliver high-quality communication and sensing services in maritime emergency scenarios. In practice, however, the real-time transmission of ISAC signals at the UAV side cannot be realized unless the reliable wireless fronthaul link between the terrestrial base station and UAV are available. This paper proposes a multicarrier-division duplex based joint fronthaul-access scheme, where mutually orthogonal subcarrier sets are leveraged to simultaneously support four types of fronthaul/access transmissions. In order to maximize the end-to-end communication rate while maintaining an adequate sensing quality-of-service (QoS) in such a complex scheme, the UAV trajectory, subcarrier assignment and power allocation are jointly optimized. The overall optimization process is designed in two stages. As the emergency area is usually far away from the coast, the optimal initial operating position for the UAV is first found. Once the UAV passes the initial operating position, the UAV’s trajectory and resource allocation are optimized during the mission period to maximize the end-to-end communication rate under the constraint of minimum sensing QoS. Simulation results demonstrate the effectiveness of the proposed scheme in dealing with the joint fronthaul-access optimization problem in maritime ISAC networks, offering the advantages over benchmark schemes.

Integrated sensing and communication (ISAC)-enabled low-altitude Internet of Things (IoT) networks hold significant potential for applications in smart cities and emergency communication systems. However, achieving secure and energy-efficient communication under complex environments, particularly in the presence of the mobile full-duplex eavesdropper (MFDE), presents significant challenges. This study investigates the optimization of secure rate energy efficiency (SREE) in ISAC-enabled low-altitude IoT networks, where the problem is further complicated by the strong coupling between unmanned aerial vehicles (UAV) trajectory design, power allocation, and artificial noise (AN) generation, leading to an optimization issue marked by significant dimensionality and a lack of convexity. To tackle this challenge, a power cost factor-based Twin Delayed Deep Deterministic Policy Gradient (CTD3) algorithm is developed, which incorporates a game-theoretic power allocation strategy into the TD3 framework to efficiently handle the high-dimensional coupled optimization problem. The algorithm reformulates part of the high-dimensional continuous optimization process into a strategy interaction problem and introduces a power cost factor into the utility function, effectively reducing the dimensionality of optimization variables and the overall computational burden. Furthermore, an adaptive dynamic sensing mechanism is introduced to enhance resource utilization while effectively countering the dynamic behavior of eavesdroppers. The effectiveness of the proposed strategy in enhancing SREE performance amidst environmental uncertainties is validated through extensive simulations, where it consistently outperforms baseline methods.

Deep learning (DL) has been widely utilized in automatic modulation classification (AMC), and its performance depends largely on the presence of high-quality datasets. Motivated by this fact, this work addresses the AMC challenges in data-limited IoT environments, proposing a framework combining few-shot meta-learning and federated learning for resource-constrained devices, where edge nodes use meta-learning for training with global updates via federated averaging (FedAvg). The system aggregates samples from multiple nodes while still maintaining data security. Simulations involved 11 modulation types with varying SNRs, 100 client nodes, and 10 rounds of federated learning. The iterative process includes loading pre-trained parameters, performing local training, averaging local parameters, and updating global parameters. The obtained results show 70% post-training testing accuracy, with a consistently good performance during federated iterations. The results demonstrated the effectiveness of the proposed framework in data-scarce IoT scenarios, offering a robust performance across varying signal qualities while minimizing energy consumption and communication overhead, which is crucial for IoT device longevity and network scalability, highlighting framework’s potential for real-world applications in distributed modulation recognition.

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.

