A passive jammer and a passive jamming method for channel knowledge map scenarios

Abstract

This invention discloses a fully passive jammer and a fully passive jamming method for channel knowledge map scenarios. The jammer includes: a data acquisition module for generating a multi-state channel dataset by traversing and controlling the configuration of a reconfigurable smart metasurface and performing channel simulation; a map construction module for constructing a channel path map based on channel state information; a beamforming parameter determination module for determining fixed hybrid precoding parameters based on the channel path map in an interference-free reference scenario; and a performance evaluation module for reusing the fixed precoding parameters and calculating the communication rate based on the real channel state in a dynamic scenario with disco-type reconfigurable smart metasurface interference, thereby quantifying performance loss. This invention transforms dynamic interference into an analyzable channel sequence through multiple simulation sampling, separates beam selection from rate evaluation, objectively quantifies mismatch penalties, and provides a standardized evaluation platform for countermeasure research on disco-type reconfigurable smart metasurfaces.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a fully passive jammer and a fully passive jamming method for channel knowledge map scenarios. Background Technology

[0002] In millimeter-wave massive MIMO systems, hybrid beamforming suffers from high training and computational overhead due to radio frequency chain limitations, and traditional channel estimation or full codebook scanning can lead to significant challenges. Channel path mapping (CPM), as an implementation of channel knowledge mapping (CKM), constructs a path map based on user locations, storing a set of channel paths for each location. During online communication, CPM is used to estimate and reconstruct equivalent channels for unknown points and design beamforming matrices.

[0003] Reconfigurable Intelligent Surfaces (RIS) are considered a key technology for improving the performance of wireless communication systems. RIS consists of a large number of reflective elements, and the reflection coefficient of each element can be flexibly adjusted using programmable components such as PIN diodes or varactor diodes. Existing research has focused on combining RIS with channel knowledge map (CKM)-assisted beamforming techniques to further optimize system performance. This paper presents a CKM-based environment-aware joint active / passive beamforming scheme to improve spectral and energy efficiency in millimeter-wave RIS-assisted multi-user downlink systems with blocked direct links. This method utilizes the location and environmental information provided by CKM, transforming the non-convex energy efficiency maximization problem into an iteratively solvable convex optimization subproblem through fractional programming and quadratic transformation, thereby designing a beamforming algorithm with low training overhead.

[0004] However, the introduction of RIS technology has also brought new and significant risks to physical layer security. Among them, a novel threat called "Disco Reconfigurable Intelligent Surface" (DRIS) is particularly noteworthy. This concept proposes that it can launch completely passive jamming attacks without obtaining legitimate user channel state information or relying on additional jamming power. Specifically, DRIS disrupts the channel reciprocity of time-division duplex systems by causing its reflection coefficient to continuously and randomly vary over time, similar to the optical scattering characteristics of a "disco ball," thus making the channel constantly changing. Summary of the Invention

[0005] This invention addresses the problems existing in the prior art by providing a fully passive jammer and a fully passive jamming method for channel knowledge map scenarios. This solves the problem in the prior art that it is difficult to accurately model and quantify the dynamic interference of disco-type reconfigurable smart metasurfaces in channel knowledge map-based communication systems.

[0006] To address the above technical problems, this invention provides the following technical solution: a fully passive jammer for channel knowledge map scenarios, comprising:

[0007] The data acquisition module is used to deploy transmitters, receivers and multiple reconfigurable smart metasurfaces in the simulation site. It performs traversal control at equal intervals within the range of reflection angle and phase configuration of the reconfigurable smart metasurfaces, and performs channel simulation after each control. It collects channel state information including receiver position coordinates, channel power, phase, angle of arrival and departure angle, and generates a multi-state channel dataset.

[0008] The map building module is used to extract the three-dimensional coordinates and multipath parameters of each receiver based on the channel state information, and to build a channel path map for each receiver that includes path gain, phase and angle.

[0009] The beamforming parameter determination module is used to perform channel prediction for test users based on the channel path map in an interference-free reference scenario, and determine the analog precoding matrix and digital precoding matrix as fixed hybrid precoding parameters according to the predicted channel and the base station radio frequency link number constraints.

[0010] The performance evaluation module is used to reuse fixed hybrid precoding parameters for signal transmission in dynamic scenarios with disco-reconfigurable smart metasurface interference, and calculate the communication rate based on the real channel state corresponding to the current dynamic scenario in the multi-state channel data set, and quantify the performance loss caused by the disco-reconfigurable smart metasurface.

