Physical-Layer Authentication (PLA) offers endogenous security, lightweight implementation, and high reliability, making it a promising complement to upper-layer security methods in Edge Intelligence (EI)-empowered Industrial Internet of Things (IIoT). However, state-of-the-art Channel State Information (CSI)-based PLA schemes face challenges in recognizing mobile multi-users due to the constantly shifting CSI distributions with user movements. To address this issue, we propose a Temporal Dynamic Graph Convolutional Network (TDGCN)-based PLA scheme, which employs Graph Neural Networks (GNNs) to capture the spatio-temporal dynamics induced by user movements. Firstly, we partition CSI fingerprints into multivariate time series and utilize dynamic GNNs to capture their associations. Secondly, Temporal Convolutional Networks (TCNs) handle temporal dependencies within each CSI fingerprint dimension. Additionally, Dynamic Graph Isomorphism Networks (GINs) and cascade node clustering pooling further enable efficient information aggregation and reduced computational complexity. Simulations demonstrate the proposed scheme's superior authentication accuracy compared to seven baseline schemes.

In the space-air-ground integrated networks of the sixth generation (6 G) systems, many satellite imagery need to be transmitted from the satellite to the ground with high resolution for further processing. However, it faces the challenges of limited available bandwidth and the poor channel conditions of satellite-to-ground links. In this paper, leveraging the benefits of semantic communication for efficient transmission under limited bandwidth and low signal-to-noise ratio (SNR) conditions, a joint Preprocessing and Multi-task driven Semantic Communication (PMSC) system for satellite imagery transmission is proposed. To efficiently use the limited bandwidth, we propose a region of interest (ROI) based preprocessing method, which focuses on only relevant regions that will be encoded into semantic information, and processes the ROIs that are pivotal for the tasks. Moreover, we formulate a multi-task driven semantic communication system with a universal joint semantic-channel encoder and distinct decoding processes, making the received semantic features of satellite imagery can be accurately and effectively utilized for different applications. The simulation results demonstrate that the proposed PMSC system has better performance in enhancing reconstruction and classification in the target regions of interest at the same compression ratio, especially under low SNR conditions.

Compatibility between semantic communication and traditional mobile communication systems remains a significant challenge. Therefore, we propose a cross-layer encrypted semantic communication (CLESC) framework for panoramic video transmission, incorporating feature extraction, encoding, encryption, cyclic redundancy check (CRC), and retransmission processes to achieve compatibility between semantic communication and traditional communication systems. Additionally, we propose an adaptive cross-layer transmission mechanism that dynamically adjusts CRC, channel coding, and retransmission schemes based on the importance of semantic information. This mechanism ensures that important information is prioritized under poor transmission conditions. To verify the aforementioned framework, we design an end-to-end adaptive panoramic video semantic transmission (APVST) network that leverages a deep joint source-channel coding (JSCC) structure and attention mechanism, integrated with a latitude adaptive module that facilitates adaptive semantic feature extraction and variable-length encoding of panoramic videos. Simulation results demonstrate that the proposed CLESC framework effectively achieves compatibility and adaptability between semantic and traditional communication systems, significantly enhancing channel robustness. Compared to traditional and artificial intelligence (AI)-based video source coding transmission schemes, our proposed CLESC achieves superior transmission performance under low signal-to-noise ratio (SNR) conditions.

Semantic Communication (SemCom) has opened a new paradigm in the 6G system. However, the performance of SemCom can be severely affected by time-varying path loss, channel noises, and other interference in wireless channels. Therefore, we propose a novel Semantic Prior aided Channel-adaptive Equalizing and De-noising SemCom (SP-EDNSC) framework, where adaptive elimination channel impact is regarded as an inverse problem. This inverse problem is addressed through semantic priors learned from score-based generative models cached in knowledge base. To reduce distortion while enhancing perceptual quality, we further combine autoencoders, adversarial learning and diffusion models to develop a latent diffusion-based (SP-Latent-Diff EDNSC) system within the SP-EDNSC framework. In the semantic space, the joint semantic equalizer and de-noiser module utilizes the proposed latent diffusion posterior sampling method. This method iteratively executes a modified reverse stochastic differential equation to sample clean semantic features, using the time-dependent score function of likelihood and semantic priors. The semantic priors are derived from pre-trained latent diffusion models, while the likelihood is approximated by a multivariate normal distribution. Simulations demonstrate that our scheme achieves superior performance in both distortion metrics like PSNR and SSIM, as well as in perceptual performance (LPIPS).

