VTC 2025 Spring Conference’s Shorts

Deep Learning for sub-THz Radio Unit Selection using sub-10 GHz Channel Information and In...

The dense and distributed deployment of sub-THz radio units (RUs) alongside sub-10 GHz access point (AP) is a promising approach to provide high data rate and reliable coverage for future 6G applications. However, beam search or RU selection for the sub-THz RUs incurs significant overhead and high power consumption. To address this, we introduce a method that leverages deep learning to infer a suitable sub-THz RU candidate from a set of sub-THz RUs using the sub-10 GHz channel characteristics. A novel aspect of this work is the consideration of inter-band beam configuration (IBBC), defined as the broadside angle between the low-band and high-band antenna patterns of the user equipment (UE). Since IBBC indicates the beamforming information or UE’s orientation, it is typically not shared with the network as a part of signalling. Therefore, we propose a solution strategy to infer a suitable sub-THz RU even when UEs do not share their IBBC information. Simulation results illustrate the performance of the inferred sub-THz RU and highlights the detrimental impact of neglecting UE orientation on the systems performance.Deep Learning for sub-THz Radio Unit Selection using sub-10 GHz Channel Information and Inferred Device BeamformingNishant Gupta, Linköping University; Muris Sarajlic, Ericsson Research, Lund, Sweden; Erik G. Larsson, Linköping University

Channel-Aware Deep Learning for Superimposed Pilot Power Allocation and Receiver Design

Superimposed pilot (SIP) schemes face significant challenges in effectively superimposing and separating pilot and data signals, especially in multiuser mobility scenarios with rapidly varying channels. To address these challenges, we propose a novel channel-aware learning framework for SIP schemes, termed CaSIP, that jointly optimizes pilot-data power (PDP) allocation and a receiver network for pilot-data interference (PDI) elimination, by leveraging channel path gain information, a form of large-scale channel state information (CSI). The proposed framework identifies user-specific, resource element-wise PDP factors and develops a deep neural network-based SIP receiver comprising explicit channel estimation and data detection components. To properly leverage path gain data, we devise an embedding generator that projects it into embeddings, which are then fused with intermediate feature maps of the channel estimation network. Simulation results demonstrate that CaSIP efficiently outperforms traditional pilot schemes and state-of-the-art SIP schemes in terms of sum throughput and channel estimation accuracy, particularly under high-mobility and low signal-to-noise ratio (SNR) conditions.Channel-Aware Deep Learning for Superimposed Pilot Power Allocation and Receiver DesignRun Gu, Southeast university; Renjie Xie, Nanjing University of Posts and Telecommunications; Wei Xu, Southeast University; Zhaohui Yang, Zhejiang University; Kaibin Huang, The University of Hong Kong

Asynchronous Multi-Agent Reinforcement Learning for Scheduling in Subnetworks

We address radio resource scheduling in a network of multiple in-X subnetworks providing wireless Ultra-Reliable Low-Latency Communication (URLLC) service. Each subnetwork is controlled by an agent responsible for scheduling resources to its devices. Agents rely solely on interference measurements for information about other agents, with no explicit coordination. Subnetwork mobility and fast-fading effects create a non-stationary environment, adding to the complexity of the scheduling problem. This scenario is modeled as a multi-agent Markov Decision Process (MDP). To address the problem, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) approach under URLLC constraints, which integrates Long Short-Term Memory (LSTM) with the Deep Deterministic Policy Gradient (DDPG) algorithm to manage non-stationarity and high-dimensional action spaces. We apply an asynchronous update strategy, where one agent is updating at a time. This reduces learning variability, resolves policy conflicts, and improves the interpretability of the MADRL approach. Simulation results demonstrate that the asynchronous update mechanism outperforms synchronous updates and baseline methods, achieving superior reliability, resource utilization, and explainability.Asynchronous Multi-Agent Reinforcement Learning for Scheduling in SubnetworksAshvin Srinivasan, Aalto University; Junshan Zhang, UC Davis; Olav Tirkkonen, Aalto University

V2XFormer: Transformer-Based Anomaly Detection for Vehicle-to-Everything Communication