A novel integrated wireless powered sensing and communication (IWPSAC) framework is proposed. Specifically, a multi-antenna transmitter utilizes a radar signal for sensing targets while enabling multiple Internet of Things (IoT) devices to harvest energy from the signal, each of which employs the collected energy to upload information to an access point (AP). Our setup further considers a reconfigurable intelligent surface (RIS) to integrate sensing, wireless energy transfer (WET) and wireless information transfer (WIT) by optimizing the phase shifts. We formulate an optimization problem to maximize the weighted sum of the communication throughput and the beampattern gain by jointly designing the energy beamforming, transmission time scheduling and RIS phase shifts. The presence of multiple coupled variables in the formulated problem renders the optimization problem non-jointly convex. To address its non-convexity, we first derive a closed-form expression for the optimal RIS phase shifts in the WIT phase. Then, an alternating optimization (AO) algorithm is proposed to solve the tradeoff problem iteratively. Concretely, this involves alternating the design of the energy beamforming and the RIS phase shifts for sensing/WET by leveraging the semidefinite programming (SDP) relaxation method. To overcome the high complexity introduced by the SDP, we introduce a low complexity AO algorithm that derives the optimal solutions for energy beamforming, transmission time scheduling, and sensing/WET phase shift using successive convex approximation (SCA), Lagrangian duality methods, Karush-Kuhn-Tucker (KKT) conditions, and the element-wise block coordinate descent (EBCD) approach. Simulation results demonstrate the performance of the proposed algorithms and underscore the superior benefits of the RIS compared to baseline schemes.

We propose a robust beamforming design methodology for integrated sensing and communications beamforms. The beamforming design aims to address channel uncertainties in the communication system by optimizing the sensing beamform. Assuming the Channel State Information (CSI) error is elliptically bounded, we investigate the robust integrated sensing and communication (ISAC) beamforming design problem, focusing on minimizing the Cramér-Rao bound (CRB) under a signal-to-noise ratio (SINR) threshold constraint. The problem is addressed through distributed optimization using the S-procedure and solved with the SDR method. Simulation results verify the effectiveness of the proposed CRB_min method.

In this paper, we study the parameter estimation performance for monostatic downlink integrated sensing and communications (ISAC) systems. In particular, we analyze the mean squared error (MSE) lower bound for target sensing in the downlink ISAC system that reveals the suboptimality in re-using the conventional communication waveform for sensing. To realize a practical dual-functional waveform, we propose a waveform augmentation strategy that imposes an extra signal structure, namely the degrees-of-freedom (DoF) completion method. The proposed approach is capable of improving the parameter estimation performance of the ISAC system and achieving the derived MSE lower bound. To improve the performance of the proposed strategy, we formulate an MSE minimization problem to design the ISAC precoder, subject to the communication users' signal-interference-plus-noise-ratio (SINR) constraints. Despite the non-convexity of the waveform design problem, we obtain its globally optimal solution via semi-definite relaxation (SDR) and the proposed constructive method. Simulation results validate the proposed DoF completion technology could achieve the derived MSE lower bound and the effectiveness of the MSE-based ISAC waveform design.

The performance of the servers directly affects the efficiency of the whole intelligent Internet of Vehicles (lo V) system. A reasonable server deployment strategy can effectively reduce network latency and improve the efficiency of IoV.Therefore, it is necessary to study the problem of server deployment in Io V.In this paper, we formulate a latency-aware server deployment problem based on Multi-Agent Reinforcement Learning (MARL) and utilize an Actor-Critic network based on Long Short-Term Memory (LSTM) encoding network to solve it. Besides, four deployment strategies (DQN, DRQN, Random and No Movement) are used as the baseline methods for performance comparison with our proposed method on two real-world datasets. The simulation results show our solution has excellent performance in reducing network latency under different scenarios.

Abstract With the continuous evolution and in‐depth integration between wireless communication and emerging technology such as internet of things (IoT), artificial intelligence (AI) etc., wireless terminals are growing exponentially, thus bringing great challenges to available spectrum resources. The contradiction between unlimited frequency needs and limited spectrum resources has become a bottleneck restricting the development of wireless communication technology. As an efficient way to improve spectrum efficiency, cognitive radio (CR) continues to be the focus of wireless communication within decades. To conduct CR, the main procedure is the discovery of available spectral holes by periodically monitoring the target authorized band, namely spectrum sensing (SS). Energy detector (ED) is widely accepted for SS due to its low complexity and high convenience. The essence of traditional ED based SS schemes consist in the adaptive variation of sensing threshold/sampling point with environmental signal‐to‐noise ratio (SNR) at the receiver of CR terminal, namely adaptive sensing threshold/sampling point based SS. However, the performance of both adaptive sensing threshold and adaptive sampling point based SS schemes are always at the expense of computation complexity due to the excessive sampling point. In addition, these two schemes are both about the optimization issue of a single variable under constraints. Actually, both detection probability and false alarm probability of ED are a two‐dimensional function of sensing threshold and sampling point for a given SNR. The optimal solution of sensing performance can not be obtained by optimizing sensing threshold or sampling point alone. Motivated by these, the joint optimization of sampling point and sensing threshold is considered for SS in this paper, where sampling point and sensing threshold are jointly adaptive with the variation of environmental SNR. In addition, Q‐learning is considered in this paper to obtain the sub‐optimal solution due to the non‐convexity of the considered optimization problem. Finally, the simulation experiments are made and the results validate the effectiveness of the proposed scheme.

