Search

Semantic communication (SemCom) can reduce data traffic for intelligent connected vehicles (ICVs), given the limited wireless spectrum available. However, it is important to recognize that deep learning-based SemCom is vulnerable to backdoor attacks, which pose significant security risks to ICVs. Therefore, it is crucial to investigate these security risks before integrating SemCom into ICVs. To this end, this study introduces a novel backdoor attack known as Covert Semantic Backdoor Attack (CSBA), specifically designed for SemCom-enabled ICVs. Unlike existing backdoor attack techniques that rely on noticeable triggers, CSBA analyzes the self-contained semantics in transmitted images to determine if they contain the target semantic required for initiating a backdoor attack. Moreover, in the event of an attack by CSBA, the target semantics disappear from the recovered image while the rest of the image remains unchanged, ensuring that the attack remains invisible. Experimental results confirm the effectiveness and the stealthiness of the proposed CSBA schemes across various wireless channel conditions and attack ratios.

With multiple-input multiple-output (MIMO) technologies widely employed in mobile communication systems, wireless signals will have higher resolution in both the time and angle domains. It makes high-precision localization gain increasing attention in MIMO integrated sensing and communication (ISAC) systems. However, the decimeter-level precise indoor localization is full of challenges due to the multi-path fading and additional noise in indoor propagations. Excessive resource overhead and channel state information (CSI) fingerprint distortion in complex channel environments are the main factors that hinder indoor high-precision localization. To solve the CSI fingerprint distortion while reducing resource consumption, we propose a Swin Transformer-based CSI data-driven indoor localization framework called Swin-Loc. In the proposed Swin-Loc, a channel fingerprint extraction scheme is formulated to enhance the CSI features. Moreover, an improved Swin Transformer-based CSI network (SwinCSINet) model is proposed to improve localization precision. Experiments are conducted on the real-world CSI dataset given by the KU Leuven lab and the simulation CSI dataset generated by the DeepMIMO platform. Simulation results demonstrate that the localization precision on all datasets in the metric of root mean squared error (RMSE) is within 0.3 m, which outperforms current deep neural networks (DNNs) based schemeand attention-based scheme. Furthermore, the storage overhead of the improved SwinCSINet model is reduced to about 33$\%$ of the DNN regression model, and the real-time performance is optimized.

Despite the potential of semantic communication for 6G, the assumption of homogeneous knowledge bases (KBs) between communication parties cannot be guaranteed. The heterogeneity of KBs (KB-heterogeneity) degrades performance. Therefore, this letter proposes a communication-efficient synchronization mechanism to mitigate the performance degradation level (PDL) caused by KB-heterogeneity, especially the varied data distribution. The proposed mechanism re-frames the problem as an estimation problem of the source model, involving transmitting the semantic information of shared data as a ‘synchronization (sync) signal’ before data transmission. Furthermore, the letter introduces Sync-signal-driven model estimation (SSME) and data-model iterative optimization (DAMIO) synchronization algorithms to utilize the sync signal for source model estimation. Compared with SSME, DAMIO improves source model estimation accuracy, when shared data and source data have significantly different distributions. Experimental results demonstrate that the proposed mechanism and algorithms effectively reduce PDL in both intra-group and inter-group KB-heterogeneity scenarios, while still meeting wireless resource constraints.

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.

Traditional communication systems treat messages’ semantic aspects and meaning as irrelevant to communication, revealing its limitations in the era of artificial intelligence (AI), such as communication efficiency and intent-sharing among different entities. Through broadening the scope of the traditional communication system and the AI-based encoding techniques, in this manuscript, we present a novel semantic communication system, which involves the essential semantic information exploration, transmission and recovery for more efficient communications. Compared to other state-of-the-art semantic communication-related works, our proposed semantic communication system is characterized by the “flow of the intelligence” via the propagation of the model. Besides, the concept of semantic slice-models (SeSM) is proposed to enable flexible model-resembling under the different requirements of the model performance, channel situation and transmission goals. Specifically, a layer-based semantic communication system for images (LSCI) is built on the simulation platform to demonstrate the feasibility of the proposed system and a novel semantic metric called semantic service quality (SS) is proposed to evaluate the semantic communication systems. We evaluate the proposed system on Cityscapes and Open Images datasets, resulting in averaged 10% and 2% bit rate reduction over JPEG and JPEG2000, respectively. In comparison to LDPC, the proposed channel coding scheme can averagely save 2dB and 5dB in AWGN channel and Rayleigh fading channel, respectively.

As a promising technology to enable effective multi-modal transmission over wireless channels, semantic communication has attracted a lot of attention from academics and industries. Different from Shannon’s information theory, based on common background knowledge provided by the knowledge base, the goal of semantic communication is transmitting intended useful information from the transmitter and recovered by the receiver at the semantic level. However, the existing studies on semantic communication rarely emphasize the essence and the usage of the knowledge base. In this paper, we propose a knowledge-enhanced semantic communication (KESC) system, where the knowledge base is cloud-edge-device collaborative cached. To solve the problem that float-type symbols are difficult to transmit directly through a radio frequency (RF) system, we adopt orthogonal frequency division multiplexing (OFDM) to transmit semantic vectors directly without some traditional signal processing techniques in semantic information transmission, and the semantic pilot is designed to assist semantic reception. Furthermore, we formulate a multi-encoder transformer based neural network model for the KESC system to support text transmission (KESC-T), where the decoder is implemented with a knowledge graph to enhance the performance of semantic decoding. Besides, we define knowledge-enhanced efficiency (KEE) to measure the gain in semantic recovery accuracy brought by per unit of knowledge. Simulation results demonstrate that the recovery accuracy of our proposed KESC outperforms the compared scheme, especially in low signal-to-noise ratio (SNR) or resource-constrained scenarios.