VTC 2025 Spring Conference’s Shorts

Semantic Communication in Vehicular Networks: A Multi-Modal Approach for Faithful Image Tr...

Semantic communication (SC) has emerged as a promising paradigm to address the bandwidth limitations of traditional wireless communication systems by transmitting only the essential meaning of data. This paper investigates an advanced SC framework for vehicular communication, employing diverse feature extraction techniques to encode multi-modal information, such as textual descriptions, object poses, semantic segmentation, and sketches, into compact semantic representations. These semantic encoders are evaluated based on their output size, faithfulness of reconstruction, and resilience to data loss. The proposed system model considers vehicular communication scenarios where vehicles transmit important information extracted from camera-collected data to other vehicles and road users, in bandwidth-constrained environments. Simulation results show the effectiveness of the proposed SC framework, with a reconstruction performance reaching 17.24 in Fréchet Inception distance (FID) and a an RMSE equal to 0.029 between the transmitted image and the reconstructed one. This performance is achieved while the data saving between the size of the original image and the transmitted semantics is equal to 92.25%.Semantic Communication in Vehicular Networks: A Multi-Modal Approach for Faithful Image TransmissionMondher Bouazizi, Riku Nagase, Yue Yin, Siyuan Yang, Tomoaki Ohtsuki, Keio University

M-JSCC: An Asymmetric Semantic Communication Architecture for 6G Intelligent Networks

Semantic communication (SC) is considered a critical technology for breaking through the Shannon limit and achieving low-latency, high-capacity 6G transmission. However, previous SC systems have typically employed a symmetrical architecture to enhance data recovery capabilities, resulting in a strong coupling between the encoder and decoder. In this paper, we introduce a novel asymmetric SC system, termed masked joint source-channel coding (M-JSCC), which significantly enhances the encoder’s versatility by allowing it to adapt to different decoder models tailored to specific task requirements. Moreover, we abandon traditional convolutional neural networks and adopt the innovative transformer to increase model capacity further. Additionally, we empower the model with data generation capabilities to combat interference and distortion during wireless transmission, achieving robust semantic transmission. As a result, extensive experiments verify that our M-JSCC achieves better semantic understanding and performance across various tasks and different channel conditions.M-JSCC: An Asymmetric Semantic Communication Architecture for 6G Intelligent NetworksPengfei Ren, Jingjing Wang, Zhiwei Wang, Beihang University; Xiangwang Hou, Tsinghua University; Xin Zhang, Hong Kong University of Science and Technology; Chunxiao Jiang, Tsinghua University

Goal-Oriented Communication with Semantic Reconstruction in Vehicular Networks

In recent years, semantic communication has received a lot of attention due to its ability to solve the challenges faced by traditional communication systems. However, little attention has been paid to the fact that during data compression and transmission, the lost data can be reconstructed by neural networks to improve transmission efficiency. In order to solve the impact of the loss of semantic information on the transmission performance in vehicular networks, this paper proposes a goal-oriented communication based on semantic reconstruction (GOCSR). By designing a semantic reconstruction network at the receiver, the lost semantic information is predicted and reconstructed, and then the complete semantic information is used to perform downstream tasks. To evaluate the efficiency of GOCSR, extensive simulation experiments are conducted using the Cityscapes dataset. Simulation results show that GOCSR can achieve higher target execution performance than the existing semantic communication schemes.Goal-Oriented Communication with Semantic Reconstruction in Vehicular NetworksZhu Jin, Tiecheng Song, Southeast University; Xiaoqin Song, Nanjing University of Aeronautics and Astronautics; Jing Hu, Southeast University

Emergency Communication: OTFS-Based Semantic Transmission with Diffusion Noise Suppression