Abstract Time‐sensitive networking (TSN) is considered one of the most promising solutions to address real‐time scheduling in in‐vehicle network due to its capabilities for providing deterministic service. The TSN working group proposed various traffic shaping mechanisms, while deterministic scheduling of hybrid traffic is still not effectively solved since the traffic requirements are difficult to satisfy by standalone or combined mechanisms with fixed time slot divisions. This article presents a time‐aware multi‐cyclicqueuing and forwarding scheduling model, that integrates the no‐wait enabled time‐aware shaper and multi‐cyclic queuing and forwarding shaping models. Then, a scheduling solution, dubbed “TSN scheduling optimizer” (TSO) is proposed that combines optimization methods and incremental techniques. TSO aims to balance the load to maximize flow schedulability while guaranteeing the service requirements of hybrid traffic. Simulation evaluations through OMNeT++ provide a performance assessment of this proposed scheduling model, which can satisfy multiple types of traffic transmission requirements. Furthermore, TSO is compared with other baseline scheduling solutions, and TSO shows efficacy regarding execution time and schedulability.

Federated learning (FL) enables collaborative training of machine learning models across distributed medical data sources without compromising privacy. However, applying FL to medical image analysis presents challenges like high communication overhead and data heterogeneity. This paper proposes novel FL techniques using explainable artificial intelligence (XAI) for efficient, accurate, and trustworthy analysis. A heterogeneity-aware causal learning approach selectively sparsifies model weights based on their causal contributions, significantly reducing communication requirements while retaining performance and improving interpretability. Furthermore, blockchain provides decentralized quality assessment of client datasets. The assessment scores adjust aggregation weights so higher-quality data has more influence during training, improving model generalization. Comprehensive experiments show our XAI-integrated FL framework enhances efficiency, accuracy and interpretability. The causal learning method decreases communication overhead while maintaining segmentation accuracy. The blockchain-based data valuation mitigates issues from low-quality local datasets. Our framework provides essential model explanations and trust mechanisms, making FL viable for clinical adoption in medical image analysis.

To solve the problems of limited computing resources and data privacy in the IoE scenario of 6G networks, this paper propose an efficient transmission and secure sharing architecture of sensing data based on federated learning. The architecture considers an integrated sensing and communication (ISAC) approach, employs knowledge distillation techniques to compress and accelerate data processing models, and implements data communication technology based on airborne computing aggregation to reduce data transmission delays and improve the efficiency of data communication and computation among nodes. To address the challenge of data sharing for largescale heterogeneous network nodes in the integrated scenario, this paper adopts a sample expansion technology of distributed remote sensing data based on WGAN-GP to address the issue of insufficient data, and considers blockchain encryption technology to protect data privacy, thus promoting progress in data privacy sharing under distributed ISAC conditions and facilitating the construction of the 6G communication network.

As an important direction in the field of information hiding, steganography is a significant means to realize secret communication. With the development of artificial intelligence, researchers have tried to use Deep Learning (DL) to design automated information hiding schemes, but the existing schemes still have shortcomings in security, hiding capacity, and robustness. To solve this problem, this paper designs a set of safe and robust image information hiding schemes by using a DL network, Quick Response (QR) coding, and introducing a noise layer mechanism. In addition, this scheme makes information hiding technology get rid of the dependence on human operation and prior professional knowledge, breaks the dilemma that information hiding needs to choose the appropriate hidden carrier and modify the carrier to embed information, and also proves that it has great potential in the field of information security.

