VTC 2025 Spring Conference’s Shorts

Spatial Resource Allocation for Optical IRS-Aided HAP-Assisted Multi-UAV Networks

This paper investigates resource allocation for optical intelligent reflecting surfaces (OIRS) on high-altitude platforms (HAPs) supporting multiple UAV-mounted base stations. We propose a rate-optimized spatial resource allocation (R-SRA) scheme that first maximizes the number of QoS-guaranteed UAVs, then allocates remaining OIRS elements to boost the total system rate. Numerical results show R-SRA outperforms conventional methods, even with UAV mobility.Spatial Resource Allocation for Optical IRS-Aided HAP-Assisted Multi-UAV Networkskhanh D. Dang, The University of Aizu; Hoang D. Le, University of Aizu; Chuyen T. Nguyen, Hanoi University of Science and Technology; Vuong Mai, University of Bradford; Anh T. Pham, University of Aizu

Adaptive UAV Deployment for Remote IoT Computation Offloading in Integrated Space-Air-Grou...

In the realm of the Internet of Things (IoT), computation offloading confronts challenges in remote areas due to scarce general-purpose edge/cloud infrastructure and insufficient terrestrial network coverage. To address this, we introduce a novel space-air-ground integrated network (SAGIN) computing architecture, designed for the efficient offloading of computation-intensive applications. Within this architecture, unmanned aerial vehicles (UAVs) conduct edge computing near users, while satellites act as a bridge to cloud computing resources. Given the limitations of UAVs in terms of battery capacity and dynamic network topology, their deployment strategy is crucial for maintaining service quality. Due to the impracticality of collecting global user information for centralized control of UAVs, we have conducted research on the adaptive deployment of UAVs under the condition that they rely solely on local observations. We propose a multi-agent softmax deep double deterministic policy gradient (MASD3) algorithm and comprehensively consider maximizing the uplink transmission rate of terrestrial IoT devices and reducing the energy consumption of UAVs during flight and communication in the optimization objective. Simulation results demonstrate that our proposed solution outperforms existing state-of-the-art baselines.Adaptive UAV Deployment for Remote IoT Computation Offloading in Integrated Space-Air-Ground NetworksXiaomin Liu, Yujie Peng, Tiecheng Song, Southeast University; Xiaoqin Song, Nanjing University of Aeronautics and Astronautics

Dynamic Pseudonym Management with Privacy-Energy Trade-offs in IoV Networks

The Internet of Vehicles (IoV) represents a significant advancement in intelligent transportation systems (ITS), enabling real-time data exchange between vehicles and infrastructure to enhance road safety, traffic efficiency, and user experiences. Vehicular edge computing (VEC) has emerged as a critical enabler, offering localized data processing and facilitating data trading among vehicles and edge servers. While data trading enhances system efficiency by enabling seamless information exchange among vehicles and infrastructure, it also introduces significant challenges in preserving user privacy, protecting against malicious tracking, and managing energy consumption effectively. To tackle these challenges, we propose a novel Energy-Efficient Pseudonym Management with Dueling Deep Q-Network (E2PM-DDQN) framework for VEC-enabled IoV, aiming to enhance the balance between privacy protection and energy efficiency during data trading. By deploying an RL agent at VEC servers, our approach dynamically manages pseudonym updates during data trading, improving the trade-off between privacy and energy consumption. Simulation results in a VEC-enabled IoV environment confirm that our proposed E2PM-DDQN framework achieves higher privacy entropy than state-of-the-art approach, enables higher rewards than baseline strategies, and ensures lower energy consumption. These results highlight how a reinforcement learning (RL) approach outperforms non-RL methods.Dynamic Pseudonym Management with Privacy-Energy Trade-offs in IoV NetworksElham Mohammadzadeh Mianji, Gabriel-Miro Muntean, Irina Tal, Dublin City University

Coverage Analysis for Directional Ad Hoc Networks With Imperfect Beam Alignment

This paper analyzes the impact of beam alignment errors (BAE) on directional ad hoc networks and proposes a new angular error model that derives angular errors from positional errors, overcoming limitations of existing models that assume fixed angular error variance. We derive the probability density function (PDF) of antenna gain for both flat-top and Gaussian antenna models and analyze the signal-to-interference-plus-noise ratio (SINR) coverage probability in Poisson networks. Numerical results show that the new model more accurately captures the effects of BAE on SINR coverage and main lobe alignment probability. The proposed model provides a more realistic understanding of beam misalignment effects in practical network scenarios.Coverage Analysis for Directional Ad Hoc Networks With Imperfect Beam AlignmentYuxin Wang, Zhejiang University; An Liu, College of ISEE, Zhejiang University; Chan Wang, Minjian Zhao, Zhejiang University

Conditional Diffusion Model as High-Dimensional Offline Resource Allocation Planner in Clu...