The Internet of Vehicles (IoV) has transformed intelligent transportation systems through vehicle-to-everything (V2X) communication, improving road safety and traffic efficiency. However, the dynamic nature of vehicular networks, with high mobility and shared wireless resources, makes them vulnerable to attacks like Denial of Service (DoS). Anomaly detection (AD) has proven effective in detecting such threats. Yet, V2X communication occurs in diverse environments with varying network coverage and vehicle speeds, leading to domain shifts and variations in feature distributions that can hinder the generalization performance of traditional anomaly detection models. To address these challenges, this paper presents V2XFormer, an unsupervised anomaly detection system based on transformer neural networks, designed to identify anomalies in V2X communication. Additionally, we introduce TV2XFormer, which integrates transfer learning to enhance adaptability across diverse network conditions and environmental variations in V2X communication. We assess the performance of the proposed approaches using the VDoS-LRS V2X dataset, employing precision, recall, and F1 score metrics. A comparison is made with five state-of-the-art unsupervised AD algorithms. Experimental results demonstrate that both V2XFormer and TV2XFormer outperform the competing algorithms, achieving the highest F1 scores (1.0). Furthermore, TV2XFormer exhibits notable robustness and generalizability to dynamic vehicular environments.V2XFormer: Transformer-Based Anomaly Detection for Vehicle-to-Everything CommunicationHarindra Sandun Mavikumbure, Victor Cobilean, Chathurika S. Wickramasinghe, Devin Drake, Milos Manic, Virginia Commonwealth University

Robust Event-Triggered Integrated Communication and Control with Graph Information Bottlen...

Integrated communication and control serves as a critical ingredient in Multi-Agent Reinforcement Learning. However, partial observability limitations will impair collaboration effectiveness, and a potential solution is to establish consensus through well-calibrated latent variables obtained from neighboring agents. Nevertheless, the rigid transmission of less informative content can still result in redundant information exchanges. Therefore, we propose a Consensus-Driven Event-Based Graph Information Bottleneck (CDE-GIB) method, which integrates the communication graph and information flow through a GIB regularizer to extract more concise message representations while avoiding the high computational complexity of inner-loop operations. To further minimize the communication volume required for establishing consensus during interactions, we also develop a variable-threshold event-triggering mechanism. By simultaneously considering historical data and current observations, this mechanism capably evaluates the importance of information to determine whether an event should be triggered. Experimental results demonstrate that our proposed method outperforms existing state-of-the-art methods in terms of both efficiency and adaptability.Robust Event-Triggered Integrated Communication and Control with Graph Information Bottleneck OptimizationZiQiong Wang, Xiaoxue Yu, Rongpeng Li, Zhejiang University; Zhifeng Zhao, Zhejiang Lab

Robust Blind Channel Estimation for Bursty Impulsive Noise with a Constrained EM Approach

Impulsive noise (IN) commonly generated by power devices can severely degrade the performance of high-sensitivity wireless receivers. Accurate channel state information (CSI) knowledge is essential for designing optimal maximum a posteriori detectors. This paper examines blind channel estimation methods based on the expectation-maximization (EM) algorithm tailored for scenarios impacted by bursty IN, which can be described by the Markov-Middleton model. We propose a constrained EM algorithm that exploits the trellis structure of the IN model and the transmitted binary phase-shift keying (BPSK) symbols. By enforcing shared variance among specific trellis states and symmetry in the transition matrix, the proposed constrained EM algorithm adapted for the bursty IN channel has an almost two times faster convergence rate and better estimation performance than the standard EM approach. We comprehensively evaluate the robustness of both standard and constrained EM estimators under different types of CSI uncertainties. The results indicate that the final estimations of both EM estimators are robust enough to mismatch Markov-Middleton model parameters. However, as the level of CSI uncertainty increases, the convergence rate decreases.Robust Blind Channel Estimation for Bursty Impulsive Noise with a Constrained EM ApproachChin-Hung Chen, Ivana Nikoloska, Wim van Houtum, Eindhoven University of Technology; Yan Wu, NXP Semiconductor; Boris Karanov, Karlsruhe Institute of Technology; Alex Alvarado, Eindhoven University of Technology

