arXiv subjects

Yameng Liu

Publications and source records attributed to Yameng Liu.

12 recordsLinked to original sources

arXiv2025

A Comprehensive Survey of 3GPP Release 19 ISAC Channel Modeling: From Empirical Features to Unified Methodology and Standardized Simulator

Integrated Sensing and Communication (ISAC) has been identified as a key 6G application by ITU and 3GPP. Channel measurement and modeling is a prerequisite for ISAC system design and has attracted widespread attention from both academia and industry. 3GPP Release 19 initiated the ISAC channel study item in December 2023 and finalized its modeling specification in May 2025 after extensive technical discussions. However, a comprehensive survey that provides a systematic overview,from empirical channel features to modeling methodologies and standardized simulators,remains unavailable. In this paper, the key requirements and challenges in ISAC channel research are first analyzed, followed by a structured overview of the standardization workflow throughout the 3GPP Release 19 process. Then, critical aspects of ISAC channels, including physical objects, target channels, and background channels, are examined in depth, together with additional features such as spatial consistency, environment objects, Doppler characteristics, and shared clusters, supported by measurement-based analysis. To establish a unified ISAC channel modeling framework, an Extended Geometry-based Stochastic Model (E-GBSM) is proposed, incorporating all the aforementioned ISAC channel characteristics. Finally, a standardized simulator is developed based on E-GBSM, and a two-phase calibration procedure aligned with 3GPP Release 19 is conducted to validate both the model and the simulator, demonstrating close agreement with industrial reference results. Overall, this paper provides a systematic survey of 3GPP Release 19 ISAC channel standardization and offers insights into best practices for new feature characterization, unified modeling methodology, and standardized simulator implementation, which can effectively supporting ISAC technology evaluation and future 6G standardization.

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arXiv2025

A Novel Multi-Reference-Point Modeling Framework for Monostatic Background Channel: Toward 3GPP ISAC Standardization

Integrated Sensing and Communication (ISAC) has been identified as a key 6G application by ITU and 3GPP. A realistic, standard-compatible channel model is essential for ISAC system design. To characterize the impact of Sensing Targets (STs), 3GPP defines ISAC channel as a combination of target and background channels, comprising multipath components related to STs and those originating solely from the environment, respectively. Although the background channel does not carry direct ST information, its accurate modeling is critical for evaluating sensing performance, especially in complex environments. Existing communication standards characterize propagation between separated transmitter (Tx) and receiver (Rx). However, modeling background channels in the ISAC monostatic mode, where the Tx and Rx are co-located, remains a pressing challenge. In this paper, we firstly conduct ISAC monostatic background channel measurements for an indoor scenario at 28 GHz. Realistic channel parameters are extracted, revealing pronounced single-hop propagation and discrete multipath distribution. Inspired by these properties, a novel stochastic model is proposed to characterizing the ISAC monostatic background channel as the superposition of sub-channels between the monostatic Tx&Rx and multiple communication Rx-like Reference Points (RPs). This model is compatible with standardizations, and a 3GPP-extended implementation framework is introduced. Finally, a genetic algorithm-based method is proposed to extract the optimal number and placement of multi-RPs. The optimization approach and modeling framework are validated by comparing measured and simulated channel parameters. Results demonstrate that the proposed model effectively captures monostatic background channel characteristics, addresses a critical gap in ISAC channel modeling, and supports 6G standardization.

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arXiv2025

The Coupling Effect of Sensing Targets on the Environment for 3GPP ISAC Channels: Observation, Modeling, and Validation

Integrated Sensing And Communication (ISAC) has been identified as a key 6G application by ITU and 3GPP, with standardization efforts already underway. Sensing tasks, such as target localization, demand more precise characterization of the sensing target (ST) in ISAC channel modeling. The ST couples complexly with environmental scatterers, potentially blocking some multipaths and generating new ones, resulting in power variations compared to the original channel. To accurately model this effect, this paper proposes a coupled ISAC channel model based on measurements and validates it through similarity analysis between simulated and measured channels. In this work, we first conduct ISAC channel measurements in an indoor factory scenario at 105 GHz, where the multipath power variations caused by the ST's interaction with the environment are clearly observed. Then, we propose an ISAC channel modeling framework that incorporates two novel parameters: the Blockage-Region Coupling Factor (BR-CF) and the Forward-Scattering (FS)-CF, which characterize the spatial region and intensity of the coupling effect, respectively. Finally, the proposed model is validated through similarity comparison with measured data, demonstrating higher accuracy for both LoS and NLoS scenarios compared to the non-coupled model. This realistic ISAC channel model provides an effective framework for capturing the ST-environment coupling effect, supporting the design and evaluation of ISAC technologies.