Integrated sensing and communication (ISAC) has received increasing attention as a potential technology to alleviate spectrum shortage. This paper studies the power-saving problem based on power assignment and subcarrier allocation in ISAC. Specifically, we propose a joint design method to jointly optimize the subcarrier and transmit power allocated to sensing services and communication services respectively. To ensure the quality of sensing service (SS) and communication service (CS), the minimum total power required by the system is obtained by optimizing the allocation of subcarriers and transmission power. Since the formulated problem is nonconvex, which is generally difficult to solve effectively. Inspired by the classical subcarrier allocation algorithm based on channel gain information, we decompose the formulated problem into two convex optimization subproblems that are easy to solve. Numerical simulation results show that the proposed algorithm can effectively improve the power-saving performance of the system compared with the existing algorithms.

Efficient and green transmission of communication and sensing (C&S) signals is a vital problem in Internet of Things (IoT) networks. In this article, we propose a variational autoencoder (VAE)-empowered deep learning (DL) network to fuse and reconstruct the integrated C&S signals. Specifically, we present a convolutional neural network to fuse the input communication data and SAR images into a combined representation, which can then be transmitted to other nodes in space–air–ground–ocean-integrated IoT networks. Instead of directly transmitting C&S data, the transmission of a fused feature vector can greatly save network resources and reduce network burden. Then, a mirrored deconvolutional network is constructed to recover C&S data from the transmitted feature representation. An end-to-end unsupervised training strategy is considered to train the proposed DL network without any label information and human labor. Qualitative and quantitative experiments demonstrate the feasibility of our proposed approach for transmitting and reconstructing integrated C&S signals. Further analysis on hyperparameter sensitivity and loss functions verifies the necessity and efficiency of the components in the proposed DL model. Note that the proposed efficient fusion and reconstruction schemes for C&S signals may provide the convenience to information sharing under the distributed scenarios.

The digital twin (DT), which effectively represents the actual real-world physical system or process, has reshaped the classic manufacturing, construction, as well as healthcare industry. As for realizing DT, both sensing and communication functionalities are demanded, which fully builds the connectivity between the physical world and the digital world. We first conducted a survey on the current situation of DT combined with communication and sensing. Inspired from this survey and the current development of communication and sensing, in this paper, we attempt to study the communication annd sensing technologies of physical layer in DT, to reduce the hardware and spectrum overhead. First, we studied the degree of freedom (DoF) problem in general communication and sensing system, and contribute to the DoF definition in the sensing system. Then, in order to improve the spectrum efficiency in DT system, we proposed an iterative optimization framework to address the coexistence of communication and sensing, and some examples are provided. Finally, in order to pursue a better integration gain, we proposed a new waveform design method based on DoF completion. The proposed optimization method can achieve the mean square error (MSE) lower bound. Simulation results demonstrate the effectiveness of various problems in the above scenarios.

With the continuous evolution of emerging technologies such as mobile network, machine learning (ML), 5G, etc., digital twins (DT) bursts out great potential by its capacity of data analysis, data tracking, data prediction, etc, building a bridge between the physical and information world. Meanwhile, mobile network is moving towards data-driven paradigm, the issue of data privacy and data security seem to be a bottleneck. As a result, federated learning (FL) and mobile network are deeply converging. However, the mobile network is time-varying and the parameters of FL-empowered mobile network is huge and continue to increase with exponential growth of wireless terminals, result in the failure of traditional modeling. In the mobile networks, DT is conducive to prototyping, testing, and optimization, enabling mobile networks to be modelled more efficiently in a virtual environment and thus providing guidance for practical application. To this end, a communication-assisted sensing scenario is considered in this paper with FL in DT-empowered mobile networks. More specifically, two communication-assisted sensing architectures are proposed to improve communication efficiency of mobile network, namely, centralized architecture of federated transfer learning (FTL) and decentralized architecture of FTL. For centralized architecture of FTL, feature extraction of sensing information is conducted by FL between partial nodes and central server while the remaining nodes are used to train the fully connected layers at the central server. Considering data safety during the communication between sensing nodes, a decentralized architecture is designed based on FTL and Blockchain, where the feature extraction module is obtained by the fusion of sharing model (by Blockchain) and local model. The performance of proposed schemes is evaluated and demonstrated by the simulations.