The future 6G systems are expected to achieve intelligent connection and interaction between various heterogeneous terminals, increasing the fragility for spoofing attacks. Due to the high security and energy efficiency, Physical Layer Authentication (PLA) has been regarded as a powerful method to verify the identity of devices. Nevertheless, due to the inaccurate identifying fingerprints caused by the imperfect estimation and variations of the limited fingerprints, most of the state-of-the-art PLA schemes have low reliability and robustness in low Signal-Noise Ratio (SNR) environments. Besides, most PLA schemes rely on the prior knowledge of attackers to establish authentication models, thus reducing the feasibility of actual communications. To address the first challenge, we propose a multi-attacker detection architecture based on multi-dimensional fingerprints, which can provide more robust identifiable spatial attributes for devices by using fingerprints observed by receivers in multi-locations. Upon the designed detection architecture, to tackle the second issue, we propose four clustering-based PLA schemes without requiring their training fingerprint sets. Considering that the aforementioned schemes can divide fingerprints from different transmitters into several disjoint clusters but can not precisely identify forged fingerprints, we further propose the graph learning-based PLA approaches with only a few labeled fingerprints. The simulation results on real industrial outdoor and indoor datasets demonstrate the superiority of the designed detection system in Adjusted Mutual Information (AMI) and authentication accurate rate (AucRate) over the single observation-based PLA schemes.

We propose a novel task-oriented and semantic-aware heterogeneous networks (TOSA-HetNets) framework for multitype Artificial Intelligence of Things (AIoT) devices with various requirements, where the dense edge servers with different transmission capabilities, computing resources, and power consumption are divided into different layers to provide on-demand collaboration for AIoT devices located in accessible areas. Moreover, we propose a device–edge collaboration intelligent tasks inference scheme between edge servers and AIoT devices in TOSA-HetNets, it includes AIoT devices performing semantic features extraction and uploading the corresponding semantic features to the associated edge servers, multiple layers of edge servers collaborating with AIoT devices to execute the intelligent tasks and transmit the intelligent task results back to AIoT devices. To investigate the performance of TOSA-HetNets in supporting device–edge collaboration intelligent tasks inference, we adopt stochastic geometry to obtain the closed-form expressions of average task success probability, power consumption, and network throughput in the downlink transmission. Furthermore, we define a metric of average achievable task back-transmission energy efficiency (TBT-EE) to measure the information bit of successfully transmitted correct intelligent task results with unit power consumption, which is a function of average task success probability, average network throughput on the unit area, and the total power consumption. Meanwhile, we maximize the average achievable TBT-EE by optimizing the density of edge servers and the average semantic compression ratio. Simulation results verify the correctness of the obtained closed-form expressions and show that the edge servers’ density and average semantic compression ratio have different influences on the performance of TOSA-HetNets.

In order to truly promote the further development of the Industrial Internet of Things (IIoT), terminal authentication of the IIoT is essential. Physical-layer authentication (PLA) has recently attracted much attention for its high security and lightweight. Nevertheless, most existing PLA schemes in conjunction only the observation of a single receiver will lead to low-reliability and low-robustness of authentication, especially in hostile time-varying wireless channels. To tackle this issue, we developed a multiobservation-multichannel-attribute (MOMCA) based multiuser authentication architecture, which considers both the observations of multireceivers and multiple channel attributes of each observation to enhance wireless security. Specifically, the proposed architecture can provide additional spatial recognition characteristics for multiusers. To better fit the channel features of multiobservations, we proposed two gradient boosting optimization-based schemes. One uses Taylor expansion to approximate objective functions and adds the regularization term to avoid overfitting issues. The other can obtain higher authentication performance by sampling the signal data with small gradient characteristics. The simulations on real industrial indoor and outdoor datasets verify the superiority of the proposed schemes in authentication accuracy over six baseline authentication schemes.

Compared with the current Shannon's Classical Information Theory (CIT) paradigm, semantic communication (SemCom) has recently attracted more attention, since it aims to transmit the meaning of information rather than bit-by-bit transmission, thus enhancing data transmission efficiency and supporting future human-centric, data-, and resource-intensive intelligent services in 6G systems. Nevertheless, channel noises are common and even serious in 6G-empowered scenarios, limiting the communication performance of SemCom, especially when Signal-to-Noise (SNR) levels during training and deployment stages are different, but training multi-networks to cover the scenario with a broad range of SNRs is computationally inefficient. Hence, we develop a novel De-Noising SemCom (DNSC) framework, where the designed de-noiser module can eliminate noise interference from semantic vectors. Upon the designed DNSC architecture, we further combine adversarial learning, variational autoencoder, and diffusion model to propose the Latent Diffusion DNSC (Latent-Diff DNSC) scheme to realize intelligent online de-noising. During the offline training phase, noises are added to latent semantic vectors in a forward Markov diffusion manner and then are eliminated in a reverse diffusion manner through the posterior distribution approximated by the U-shaped Network (U-Net), where the semantic de-noiser is optimized by maximizing evidence lower bound (ELBO). Such design can model real noisy channel environments with various SNRs and enable to adaptively remove noises from noisy semantic vectors during the online transmission phase. The simulations on open-source image datasets demonstrate the superiority of the proposed Latent-Diff DNSC scheme in PSNR and SSIM over different SNRs than the state-of-the-art schemes, including JPEG, Deep JSCC, and ADJSCC.