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arXiv2025

Research and Experimental Validation for 3GPP ISAC Channel Modeling Standardization

Integrated Sensing and Communication (ISAC) is considered a key technology in 6G networks. An accurate sensing channel model is crucial for the design and sensing performance evaluation of ISAC systems. The widely used Geometry-Based Stochastic Model (GBSM), typically applied in standardized channel modeling, mainly focuses on the statistical fading characteristics of the channel. However, it fails to capture the characteristics of targets in ISAC systems, such as their positions and velocities, as well as the impact of the targets on the background. To address this issue, this paper proposes an extended GBSM (E-GBSM) sensing channel model that incorporates newly discovered channel characteristics into a unified modeling framework. In this framework, the sensing channel is divided into target and background channels. For the target channel, the model introduces a concatenated modeling approach, while for the background channel, a parameter called the power control factor is introduced to assess impact of the target on the background channel, making the modeling framework applicable to both mono-static and bi-static sensing modes. To validate the proposed model's effectiveness, measurements of target and background channels are conducted in both indoor and outdoor scenarios, covering various sensing targets such as metal plates, reconfigurable intelligent surfaces, human bodies, UAVs, and vehicles. The experimental results provide important theoretical support and empirical data for the standardization of ISAC channel modeling.

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arXiv2025

A Novel Environment Object Modeling Method for Vehicular ISAC Scenarios

Integrated Sensing and Communication (ISAC), as a fundamental technology of 6G, empowers Vehicle-to-Everything (V2X) systems with enhanced sensing capabilities. One of its promising applications is the reliance on constructed maps for vehicle positioning. Traditional positioning methods primarily rely on Line-of-Sight (LOS), but in urban vehicular scenarios, obstructions often result in predominantly Non-Line-of-Sight (NLOS) conditions. Existing research indicates that NLOS paths, characterized by one-bounce reflection on building walls with determined delay and angle, can support sensing and positioning. However, experimental validation remains insufficient. To address this gap, channel measurements are conducted in an urban street to explore the existence of strong reflected paths in the presence of a vehicle target. The results show significant power contribution from NLOS paths, with large Environmental Objects (EOs) playing a key role in shaping NLOS propagation. Then, a novel model for EO reflection is proposed to extend the Geometry-Based Stochastic Model (GBSM) for ISAC channel standardization. Simulation results validate the model's ability to capture EO's power and position characteristics, showing that higher EO-reflected power and closer distance to Rx reduce Delay Spread (DS), which is more favorable for positioning. This model provides theoretical guidance and empirical support for ISAC positioning algorithms and system design in vehicular scenarios.

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arXiv2024

BUPTCMCC-6G-CMG+: A GBSM-Based ISAC Standard Channel Model Generator

Integrated sensing and communication (ISAC) has been recognized as the key technology in the vision of the sixth generation (6G) era. With the emergence of new concepts in mobile communications, the channel model is the prerequisite for system design and performance evaluation. Currently, 3GPP Release 19 is advancing the standardization of ISAC channel models. Nevertheless, a unified modeling framework has yet to be established. This paper provides a simulation diagram of ISAC channel modeling extended based on the Geometry-Based Stochastic Model (GBSM), compatible with existing 5G channel models and the latest progress in the 3rd Generation Partnership Project (3GPP) standardization. We first introduce the progress of the ISAC channel model standardization in general. Then, a concatenated channel modeling approach is presented considering the team's standardization proposals, which is implemented on the BUPTCMCC-6G-CMG+ channel model generator. We validated the model in cumulative probability density function (CDF) in statistical extension of angle and delay, and radar cross section (RCS). Simulation results show that the proposed model can realistically characterize the feature of channel concatenation and RCS within the ISAC channel.

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arXiv2023

How to Extend 3D GBSM to Integrated Sensing and Communication Channel with Sharing Feature?

Integrated Sensing and Communication (ISAC) is a promising technology in 6G systems. The existing 3D Geometry-Based Stochastic Model (GBSM), as standardized for 5G systems, addresses solely communication channels and lacks consideration of the integration with sensing channel. Therefore, this letter extends 3D GBSM to support ISAC research, with a particular focus on capturing the sharing feature of both channels, including shared scatterers, clusters, paths, and similar propagation param-eters, which have been experimentally verified in the literature. The proposed approach can be summarized as follows: Firstly, an ISAC channel model is proposed, where shared and non-shared components are superimposed for both communication and sensing. Secondly, sensing channel is characterized as a cascade of TX-target, radar cross section, and target-RX, with the introduction of a novel parameter S for shared target extraction. Finally, an ISAC channel implementation framework is proposed, allowing flexible configuration of sharing feature and the joint generation of communication and sensing channels. The proposed ISAC channel model can be compatible with the 3GPP standards and offers promising support for ISAC technology evaluation.

