公开(公告)号：US20250080259A1

公开(公告)日：2025-03-06

申请号：US18815239

申请日：2024-08-26

Applicant: Southeast University ,  PURPLE MOUNTAIN LABORATORIES

Inventor： Chengxiang WANG ,  Duoxian HUANG ,  Lijian XIN ,  Jie HUANG

IPC: H04B17/391 ,  H04B7/0426 ,  H04L25/02

Abstract: A method for designing a time-domain non-stationary V2V MIMO communication channel emulator includes determining basic parameters for the V2V MIMO communication channel; generating a V2V 2D time-domain non-stationary communication channel environment, by using a MATLAB, that is, the numbers of the scatterers and the positions of the scatterers and the like; importing parameters generated in the previous step into a hardware simulation platform to calculate communication channel parameters for clusters, such as an angle distribution and a power distribution, writing a Verilog code for running, and eventually calculating to obtain a channel impulse response of the time-domain non-stationary V2V MIMO communication channel; and comparing with a statistical characteristic of a theoretical communication channel model, and designing an appropriate hardware diagram of a communication channel emulator. The method supports the simulation of time-domain non-stationary V2V MIMO communication channel, filling the gap in the field of communication channel emulators.

公开(公告)号：US20250088296A1

公开(公告)日：2025-03-13

申请号：US18816739

申请日：2024-08-27

Applicant: Southeast University ,  PURPLE MOUNTAIN LABORATORIES

Inventor： Chengxiang WANG ,  Zixin LI ,  Hengtai CHANG ,  Jie HUANG

IPC: H04B17/391 ,  H04B17/309

Abstract: A satellite communication-oriented geometry-based stochastic channel modeling method includes steps: S1: establishing a satellite channel simulation scenario, and setting scenario layout parameters; S2: initializing the trajectory and speed of a satellite and a receiving end; S3: calculating spatially consistent large-scale parameters, and calculating an effect of rainfall on the large-scale parameters; S4: calculating a path loss, a shadow fading, an atmospheric absorption, and a rainfall attenuation; S5: initializing the central positions of a cluster and a scatterer, and calculating the delay, angle and power of the cluster according to the geometric position information of the transmitting and receiving ends and the scatterer, to generate a channel coefficient; S6: updating the large-scale and small-scale parameters according to the movement of the transmitting and receiving ends and the birth-death process of the cluster, to generate a new channel coefficient; and S7: deriving the statistical characteristics of the channel, and performing simulation analysis.

公开(公告)号：US20240259121A1

公开(公告)日：2024-08-01

申请号：US18564273

申请日：2023-03-19

Applicant: Southeast University

Inventor： Chengxiang WANG ,  Zheao LI ,  Jie HUANG ,  Wenqi ZHOU ,  Chen HUANG

IPC: H04B17/391 ,  H04W24/06

CPC classification number: H04B17/3913 ,  H04B17/3912 ,  H04W24/06

Abstract: Disclosed in the present disclosure is a predictive channel modeling method based on a generative adversarial network and a long short-term memory artificial neural network, which method effectively achieves a channel prediction function in different frequency bands and scenarios, and generates a large number of channel data sets for simulation experiments. The method comprises: firstly, inputting channel measurement data for existing frequency bands and scenarios for training; then, learning true channel data using a long short-term memory artificial neural network, and acquiring a channel time sequence feature; by means of adversarial learning of a generative adversarial network, greatly eliminating redundant information of the channel data, and on the basis of the measurement data, generating accurate channel data, and acquiring massive channel information; and finally, achieving the balance between a generative model and a discriminative model during the continuous iteration of the generative adversarial network, and then outputting a trained predictive channel model. A statistical channel feature obtained by means of prediction by a model can clearly specify the predictive learning for a channel distribution feature in the present disclosure, and real-time and complex prediction problems in wireless communication can be solved.