Due to their flexibility and dynamic coverage capabilities, Unmanned Aerial Vehicles (UAVs) have emerged as vital platforms for emergency communication in disaster-stricken areas. However, the complex channel conditions in high-speed mobile scenarios significantly impact the reliability and efficiency of traditional communication systems. This paper presents an intelligent emergency communication framework that integrates Orthogonal Time Frequency Space (OTFS) modulation, semantic communication, and a diffusion-based denoising module to address these challenges. OTFS ensures robust communication under dynamic channel conditions due to its superior anti-fading characteristics and adaptability to rapidly changing environments. Semantic communication further enhances transmission efficiency by focusing on key information extraction and reducing data redundancy. Moreover, a diffusion-based channel denoising module is proposed to leverage the gradual noise reduction process and statistical noise modeling, optimizing the accuracy of semantic information recovery. Experimental results demonstrate that the proposed solution significantly improves link stability and transmission performance in high-mobility UAV scenarios, achieving at least a 3dB SNR gain over existing methods.Emergency Communication: OTFS-Based Semantic Transmission with Diffusion Noise SuppressionKexin Zhang, northwestern polytechnical university; Xin Zhang, Lixin Li, Wensheng Lin, Northwestern Polytechnical University; Wenchi Cheng, Xidian University; Qinghe Du, Xi'an Jiaotong University

Efficient CKM Exchange via Semantic Communications

Channel knowledge map (CKM) is a novel technique for achieving environment-aware wireless communication and sensing. CKM exchange among different base stations (BSs) is needed to achieve efficient BS cooperations. This paper proposes an efficient CKM exchange method based on semantic communication to facilitate the utilization of CKM at dynamic users. The proposed semantic communication framework achieves the tradeoff between the bandwidth consumption and reconstruction quality of CKM. Compared to traditional image compression and transmission methods, the semantic communication approach demonstrates higher stability and reliability in CKM exchange, especially under low SNR region. Besides, semantic communication based on deep learning can also effectively mitigate the measurement errors associated with CKM reconstruction. Simulation results show that under 4 times compression ratio, the proposed method outperforms traditional methods in terms of stability and can effectively transmit severely corrupted data, recovering CKMs close to the original values.Efficient CKM Exchange via Semantic
CommunicationsYiou Shen, Shiyu Wang, xiaoli Xu, Yong Zeng, Southeast University

Precoder Design for RIS-Assisted Interference Suppression in Full-Duplex Integrated Satell...

This article investigates the performance of a reconfigurable intelligent surface (RIS)-assisted full-duplex (FD) integrated satellite-terrestrial network (ISTN). Particularly, the RIS structure is incorporated into the ISTN topology to curb the effects of the undesirable self-interference (SI) resulting from the FD operation, cross-tier interference (CTI), and intra-cell interference (ICI). To examine this network performance, an optimization problem is formulated to maximize the sum rate of users by jointly optimizing the active beamforming and RIS reflection coefficients. To address the non-convexity of the optimization problem, it is decomposed into two subproblems: one which involves applying the fractional programming (FP) technique to find the optimal solution for the active beamforming variable, while the other subproblem constitutes the use of successive convex approximation (SCA) to optimize the passive beamforming variable. These subproblems are solved alternately until convergence. Numerical results presented herein confirm that the proposed algorithm outperforms the benchmark schemes.Precoder Design for RIS-Assisted Interference Suppression in Full-Duplex Integrated Satellite-Terrestrial NetworksJulius Ssimbwa, Korea University; Byungju Lim, Pukyong National University, Busan, South Korea; Young-Chai Ko, Korea University

Max-Min Strategy for Handover in LEO based Non-Terrestrial Networks

In Low-Earth Orbit (LEO)-based Non-Terrestrial Networks (NTN), stable and continuous connectivity through efficient handover (HO) mechanisms should be ensured, especially given the high mobility and dynamic satellite coverage in these networks. Conventional HO optimization approaches, such as the Service Continuity Dynamic Programming (SCDP) strategy, optimize HO but may leave some User Equipments (UEs) experiencing low link rates, reduced service time, or frequent handovers. To address this, we propose the Service Continuity Max-Min (SCMM) strategy, which employs the Max-Min algorithm to reduce the number of UEs that have poor connectivity experiences, using a uniquely defined reward function that incorporates link rate, service availability time, and HO frequency. The simulation results demonstrate that SCMM improves the minimum reward across UEs while maintaining an overall performance comparable to that of the existing SCDP methods. This approach offers a robust solution to improve service continuity and user satisfaction in NTN, making it an effective strategy for next-generation satellite networks.Max-Min Strategy for Handover in LEO based Non-Terrestrial NetworksRiku Nagase, Siyuan Yang, Tomoaki Ohtsuki, Keio University