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arXiv2023

6G Channel Modeling: Requirement, Measurement, Methodology and Simulator

Sixth-generation (6G) mobile communications have attracted substantial attention in the global research community of information and communication technologies (ICTs). 6G systems are expected to support not only extended 5G usage scenarios but also new usage scenarios, such as integrated sensing and communication (ISAC), integrated artificial intelligence (AI) and communication, and communication and ubiquitous connectivity. To achieve this goal, channel characteristics must be comprehensively studied and properly exploited to promote the design, standardization, and optimization of 6G systems. In this paper, we first summarize the requirements and challenges in 6G channel research. Our focus is on channels for six promising technologies enabling 6G, including ISAC, extremely large-scale MIMO (XL-MIMO), mid-band and terahertz (THz) technologies, reconfigurable intelligent surfaces (RISs), and space-air-ground integrated networks (SAGINs). A survey of the progress in 6G channel research regarding the above six promising technologies is presented in terms of the latest measurement campaigns, new characteristics, modeling methods, and research prospects. To support testing, optimization and evaluation, existing 6G channel simulators are summarized. Then, BUPTCMCCCMG-IMT2030 is introduced as an example of a simulator that was developed on the basis of the ITU/3GPP 3D geometry-based stochastic model (GBSM) methodology. We also address open issues covering standardization activities, AI-enabled methods, and system performance analysis in the context of 6G channel research. This paper offers in-depth, hands-on insights into the best practices of channel measurements, modeling, and simulations for the evaluation of 6G technologies, the development of 6G standards, and the implementation and optimization of 6G systems.

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arXiv2022

A Shared Cluster-based Stochastic Channel Model for Integrated Sensing and Communication Systems

Integrated Sensing And Communication (ISAC) has been recognized as a promising technology in the 6G communication. A realistic channel model is a prerequisite for designing ISAC systems. Most existing channel models independently generate the communication and sensing channels under the same framework. However, due to the multiplexing of hardware resources and the same environment, signals enabled for communication and sensing may experience shared propagation scatterers. This practical sharing feature necessities the joint generation of communication and sensing channels for realistic modeling, where the shared clusters (contributed by the shared scatterers) should be reconstructed.In this paper, we first conduct communication and sensing channel measurements for an indoor scenario at 28 GHz. The power-angular-delay profiles of multipath components are obtained, and the shared scatterers by communication and sensing channels are intuitively observed. Then, a stochastic ISAC channel model is proposed to capture the sharing feature, where shared and non-shared clusters by the two channels are dfined and superimposed. To extract those clusters from measured ISAC channels, a KPowerMeans-based joint clustering algorithm is novelly introduced. Finally, stochastic channel characteristics are analyzed, and empirical simulations validate that the channel Sharing Degree (SD) increases with more shared clusters. The proposed model can realistically capture the sharing feature of ISAC channels and is able to evaluate and simulate the channel SD values, which is valuable for the design and deployment of ISAC systems.

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arXiv2021

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference

A classical problem in causal inference is that of matching, where treatment units need to be matched to control units based on covariate information. In this work, we propose a method that computes high quality almost-exact matches for high-dimensional categorical datasets. This method, called FLAME (Fast Large-scale Almost Matching Exactly), learns a distance metric for matching using a hold-out training data set. In order to perform matching efficiently for large datasets, FLAME leverages techniques that are natural for query processing in the area of database management, and two implementations of FLAME are provided: the first uses SQL queries and the second uses bit-vector techniques. The algorithm starts by constructing matches of the highest quality (exact matches on all covariates), and successively eliminates variables in order to match exactly on as many variables as possible, while still maintaining interpretable high-quality matches and balance between treatment and control groups. We leverage these high quality matches to estimate conditional average treatment effects (CATEs). Our experiments show that FLAME scales to huge datasets with millions of observations where existing state-of-the-art methods fail, and that it achieves significantly better performance than other matching methods.

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arXiv2019

Interpretable Almost-Matching-Exactly With Instrumental Variables

Uncertainty in the estimation of the causal effect in observational studies is often due to unmeasured confounding, i.e., the presence of unobserved covariates linking treatments and outcomes. Instrumental Variables (IV) are commonly used to reduce the effects of unmeasured confounding. Existing methods for IV estimation either require strong parametric assumptions, use arbitrary distance metrics, or do not scale well to large datasets. We propose a matching framework for IV in the presence of observed categorical confounders that addresses these weaknesses. Our method first matches units exactly, and then consecutively drops variables to approximately match the remaining units on as many variables as possible. We show that our algorithm constructs better matches than other existing methods on simulated datasets, and we produce interesting results in an application to political canvassing.

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arXiv2019

Interpretable Almost Matching Exactly for Causal Inference

We aim to create the highest possible quality of treatment-control matches for categorical data in the potential outcomes framework. Matching methods are heavily used in the social sciences due to their interpretability, but most matching methods do not pass basic sanity checks: they fail when irrelevant variables are introduced, and tend to be either computationally slow or produce low-quality matches. The method proposed in this work aims to match units on a weighted Hamming distance, taking into account the relative importance of the covariates; the algorithm aims to match units on as many relevant variables as possible. To do this, the algorithm creates a hierarchy of covariate combinations on which to match (similar to downward closure), in the process solving an optimization problem for each unit in order to construct the optimal matches. The algorithm uses a single dynamic program to solve all of the optimization problems simultaneously. Notable advantages of our method over existing matching procedures are its high-quality matches, versatility in handling different data distributions that may have irrelevant variables, and ability to handle missing data by matching on as many available covariates as possible.

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