[0011] Furthermore, the aforementioned data acquisition module traverses the configuration of the reconfigurable smart metasurface within a preset range of reflection angle and phase change, according to a set step size. Each time a configuration adjustment is completed, a ray tracing simulation is performed, and the acquired channel data is used as a channel snapshot in the disco-style reconfigurable smart metasurface state corresponding to that configuration, thereby constructing a channel dataset that changes with the configuration.

[0012] Furthermore, the aforementioned map construction module is specifically used for: reading ray tracing data and extracting the receiver's three-dimensional coordinates; parsing the multipath parameter file to obtain the received power, phase, and angle information of each path; normalizing the received power to path gain by subtracting the transmitted power; sorting and filtering several of the strongest paths for each receiver by gain, and merging their gain, phase, and angle into a feature vector; and constructing a mapping database of geographic location and channel information using the receiver's two-dimensional location as the key and the corresponding feature vector as the value.

[0013] Furthermore, the aforementioned beamforming parameter determination module includes:

[0014] The channel prediction unit is used to introduce a positioning error into the test user under a given expected positioning error, obtain an estimated position, and use the distance-weighted K-nearest neighbor algorithm to predict the strongest path feature from the channel path map, thereby reconstructing the predicted channel.

[0015] The simulated precoding determination unit is used to construct a codebook based on the transmitter antenna array, select the codeword index corresponding to the maximum projection gain for each user on the prediction channel, and select codewords not exceeding the constraint number from all the codeword indexes selected by all users according to the constraint number of base station radio frequency links. If there are not enough, they are supplemented according to the total projection energy in the group from large to small to obtain the radio frequency codeword index set, and construct the simulated precoding matrix.

[0016] The digital precoding determination unit is used to obtain the equivalent channel based on the analog precoding matrix, and to obtain the digital precoding matrix after power normalization by using zero-forcing digital precoding with regularization terms.

[0017] Furthermore, the aforementioned performance evaluation module is specifically used to: in dynamic scenarios, directly reuse the RF codeword index set and digital precoding matrix stored in the reference scenario to construct the total precoding matrix; and calculate the signal-to-interference-plus-noise ratio and achievable rate of each user on the real channel matrix in dynamic scenarios; and finally, perform statistical averaging on all users, all user groups and all dynamic scenario states to obtain the average communication rate.

[0018] This invention also provides a fully passive jamming method for channel knowledge map scenarios, applied to the jammer described in this invention, comprising the following steps:

[0019] Step S1: Deploy a transmitter, a receiver, and multiple reconfigurable smart metasurfaces in the simulation site. Perform ergonomic control at equal intervals within the range of the reflection angle and phase configuration of the reconfigurable smart metasurfaces, and perform channel simulation after each control. Collect channel state information including receiver position coordinates, channel power, phase, angle of arrival, and departure angle to generate a multi-state channel dataset.

[0020] Step S2: Based on the channel state information, extract the three-dimensional coordinates and multipath parameters of each receiver, and construct a channel path map including path gain, phase and angle for each receiver;

[0021] Step S3: In an interference-free reference scenario, channel prediction is performed on the test user based on the channel path map, and the analog precoding matrix and digital precoding matrix are determined as fixed hybrid precoding parameters according to the predicted channel and the base station radio frequency link number constraints.

[0022] Step S4: In a dynamic scenario with interference from the disco-reconfigurable smart metasurface, the fixed hybrid precoding parameters are reused for signal transmission, and the communication rate is calculated based on the real channel state corresponding to the current dynamic scenario in the multi-state channel data set, so as to quantify the performance loss caused by the disco-reconfigurable smart metasurface.

[0023] Further, step S1 includes: within a preset range of reflection angle and phase change, traversing the configuration of the reconfigurable smart metasurface by a set step size, performing a ray tracing simulation once after each configuration adjustment, and taking the collected channel data as a channel snapshot in the disco-style reconfigurable smart metasurface state corresponding to the current configuration, thereby constructing a channel dataset that changes with the configuration.

[0024] Furthermore, the aforementioned step S3 includes the following sub-steps:

[0025] Step S31: Introduce a positioning error to the test user under a given expected positioning error to obtain an estimated position, and use the distance-weighted K-nearest neighbor algorithm to predict its strongest path feature from the channel path map, thereby reconstructing the predicted channel;

[0026] Step S32: Construct a codebook based on the transmitter antenna array, select the codeword index corresponding to the maximum projection gain for each user on the predicted channel, and select codewords not exceeding the constraint number from all the codeword indexes selected by all users according to the constraint number of base station radio frequency links. If insufficient, supplement the codewords according to the total projection energy in the group from large to small to obtain the radio frequency codeword index set, and construct the simulated precoding matrix.

