VTC 2025 Spring Conference’s Shorts

Analysis of PAPR-Aware OFDM Waveforms for Integrated Sensing and Communication

This paper addresses the peak-to-average power ratio (PAPR) reduction of Orthogonal Frequency Division Multiplexing (OFDM) waveforms under unit modulus constraint for Integrated Sensing and Communication systems (ISAC). We conduct a comparative analysis of radar and communication performance for waveforms optimized using classical PAPR reduction techniques, primarily Tone Reservation (TR) and Selected Mapping (SLM), as well as their state-of-the-art sensing-aware variants, across radar- and communication-centric scenarios. Additionally, we investigate the pilot-aided case with pseudorandom pilot tones. The results reveal a trade-off between PAPR reduction, radar sensing capability, and computational cost, offering practical insights for efficient OFDM-based ISAC system design.Analysis of PAPR-Aware OFDM Waveforms for Integrated Sensing and CommunicationEya Gourar, Yahia Medjahdi, IMT Nord Europe; Laurent Clavier, Institut Mines-Telecom Telecom Lille; Abdul karim Gizzini, SogetiLabs Research and Innovation (part of Capgemini)

AI-Assisted NLOS Sensing for RIS-Based Indoor Localization in Smart Factories

In the era of Industry 4.0, precise indoor localization is vital for automation and efficiency in smart factories. Reconfigurable Intelligent Surfaces (RIS) are emerging as key enablers in 6G networks for joint sensing and communication. However, RIS faces significant challenges in Non-Line-of-Sight (NLOS) and multipath propagation, particularly in localization scenarios, where detecting NLOS conditions is crucial for ensuring not only reliable results and increased connectivity but also smart factory personnel’s safety. This study introduces an AI-assisted framework employing a Convolutional Neural Network (CNN) customized for accurate Line-of-Sight (LOS) and NLOS classification to enhance RIS-based localization using measured, synthetic, mixed-measured, and mixed-synthetic experimental data, that is, original, augmented, slightly noisy, and highly noisy data, respectively. Validated through such data from three different environments, the proposed customized-CNN (cCNN) model achieves 95.0%-99.0% accuracy, outperforming standard pre-trained models like Visual Geometry Group 16 (VGG-16) with an accuracy of 85.5%-88.0%. By addressing RIS limitations in NLOS scenarios, this framework offers scalable and high-precision localization solutions for 6G-enabled smart factories.AI-Assisted NLOS Sensing for RIS-Based Indoor Localization in Smart FactoriesTaofeek Yusuf, NEC Laboratories Europe in Germany, Aalborg University of Denmark; Sigurd S. Petersen, Aalborg University; Puchu Li, Aalborg University of Denmark; Jian Ren, Xidian University; Placido Mursia, NEC; Vincenzo Sciancalepore, NEC Laboratories Europe GmbH; Xavier Costa Pérez, NEC Laboratories Europe, i2cat Foundation and ICREA; Gilberto Berardinelli, Ming Shen, Aalborg University

ST-SAM: A Lightweight Satellite-Tailored Service-Adaptive Multicast TE Protocol

Multicast traffic engineering (TE) over Segment Routing (SR) is crucial for large-scale Low Earth Orbit (LEO) satellite networks. However, introducing multicast Source Routing TE to the LEO satellite network faces challenges due to constrained computational resources and the need for service adaptability. To address these issues, we propose the Satellite-Tailored Service-Adaptive Multicast TE protocol, abbreviated as ST-SAM, which emphasizes efficient TE through a lightweight design and adaptability. ST-SAM introduces a protocol that partitions the network into two domains to provide depth and breadth control of the multicast service tree. Furthermore, it incorporates a dynamic domain boundary adjustment mechanism that optimizes domain placement based on satellite node density and multicast service characteristics. Simulations show that our proposed ST-SAM effectively reduces network overhead by up to 53.68% compared to existing solutions and achieves optimal lightweight performance under over 75% of evaluated conditions, including various network scales and multicast service demands.ST-SAM: A Lightweight Satellite-Tailored Service-Adaptive Multicast TE ProtocolYuxin Jiang, Beijing University of Posts and Telecommunications; Weihong Wu, University of Electronic Science and Technology; Yutong Zhao, Yue Liu, Beijing University of Posts and Telecommunications; Ying Wang, Jiang Liu, Beijing University of Posts and telecommunications

