A 5G communication method and system based on environmental adaptation

Abstract

The present application provides a 5G communication method and system based on environmental adaptation, in which a dynamic environmental perception matrix is generated by fusing electromagnetic field strength distribution, terminal density heat map and obstacle reflection data through distributed sensing nodes. Based on this matrix, a beam pointing model of the user equipment cluster motion trajectory is established, the main lobe radiation direction of the beam is dynamically adjusted and a null area is created. Combined with the multipath phase characteristics of the real-time channel, the non-uniformly spaced subcarrier distribution is reconstructed and the multipath phase is compensated. The main lobe radiation direction and the subcarrier distribution pattern are integrated into the physical layer structure, and the spatial multiplexing density of the time-frequency resource block is dynamically adjusted according to the service priority. Through the closed-loop interaction of the forward channel response characteristics and the reverse control instructions, the collaborative matching thresholds of each parameter are iteratively optimized. The present application improves the anti-interference capability and spectrum efficiency of the communication system.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of environmental adaptation technology, and in particular to a 5G communication method and system based on environmental adaptation. Background Art

[0002] In 5G communication systems, the rapid development of emerging applications such as the Internet of Things, augmented reality, and virtual reality is driving an increasing demand for high-bandwidth, low-latency, and highly reliable communications. In dense urban environments, large public venues, and industrial automation scenarios, the dynamic mobility of user device clusters, complex electromagnetic environments, and multipath effects pose significant challenges to wireless communications. To meet the demands of these application scenarios, efficient, flexible, and adaptive beam management and resource allocation strategies must be implemented to ensure high-quality service.

[0003] Current advanced question-answering systems typically rely on deep learning models and natural language processing techniques, such as large-scale pre-trained language models based on the Transformer architecture. These models are pre-trained on large datasets and then fine-tuned for specific tasks, enabling them to understand and generate high-quality question-answer pairs to a certain extent. Furthermore, some systems employ data augmentation techniques to expand the diversity of training data and utilize multi-turn dialogue frameworks to optimize the directionality of question generation. However, despite significant progress in many areas, these approaches still face certain limitations.

[0004] Although existing advanced solutions have improved system performance to a certain extent, they still have several shortcomings. First, in complex and changing environments, existing beam management strategies may not be able to respond quickly to environmental changes, resulting in inaccurate beam pointing and affecting communication quality. Second, although adaptive subcarrier allocation strategies are adopted, their optimization process often relies on static or semi-static channel models, making it difficult to adapt to instantaneous multipath conditions in real time. Finally, when handling service priority scheduling, existing solutions often use preset rules rather than real-time optimization mechanisms. As a result, in the event of sudden high-priority service bursts, the system cannot adjust resource allocation in a timely manner, which in turn affects service quality. Summary of the Invention

[0005] The embodiments of the present application provide a 5G communication method and system based on environmental adaptation to solve the problems of low anti-interference capability and low spectrum efficiency of communication systems in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a 5G communication method based on environment adaptation, including:

[0007] Generate a dynamic environment perception matrix by integrating electromagnetic field intensity distribution, terminal density heat map and obstacle reflection data in real time through distributed sensor nodes;

[0008] Establishing a beam pointing model of the user equipment cluster motion trajectory based on the dynamic environment perception matrix, dynamically adjusting the main lobe radiation direction of the beam according to the beam pointing model and creating a null area pointing to the interference source;

[0009] Reconstructing a subcarrier distribution pattern with non-uniform spacing and compensating for multipath phase according to the main lobe radiation direction and the null region in combination with real-time channel multipath phase characteristics;

[0010] Combining the main lobe radiation direction, the subcarrier distribution pattern and the physical layer structure, and dynamically adjusting the spatial multiplexing density of the time-frequency resource block based on service priority;

[0011] Through the closed-loop interaction of the forward channel response characteristics and the reverse control instructions, the collaborative matching threshold of the main lobe radiation direction, the subcarrier distribution and the spatial multiplexing density is iteratively optimized.

