Semantic Communication Research Articles

Joint optimization trajectory and resource allocation for UAV-assisted semantic communications

Semantic communication can effectively save bandwidth, and enhance communication capabilities by transmitting semantic features. However, semantic communication has certain limitations, such as limited application scenarios and inflexible deployment. To this end, we investigate an unmanned aerial vehicle (UAV)-assisted semantic communication system in this paper. An UAV serves as a mobile base station to service users in designated area. Each user has different requirements for transmission delay and performance, and the UAV has a limited maximum flight time. We need to achieve the communication goals of all users in the shortest possible time, that is, to ensure that the information received by each user meets latency and quality requirements. This is a non-convex optimization problem, which is very complicated to solve using traditional methods. In order to solve this problem, we propose a deep reinforcement learning algorithm based on Proximal Policy Optimization 2. The simulation results confirm the effectiveness of our proposed algorithm.

NeuroBCI: Multi-Brain to Multi-Robot Interaction Through EEG-Adaptive Neural Networks and Semantic Communications

NeuroBCI: Multi-Brain to Multi-Robot Interaction Through EEG-Adaptive Neural Networks and Semantic Communications

Adversarial Reinforcement Learning Based Data Poisoning Attacks Defense for Task-Oriented Multi-User Semantic Communication

Adversarial Reinforcement Learning Based Data Poisoning Attacks Defense for Task-Oriented Multi-User Semantic Communication

Goal-Oriented Semantic Communications for Robotic Waypoint Transmission: The Value and Age of Information Approach

Goal-Oriented Semantic Communications for Robotic Waypoint Transmission: The Value and Age of Information Approach

On Forecasting-Oriented Time Series Transmission: A Federated Semantic Communication System

On Forecasting-Oriented Time Series Transmission: A Federated Semantic Communication System

Sentiment-devoid lexicons: A novel method for domain-specific textual analysis in business and governance documents

Our study proposes and tests a method for developing domain-specific dictionaries tailored for textual analysis in information systems research. Traditionally, dictionaries have been widely used for content classification according to sentiment; however, we introduce an alternative approach focused on creating dictionaries from sentiment-devoid documents. We demonstrate this method by developing a dictionary specific to Securities and Exchange Commission (SEC) investigations. Analyzing 150,432 publicly available SEC documents, we gained insights into the semantics of communications between the SEC and firms. To evaluate the dictionary, we analyzed SEC comment letters to predict the likelihood of firms reporting information technology control weaknesses (ITCWs), information technology audit fees, and cyber risks. Our dictionary outperformed five benchmarking dictionaries, explaining a higher proportion of variance in ITCW likelihood, information technology audit fees, and cyber risks. This study enhances the effectiveness of dictionaries in analyzing sentiment-devoid business and governance documents and results in a specialized dictionary for SEC communications.

A Semantic Communication Approach for Multiscene Target Detection in Intelligent Vehicle Networks

A Semantic Communication Approach for Multiscene Target Detection in Intelligent Vehicle Networks

Attention-Aware Semantic Communications for Collaborative Inference

Attention-Aware Semantic Communications for Collaborative Inference

Practical hardware demonstration of a multi-sensor goal-oriented semantic signal processing and communications network

Recent advancements in machine learning, particularly real-time extraction of rich semantic information, reshape signal processing techniques and related hardware architectures. To address the highly challenging requirements of next-generation signal processing applications in networked platforms, we investigate low-power hardware implementation alternatives for a multi-sensor, goal-oriented semantic communications network. Specifically, we focus on cost-effective Raspberry Pis in a multi-sensor semantic video communication application, showcasing adaptability from traditional CPU/GPU configurations. Additionally, we provide a preliminary investigation on implementing semantic extraction tasks through in-memory computation using memristor arrays to further emphasize the potential future of low-power low-cost semantic signal processing. Hardware demonstrations using Raspberry Pi 4Bs and simulations with in-memory computation architectures offer promising hardware architectures with cost-effective and low-power sensor alternatives to the next-generation semantic signal processing applications and semantic communication systems.

An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication

Semantic communication (SC) is a communication paradigm that has gained significant attention, as it offers a potential solution to move beyond Shannon’s formulation in bandwidth-limited communication channels by delivering the semantic meaning of the message rather than its exact form. In this paper, we propose an autoencoder-based SC system for transmitting images between two machines over error-prone channels to support emerging applications such as VIoT, XR, M2M, and M2H communications. The proposed autoencoder architecture, with a semantically modeled encoder and decoder, transmits image data as a reduced-dimension vector (latent vector) through an error-prone channel. The decoder then reconstructs the image to determine its M2M implications. The autoencoder is trained for different noise levels under various channel conditions, and both image quality and classification accuracy are used to evaluate the system’s efficacy. A CNN image classifier measures accuracy, as no image quality metric is available for SC yet. The simulation results show that all proposed autoencoders maintain high image quality and classification accuracy at high SNRs, while the autoencoder trained with zero noise underperforms other trained autoencoders at moderate SNRs. The results further indicate that all other proposed autoencoders trained under different noise levels are highly robust against channel impairments. We compare the proposed system against a comparable JPEG transmission system, and results reveal that the proposed system outperforms the JPEG system in compression efficiency by up to 50% and in received image quality with an image coding gain of up to 17 dB.