Due to network delays and scalability limitations, clustered ad hoc networks widely adopt Reinforcement Learning (RL) for on-demand resource allocation. Albeit its demonstrated agility, traditional Model-Free RL (MFRL) solutions struggle to tackle the huge action space, which generally explodes exponentially along with the number of resource allocation units, enduring low sampling efficiency and high computational complexity. To mitigate these limitations, Model-Based RL (MBRL) offers a solution by generating simulated samples through an environment model, which boosts sample efficiency and stabilizes the training by avoiding extensive real-world interactions. However, establishing an accurate dynamic model for complex and noisy environments necessitates a careful balance between model accuracy and computational complexity & stability. To address these issues, we propose a conditional Diffusion Model (DM) as high-dimensional offline resource allocation planner in multi-frequency time division multiple access (MF-TDMA) wireless ad hoc networks. By leveraging the astonishing generative capability of DMs, our approach takes advantage of generated high-quality samples to guide exploration and learn optimal policy. Extensive experiments show that our model outperforms MFRL in average reward and Quality of Service (QoS) while demonstrating comparable performance to other MBRL algorithms.Conditional Diffusion Model as High-Dimensional Offline Resource Allocation Planner in Clustered MF-TDMA Ad Hoc NetworksKechen Meng, Sinuo Zhang, Rongpeng Li, Chan Wang, Ming Lei, Minjian Zhao, Zhejiang University; Zhifeng Zhao, Zhejiang Lab

Performance assessment of Ethernet 10BASE-T1S networks in automotive applications

The IEEE 802.3cg-2019 standard defines Ethernet 10BASE-T1S. With a data rate of 10 Mbps in half-duplex mode on a multidrop bus, Ethernet 10BASE-T1S is a suitable candidate for in-vehicle networks with zonal architectures to interconnect multiple sensors with the relevant zonal controller. The standard provides optional Physical Layer Collision Avoidance (PLCA) capabilities that support cyclic transmissions without collisions and provide short transmission delays. However, PLCA lacks explicit priority support, while priority is useful in automotive communications to handle different flows with diverse timing requirements. In this context, this work presents a simulative performance assessment of Ethernet 10BASE-T1S with PLCA enabled in a realistic zonal-based automotive scenario. Moreover, the paper addresses how to leverage the PLCA to offer a better Quality of Service (QoS) to the nodes that generate the most time-critical flows, discussing the delays obtained with different network configurations.Performance assessment of Ethernet 10BASE-T1S networks in automotive applicationsFrancesco Cerruto, Luca Leonardi, Lucia Lo Bello, Gaetano Patti, University of Catania

Task-oriented Adaptive Distributed Node State Exchange Framework in Mobile Edge Networks

With the development of mobile edge computing, task collaboration among edge nodes is an effective way to address the limited edge resources. The critical step in resource collaboration is obtaining and updating edge nodes eligible for collaboration. However, the dynamic nature of the mobile edge network makes acquiring an accurate and timely node state challenging. This paper introduces a Task-oriented Adaptive Distributed (TAD) node state exchange framework to improve network service quality and system energy efficiency. Two main components of the TAD framework are the Scheduled State Update (SSU) Algorithm, which periodically maintains state information, and the Task-driven Resource Discovery (TRD) Algorithm, which updates outdated information to ensure the network adapts to changes. In addition, the Adaptive Dynamic Counter (ADC) is designed to make the update frequency of SSU dynamically adjustable according to the task execution and node state. The framework significantly improves the task completion rate and reduces energy consumption. Compared to the existing state-of-the-art methods, the task completion rate demonstrates an improvement of up to 36.70%, while the energy consumption exhibits a reduction rate of up to 33.38%.Task-oriented Adaptive Distributed Node State Exchange Framework in Mobile Edge NetworksLiangjie Zhao, Institute of Computing, Chinese Academy of Sciences; Nina Wang, ICT/CAS, China; Zongshuai Zhang, Institute of Computing Technology, Chinese Academy of Sciences, China; Yu Tian, Institute of Computing Technology, Chinese Academy of Science; Beixi Ning, Institute of Computing, Chinese Academy of Sciences; Lin Tian, Institute of Computing Technology, Chinese Academy of Sciences