[0012] Optionally, establishing a beam pointing model of the user equipment cluster motion trajectory based on the dynamic environment perception matrix, dynamically adjusting the main lobe radiation direction of the beam according to the beam pointing model, and creating a null area pointing to the interference source includes:

[0013] Separating the spatial distribution characteristics of the user device cluster and the spatial location parameters of the interference source from the dynamic environment perception matrix, and constructing a vector prediction model of the user cluster's motion trajectory based on the time-domain gradient change of the user device density heat map to calculate the motion direction angle and velocity attenuation coefficient of the user cluster in three-dimensional space;

[0014] Based on the vector prediction model and the motion direction angle and velocity attenuation coefficient of the user cluster, a dynamic matching mechanism is established between the main lobe radiation direction and the motion trajectory of the user cluster. By minimizing the spatial offset error between the main lobe coverage center point and the user cluster mass center, a beam main lobe pointing angle sequence that is synchronously updated with the user cluster movement is generated;

[0015] Extracting the azimuth distribution and reflection intensity of the interference source based on obstacle reflection profile data, constructing a spatial feature matrix of the interference source, and calculating the null area of the beam weight vector based on the spatial feature matrix and the motion direction angle and velocity attenuation coefficient of the user cluster in combination with a multi-constraint optimization algorithm, so that the central axis of the null area coincides with the azimuth of the interference source and the null width has a nonlinear mapping relationship with the reflection intensity;

[0016] The beam main lobe pointing angle sequence and the null area are input into the beamforming parameter fusion device. By dynamically balancing the conflicting goals of maximizing the main lobe gain and the null suppression strength, a shaping weight vector set that jointly controls the radiation direction of the beam main lobe pointing angle sequence and the distribution of the null area is generated and loaded into the antenna array unit in real time.

[0017] Optionally, the method of establishing a dynamic matching mechanism between the main lobe radiation direction and the user cluster motion trajectory based on the vector prediction model and the motion direction angle and velocity attenuation coefficient of the user cluster, and generating a beam main lobe pointing angle sequence that is synchronously updated with the user cluster motion by minimizing the spatial offset error between the main lobe coverage center point and the user cluster mass center, includes:

[0018] Based on the three-dimensional motion direction angle, velocity attenuation coefficient, and distribution density of the user device cluster output by the vector prediction model, a user cluster centroid motion trajectory function weighted by a time-varying weight factor is constructed. The weight factor is negatively correlated with the regional signal quality attenuation rate of the device density heat map.

[0019] Based on the user cluster centroid motion trajectory function, a sliding time window mechanism is used to predict the discrete position sequence of the user cluster centroid within a preset time interval. Combined with the antenna array's beamwidth adaptive adjustment rule, a dynamic offset error model is established between the mainlobe coverage center point and the user cluster centroid position. The beamwidth adaptive adjustment rule is dynamically calculated based on the spatial discreteness of the user cluster distribution range and a preset coverage redundancy threshold.

[0020] An iterative optimization algorithm for azimuth angle corrections is introduced, with the real-time error value output by the dynamic offset error model as the objective function. In each iteration, the correction is jointly calculated based on the gradient direction of the discretized position sequence and the antenna array steering delay parameter to obtain the optimal solution sequence for the beam pointing angle.

[0021] The optimal solution sequence is input into the beamform synthesizer. Based on the reconfigurable pattern characteristics of the antenna array unit, a beam mainlobe pointing angle sequence that matches the spatial distribution range of the user cluster is generated, so that the gain attenuation rate at the edge of the mainlobe coverage area is consistent with the spatial gradient of the user cluster distribution density.

[0022] Optionally, the azimuth distribution and reflection intensity of the interference source are extracted based on the obstacle reflection profile data, a spatial feature matrix of the interference source is constructed, and a null area of the beam weight vector is calculated based on the spatial feature matrix and the motion direction angle and velocity attenuation coefficient of the user cluster in combination with a multi-constraint optimization algorithm, so that the central axis of the null area coincides with the azimuth of the interference source and the null width and the reflection intensity have a nonlinear mapping relationship, including:

[0023] Based on the obstacle reflection profile data, the azimuth distribution cluster of the interference source and the corresponding reflection intensity level are extracted by estimating the arrival angle of the multipath reflection path and analyzing the signal attenuation factor;

[0024] Constructing a spatial feature matrix based on the azimuth distribution clusters and the reflection intensity levels, wherein the row vectors of the spatial feature matrix represent the centers of the azimuth distribution clusters, the column vectors represent the nonlinear quantization intervals of the reflection intensity levels, and the element values of the spatial feature matrix are the cumulative values of the reflection intensities weighted by the number of interference sources in each cluster;