An Energy-Efficient Reliable Distributed Routing Algorithm for 6G LEO Satellite Networks

Low Earth Orbit (LEO) satellite networks, known for their low signal latency, are ideal for 6G real-time communications and Internet access. However, they are confronted with considerable challenges, including instability and restricted power resources. It is importance to guarantee reliable and energy-efficient operations in order to maintain uninterrupted and high-quality service. We propose an Energy-Efficient Reliable Distributed Routing Algorithm (EERRA) that dynamically updates satellite information, prevents data loops, and optimizes transmission paths to avoid eclipse regions. This approach extends satellite lifetime and improves network performance. The simulation results show that EERRA outperforms existing algorithms, including Global, DRA, and DSRA, across multiple metrics. EERRA demonstrates exceptional performance in terms of average end-to-end delay, average hop count, signaling overhead, and satellite lifespan. Compared to DRA and DSRA, EERRA achieves lower average end-to-end delay and hop count, and significantly reduces signaling overhead compared to the Global algorithm. Furthermore, EERRA excels in maintaining the highest average residual energy and the lowest life cycle consumption. These attributes make EERRA a highly efficient solution for managing the complex dynamics of LEO satellite networks, ensuring sustainable and reliable network operations over extended periods.An Energy-Efficient Reliable Distributed Routing Algorithm for 6G LEO Satellite NetworksYu-Ya Su, National Tsing Hua University, Taiwan; Shun-Ren Yang, National Tsing Hua University; Chai-Hien Gan, Information and Communications Research Laboratories, ITRI; Phone Lin, Xizhe Qiu, National Taiwan University

An Efficient Parallel Simulation Architecture for Ultra-Mega-Scale LEO Satellite Networks

As Low Earth Orbit (LEO) satellite constellations continue to expand in scale, traditional satellite network simulation approaches will face limitations in performance and scalability when simulating large-scale satellite networks. To address this issue, we propose an efficient parallel simulation architecture named HyperLEO, specifically designed for ultra-large-scale LEO satellite networks. The architecture leverages MPI-based parallelization techniques to significantly enhance the simulation efficiency. We further introduce a parallel discrete event simulation (PDES) synchronization algorithm tailored to the dynamic characteristics of satellite networks and develop a load-balanced partitioning algorithm. Experimental results show that HyperLEO achieves up to a 19.7× speedup using 32 processes over serial simulations at the LEO-2500 scale and is capable of supporting simulations of constellations with up to 20,000 satellites. Our work provides an efficient and scalable approach for protocol development and performance evaluation in ultra-large-scale LEO satellite networks.An Efficient Parallel Simulation Architecture for Ultra-Mega-Scale LEO Satellite NetworksTianyi Pei, Xiaoyu Liu, Junkai Zeng, Yuze Liu, Haibo Zhou, Nanjing University

RIS-assisted Hybrid FSO/THz System with Maximal Ratio Combining

This study explores a hybrid framework that merges terahertz (THz) and free space optics (FSO) technologies in a backhaul network. The combined FSO and THz systems leverage reconfigurable intelligent surfaces (RIS) and employ maximal ratio combining (MRC) at the receiver. Specifically, the closed-form expressions for cumulative distribution function (CDF) of channel and signal-to-noise ratio (SNR) for RIS-assisted FSO and THz channels are derived by assuming the impediments such as atmospheric turbulence, fading, pointing errors, and pathloss. Using these statistics, outage probability, average symbol error rate (SER), and outage capacity are computed for the proposed RIS-assisted hybrid FSO/THz system. Numerical results compare the proposed hybrid FSO/THz system against various wireless systems, including RIS-assisted FSO, RIS-assisted THz, and hybrid FSO/THz systems without RIS.RIS-assisted Hybrid FSO/THz System with Maximal Ratio CombiningNarendra Vishwakarma, Rithwik Premanand, Nanyang Technological University, Singapore; Swaminathan R, Indian Institute of Technology Indore, India; A.S. Madhukumar, Nanyang Technological University