Nowadays, a possible approach to designing a commercial-attractive sensing solution is integrating sensing capability into widely deployed communication systems, e.g., the force coming fifth-generation (5G) new radio (NR), by slightly modifying the standard. To this end, in this paper, we firstly investigate the possibility of re-using the NR waveform for sensing by reviewing current NR frame structure. Then, the self-ambiguity and cross-ambiguity functions are analyzed to exploit the NR waveform performance limitations. Several synchronizations and reference NR signal structures are considered for both downlink and uplink NR transmissions. Finally, the simulated NR frame and its self- and cross-ambiguity simulation results demonstrate the performance limitations.

This paper investigates the problem of fairness in Dual-Function radar-communications system (DFRC) which exploits orthogonal frequency division multiplexing (OFDM) waveforms for performing radar and communication operations simultaneously. A novel iterative algorithm are proposed to maximize the fairness among different communication users (CUs) with the constraint on radar user’s (RU) perfermance. In our considered system, the optimization problem falls into a mixed integer nonlinear programming (MINLP) problem, which is generally NP-hard. In order to make this thorny problem easy to deal with, we propose an approximate algorithm based on BSUM theory to obtain the feasible solution of the original problem in polynomial time. It is shown, using simulation results, that the proposed optimization strategy outperform other strategies in term of fairness.

Abstract Recently, deep learning (DL) based spectrum sensing (SS) has drawn much attention due to its better capacity of feature extraction and superb performance. However, the model robustness of the DL based scheme is limited by reason of the dynamic radio environment, leading to the floating of sensing performance. Motivated by this, adversarial transfer learning is applied to SS here, where the model is pre‐trained at the central node firstly and fine‐tuned at the local nodes. More specifically, a 2D dataset of the observed signal is constructed under various signal‐to‐noise‐ratio (SNRs) and a convolution neural network (CNN) model is designed. Then a part of samples with various SNRs in the constructed dataset are employed to pre‐train the proposed CNN model. After that, the pre‐trained CNN model is distributed to local nodes with different SNRs and the pre‐trained CNN model is fine‐tuned. The proposed CNN model is pre‐trained based on the samples under various SNRs, resulting in its stronger adaptability at the local node. The simulation experiments validate the effectiveness of the proposed scheme.

Gesture recognition is the central enabler of human-computer interaction (HCI). In addition to the semantic information contained in gestures, gesture-based user identification can effortlessly enhance HCI system security. Recently, the Wi-Fi-integrated sensing and communication (ISAC) technology has shown great potential in a field hitherto occupied by computer vision and radar sensing. In this work, leveraging Wi-Fi sensing, we propose a system called WiGesID that achieves joint gesture recognition and human identification (JGRHI). The basic idea behind WiGesID is to identify personalized spatiotemporal dynamic patterns from the gestures of different users. Moreover, we develop an effective approach to recognize new categories of gestures and users by computing relation scores between the features of the new category samples and the support samples. To evaluate the performance, we implemented WiGesID and conducted extensive experiments. The results demonstrate that our system outperforms the state-of-the-art method for cross-domain sensing and accurately recognizes new categories, which promotes the use of this application of Wi-Fi sensing in HCI.

The facial expression contains abundant emotional information, mirrors the psychological state of people and plays an important role in daily communication. Facial emotion recognition (FER) has been a popular research field with great significance. However, FER has been regarded as a challenging issue due to individual variation, illumination, occlusion, etc. In this paper, a novel network SferNet is proposed for Static Facial Emotion Recognition (SFER), where data enhancement, Batch Normalization (BN) and Dropout modules are considered. SferNet improves the generalization capacity of FER and applies to non-peak expression recognition. Extensive comparisons on two popular datasets, CK+ and JAFFE, demonstrate the proposed framework makes a trade-off between recognition rate and model complexity.

In this paper, an Unmanned Aerial Vehicles (UAV) control signal detection scheme is proposed with Convolutional Neural Network (CNN). More specifically, the sampled signal images of UAV control signal are considered to train the classical LeNet network under various signal-to-noise ratios (SNR). The simulation experiments state that the detection performance of UAV control signal is greatly improved. In addition, the conclusion is drawn that the increase in signal image size helps to improve the detection performance.