Will 6G Be Semantic Communications? Opportunities and Challenges From Task Oriented and Secure Communications to Integrated Sensing

Will 6G Be Semantic Communications? Opportunities and Challenges From Task Oriented and Secure Communications to Integrated Sensing

Lightweight and Robust Wireless Semantic Communications

Lightweight and Robust Wireless Semantic Communications

Toward Effective and Interpretable Semantic Communications

Toward Effective and Interpretable Semantic Communications

Task-Oriented Scene Graph-Based Semantic Communications With Adaptive Channel Coding

Task-Oriented Scene Graph-Based Semantic Communications With Adaptive Channel Coding

STAR-RIS Assisted Information Transmission Based on Fairness in Semantic Communication Systems

STAR-RIS Assisted Information Transmission Based on Fairness in Semantic Communication Systems

A Knowledge Base Driven Task-Oriented Image Semantic Communication Scheme

With the development of artificial intelligence and computer hardware, semantic communication has been attracting great interest. As an emerging communication paradigm, semantic communication can reduce the requirement for channel bandwidth by extracting semantic information. This is an effective method that can be applied to image acquisition of unmanned aerial vehicles, which can transmit high-data-volume images within the constraints of limited available bandwidth. However, the existing semantic communication schemes fail to adequately incorporate the guidance of task requirements into the semantic communication process and are difficult to adapt to the dynamic changes of tasks. A task-oriented image semantic communication scheme driven by knowledge base is proposed, aiming at achieving high compression ratio and high quality image reconstruction, and effectively solving the bandwidth limitation. This scheme segments the input image into several semantic information unit under the guidance of task requirements by Yolo-World and Segment Anything Model. The assigned bandwidth for each unit is according to the task relevance scores, which enables high-quality transmission of task-related information with lower communication overheads. An improved metric weighted learned perceptual image patch similarity (LPIPS) is proposed to evaluate the transmission accuracy of the novel scheme. Experimental results show that our scheme achieves a notable performance improvement on weighted LPIPS while the same compression ratio compared with traditional image compression schemes. Our scheme has a higher target capture ratio than traditional image compression schemes under the task of target detection.

Adaptive Wireless Image Transmission Transformer Architecture for Image Transmission and Reconstruction.

The advancement of 6G (6th Generation Mobile Networks) communication technology has posed challenges for traditional communication network architectures in meeting the demands for communication efficiency and quality. Semantic communication technology, characterized by its "understand before transmit" approach, has emerged as a pivotal technology driving the progress of 6G due to its ability to enhance communication efficiency and quality. The Wireless Image Transmission Transformer (WITT) model, which operates as a semantic communication system leveraging vision transformer technology for the transmission of semantic images, has shown efficacy in transmitting input images through processes of feature extraction and channel adaptation. This study introduces an advanced channel adaptive module that is informed by deep learning methodologies and the adaptive modulation principles of the Variational Information Bottleneck (VIB). This innovation enhances the original WITT model, resulting in the development of the Adaptive Wireless Image Transmission Transformer (ADWITT) architecture. Comprehensive experimental results have unequivocally shown that the transmission performance of the ADWITT architecture substantially surpasses that of the conventional WITT (Wavelet Image Transmission Technique) model, particularly in scenarios characterized by harsh and detrimental channel conditions. These findings underscore the robustness and adaptability of the ADWITT approach, which is poised to improve the field of image transmission by offering superior performance and resilience in environments where traditional methods falter.

An Introduction to Semantic Communication and Semantic-Aware Networking Standardization for 6G

Recent developments of networking technologies have witnessed a growing interest on human-oriented services such as Tactile Internet and VR/AR/XR, most of which are data-hungry and computationally expensive. Semantic communication and semantic-ware networking (SAN) are promising solutions to meet the above need and support emerging smart applications and services based on the network and user generated data. In this paper, we provide an overview of the ongoing standardization activity of semantic communication and semantic-aware networking at IMT-2030 (6G) standard promotion group, including the overview of the concepts, key functional components, reference architectures, and use cases.

Semantic Communication for Cooperative Multi-Task Processing Over Wireless Networks

Semantic Communication for Cooperative Multi-Task Processing Over Wireless Networks

Secure Semantic Communications: From Perspective of Physical Layer Security

Secure Semantic Communications: From Perspective of Physical Layer Security