[0025] Combining the motion direction angle and velocity attenuation coefficient of the user cluster, predicting the relative motion trajectory of the user cluster and the interference source, establishing a dynamic azimuth offset model of the interference source, and calculating the real-time correction value of the null center axis;

[0026] Based on the spatial feature matrix and the dynamic offset model, a multi-objective optimization problem is constructed with the coincidence of the null center axis as a hard constraint and the nonlinear mapping between the null width and the reflection intensity as a soft constraint. The feasible solution set of the beam weight vector is solved by the weighted least squares method, where the null width function is designed as a piecewise exponential function of the reflection intensity to adapt to the suppression range requirements under different interference intensities;

[0027] The feasible solution set of the beam weight vector is input into the antenna array response controller, and a three-dimensional radiation suppression pattern of the null area is generated according to the array aperture phase distribution. The conflict threshold between the null depth and the quality of the user cluster received signal is monitored in real time. When performance degradation is detected, dynamic backtracking calibration of the beam weight vector is triggered.

[0028] Optionally, based on the user cluster centroid motion trajectory function, a sliding time window mechanism is used to predict the discrete position sequence of the user cluster centroid within a preset time interval, and combined with the beam width adaptive adjustment rule of the antenna array, a dynamic offset error model between the main lobe coverage center point and the user cluster centroid position is established. The beam width adaptive adjustment rule is dynamically calculated based on the spatial discreteness of the user cluster distribution range and a preset coverage redundancy threshold, including:

[0029] Based on the user cluster centroid motion trajectory function, a sliding time window with a fixed step size is used to divide the trajectory segments. In each window, a neural differential equation is used to model the spatiotemporal dynamic evolution law, and a discretized centroid position sequence within a preset time interval is output;

[0030] Calculate the spatial dispersion index of the current user cluster distribution in real time, use a clustering algorithm to dynamically group user location coordinates, and determine the beamwidth adjustment coefficient based on the ratio of the standard deviation of the intra-cluster distance of the clustering result to the preset coverage redundancy threshold;

[0031] According to the beam width adjustment coefficient, the phase distribution of the excitation signal of the antenna array is reconstructed through a phase difference compensation algorithm to generate a main lobe beam width parameter that meets the current user distribution range, and the actual coverage area boundary coordinates are recorded;

[0032] Based on the discretized centroid position sequence, the main lobe beamwidth parameter and the actual coverage area boundary coordinates, a dynamic offset error model between the main lobe coverage center point and the predicted centroid position is established.

[0033] Optionally, reconstructing a subcarrier distribution pattern with non-uniform spacing and compensating for multipath phase according to the main lobe radiation direction and the null region in combination with multipath phase characteristics of a real-time channel includes:

[0034] Based on the coverage range of the main lobe radiation direction and the suppression angle of the null area, the arrival angle distribution and phase delay characteristics of the multipath propagation path in the current channel environment are extracted to construct a multipath phase characteristic matrix;

[0035] Calculating the phase offset of each subcarrier on the multipath propagation path according to the multipath phase characteristic matrix, and generating a non-uniform subcarrier spacing allocation strategy based on the user equipment distribution density within the main lobe coverage area;

[0036] Based on the non-uniform subcarrier spacing allocation strategy, a phase compensation function is constructed, and an initial phase offset of each subcarrier is iteratively adjusted by the phase offset, so that the phase delays on the multipath propagation path are aligned at the receiving end based on the initial phase offset;

[0037] The non-uniform subcarrier spacing allocation strategy and the phase compensation function are input into an orthogonal frequency division multiplexing modulator to generate a pilot sequence matching the current channel environment, and the subcarrier spacing and the parameters of the phase compensation function are corrected in real time based on the pilot sequence in combination with a closed-loop feedback mechanism.

[0038] Optionally, combining the main lobe radiation direction, the subcarrier distribution pattern, and a physical layer structure to dynamically adjust the spatial multiplexing density of time-frequency resource blocks based on service priority includes:

[0039] Based on service delay sensitivity, bandwidth requirements, and reliability levels, a multidimensional weight vector is generated through the analytic hierarchy process, which outputs a discrete service priority label and associates it with the logical channel identifier of the physical layer structure.