Inter-cell Interference Mitigation for Time of Arrival Estimation in LTE Positioning

In this paper, the effect of inter-cell interference to the correlation of Long Term Evolution positioning reference signals’ for time of arrival estimation is analyzed. First, the expectation of inter-cell interference power in ToA estimation is derived. After that, a simple scheme to remove inter-cell interference is proposed by sacrificing signal power and provide a metric when the proposed scheme outperforms conventional correlation based scheme in terms of inter-cell interference power. The simulation results show that the proposed scheme outperforms the conventional correlation based scheme in most of scenarios.Inter-cell Interference Mitigation for Time of Arrival Estimation in LTE PositioningJungho So, Yejin Lee, Joonsung Kim, Jin Ho Kim, Hui Won Je, Jungwon Lee, Samsung Electronics

High-Precision Positioning Based on Carrier Phase and Unscented Kalman Filter in the Prese...

In scenarios where high-precision localization is required, even tiny calibration errors of the base station (BS) can directly and significantly impact the positioning accuracy of the device. Inspired by the excellent precision of carrier phase positioning, this paper proposes a high-precision positioning algorithm based on carrier phase and unscented Kalman filter (UKF) to address BS calibration errors. The state vector of the positioning system is initialized with the initial coordinates of the terminal, which is estimated via Chan’s algorithm. The coordinates of the BSs and the terminal are then iteratively refined by combining the double-differential carrier phase and time difference of arrival (TDoA) measurements of multiple moments. Numerical simulations demonstrate that the proposed algorithm achieves centimeter-level positioning accuracy even in the presence of significant BS calibration errors, which effectively overcomes the impact of BS calibration errors on positioning performance.High-Precision Positioning Based on Carrier Phase and Unscented Kalman Filter in the Presence of Base Station Calibration ErrorsShuran Huang, Shaoshuai Fan, Tian Hui, Weimeng Jiao, Boyang Hu, Beijing University of Posts and Telecommunications

Constellation Design of LEO Satellite Internet of Things with QoS Provision

Low earth orbit (LEO) satellite Internet of Things (IoT) has been recognized as a pivotal element within the realm of sixth-generation (6G) non-terrestrial networks (NTN), aimed at delivering ubiquitous connectivity. Due to the low orbit altitude and fast movement speed, a massive number of satellites are needed to form a satellite constellation, resulting in substantial construction costs. To this end, this paper proposes a LEO satellite IoT constellation design algorithm with the goal of minimizing the total cost while satisfying quality of service (QoS) requirements in terms of coverage ratio and communication quality. Simulation results validate the efficiency of the proposed algorithm in LEO satellite IoT constellation.Constellation Design of LEO Satellite Internet of Things with QoS ProvisionMing Ying, Xiaoming Chen, Zhejiang University; Qiao Qi, Hangzhou Normal University; Zhaoyang Zhang, Zhejiang University

Advanced Positioning in 5G and Beyond: Leveraging Deep Learning Techniques

Recent advancements in machine learning have improved device positioning in challenging environments where traditional methods often fall short. This work introduces a deep learning-based approach to predict the location of user equipment in a wireless radio network. The model leverages data samples derived from channel impulse responses as input features. Additionally, the proposed sample-based method is compared to conventional path-based data estimated through a channel estimator. Performance evaluations are conducted under different conditions to demonstrate the impact of input types, network densification, and available bandwidth in an indoor factory scenario.Advanced Positioning in 5G and Beyond: Leveraging Deep Learning TechniquesYuxin Zhao, Ericsson AB; Jung-Fu (Thomas) Cheng, Ericsson Research; Atieh Rajabi Khamesi, Ericsson