[0027] Step S33: Obtain the equivalent channel based on the analog precoding matrix, and use zero-forcing digital precoding with regularization terms to obtain the digital precoding matrix after power normalization.

[0028] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described in the present invention.

[0029] The present invention also provides a passive jamming device for channel knowledge map scenarios, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the methods described in the present invention.

[0030] Compared with the prior art, the beneficial technical effects of the present invention using the above technical solution are as follows:

[0031] Modeling and Multi-State Analysis of Time-Varying Channel Attacks for Dynamically Reconfigurable Smart Metasurfaces: This invention addresses the time-varying and rapid aging channel problems caused by the continuous changes in reflection parameters of dynamically reconfigurable smart metasurfaces (DRIS), proposing a DRIS scenario modeling method based on multiple simulation sampling. Unlike traditional smart metasurfaces with fixed reflection parameters, the reflection angle and phase of DRIS are continuously changing, making it difficult to establish a static channel model through a single offline simulation. Using ray tracing methods with fixed configurations only yields a snapshot of the channel at a specific instant, failing to accurately reflect the continuous dynamic impact of DRIS on multipath structures, signal coherence, and angular domain distribution in actual operation, thus hindering accurate assessment of its effect on system communication performance.

[0032] To address the aforementioned issues, this solution employs a "multiple simulation sampling" modeling approach: within a preset range of reflection angle and phase variation, the RIS configuration is traversed at set step sizes. Each configuration adjustment triggers a ray tracing simulation, and corresponding channel data is collected. Through this process, a channel dataset varying with the DRIS state can be obtained. ,in Indicates the first This method describes a DRIS configuration state. It transforms the dynamic perturbation process of DRIS into a storable, reproducible, and statistically analyzable channel state sequence. It can not only systematically describe the time-varying and mismatch characteristics of the channel caused by DRIS, but also achieve fair comparison and performance evaluation of different DRIS perturbation intensities, array sizes, and algorithm strategies on a unified data basis. It has strong reproducibility and scalability.

[0033] Beam mismatch naturally presented in closed-loop evaluation: objective quantification of DRIS hazards: This invention addresses the performance evaluation problem of covert communication systems under dynamic reconfigurable smart metasurface (DRIS) interference by proposing an objective quantification method for mismatch penalty based on fixed beam multiplexing.

[0034] In practical communication systems, because the dynamic changes of the channel caused by the DRIS cannot be perceived in real time, the transmitter typically continues to use the beam index selected by channel prediction and codebook matching based on a reference static scenario—such as a fixed intelligent metasurface (RIS) assisted channel—without frequently retraining and updating the beam as the DRIS reflection angle and phase continuously change. Therefore, this scheme strictly separates "beam selection" and "rate evaluation" into two independent stages: first, the transmit beam is determined and fixed in the static reference scenario; then, this fixed beam is directly reused for signal transmission in the DRIS dynamic disturbance environment; and the system performance must be calculated in real time based on the current actual channel state.

[0035] Because DRIS continuously alters the amplitude, phase, and angular distribution of multipath signals, the actual channel gradually deviates from the predicted results under the static reference scenario. This leads to a mismatch between the fixed beam and the main energy direction of the real-time channel, resulting in a decrease in received signal gain and a reduction in communication rate. The performance loss obtained by this method is actually a real "mismatch penalty" caused by the dynamic perturbation of DRIS in the closed loop of system simulation, rather than a performance degradation based on artificial assumptions or preset conditions. Therefore, it can more objectively and reproducibly quantify the key impact of DRIS on the rate performance of communication systems.

[0036] This method features a closed evaluation process, strong repeatability of results, and consistency with actual system behavior, making it suitable for accurate evaluation and comparative analysis of communication link performance under DRIS interference.

[0037] Provide a reproducible, standardized evaluation platform for DRIS detection, robust beamforming, and anti-interference strategy research:

[0038] This invention relates to the field of dynamic reconfigurable intelligent metasurface interference countermeasure technology, specifically providing a method for constructing a DRIS attack and defense integrated evaluation platform based on multi-state channel simulation and fixed strategy reuse.

[0039] This scheme can generate multi-state realistic channel datasets that continuously change with DRIS configuration, while maintaining the channel prediction model, pre-selected codebook, and beam index determined under the reference static scenario on the communication side. This naturally constructs an integrated data and indicator system encompassing "DRIS dynamic perturbation - communication strategy lag - performance closed-loop evaluation" in the simulation environment. This scheme forms a reproducible and scalable standardized evaluation platform for DRIS countermeasures and defenses, providing a consistent evaluation basis for related technology research and significantly enhancing the engineering applicability and technological expansion potential of the patented solution. Attached Figure Description

[0040] Figure 1 This is a flowchart of a fully passive interference method for channel knowledge map scenarios.