[0040] Based on the real-time location distribution of user clusters and the service priority labels, a deep reinforcement learning algorithm is used to build a beam pointing strategy network, output the main lobe radiation direction angle and beamforming weight matrix, and record the beam switching delay parameters;

[0041] Based on the beamforming weight matrix and the service priority label, the subcarriers are divided into a high-priority service dedicated cluster, a low-priority shared cluster, and a guard interval using an improved graph theory clustering algorithm, a subcarrier resource block mapping relationship table is generated, and an interference suppression factor within each cluster is calculated;

[0042] Dynamically divide the time slot length and symbol interval according to the subcarrier resource block mapping relationship table and the beam switching delay parameter, adopt an asymmetric cyclic prefix mechanism to align the transmission window of the high-priority service dedicated cluster, and output the spatial multiplexing density of the time-frequency resource block;

[0043] Based on the spatial multiplexing density and the interference suppression factor, a multi-objective optimization algorithm is used to solve the modulation order and code rate combination that meets the service priority constraint, and a binding relationship configuration instruction between the antenna port and the resource block is generated. The spatial multiplexing density is dynamically adjusted based on the binding relationship configuration instruction.

[0044] In a second aspect, an embodiment of the present application provides a 5G communication system based on environment adaptation, including:

[0045] A generation module is used to generate a dynamic environment perception matrix by integrating electromagnetic field intensity distribution, terminal density heat map and obstacle reflection data in real time through distributed sensor nodes;

[0046] An establishment module is used to establish a beam pointing model of the user equipment cluster motion trajectory based on the dynamic environment perception matrix, dynamically adjust the main lobe radiation direction of the beam according to the beam pointing model, and create a null area pointing to the interference source;

[0047] A reconstruction module, configured to reconstruct a subcarrier distribution pattern with non-uniform spacing and compensate for the multipath phase according to the main lobe radiation direction and the null region, in combination with the multipath phase characteristics of the real-time channel;

[0048] An adjustment module, configured to combine the main lobe radiation direction, the subcarrier distribution pattern, and the physical layer structure to dynamically adjust the spatial multiplexing density of the time-frequency resource block based on service priority;

[0049] An optimization module is used to iteratively optimize the collaborative matching threshold of the main lobe radiation direction, the subcarrier distribution and the spatial multiplexing density through closed-loop interaction between the forward channel response characteristics and the reverse control instructions.

[0050] In a third aspect, an embodiment of the present application provides a computing device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a 5G communication method based on environment adaptation as described in any one of the first aspects.

[0051] In a fourth aspect, an embodiment of the present application provides a computer storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement a 5G communication method based on environment adaptation as described in any one of the first aspects.

[0052] In an embodiment of the present application, a dynamic environment perception matrix is generated by real-time fusion of electromagnetic field strength distribution, terminal density heat map and obstacle reflection data through distributed sensor nodes; a beam pointing model of the user equipment cluster motion trajectory is established based on the dynamic environment perception matrix, and the main lobe radiation direction of the beam is dynamically adjusted according to the beam pointing model and a null area pointing to the interference source is created; based on the main lobe radiation direction and the null area, combined with the real-time channel multipath phase characteristics, a subcarrier distribution pattern with non-uniform spacing is reconstructed and the multipath phase is compensated; the main lobe radiation direction, the subcarrier distribution pattern and the physical layer structure are combined to dynamically adjust the spatial multiplexing density of the time-frequency resource block based on the service priority; through the closed-loop interaction of the forward channel response characteristics and the reverse control instructions, the collaborative matching threshold of the main lobe radiation direction, the subcarrier distribution and the spatial multiplexing density is iteratively optimized.

[0053] The technical solution of this application has the following beneficial effects:

[0054] This application utilizes precise dynamic environment perception, intelligent beam management, and resource allocation strategies to improve the communication system's anti-interference capabilities and spectrum efficiency while effectively reducing the risk of multipath interference and signal loss. Furthermore, a closed-loop feedback mechanism ensures the system can adaptively optimize in a constantly changing environment, providing stable and high-quality services and significantly enhancing user experience and service quality.