Range Estimation Under Low Sampling Frequency with FDA-PA System

Target localization holds a key position in autonomous driving and intelligent transportation systems, and has drawn increasing attention in recent years with the widespread use of millimeter-wave radar. To achieve large-scale, high-precision, unambiguous parameter estimation, the combination of frequency diverse arrays (FDA) and phased arrays (PA) has been proposed as FDA-PA radar. In this paper, we introduce a novel target range estimation method for the FDA-PA radar, where an initial coarse estimate is obtained from the FDA and refined by the PA. The method fully exploits the beamforming gains of the PA and the extended unambiguous range of the FDA, enabling accurate, unambiguous range estimation even at extended distances. Additionally, by mitigating range ambiguity with the FDA, the system allows the use of a low-sampling-rate ADC for sampling de-chirped signals. We derive the Cramér-Rao Bound (CRB) for range estimation errors to identify the conditions necessary for effective unambiguous estimation. Simulation results validate the superiority of the proposed method and outline the key system constraints.Range Estimation Under Low Sampling Frequency with FDA-PA SystemMengjiang Sun, Peng Chen, Southeast University; Yingfe Rong, State Grid Naniing Power Supply Company; Zhimin Chen, Shanghai Dianji University; Zhenxin Cao, Southeast University

MIMO-OFDM ISAC with Spatial Multiplexing

For orthogonal frequency division multiplexing (OFDM) integrated sensing and communication (ISAC), in order to perform efficient delay and Doppler estimation, the equivalent data symbols on the time-frequency resource element need to be removed, so that the resulting signal obeys the manifold structure with phases linearly increasing across subcarriers/OFDM symbols. However, this task becomes more challenging for multiple-input and multiple-output (MIMO)-OFDM ISAC with spatial multiplexing, since the received equivalent data symbols in time-frequency resource element not only depend on the transmitted information-bearing symbols, but also depend on the transmit steering vectors and the precoding matrix. To address such challenges, this paper proposes an effective sensing signal processing method for MIMO-OFDM ISAC with spatial multiplexing. Specifically, we first estimate the angle of arrival, followed by channel matrix decomposition to obtain transmit steering vector, then reconstruct the equivalent symbol. After removing the equivalent symbol from the received signal, the classical periodogram algorithm can be used to estimate the range and Doppler of targets. Simulation results show that the proposed method achieves effective parameter estimation for MIMO-OFDM ISAC with spatial multiplexing.MIMO-OFDM ISAC with Spatial MultiplexingHaoyu Jiang, xiaoli Xu, Yong Zeng, Southeast University

Aggregation and Multicast Coded Repair Technique for LEO Cloud Storage Constellation

LEO cloud storage constellation (LCSC) has gained significant popularity thanks to its on-board data storage capability and extensive inter-satellite connectivity. Unfortunately, the satellite disks will fail occasionally due to cosmic radiation and energy depletion, which leads to data loss and network unavailable. However, multi-node repair in the LCSC leads to the larger repair delay and the higher energy cost. To address this, we introduced the aggregation and multicast coded repair (AMCR) to fast recover the stored data using Reed-Solomon (RS) codes. We first propose the multi-weight multi-node repair tree (MWNRT) model, i.e., a staged tree graph containing edge weights, to measure the multiple factors affecting repair performance. Next, we analyze the repair delay and the energy cost associated with multi-node repair leveraging AMCR. Then, to minimize repair delay while reducing energy cost, the aggregation-based multiple single-node repair trees construction (A-MSRT) algorithm is designed to construct multiple single-node repair trees based on the shortest-path principles. While the multicast-based aggregation of multiple repair trees (M-AMRT) algorithm is designed to select the repair tree with the longest delay from the output of A-MSRT as the initial repair tree, then adds the remaining replacement nodes. And the complexity of the two algorithms is elaborated and proven to be reasonable. Simulations show that AMCR scheme outperforms other schemes under different network conditions in LCSC.Aggregation and Multicast Coded Repair Technique for LEO Cloud Storage ConstellationGuixiang Lei, Shushi Gu, Harbin Institute of Technology (Shenzhen); zhikai zhang, Pengcheng Laboratory; Wenjing Mou, Harbin Institute of Technology, Shenzhen; Zhang Qinyu, Harbin Institute of Tech.; Wei Xiang, La Trobe University