VTC 2025 Spring Conference’s Shorts

OLIVE: Adaptive Containerized Architecture for Multi-Factor Authentication in V2X

OLIVE is a containerized Multi-Factor Authentication (MFA) architecture designed to adapt to diverse security requirements in Vehicle-to-Everything (V2X) communications. By integrating multiple authentication factors, it provides adaptability and scalability to match the required level of security. Beyond the standard three authentication factors, OLIVE supports additional custom factors housed in independent containers. The nature of this modular design and its alignment with current security standards enable efficient resource management while ensuring compatibility with both autonomous and non-autonomous vehicles. OLIVE’s adaptability positions it as a versatile solution for various applications, including dynamic Electric Vehicle (EV) charging and zero-trust architectures.OLIVE: Adaptive Containerized Architecture for Multi-Factor Authentication in V2XChiara Bodei, University of Pisa; Marco De Vincenzi, IIT CNR; Ilaria Matteucci, IIT-CNR

UAV Trajectory Optimization for Radio Map Updating: A Transformer-Based DRL Approach

The deployment of unmanned aerial vehicles (UAVs) to assist in measurement collection for radio map construction has significant potential. In this work, we investigate the UAV-assisted radio map updating system, where the UAV has to collect informative measurements to improve radio map accuracy and reach the destination within the constraint of limited onboard energy. We apply Ordinary Kriging to construct the radio map and use Kriging variance as a metric to evaluate the accuracy of the map. We then formulate a finite-horizon Markov Decision Process (MDP) that optimizes the UAVs trajectory, aiming to maximize the total reduction in Kriging variance under the system’s constraints. The MDP is challenging due to its sparse reward and large, continuous state space. To address this, we propose an AT-DQN algorithm that utilizes reward shaping and combines Agent Transformer (AT) with Dueling DQN for effective trajectory learning. Finally, through numerical experiments, we verify the efficiency of the proposed algorithm in both radio map updating and trajectory optimization.UAV Trajectory Optimization for Radio Map Updating: A Transformer-Based DRL ApproachJiahao Li, Bo Zhou, Xinyi Ma, Qihui Wu, Nanjing University of Aeronautics and Astronautics

Phase-Reconfigured Magnitude-Selective Affine Function-Based Digital Self-Interference Can...

In 5G new radio, the number of band combinations increases and bandwidths are wider to meet a demand for a high data rate. As a result, a structure of a mobile front-end has become complex. Hence, transmitter self-interferences have significantly increased and their models have become more complicated. To efficiently approximate a highly nonlinear interference model, a phase-reconfigured magnitude-selective affine (PR-MSA) function is proposed. The PR-MSA model is a modified magnitude-selective affine model to resolve a phase transition of input signals caused by an intermodulation at interference frequencies. In addition, a digital self-interference cancellation (DSC) system using PR-MSA model is proposed to verify a modeling capability. Experimental measurements have shown that a normalized mean square error of PR-MSA model is improved up to 1.2dB for the highly nonlinear interference with lower complexity compared to a memory polynomial-based model. And the proposed PR-MSA function-based DSC has demonstrated an improvement of 0.25dB in signal-to-noise at uncoded bit-error-rate.Phase-Reconfigured Magnitude-Selective Affine Function-Based Digital Self-Interference Cancellation SystemDaeyoung Kim, Samsung electronics; Hyunseok Yu, Joohyun Do, Hui Won Je, Jungwon Lee, Samsung Electronics

Mismatched Processing of Composite Waveforms with Small Neural Networks

Linear Frequency-Modulated (LFM) waveforms are commonly utilized in radar systems, however, they are also vulnerable to replication by modern jammers. Composite waveforms, such as a primary (LFM) waveform with a secondary noise component, offer the prospect of designing low-probability-of-intercept systems but can also lead to elevated sidelobe levels and lower Doppler tolerance. In this paper, we explore receiver processing of composite waveforms with a trained neural network (NN) as an alternative to the traditional matched-filter. It is shown that fully connected feedforwarding NNs can be trained to extract the primary waveform’s characteristics while minimizing the side effects caused by the secondary waveform. The development of NN structures to create novel types of mismatched filters is thus demonstrated. Through simulations, including constant false alarm rate (CFAR) tests, we substantiate the capabilities attainable through neural network-based mismatched filtering.Mismatched Processing of Composite Waveforms with Small Neural NetworksJabran Akhtar, Norwegian Defence Research (FFI)

Max-Min Fairness in Intelligent Reflecting Surface-aided Multi-Operator Networks with Coop...

This study investigates a scenario where multiple mobile network operators (MNOs) share an intelligent reflecting surface (IRS), a technology that efficiently manipulates electromagnetic waves. Although IRSs are energy-efficient, constraints on their installation lead to competition among MNOs, increasing redundancy and energy consumption. In a multi-MNO environment, achieving fairness and optimizing performance is crucial. To address these challenges, this study proposes a cooperative passive beamforming strategy for maximizing the minimum achievable rate among users of different MNOs. An algorithm based on the projected gradient ascent (PGA) method is introduced to resolve the inherent complexity of this max-min fairness problem. Numerical evaluations demonstrate that the proposed IRS-sharing approach outperforms traditional single MNO-specific and sequential IRS control schemes. These findings highlight the potential benefits of IRS sharing, underscoring its practical value in enhancing network efficiency and fairness in multi-operator scenarios.Max-Min Fairness in Intelligent Reflecting Surface-aided Multi-Operator Networks with Cooperative Passive BeamformingHiroaki Hashida, Yuichi Kawamoto, Nei Kato, Tohoku University