[0055] Furthermore, the embodiment of the present application also separates the spatial distribution characteristics of the user device cluster and the spatial position parameters of the interference source based on the dynamic environment perception matrix, and uses the time domain gradient change of the user density heat map to construct a vector prediction model of the user cluster motion trajectory, and calculates the motion direction angle and speed attenuation coefficient of the user cluster in three-dimensional space. According to the vector prediction model and the motion characteristics of the user cluster, a dynamic matching mechanism is established between the main lobe radiation direction and the user cluster motion trajectory, and a beam main lobe pointing angle sequence that is updated synchronously with the user cluster movement is generated. By extracting the obstacle reflection profile data, the spatial feature matrix of the interference source is constructed, and the null area of the beam weight vector is calculated in combination with the multi-constraint optimization algorithm, so that the central axis of the null area coincides with the azimuth angle of the interference source and the null width is adjusted to adapt to the reflection intensity. Finally, the beam main lobe pointing angle sequence and the null area are input into the beam shaping parameter fusion device to generate a shaping weight vector set that jointly controls the beam main lobe pointing angle and the null area distribution, and is loaded into the antenna array unit in real time.

[0056] This method, through precise dynamic environment perception and intelligent beam management strategies, achieves accurate tracking of the motion trajectory of user device clusters and real-time adjustment of the beam mainlobe direction, significantly improving the accuracy and stability of beam coverage. Furthermore, by constructing a spatial feature matrix of the interference source and combining it with a multi-constraint optimization algorithm, an effective null region is generated, effectively suppressing the impact of interfering signals. A closed-loop feedback mechanism ensures the dynamic balance of beamforming parameters, maximizing mainlobe gain while enhancing null suppression, thereby improving the communication system's anti-interference capability and spectrum efficiency, and providing a more stable and high-quality service experience.

[0057] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0059] Figure 1 A flowchart of a 5G communication method based on environment adaptation provided in an embodiment of the present application;

[0060] Figure 2 A schematic diagram of the structure of a 5G communication system based on environmental adaptation provided in an embodiment of the present application;

[0061] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] In order to enable people skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0063] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0064] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0065] Figure 1 A flowchart of a 5G communication method based on environment adaptation is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0066] Step 101: Generate a dynamic environment perception matrix by integrating electromagnetic field intensity distribution, terminal density heat map, and obstacle reflection data in real time through distributed sensor nodes;

[0067] In this step, the dynamic environment perception matrix is a data structure that integrates electromagnetic field strength distribution, terminal density heat map, and obstacle reflection data to describe various dimensions of the communication environment. The electromagnetic field strength distribution represents the intensity variations of electromagnetic signals within the area; the terminal density heat map shows the concentration of user devices; and the obstacle reflection data records the reflection characteristics of obstacles in the environment to wireless signals. Together, this information forms a comprehensive matrix reflecting the current environmental status, which is used for subsequent beam management and resource scheduling.

[0068] In practice, the system first uses distributed sensor nodes to collect real-time data on electromagnetic field strength, user device density, and obstacle reflections. Data fusion technology then integrates this information into a dynamic environmental perception matrix. This matrix not only reflects the current state of the environment but also includes potential future trends, providing foundational data support for subsequent steps.

[0069] For example, in a smart city application scenario, a sensor network is deployed across the city to monitor electromagnetic field strength, user device density, and its changes in real time around 5G base stations. It also records signal reflection paths from buildings and other obstacles. During a large-scale public event, sensors may detect a large number of users concentrating in a certain area, as well as strong electromagnetic interference in certain locations. This information is aggregated and used to generate a dynamic environmental perception matrix, providing foundational data for subsequent beam management and resource scheduling.

[0070] Step 102: establishing a beam pointing model of the user equipment cluster motion trajectory based on the dynamic environment perception matrix, dynamically adjusting the main lobe radiation direction of the beam according to the beam pointing model, and creating a null area pointing to the interference source;

[0071] In this step, a beam pointing model is constructed based on the dynamic environment perception matrix. This mathematical model predicts the movement trajectory of a user device cluster. It incorporates the spatial distribution characteristics of the user devices and the spatial location parameters of the interference source. The vector prediction model calculates the motion direction angle and velocity attenuation coefficient of the user cluster in three-dimensional space. This guides the adjustment of the beam main lobe radiation direction and creates a null zone pointing towards the interference source to reduce interference impact.

[0072] In practice, the system extracts the spatial distribution characteristics of user device clusters and the spatial location parameters of interference sources from a dynamic environment perception matrix. It then constructs a vector prediction model of user cluster motion trajectories based on the temporal gradient of the user device density heat map. Based on this model, the system dynamically adjusts the main lobe radiation direction of the beam to consistently cover the user cluster's centroid. A multi-constraint optimization algorithm is then used to generate null zones to suppress interference sources.