[0041] Figure 2 This is a curve showing the change in beamforming communication rate after adding DRIS with the aid of a channel path map. Detailed Implementation

[0042] To better understand the technical content of the present invention, specific embodiments are described below in conjunction with the accompanying drawings.

[0043] In this invention, various aspects of the invention are described with reference to the accompanying drawings, in which numerous illustrative embodiments are shown. Embodiments of the invention are not limited to those depicted in the drawings. It should be understood that the invention is implemented through any of the various concepts and embodiments described above, as well as the concepts and embodiments described in detail below, because the concepts and embodiments disclosed herein are not limited to any particular implementation. Furthermore, some aspects of the invention disclosed may be used alone or in any suitable combination with other aspects of the invention disclosed.

[0044] Example 1

[0045] This embodiment provides a passive jammer for channel knowledge map scenarios, including:

[0046] The data acquisition module is used to deploy transmitters, receivers, and multiple reconfigurable smart metasurfaces in the simulation site. It performs equally spaced traversal adjustments within the reflection angle and phase configuration range of the reconfigurable smart metasurfaces, and executes channel simulation after each adjustment. This process collects channel state information, including receiver position coordinates, channel power, phase, angle of arrival, and departure angle, generating a multi-state channel dataset. Specifically, the data acquisition module traverses the configuration of the reconfigurable smart metasurfaces within a preset range of reflection angle and phase changes, step by step. Each configuration adjustment is followed by a ray-tracing simulation, and the collected channel data is used as a channel snapshot under the disco-style reconfigurable smart metasurface state corresponding to that configuration, thereby constructing a channel dataset that changes with the configuration.

[0047] The map construction module is used to extract the three-dimensional coordinates and multipath parameters of each receiver based on the channel state information, and to construct a channel path map containing path gain, phase, and angle for each receiver. Specifically, the map construction module is used to: read ray tracing data and extract the three-dimensional coordinates of the receivers; parse the multipath parameter file to obtain the received power, phase, and angle information of each path; normalize the received power to path gain by subtracting the transmitted power; sort and filter several of the strongest paths for each receiver by gain, and merge their gain, phase, and angle into a feature vector; and construct a mapping database between geographical location and channel information, using the receiver's two-dimensional location as the key and the corresponding feature vector as the value.

[0048] The beamforming parameter determination module is used to perform channel prediction for test users based on the channel path map in an interference-free reference scenario, and determine the analog precoding matrix and digital precoding matrix as fixed hybrid precoding parameters according to the predicted channel and the base station radio frequency link number constraints.

[0049] Preferably, the beamforming parameter determination module includes: a channel prediction unit, used to introduce a positioning error into the test user under a given expected positioning error, obtain an estimated position, and use a distance-weighted K-nearest neighbor algorithm to predict the strongest path feature from the channel path map, thereby reconstructing the predicted channel; an analog precoding determination unit, used to construct a codebook based on the transmitter antenna array, select the codeword index corresponding to the maximum projection gain for each user on the predicted channel, and, according to the base station RF link number constraint, preferentially remove duplicates from all the codeword indices selected by the users and select codewords not exceeding the constraint number. If insufficient, they are supplemented according to the total projection energy within the group from largest to smallest to obtain the RF codeword index set, and construct an analog precoding matrix; and a digital precoding determination unit, used to obtain the equivalent channel based on the analog precoding matrix, and use zero-forcing digital precoding with regularization terms, and obtain the digital precoding matrix after power normalization.

[0050] The performance evaluation module is used to reuse fixed hybrid precoding parameters for signal transmission in dynamic scenarios with disco-reconfigurable smart metasurface interference. Based on the real channel states corresponding to the current dynamic scenario in the multi-state channel dataset, it calculates the communication rate and quantifies the performance loss caused by the disco-reconfigurable smart metasurface. Specifically, the performance evaluation module is used to: construct a total precoding matrix by directly reusing the RF codeword index set and digital precoding matrix stored in the reference scenario in the dynamic scenario; calculate the signal-to-interference-plus-noise ratio (SIR) and achievable rate for each user on the real channel matrix in the dynamic scenario; and finally, perform statistical averaging on all users, all user groups, and all dynamic scenario states to obtain the average communication rate.