Multi-Task Hypergraph-Attention Framework for Multimodal Sentiment Analysis

Multimodal sentiment analysis has emerged as a critical research area. However, existing methods face significant challenges: (1) Unimodal feature extraction techniques often fail to capture the topological structure within data, and do not effectively integrate local and global information, leading to information loss. (2) Traditional multimodal fusion methods, such as concatenation, addition, and multiplication, struggle to model modality differences and inter-modal correlations. In this paper, we propose a novel multi-task hypergraph-attention framework (MTHA) to improve feature discrimination and model performance. Experimental results demonstrate that MTHA outperforms most baseline models in both sentiment classification and regression.Multi-Task Hypergraph-Attention Framework for Multimodal Sentiment AnalysisYibing Wang, Zhutian Yang, Linhan Wang, Harbin Institute of Technology; Mingqian Liu, Xidian University; Yushi Chen, Harbin Institute of Technology

Joint Optimization of 3D Trajectory and Resource Allocation in Multi-UAV Systems via Graph...

With their high mobility and ease of deployment, unmanned aerial vehicle (UAV)-assisted communication systems have emerged as a prominent area of academic research and a cornerstone technology for Sixth-Generation (6G) mobile communication networks. This paper investigates a multi-UAV downlink wireless communication system in which users exhibit random movement on the ground. To maximize the sum-rate of all users over the observation period, we propose a joint optimization framework that integrates user association, UAV 3D trajectory design, and power allocation, while addressing channel estimation across different timescales. In the long timescale, we model the UAV-user connections as a graph and utilize a graph neural network to jointly optimize user association and UAV trajectories. In the short timescale, we deploy a deep unfolding network for efficient channel estimation and power allocation. Simulation results validate the effectiveness of the proposed approach, showcasing significant performance improvements.Joint Optimization of 3D Trajectory and Resource Allocation in Multi-UAV Systems via Graph Neural NetworksJingwei Peng, Yunlong Cai, Zhejiang University; Jiantao Yuan, Hangzhou City University; Kai Ying, Shanghai Jiao Tong University; Rui Yin, Zhejiang University City College

Generative CKM Construction using Partially Observed Data with Diffusion Model

Channel knowledge map (CKM) is a promising technique that enables environment-aware wireless networks by utilizing location-specific channel prior information to improve communication and sensing performance. A fundamental problem for CKM construction is how to utilize partially observed channel knowledge data to reconstruct a complete CKM for all possible locations of interest. This problem resembles the long-standing ill-posed inverse problem, which tries to infer from a set of limited observations the cause factors that produced them. By utilizing the recent advances of solving inverse problems with generative artificial intelligence (AI), in this paper, we propose generative CKM construction method using partially observed data by solving inverse problems with diffusion models. Simulation results show that the proposed method significantly improves the performance of CKM construction compared with benchmarking schemes.Generative CKM Construction using Partially Observed Data with Diffusion ModelShen Fu, Zijian Wu, Southeast University; Di Wu, Southeast University, Purple Mountain Laboratories; Yong Zeng, Southeast University

End-to-End Learning for RIS Profile Design and Channel Parameter Estimation under Pixel Fa...

Reconfigurable intelligent surfaces (RISs) have emerged as a transformative technology for sixth-generation (6G) communication networks, offering the ability to dynamically shape wireless propagation environments and thus efficiently enhance received signal quality. However, practical implementation of RIS faces challenges, including potential failures of individual elements (pixels), which can degrade the performance significantly. This paper leverages autoencoders and end-to-end (E2E) learning in RIS-aided systems to jointly optimize the RIS phase profiles and receiver angle-of-departure (AoD) estimation in the presence of pixel failures. The proposed E2E approach demonstrates resilience against practical pixel errors while is shown to achieve performance close to the fundamental bounds, thereby advancing the state-of-the-art in RIS-aided systems towards the 6G era.End-to-End Learning for RIS Profile Design and Channel Parameter Estimation under Pixel FailuresMehmet C. Ilter, Tampere University; Furkan Keskin, Chalmers University; José Miguel Mateos-Ramos, Chalmers University of Technology, Sweden; Christian Häger, Chalmers University of Technology; Mikko Valkama, Tampere University; Henk Wymeersch, Chalmers University of Technology