公开(公告)号：US20250038868A1

公开(公告)日：2025-01-30

申请号：US18782376

申请日：2024-07-24

Applicant: Southeast University ,  PURPLE MOUNTAIN LABORATORIES

Inventor： Chengxiang WANG ,  Wenxie JI ,  Jie HUANG ,  Yue YANG ,  Chen HUANG

IPC: H04B17/391 ,  H04B7/0426 ,  H04L5/00

Abstract: The present disclosure discloses a beam domain channel modeling method for an orbital angular momentum wireless communication. The method comprises: 1) establishing a geometry-based stochastic model and considering a near-field effect and a mutual coupling; 2) deriving a beam sampling matrix by utilizing a beamforming matrix, and establishing a beam domain channel model under a spatial multiplexing; and 3) implementing a simulation channel model based on a channel transfer function, and deriving and analyzing channel statistical properties. The beam domain channel model for the orbital angular momentum wireless communication established in the present disclosure is an extension of the channel models based on the plane wave, which considers the near-field effect and the mutual coupling, supports the spatial multiplexing, enriches the modeling methods for the orbital angular momentum channel in the non-line-of-sight scenarios, and is reduced in computation complexity compared with the geometry-based stochastic model. The simulation statistical properties have the reference value for the design of the orbital angular momentum wireless communication system.

公开(公告)号：US20240372638A1

公开(公告)日：2024-11-07

申请号：US18681777

申请日：2023-04-07

Applicant: SOUTHEAST UNIVERSITY

Inventor： Chengxiang WANG ,  Runruo YANG ,  Jie HUANG

IPC: H04B17/391 ,  G01S7/00 ,  G01S13/86

Abstract: Disclosed is a novel integrated sensing and communication channel modeling method combining forward scattering and backward scattering. The method includes the following steps: setting application scenarios and antenna parameters; estimating channel state information through a mono-static sensing means, and determining positions of a communication terminal and positions and motion information of scatterers that are backward scattered in environment; dividing non-line-of-sight paths of the communication channel into forward scattering paths and backward scattering paths based on whether the scatterers can be sensed by a sensing channel, generating forward scattering paths by adopting a geometric random modeling method and generating the backward scattering paths by adopting a geometric modeling method based on obtained sensing information parameters; weighted-summing the line-of-sight, the forward scattering paths, and the backward scattering paths according to probabilities to obtain a complete communication channel impulse response. The present disclosure proposes a relatively comprehensive integrated sensing and communication channel modeling method for the first time, and the simulation results of the channel model are in good agreement with measurement data and have high accuracy.

公开(公告)号：US20250080257A1

公开(公告)日：2025-03-06

申请号：US18814842

申请日：2024-08-26

Applicant: Southeast University ,  PURPLE MOUNTAIN LABORATORIES

Inventor： Chengxiang WANG ,  Yuxiao LI ,  Li ZHANG ,  Songjiang YANG ,  Yinghua WANG ,  Jie HUANG

IPC: H04B17/391 ,  H04B7/0452

Abstract: Disclosed by the present disclosure is a geometry-based stochastic channel modeling method for an IIoT channel. The method includes the following steps: S1, setting a propagation scenario, propagation conditions, model parameters, an antenna configuration, and the like, S2, generating large-scale parameters with a spatial consistency; S3, determining a number of initial clusters, a number of specular multipath components generated in each of clusters and a number of dense multipath components generated in each of the clusters, determining a visibility of an array antenna to the clusters, generating an initial delay of the clusters, an angle of the clusters, and a power of the clusters, and generating channel coefficients between each pair of transmitter antennas and receiver antennas; S4, updating the positions of the transmitters and the positions of the receivers as well as values for the large-scale parameters according to the motion trajectories of the transmitters and the motion trajectories of the receivers; S5, applying a birth and death process of the clusters to initialize new clusters and update angles, delays and powers of surviving clusters, and generating the channel coefficients; and S6, returning to Step S4, until traversing motion trajectories of the transmitters and the motion trajectories of the receivers; calculating statistical characteristics of the channel, and verifying channel model according to actual measurement data. For the first time, the present disclosure considers 6G channel modeling requirements and dense multipath characteristics, and are verified through actual measurements, which is of great significance for the standardization of IIoT channel models.

公开(公告)号：US20240340098A1

公开(公告)日：2024-10-10

申请号：US18681770

申请日：2023-04-12

Applicant: Southeast University

Inventor： Chengxiang WANG ,  Yue YANG ,  Yi ZHENG ,  Jie HUANG

IPC: H04B17/391 ,  H04B7/0413

CPC classification number: H04B17/3912 ,  H04B7/0413

Abstract: A method for calculating the spatial non-stationary wireless channel capacity for large-scale antenna array communications, includes the following steps: first, constructing a spatial non-stationary channel model with a large-scale antenna array having the mutual coupling effect; building a channel measurement system for the large-scale antenna array, and obtaining measurement data; next, optimizing parameters of the channel model, and simulating the spatial cross-correlation function, then calculating the spatial stationary interval and calculating the channel capacity within the interval and the total channel capacity; and finally comparing simulation results with measurement results, to verify the correctness of the calculation method. The method for calculating the channel capacity of a spatial non-stationary large-scale antenna array provided in the present invention can be effectively applied to a channel having non-stationary characteristics, thereby solving the limitation of Shannon channel capacity formula calculation.