[0051] Example 2

[0052] like Figure 1 As shown, this embodiment also provides a fully passive jamming method for channel knowledge map scenarios, applied to the jammer in this invention, including the following steps:

[0053] Step S1: Deploy a transmitter, a receiver, and multiple reconfigurable smart metasurfaces in the simulation site. Perform ergonomic control at equal intervals within the range of the reflection angle and phase configuration of the reconfigurable smart metasurfaces, and perform channel simulation after each control. Collect channel state information including receiver position coordinates, channel power, phase, angle of arrival, and departure angle to generate a multi-state channel dataset.

[0054] Step S2: Based on the channel state information, extract the three-dimensional coordinates and multipath parameters of each receiver, and construct a channel path map including path gain, phase and angle for each receiver;

[0055] Step S3: In an interference-free reference scenario, channel prediction is performed on the test user based on the channel path map, and the analog precoding matrix and digital precoding matrix are determined as fixed hybrid precoding parameters according to the predicted channel and the base station radio frequency link number constraints.

[0056] Step S4: In a dynamic scenario with interference from the disco-reconfigurable smart metasurface, the fixed hybrid precoding parameters are reused for signal transmission, and the communication rate is calculated based on the real channel state corresponding to the current dynamic scenario in the multi-state channel data set, so as to quantify the performance loss caused by the disco-reconfigurable smart metasurface.

[0057] As a preferred embodiment of step S1 in Example 2, step S1 includes the following sub-steps:

[0058] S1.1 Place one transmitter and 3600 receivers in the simulation site; the receivers are evenly distributed within the site with a grid spacing of 5 m, and all receivers are installed at the same height of 1.5 m; the transmitter is installed at a height of 15 m.

[0059] S1.2 Both the transmitter and receiver are equipped with a single omnidirectional antenna and a single radio frequency link. It is assumed that both the transmitter and receiver are equipped with a single RF chain. The system operates at a frequency of 28 GHz with a bandwidth of 100 MHz.

[0060] S1.3 In the simulation environment, five reconfigurable smart metasurfaces were randomly deployed. By performing equally spaced ergonomic adjustments within their predefined reflection angle and phase configuration ranges, and executing a channel simulation after each adjustment to collect corresponding channel state information, the impact of DRIS on channel characteristics in a real propagation scenario was effectively simulated.

[0061] S1.4 The simulation output data includes receiver position coordinates, as well as key channel parameters such as channel power, phase, angle of arrival (AoA), and angle of departure (AoD).

[0062] As a preferred embodiment of step S2 in Example 2, to construct the channel path map, ray tracing data is first read to extract the three-dimensional coordinates of the receivers; then, the multipath parameter file is parsed to obtain the received power, phase, and angle information of each path for each receiver, forming a global channel knowledge map and counting the number of paths. Subsequently, the received power is normalized to path gain by subtracting the transmit power for use in channel reconstruction. Then, the three strongest paths for each node are sorted by gain, and their gain, phase, and angle are merged into a 12-dimensional feature vector; if there are fewer than three, preset values ​​are used to fill the gaps. Finally, receivers without paths are removed and the dataset is divided, using the two-dimensional location of the training nodes as the key and the corresponding path features as the value to construct a "geographic location-channel information" mapping database.

[0063] As a preferred embodiment of step S3 in Example 2, step S3 includes the following sub-steps:

[0064] S3.1 DRIS Data Construction: Because DRIS continuously changes its reflection angle and phase configuration during actual propagation, causing the channel state to age rapidly over time, it is impossible to add DRIS as a fixed component and complete stable offline modeling in a ray tracing platform. To characterize this dynamic perturbation effect, this method uses multiple simulation sampling to model the DRIS scene. Within a predefined range of reflection angle and phase configuration, the RIS is traversed and configured. Each configuration adjustment is performed followed by a ray tracing simulation, collecting the corresponding channel data. This results in a set of channel data that changes with the RIS configuration, denoted as . , where s represents the s-th RIS configuration, corresponding to one "DRIS state".

[0065] S3.2 In the reference scenario, a CPM database is first constructed based on ray tracing data. The mean positioning error of the test users is then calculated based on the given expected positioning error. Introducing two-dimensional positioning error To obtain the estimated location , This indicates the exact location of the receiver in the simulation data.

[0066] Subsequently, distance-weighted KNN is used to predict the L strongest path features, and the predicted channel is reconstructed accordingly. In a fixed array size Below is the codebase for the build:

[0067] ,

[0068] In the formula, These represent the number of antennas in the y-direction and the number of antennas in the z-direction, respectively. Represents the candidate beam vector;

[0069] And in the prediction channel of any user group u First, select the codeword index corresponding to the maximum projection gain for each user:

[0070] ,

[0071] In the formula, This represents the channel vectors of the K users within the user group, where K represents the number of users in the user group. This represents the channel vector of the k-th user in the u-th user group.