[0073] For example, continuing with the aforementioned smart city scenario, the system predicts the movement direction and speed of the user device cluster based on the dynamic environmental perception matrix generated in step 101, and dynamically adjusts the main lobe radiation direction of the beam to consistently cover areas with high user density. Simultaneously, the system identifies interference sources within specific areas and creates null zones to suppress these interference sources, ensuring high-quality communication services.

[0074] Step 103: reconstructing a subcarrier distribution pattern with non-uniform spacing and compensating for multipath phase according to the main lobe radiation direction and the null region and in combination with multipath phase characteristics of a real-time channel;

[0075] In this step, the multipath phase characteristic matrix is a data structure that contains the arrival angle distribution and phase delay characteristics of multipath propagation paths, which is used to describe the multipath effect of the channel. The non-uniform subcarrier spacing pattern dynamically adjusts the subcarrier spacing based on the user device distribution density and channel conditions, aiming to improve spectrum efficiency and multipath interference mitigation. The phase compensation function is used to correct the phase offset caused by multipath effects, ensuring correct signal decoding at the receiver.

[0076] In practice, the system combines the mainlobe radiation direction and null region, leveraging the real-time channel multipath phase characteristics to reconstruct the distribution pattern of non-uniformly spaced subcarriers and compensate for multipath phase. Specifically, the system analyzes channel state information, extracts the arrival angle distribution and phase delay characteristics of the multipath propagation paths, and generates a multipath phase characteristic matrix. Based on this matrix, the phase offset of each subcarrier is calculated, and a phase compensation function is designed to align the phase delays along the multipath propagation paths at the receiver.

[0077] For example, in a smart city scenario, the system reconstructs the subcarrier distribution pattern based on the mainlobe radiation direction and null region obtained in step 102, combined with the real-time channel multipath phase characteristics. In high-density user areas, denser subcarrier spacing is used to improve spectral efficiency, while in low-density areas, the subcarrier spacing is increased to reduce multipath interference. The system also uses a phase compensation algorithm to ensure correct signal decoding at the receiver.

[0078] Step 104: combining the main lobe radiation direction, the subcarrier distribution pattern, and the physical layer structure, and dynamically adjusting the spatial multiplexing density of the time-frequency resource block based on service priority;

[0079] In this step, the physical layer structure is the basic unit used for data transmission in wireless communication systems. Its design needs to consider factors such as service priority and the spatial reuse density of time-frequency resource blocks. The spatial reuse density of time-frequency resource blocks refers to the number of frequency resources that can be shared by different users in the same time period. Dynamically adjusting this parameter based on service priority can maximize system performance while meeting the needs of high-priority services.

[0080] In practice, the system combines the mainlobe radiation pattern, subcarrier distribution pattern, and physical layer structure to dynamically adjust the spatial multiplexing density of time-frequency resource blocks based on service priority. This involves generating a multidimensional weight vector, outputting discretized service priority labels, and using a deep reinforcement learning algorithm to construct a beam-steering strategy network based on these labels and generate a subcarrier-resource block mapping table. Finally, the system dynamically divides the time slot length and symbol interval, and uses an asymmetric cyclic prefix mechanism to align the transmission windows of high-priority services.

[0081] For example, in a smart city scenario, the system dynamically adjusts the spatial multiplexing density of time-frequency resource blocks based on the subcarrier distribution pattern generated in step 103 and the beam management results in step 102, taking into account service priority. For high-priority services such as emergency rescue communications, the system allocates more resource blocks and shortens the time slot length to ensure low latency and high reliability. For lower-priority services, the system shares the remaining resource blocks to improve overall resource utilization.

[0082] Step 105: Iteratively optimize the collaborative matching threshold of the main lobe radiation direction, the subcarrier distribution, and the spatial multiplexing density through closed-loop interaction between the forward channel response characteristics and the reverse control instructions.

[0083] In this step, closed-loop interaction refers to the process of continuously optimizing system parameters through a feedback mechanism that combines forward channel response characteristics with reverse control instructions. The coordinated matching threshold is the optimal matching value between multiple system parameters (such as beam mainlobe pointing angle, subcarrier distribution, and spatial multiplexing density). Optimal performance is achieved through iterative optimization.