公开(公告)号：US20250088267A1

公开(公告)日：2025-03-13

申请号：US18813519

申请日：2024-08-23

Applicant: Southeast University ,  PURPLE MOUNTAIN LABORATORIES

Inventor： Chengxiang WANG ,  Xiuming ZHU ,  Jun WANG ,  Rui FENG ,  Jie HUANG

IPC: H04B10/073 ,  H04B10/114 ,  H04B10/50 ,  H04B17/391

Abstract: The present application discloses an indoor optical wireless communications-oriented general geometry-based stochastic channel modeling method, which belongs to the field of wireless communication channel modeling. The method includes: setting scenario layout and frequency band related parameters; generating an object reflection cluster birth-death process matrix and random numbers for controlling a blocking effect and propagation component classification; initializing a scattering cluster and intra-cluster scatterers; updating and calculating model parameters varying with space and time; calculating a light source radiation intensity, the power distributions of object reflection and particle scattering, and an equivalent reflection coefficient; and calculating a subchannel impulse response, and determining whether a propagation component exists, to generate a final channel impulse response. The general geometry-based stochastic channel modeling method for indoor optical wireless communications of the present invention can utilize the common characteristics of the wireless frequency bands of light and the unique characteristics of the frequency bands of infrared light, visible light and ultraviolet light. By setting corresponding parameters, the established model can support different frequency bands to be flexibly applied to the simulation and performance evaluation of 6G indoor optical wireless communication systems.

公开(公告)号：US20250080250A1

公开(公告)日：2025-03-06

申请号：US18816830

申请日：2024-08-27

Applicant: Southeast University ,  PURPLE MOUNTAIN LABORATORIES

Inventor： Chengxiang WANG ,  Yingjie XU ,  Yingzhuo SUN ,  Jialing HUANG ,  Lijian XIN ,  Jie HUANG

IPC: H04B17/309 ,  H04B7/10

Abstract: A method for estimating channel parameters of a reconfigurable intelligent surface based on a spherical wave assumption includes the following steps. In Step 1, a signal transmission model of a RIS-assisted near-field communication is constructed based on the spherical wave assumption; in Step 2, channel measurement data in different RIS transmission modes are obtained; in Step 3, a delay, an angle of arrival, an angle of departure, a Doppler shift and a polarization matrix of multipath in channels are estimated based on a space-alternating generalized expectation maximization algorithm, and angle parameters, distance parameters and coupling polarization matrices of the multipath at a RIS end are estimated based on a maximum likelihood principle; and in Step 4, the estimated parameters are updated and iterated subsequently. The method can estimate all important channel parameters in the RIS-assisted near-field communication scenario more accurately.

公开(公告)号：US20250080192A1

公开(公告)日：2025-03-06

申请号：US18816232

申请日：2024-08-27

Applicant: Southeast University ,  PURPLE MOUNTAIN LABORATORIES

Inventor： Chengxiang WANG ,  Lin HOU ,  Hengtai CHANG ,  Jie HUANG

IPC: H04B7/06 ,  H04B7/0413

Abstract: A method for estimating a beam domain channel in a spatial non-stationary massive MIMO system includes constructing a beam domain channel model for the spatial non-stationary massive MIMO system by using a visibility region; transforming a problem for estimating the beam domain channel into a problem for reconstructing a sparse channel based on a sparsity of beam domain channel and an influence of power leakage; proposing a beam domain structure-based sparsity adaptive matching pursuit scheme according to a cross-block sparse structure and a power ratio threshold of the beam domain channel; and verifying that the proposed scheme has a lower pilot overhead, a higher accuracy and a higher effectiveness compared to the traditional schemes in simulation results. The method can be effectively applied to communication channel estimation with non-stationary characteristics, and has obvious advantages in estimation accuracy and complexity.