[0072] Considering the base station RF link number constraint From codeword index Prioritize deduplication and select no more than One codeword; if insufficient, then based on the total projected energy within the group:

[0073] ,

[0074] Padding the codewords from largest to smallest, we obtain the RF codeword index set:

[0075] ,

[0076] In the formula, This represents the individual indices in the final selected set of RF codeword indices.

[0077] And construct the analog precoding matrix:

[0078] ,

[0079] In the formula, This represents the beam vector selected by index i. Represents a set, Indicates the number of transmitting antennas. Indicates the number of radio frequency chains.

[0080] In the digital domain, the equivalent channel is first obtained:

[0081] ,

[0082] In the formula, The superscript 0 indicates the reference scene number, and the table below RF represents the analog precoding matrix. This represents the predicted channel for user group u, where the superscript 0 indicates the reference scenario number, the subscript u represents the user group number, and H represents the equivalent matrix. ZF digital precoding with regularization is employed.

[0083]

[0084] In the formula, I represents the identity matrix. Power normalization is then performed to make... Thus, the total precoding is obtained. .

[0085] Therefore, in the reference scenario, for each array size and error level... This method stores a pair of parameters corresponding to each user group:

[0086] That is, the RF codeword index set and the digital precoding matrix.

[0087] Subsequently, in the DRIS dynamic scene, to ensure strict alignment evaluation, the reference scene's saved data was directly reused. and Construct precoding and calculate the rate for each user on a real channel in a dynamic scenario. This indicates that, under a reference scenario and given a positioning error level... At that time, the first The set of RF codeword indices corresponding to each user group; This indicates that, under a reference scenario and given a positioning error level... At that time, the first The set of RF codeword indices corresponding to each user group.

[0088] S3.3 Channel Model: In a multi-user multiple-input single-output (MU-MISO) downlink system based on channel path map (CPM) under Disco Reconfigurable Smart Metasurface (DRIS) interference, the channel of the k-th user can be decomposed into the sum of the direct link component and the channel aging component caused by DRIS interference, i.e.:

[0089] h k = h d,k + h aca,k (1)

[0090] Among them, h d,k h represents the direct link channel for the k-th user. aca,k This represents the active channel aging (ACA) component introduced by DRIS.

[0091] The direct link channel for the k-th user is represented as:

[0092] (2)

[0093] Where D_k represents the set of direct paths for the k-th user, ρ k,ℓ Let ϑ represent the complex gain of the ℓth path. t k,ℓ and φt k,ℓ These represent the elevation and azimuth angles on the base station side, respectively. t (·) represents the base station transmit array response vector.

[0094] For the ACA component, its effective class connection number is expressed as:

[0095] (3)

[0096] Among them, (ϑi n , φ in ) represents the elevation and azimuth angles of the signal incident from the base station to DRIS, (ϑ out , φ out ) represents the pitch and azimuth angles in the direction of DRIS reflection, a in (·) and a out (·) represent the incident array response vector and the output array response vector on the DRIS side, respectively, Φ represents the DRIS reflection matrix, and H represents the conjugate transpose.

[0097] Based on the above effective level connection number, the ACA channel component of the k-th user is represented as:

[0098] (4)

[0099] Where C_k represents the set of paths affected by DRIS for the k-th user, and N D N represents the total number of DRIS reflective units, η represents the effective level connection number, i.e., the reflectivity of the DRIS. t This represents the number of transmitting antennas.

[0100] Furthermore, the DRIS reflection matrix is ​​constructed as follows:

[0101] (5)

[0102] in, (6)

[0103] ⊙ represents the Hadamard product. Based on the above channel model, the time-varying effects introduced by DRIS can be uniformly characterized as the combined effect of cascaded channel coefficients and direct path components, thus providing a channel modeling foundation for subsequent beamforming parameter multiplexing, dynamic scene rate assessment, and performance loss quantification. Figure 2 The curves showing the change in beamforming communication rate after adding DRIS with channel path map assistance are displayed.

[0104] S3.4 Codebook Construction: The codebook construction process can be described as discrete angular domain sampling and array response mapping based on the UPA array. Given the size of the uniform planar array at the transmitting end. and the range obtained from the zenith angle statistics of effective paths in the channel path map. First of all and Within the variable domain, respectively , Perform uniform sampling, where , , Indicates in The sampling step size over this variable domain, Represents the direction domain variables corresponding to the pitch angle / zenith angle. Represents the direction domain variable corresponding to the azimuth angle;

[0105] Subsequently passed and The sampling points are mapped back to the angle domain to form a two-dimensional angle grid. For each pair of angles in the grid, a normalized UPA array response vector is constructed as a candidate beam codeword: ,

[0106] in, , And the spacing between array elements is taken .