Map-Fusion based Multi-Target Tracking in Cooperative ISAC Networks

The Integrated Sensing and Communication (ISAC) technique combines communication and environmental sensing in future wireless networks. Since multi-static sensing advantages in spatial diversity, cooperative ISAC networks have garnered extensive attention and investigation, but face challenges such as separation, extraction, and cooperative fusion of multi-target sensing information. To address these challenges, a code division approach is proposed to differentiate targets and signal source base stations. Furthermore, a map-fusion based cooperating localization approach integrating Direction of Arrival (DOA) estimation is introduced. Finally, numerical simulations validate the effectiveness of the proposed approaches. Code division reduces the Root Mean Squared error (RMSE) for angle, range, and velocity estimation by 86.4%, 91.7%, and 52.4% compared to the baseline approach. Additionally, the map-fusion based approach reduces localization RMSE by 83.4% to 95.1% compared to the existing localization approaches.Map-Fusion based Multi-Target Tracking in Cooperative ISAC NetworksZihe Wang, Ning Jiang, Shi Yan, Beijing University of Posts and Telecommunications

Empirical Analysis of Statistical Variation in Channel Data of WiGig Networks towards 6G

Emerging wireless local area networks, such as WiGig that operate in the extremely high-frequency band (60 GHz) hold significant potential for the development of next-generation 6G networks by offering high throughput and low latency. However, the 60 GHz band is prone to severe signal degradation due to channel blockages, leading to frequent handovers and challenges in maintaining seamless connectivity. Reactive handover strategies can result in service delays due to overhead and decision-making latency. To tackle these issues, proactive approaches that utilize machine learning (ML) and deep learning (DL) are becoming increasingly popular for network optimization in WiGig networks. However, existing ML/DL models are often tailored to specific network environments, making them susceptible to concept drift — a phenomenon where even minor environmental changes can significantly degrade network performance due to incorrect decision-making. This paper investigates scenarios and environmental changes that can trigger concept drift in WiGig networks. We conduct real-world experiments to analyze the statistical behavior of received signal strength, highlighting the potential for concept drift. Based on our findings, we propose a direction for identifying concept drift in WiGig networks.Empirical Analysis of Statistical Variation in Channel Data of WiGig Networks towards 6GShikhar, Tohoku University; Tiago Koketsu Rodrigues, Graduate School of Information Sciences, Tohoku University; Nei Kato, Tohoku University; Mostafa M. Fouda, Idaho State University; Muhammad Ismail, TnTech, USA

Complexity/Performance trade-off of Kalman Smoothing for Iterative Equalization based on E...

Kalman smoothers have been successfully used in many digital communication receiver algorithms, including channel equalization. In this paper, we propose low complexity Kalman equalizers based on expectation propagation (EP), and we compare them with state-of-the-art EP-based Kalman equalizers to identify the structure with the most attractive complexity/performance trade-off. In addition, we provide a practical approach for further reducing the computational complexity of Kalman equalizers in time-varying channels. Our analysis show that the Kalman equalizer we derived based on the backward information forward marginals (BIFM) structure is the most efficient in terms of complexity/performance trade-off and numerical results attest its superiority in high-speed vehicle cellular communication scenarios above 50 km/h.Complexity/Performance trade-off of Kalman Smoothing for Iterative Equalization based on EPLaëtitia JADOT, University of Bordeaux; Serdar SAHIN, Thales; Romain TAJAN, IMS Laboratory; Guillaume Ferré, University of Bordeaux; Pascal CHEVALIER, CNAM

Communication Range Expansion via Superimposed Iterative and Parallel Delay Transmission ...

This study aims to enhance flexibility in the communication range of rolling shutter based visible light communication (RS-VLC). One of the main challenges in RS-VLC is the trade-off between the data rate and communication distance. To address this issue, we proposed a method that integrates superimposed iterative transmission with parallel delay transmission. Superimposed iterative transmission overlays two datasets, one optimized for short-range communication and the other for long-range. This strategy enhances communication over longer distances without significantly reducing the data rate at shorter ranges. Meanwhile, the parallel delay transmission sends signals with varying delays simultaneously across multiple LEDs, facilitating efficient transmission. By combining these two techniques, we aim to extend the communication range. Experimental results illustrate that the proposed approach enables data reception at long distances (up to 270 cm) while significantly reducing data rate degradation at shorter distances. This effectively mitigates the trade-off between data rate and communication range. Additionally, over communication distances of 50 to 290 cm, the average throughput of our method improved by approximately 73 % compared to conventional methods.Communication Range Expansion via Superimposed
Iterative and Parallel Delay Transmission in Rolling
Shutter Based Visible Light CommunicationHikari Koga, Masayuki Kinoshita, Koji Kamakura, Chiba Institute of Technology; Takaya Yamazato, Nagoya University

Carrier Synchronization in SC-FDMA System for Satellite Uplink

Single carrier frequency division multiple access (SC-FDMA) is a multicarrier modulation technique known for its low peak-to-average ratio (PAPR) and high spectral efficiency. However, SC-FDMA is more sensitive to carrier frequency offset than single carrier systems. In this paper, a method is proposed to analyse the frequency offset tolerance of SC-FDMA systems under different constellation modulations, and to use it as a theoretical guide for selecting the frequency offset estimation method. An none-data-aided (NDA) method is proposed and the estimation performance of data-aided (DA) and NDA methods are compared. It is found that the proposed carrier frequency offset synchronization method can satisfy the packet error rate (PER) performance with a difference of about 0.2 dB from the ideal case.Carrier Synchronization in SC-FDMA System for Satellite UplinkKe Zhou, Jie Wang, Chen Ming, Nengtang Hua, Huilin Song, Southeast University