[0084] In practice, the system iteratively optimizes the coordinated matching thresholds for the mainlobe radiation direction, subcarrier distribution, and spatial multiplexing density through a closed-loop interaction between forward channel response characteristics and reverse control commands. Specifically, this involves using the forward channel response characteristics and reverse control commands to form a closed-loop feedback mechanism. Through continuous iterative optimization, the parameters of the beam mainlobe direction, subcarrier distribution, and spatial multiplexing density are adjusted to achieve the optimal match.

[0085] For example, in a smart city scenario, the system continuously monitors and adjusts the beam main lobe direction, subcarrier distribution, and spatial multiplexing density through a closed-loop feedback mechanism based on the resource allocation results generated in step 104. During the activity period, the system continuously optimizes beam pointing and resource allocation based on real-time feedback data, ensuring stable and efficient communication services even when the density and distribution of user devices are rapidly changing.

[0086] Through the close collaboration of the five steps described above, the system achieves intelligent optimization of the entire process, from environmental perception to beam management to resource allocation. The dynamic environmental perception matrix provides accurate environmental data, the beam pointing model ensures precise tracking of the beam main lobe direction and effective suppression of interference sources, the non-uniformly spaced subcarrier distribution pattern improves spectrum efficiency, the optimization of the physical layer structure ensures the efficient satisfaction of different business needs, and the closed-loop feedback mechanism achieves adaptive optimization of the system. This systematic approach significantly improves the communication system's anti-interference capability and spectrum efficiency, enhances the system's flexibility and stability, and provides a solid guarantee for efficient communication in complex and dynamic environments.

[0087] In order to further improve the accuracy of beam pointing and the interference suppression effect, in some embodiments, the step 102 of establishing a beam pointing model of the user equipment cluster motion trajectory based on the dynamic environment perception matrix, dynamically adjusting the main lobe radiation direction of the beam according to the beam pointing model and creating a null area pointing to the interference source includes:

[0088] The spatial distribution characteristics of the user device cluster and the spatial position parameters of the interference source are separated from the dynamic environment perception matrix, and a vector prediction model of the user cluster motion trajectory is constructed based on the time domain gradient change of the user device density heat map to calculate the motion direction angle and speed attenuation coefficient of the user cluster in three-dimensional space; according to the vector prediction model and the motion direction angle and speed attenuation coefficient of the user cluster, a dynamic matching mechanism between the main lobe radiation direction and the user cluster motion trajectory is established, and by minimizing the spatial offset error between the main lobe coverage center point and the user cluster mass center, a beam main lobe pointing angle sequence that is synchronously updated with the user cluster movement is generated; the direction of the interference source is extracted based on the obstacle reflection profile data. The spatial feature matrix of the interference source is constructed based on the azimuth distribution and reflection intensity. According to the spatial feature matrix and the motion direction angle and velocity attenuation coefficient of the user cluster, the null area of the beam weight vector is calculated in combination with a multi-constraint optimization algorithm, and the central axis of the null area coincides with the azimuth angle of the interference source and the null width and the reflection intensity are nonlinearly mapped. The main lobe pointing angle sequence of the beam and the null area are input into the beam shaping parameter fusion device. By dynamically balancing the conflicting goals of maximizing the main lobe gain and the null suppression intensity, a shaping weight vector set that jointly controls the radiation direction of the main lobe pointing angle sequence of the beam and the distribution of the null area is generated and loaded into the antenna array unit in real time.

[0089] In this embodiment, the dynamic environment perception matrix includes information such as the spatial distribution characteristics of user device clusters (such as user device density heat maps) and the spatial location parameters of interference sources (such as obstacle reflection profile data). A vector prediction model is used to describe the motion trajectory of user clusters. By analyzing the temporal gradient changes of the user device density heat map, the motion direction angle and velocity attenuation coefficient of the user cluster in three-dimensional space are predicted. The spatial characteristic matrix of the interference source records the azimuth distribution of the interference source and its reflection intensity, which is used to calculate the null region of the beam weight vector.