[0107] Finally, all codewords are stacked row by row to obtain the beamforming codebook matrix. ,in This is used for subsequent beam search and selection. Representing the total number of candidate beams, Number of sampling points in the corner domain Number of sampling points in the corner domain.

[0108] S3.5 When the propagation environment switches to the DRIS dynamic scenario, the actual channel at the receiver will change. Assume a dynamic scenario with a given array size... At that time, the actual downlink channel matrix corresponding to the u-th user group is

[0109] in Let s represent the number of users in the group. The first DRIS dynamic scene, u represents the first... User groups.

[0110] In actual communication, due to DRIS interference, the channel ages rapidly, and the transmitter cannot obtain the real channel of the dynamic scenario in real time. Therefore, hybrid precoding parameters determined based on CPM prediction in the reference scenario are used for transmission. Specifically, for each positioning error level... For each user group u, the reference scenario has stored its RF codeword index set and digital precoding matrix, namely:

[0111] and by index set Construct a simulated precoding matrix on the same codebook:

[0112] , These represent the user group number and the RF link number, respectively.

[0113] This yields the total precoding matrix reused in the dynamic scene D:

[0114] ,

[0115] And keeping this precoding unchanged during the dynamic scenario evaluation phase, the received SINR of the k-th user in the dynamic scenario is expressed as:

[0116] in for The kth column, For transmission power, For noise power, Indicates the first The first user group The user, in the 1st The real downlink channel vector in a dynamic scenario Represents the total precoding matrix The Column, corresponding to the first Precoded vectors for each user Indicates the first The first user group The user, in the 1st The real downlink channel vector in a dynamic scenario.

[0117] The corresponding achievable rate is:

[0118] ,

[0119] In the formula, Indicates the first The first user group The user, in the 1st A dynamic scenario, given positioning error level Downlink signal-to-noise ratio.

[0120] Finally, to obtain the average performance under DRIS dynamic scenarios, the code performs a statistical average over all users, all random user groups, and all dynamic scenario sets, i.e.

[0121] , in, This represents a dynamic set of scenarios, where s represents the total number of scenarios and U represents the number of user groups.

[0122] While the present invention has been described above with reference to preferred embodiments, it is not intended to limit the invention. Those skilled in the art can make various modifications and refinements without departing from the spirit and scope of the invention. Therefore, the scope of protection of the present invention shall be determined by the claims.

Claims

1. A full passive jammer oriented to a channel knowledge map scenario, characterized in that, include: The data acquisition module is used to deploy transmitters, receivers and multiple reconfigurable smart metasurfaces in the simulation site. It performs traversal control at equal intervals within the range of reflection angle and phase configuration of the reconfigurable smart metasurfaces, and performs channel simulation after each control. It collects channel state information including receiver position coordinates, channel power, phase, angle of arrival and departure angle, and generates a multi-state channel dataset. The map building module is used to extract the three-dimensional coordinates and multipath parameters of each receiver based on the channel state information, and to build a channel path map for each receiver that includes path gain, phase and angle. The beamforming parameter determination module is used to perform channel prediction for test users based on the channel path map in an interference-free reference scenario, and determine the analog precoding matrix and digital precoding matrix as fixed hybrid precoding parameters according to the predicted channel and the base station radio frequency link number constraints. The performance evaluation module is used to reuse fixed hybrid precoding parameters for signal transmission in dynamic scenarios with disco-reconfigurable smart metasurface interference, and calculate the communication rate based on the real channel state corresponding to the current dynamic scenario in the multi-state channel data set, and quantify the performance loss caused by the disco-reconfigurable smart metasurface.

2. The passive jammer for channel knowledge map scenarios according to claim 1, characterized in that, Within a preset range of reflection angle and phase change, the data acquisition module traverses the configuration of the reconfigurable smart metasurface at a set step size. Each time a configuration adjustment is completed, a ray tracing simulation is performed, and the acquired channel data is used as a channel snapshot of the disco-style reconfigurable smart metasurface state corresponding to that configuration, thereby constructing a channel dataset that changes with the configuration.