Network Planning for IIoT Scenarios Based on Ray Tracing

The Industrial Internet of Things (IIoT) is a promising scenario for Industry 4.0, where the existence of rich scatterers can affect the quality of communication in the factory. In this paper, we investigate the multiple base stations (BSs) deployment problem based on the ray tracing (RT) method in IIoT scenarios considering coverage. We simplify the construction of IIoT scenarios by considering the geometric and channel characteristics to improve computational efficiency in RT simulation. Then, the BS deployment problem is formulated subject to the path loss and energy efficiency. The particle swarm optimization (PSO) algorithm with the surrogate model from RT simulation is proposed to solve the formulated problem. Moreover, the surrogate model is used to replace the all-time RT simulation in the PSO algorithm. The simulation results demonstrate that the proposed method effectively addresses BS deployment challenges in IIoT scenarios.Network Planning for IIoT Scenarios Based on Ray TracingXiaohui Yin, Zewei Zhang, Shizhuo Fu, Guilin Hu, Southeast University; Songjiang Yang, Yinghua Wang, Purple Mountain Laboratories; Jie Huang, Cheng-Xiang Wang, Southeast University

Multi-user Full-Duplex Reconfigurable Intelligent Surface Assisted Wireless Powered IIoT N...

This paper investigates a full-duplex (FD) reconfigurable intelligent surface (RIS)-assisted wireless powered communication network (WPCN) in which FD multi-user (FD-MUs) industrial internet-of-things (IIoT) sensor devices with energy harvesting (EH) capabilities communicate with an FD hybrid-access point (FD-AP). The FD-AP with dual-set of multiple antennas use a set of antennas to beamform energy in downlink (DL) transmission and the other set of antennas to receive information signals in uplink (UL) transmission from the dual-set of single antenna FD-MUs. The FD-MUs use one single antenna to receive energy signals, while the other single antenna is used to transmit information. For comparison, two special cases of the FD-FD RIS-assisted WPCN IIoT network are considered. The first scenario considers an FD-HD (half-duplex) RIS-assisted WPCN IIoT network made up of a FD-AP and HD-MUs while the second comprises an HD-HD RIS-assisted WPCN IIoT network consisting of HD-AP and HD-MUs. We investigate the sum-rate performance of the FD-FD system with random IRS phase shifts and compare it to FD-HD and HD-HD system models. It is shown from simulation results that the FD-FD system outperforms the two special cases in the low transmit power regime.Multi-user Full-Duplex Reconfigurable Intelligent Surface Assisted Wireless Powered IIoT NetworksReynah Akwafo, Samuel Kwamena Menanor, Hanbat National University; Derek Kwaku Pobi Asiedu, IMT-Atlantique; Samir Saoudi, IMT Atlantique Bretagne-Pays de la Loire; Kyoung-Jae Lee, Hanbat National University

Enhancing Mobile Crowdsensing Efficiency: A Coverage-aware Resource Allocation Approach

In this study, we investigate the resource management challenges in next-generation mobile crowdsensing networks with the goal of minimizing task completion latency while ensuring coverage performance, i.e., an essential metric to ensure comprehensive data collection across the monitored area, yet it has been commonly overlooked in existing studies. To this end, we formulate a weighted latency and coverage gap minimization problem via jointly optimizing user selection, subchannel allocation, and sensing task allocation. The formulated minimization problem is a non-convex mixed-integer programming issue. To facilitate the analysis, we decompose the original optimization problem into two subproblems. One focuses on optimizing sensing task and subband allocation under fixed sensing user selection, which is optimally solved by the Hungarian algorithm via problem reformulation. Building upon these findings, we introduce a time-efficient two-sided swapping method to refine the scheduled user set and enhance system performance. Extensive numerical results demonstrate the effectiveness of our proposed approach compared to various benchmark strategies.Enhancing Mobile Crowdsensing Efficiency: A Coverage-aware Resource Allocation ApproachYaru Fu, Hong Kong Metropolitan University; Yue Zhang, Shantou University; Zheng Shi, Jinan University; Yongna Guo, KTH Royal Institute of Technology; Yalin Liu, Hong Kong Metropolitan University