As the basis of cognitive radio technology, Spectrum sensing (SS) has received widespread attention because it is very important to improve spectrum efficiency. However, the limited sensing time makes it difficult to obtain sufficient sample data, which will seriously affect the performance of the spectrum sensing model. In this paper, SS is considered to be a binary classification problem, in which Deep Convolutional Generative Adversarial Networks(DCGAN) is improved and used to expand the training set to cope with the shortage of sample data. More specifically, the sampling covariance matrix of the received signal is firstly transformed into the true color picture which is divided into a training set and a test set. After that, the obtained training set is expanded with the improved DCGAN. Finally, the LeNet network is trained based on the extended data. Simulation results show that the proposed scheme greatly improve the sensing accuracy. The probability of detection(PD) and the probability of false alarm(PFA) fluctuate less after expanding the dataset with DCGAN. Especially when SNR= -4dB, the minimum value of PD increases by 0.2.

To improve detection performance, this paper proposes a non-cooperative unmanned aerial vehicle (UAV) detection strategy based on multichannel detection of remote signal with energy detector (ED). More specifically, the sampling point of remote signal on each subchannel adaptively varies with environmental signal-to-noise (SNR) within constrained scope. The decision on the presence or absence of remote signal is made at the fusion center (FC) based on the majority voting rule. Both theoretical derivation and simulation experiments validate the effectiveness of the proposed scheme.

The signal-to-noise ratio (SNR) is an effective evaluation index for channel status and communication quality, and plays an important role in signal analysis. Under the gradual complexity of the unmanned aerial vehicle (UAV) remote control signal environment and the rapid development of neural network models in deep learning, this paper proposes a convolutional neural network (CNN) model-based SNR estimation method of UAV remote control signal environment. We construct a simulation dataset of UAV remote control signal with different SNRs, then train the model and its parameters, save the model with better performance and use the test set to verify the performance of the algorithm finally. The experimental result shows that the performance of the algorithm is improved compared to the two known algorithm.

The dual-function radar communication system develops rapidly with the integration of sensing function and communication function, the combination of vehicle tracking and positioning and vehicle communication leads to a more efficient vehicle networking system in the future. This paper proposes a beam tracking prediction scheme for intergraded sensing and communications (ISAC) aided vehicle to infrastructure communications. In detail, we focus on the beam misalignment problem between roadside units (RSU) and high dynamic passing vehicles. To solve this problem, we propose a particle filter-based predictive beamforming method that can predict the motion parameters of vehicles by using transmitted ISAC signals and received the vehicle echoes. The simulation results show that the proposed particle filter algorithm can reduce the overhead and predict the vehicle motion parameters and the vehicle's angle relative to the RSU when the vehicle is moving.

Currently, developments in wireless sensing technologies have shown that wireless signals can be employed to transmit information between wireless communication devices and are also able to realize passive target wireless sensing. Wireless sensing has diverse Internet-of-Things applications in indoor human detection, such as in device-free localization, activity recognition and fall detection, respiration detection, gait recognition, user identification, and so forth. Deep learning (DL), with the latest breakthroughs in machine learning (ML) and artificial intelligence (AI), seems to be a feasible technique for device-free wireless sensing (DFWS) and human detection in a more intelligent and autonomous manner. Although DL has attracted wide spread attention in computer vision (CV), AI games, speech recognition, automated vehicles, and other fields, its application in wireless sensing systems (WSSs) is relatively new, and little attention has been paid to it. Motivated by these developments, this article clarifies the motivation and mechanism of the DL-aided WSSs for human detection. First, we survey the most advanced architecture of DL that may be powerful for WSSs. We also review conventional ML and DL approaches to human detection based on red green blue (RGB)/depth camera and radar: one reason is to introduce the successful experience in these areas to the field of wireless sensing and another reason is that the possibility of combining and fusing information from the heterogeneous types of sensors is expected to improve the overall performance of practical human detection systems. We provide a comprehensive survey of the state-of-the-art research on wireless sensing for human detection with a focus on WSSs. Furthermore, a general structure of the DL-based WSS is introduced in detail for hitherto unexplored applications and future wireless sensing scenarios. We also discuss some open research issues in wireless sensing for human detection, including data acquisition for DL model training, calibration of signals from commercial devices, multimodal sensing, simultaneous user identification and activity recognition, multiuser human detection, and generalization ability of DL models, to indicate future research directions.