3. The passive jammer for channel knowledge map scenarios according to claim 1, characterized in that, The map building module is specifically used for: reading ray tracing data and extracting the receiver's 3D coordinates; parsing multipath parameter files to obtain the received power, phase, and angle information of each path; normalizing the received power to path gain by subtracting the transmitted power; sorting and filtering several strongest paths for each receiver by gain, and merging their gain, phase, and angle into a feature vector; and constructing a mapping database of geographic location and channel information using the receiver's 2D location as the key and the corresponding feature vector as the value.

4. The passive jammer for channel knowledge map scenarios according to claim 1, characterized in that, The beamforming parameter determination module includes: The channel prediction unit is used to introduce a positioning error into the test user under a given expected positioning error, obtain an estimated position, and use the distance-weighted K-nearest neighbor algorithm to predict the strongest path feature from the channel path map, thereby reconstructing the predicted channel. The simulated precoding determination unit is used to construct a codebook based on the transmitter antenna array, select the codeword index corresponding to the maximum projection gain for each user on the prediction channel, and select codewords not exceeding the constraint number from all the codeword indexes selected by all users according to the constraint number of base station radio frequency links. If there are not enough, they are supplemented according to the total projection energy in the group from large to small to obtain the radio frequency codeword index set, and construct the simulated precoding matrix. The digital precoding determination unit is used to obtain the equivalent channel based on the analog precoding matrix, and to obtain the digital precoding matrix after power normalization by using zero-forcing digital precoding with regularization terms.

5. The passive jammer for channel knowledge map scenarios according to claim 1, characterized in that, The performance evaluation module is specifically used to: in dynamic scenarios, directly reuse the RF codeword index set and digital precoding matrix stored in the reference scenario to construct the total precoding matrix; and calculate the signal-to-interference-plus-noise ratio and achievable rate of each user on the real channel matrix in dynamic scenarios; and finally, perform statistical averaging on all users, all user groups and all dynamic scenario states to obtain the average communication rate.

6. A fully passive jamming method for channel knowledge map scenarios, applied to the jammer according to any one of claims 1 to 5, characterized in that, Includes the following steps: Step S1: Deploy a transmitter, a receiver, and multiple reconfigurable smart metasurfaces in the simulation site. Perform ergonomic control at equal intervals within the range of the reflection angle and phase configuration of the reconfigurable smart metasurfaces, and perform channel simulation after each control. Collect channel state information including receiver position coordinates, channel power, phase, angle of arrival, and departure angle to generate a multi-state channel dataset. Step S2: Based on the channel state information, extract the three-dimensional coordinates and multipath parameters of each receiver, and construct a channel path map including path gain, phase and angle for each receiver; Step S3: In an interference-free reference scenario, channel prediction is performed on the test user based on the channel path map, and the analog precoding matrix and digital precoding matrix are determined as fixed hybrid precoding parameters according to the predicted channel and the base station radio frequency link number constraints. Step S4: In a dynamic scenario with interference from the disco-reconfigurable smart metasurface, the fixed hybrid precoding parameters are reused for signal transmission, and the communication rate is calculated based on the real channel state corresponding to the current dynamic scenario in the multi-state channel data set, so as to quantify the performance loss caused by the disco-reconfigurable smart metasurface.

7. The passive interference method for channel knowledge map scenarios according to claim 6, characterized in that, Step S1 further includes: within a preset range of reflection angle and phase change, traversing the configuration of the reconfigurable smart metasurface by a set step size, performing a ray tracing simulation once after each configuration adjustment, and taking the collected channel data as a channel snapshot in the disco-style reconfigurable smart metasurface state corresponding to the current configuration, thereby constructing a channel dataset that changes with the configuration.

8. The passive interference method for channel knowledge map scenarios according to claim 6, characterized in that, Step S3 further includes the following sub-steps: Step S31: Introduce a positioning error to the test user under a given expected positioning error to obtain an estimated position, and use the distance-weighted K-nearest neighbor algorithm to predict its strongest path feature from the channel path map, thereby reconstructing the predicted channel; Step S32: Construct a codebook based on the transmitter antenna array, select the codeword index corresponding to the maximum projection gain for each user on the predicted channel, and select codewords not exceeding the constraint number from all the codeword indexes selected by all users according to the constraint number of base station radio frequency links. If insufficient, supplement the codewords according to the total projection energy in the group from large to small to obtain the radio frequency codeword index set, and construct the simulated precoding matrix. Step S33: Obtain the equivalent channel based on the analog precoding matrix, and use zero-forcing digital precoding with regularization terms to obtain the digital precoding matrix after power normalization.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 6 to 8.

10. A fully passive jamming device for channel knowledge map scenarios, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the method according to any one of claims 6 to 8.

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