Spectrum sensing (SS) has attracted much attention in the field of Internet of things (IoT) due to its capacity of discovering the available spectrum holes and improving the spectrum efficiency. However, the limited sensing time leads to insufficient sampling data due to the tradeoff between sensing time and communication time. In this paper, deep learning (DL) is applied to SS to achieve a better balance between sensing performance and sensing complexity. More specifically, the two‐dimensional dataset of the received signal is established under the various signal‐to‐noise ratio (SNR) conditions firstly. Then, an improved deep convolutional generative adversarial network (DCGAN) is proposed to expand the training set so as to address the issue of data shortage. Moreover, the LeNet, AlexNet, VGG‐16, and the proposed CNN‐1 network are trained on the expanded dataset. Finally, the false alarm probability and detection probability are obtained under the various SNR scenarios to validate the effectiveness of the proposed schemes. Simulation results state that the sensing accuracy of the proposed scheme is greatly improved.

As a key technology to discover idle spectrum in cognitive radio (CR) networks, classical spectrum sensing schemes mainly focus on the improvement of sensing accuracy and available throughput. However, both sensing performance and sensing complexity are significant elements that influence the performance of a CR in computation‐constrained scenarios. Motivated by this, theproposed study is devoted to computation‐constrained spectrum sensing and a tradeoff is considered between sensing performance and sensing complexity. First, the authors give two functions to evaluate the detection efficiency and communication efficiency of a CR. On this basis, two specific models are provided to obtain the optimal sensing operations in computation‐constrained conditions, respectively. Then they analyse these two models and conclude their advantages and disadvantages. Finally, simulation experiments validate the effectiveness of the proposed schemes. Note that computation‐constrained spectrum sensing works as a significant issue in the internet of things (IoT)‐based applications and the proposed schemes provide effective solutions for it.

Multistage detection has inspired a heated debate due to its capacity to take full advantage of each detector. Motivated by this, an investigation into multistage spectrum sensing is conducted and a two‐stage spectrum detector is proposed based on energy detector and covariance absolute value (CAV) detector here. The two‐stage spectrum detector periodically determines which detector is appropriate for current radio environment according to the comparison between estimated SNR and SNR threshold provided here. The total blindness of CAV detector and available noise variance by SNR estimation scheme results in the blindness of the proposed detector to the characteristics of observed signal and noise. Simultaneously, the proposed scheme balances the sensing complexity and detection accuracy and provides higher practicability in consequence. Simulations validate the performance of the proposed method.

As an issue under heated discussion, spectrum sensing (SS) exhibits its particular significance in various application scenarios due to its limited spectrum resource and low spectrum utilisation. For a given SS scheme, detection probability, false alarm probability and available throughput jointly determine its performance. To consider these three factors as a whole, the possible performance improvement of an SS scheme will make a great difference in active demand for idle spectrum, especially in Internet of things‐based domains. Motivated by this, this study proposes a new strategy on SS to further improve its sensing performance, where the optimal sensing performance is demonstrated to be better than traditional schemes when signal‐to‐noise ratio is higher than 1.76 dB. Additionally, the authors extend the proposed scheme to multiple secondary user case and give optimal N for N ‐out‐of‐ K fusion rule. Both theoretical derivation and simulation experiments validate the effectiveness of the proposed scheme.

Random numbers are widely used in 5G communication security. In this paper, we propose a wireless physical layer (PHY-layer) characteristics based random number generator in vehicular networks. Firstly, the closed form expression of random transmission success probability is derived under the presence of multiple jamming attackers in a Nakagami-m fading channel. Secondly, a novel Random Transmission Success Probability based Physical Random Number Generator (RTSP-PhRNG) is presented. Finally, numerical results are conducted and a Universal Software Radio Peripheral (USRP) based prototype is implemented to validate our proposed method. Furthermore, the standard randomness test suite from NIST shows that our proposed PhRNG reveals good randomness.