[0090] In an embodiment of the present application, the system first extracts the spatial distribution characteristics of the user device cluster and the spatial position parameters of the interference source from the dynamic environment perception matrix, and uses the time domain gradient change of the user device density heat map to construct a vector prediction model of the user cluster motion trajectory, and calculates the motion direction angle and velocity attenuation coefficient of the user cluster. Then, based on the vector prediction model, a dynamic matching mechanism is established between the main lobe radiation direction and the user cluster motion trajectory, and by minimizing the spatial offset error between the main lobe coverage center point and the user cluster mass center, a beam main lobe pointing angle sequence that is updated synchronously with the user cluster movement is generated. At the same time, the system extracts the azimuth distribution and reflection intensity of the interference source based on the obstacle reflection profile data, constructs the spatial feature matrix of the interference source, and calculates the null area of the beam weight vector in combination with the multi-constraint optimization algorithm, so that the central axis of the null area coincides with the azimuth of the interference source and adjusts the null width to adapt to the reflection intensity. Finally, the system inputs the beam main lobe pointing angle sequence and the null area into the beamforming parameter fuser. By dynamically balancing the goals of maximizing the main lobe gain and the null suppression strength, it generates a set of shaping weight vectors that jointly control the radiation direction of the beam main lobe pointing angle sequence and the distribution of the null area, and loads them into the antenna array unit in real time.

[0091] The following is a specific embodiment:

[0092] At a large-scale public event, a sensor network is deployed throughout the venue, monitoring the electromagnetic field strength, user device density, and its dynamics around 5G base stations in real time. It also records signal reflection paths from buildings and other obstacles. The system extracts the spatial distribution characteristics of user device clusters and the spatial location parameters of interference sources from the dynamic environmental perception matrix. Using the temporal gradient of the user device density heat map, it constructs a vector prediction model for the user cluster's motion trajectory, predicting the angular direction and velocity attenuation coefficient of the user cluster in three-dimensional space. For example, at the event, the system detected a large number of users concentrated in a certain area and moving toward the stage. It then dynamically adjusted the main lobe radiation direction of the beam to consistently cover the high-density user area. Simultaneously, the system identifies interference sources within specific areas and creates null zones to suppress these interference sources. Based on obstacle reflection profile data, the system extracts the azimuth distribution and reflection intensity of the interference sources, constructs a spatial feature matrix for the interference sources, and uses a multi-constraint optimization algorithm to calculate the null zone for the beam weight vector, ensuring that the central axis of the null zone coincides with the azimuth of the interference source. The system also inputs the beam mainlobe pointing angle sequence and the null area into the beamforming parameter fuser. By dynamically balancing the goals of maximizing the mainlobe gain and the null suppression strength, it generates a set of shaping weight vectors that jointly control the radiation direction of the beam mainlobe pointing angle sequence and the distribution of the null area, and loads it into the antenna array unit in real time, thereby providing stable and efficient communication services in complex environments.

[0093] To further improve the accuracy and dynamic adaptability of beam pointing, in certain embodiments, step 102 includes establishing a dynamic matching mechanism between the main lobe radiation direction and the user cluster motion trajectory based on the vector prediction model and the motion direction angle and velocity attenuation coefficient of the user cluster, and generating a beam main lobe pointing angle sequence that is synchronously updated with the user cluster motion by minimizing the spatial offset error between the main lobe coverage center point and the user cluster mass center. The method further includes:

[0094] Based on the three-dimensional motion direction angle, velocity attenuation coefficient and distribution density of the user device cluster output by the vector prediction model, a user cluster centroid motion trajectory function weighted by a time-varying weight factor is constructed, wherein the weight factor is negatively correlated with the regional signal quality attenuation rate of the device density heat map; according to the user cluster centroid motion trajectory function, a sliding time window mechanism is used to predict the discrete position sequence of the user cluster centroid within a preset time interval, and combined with the beam width adaptive adjustment rule of the antenna array, a dynamic offset error model between the main lobe coverage center point and the user cluster centroid position is established, and the beam width adaptive adjustment rule is based on the user cluster distribution range. The spatial discreteness of the range and the preset coverage redundancy threshold are dynamically calculated; an iterative optimization algorithm for the directional angle correction is introduced, and the real-time error value output by the dynamic offset error model is used as the objective function. In each iteration, the correction is jointly calculated according to the gradient direction of the discretized position sequence and the antenna array steering delay parameter to obtain the optimal solution sequence of the beam pointing angle; the optimal solution sequence is input into the beam morphology synthesizer, and based on the reconfigurable directional pattern characteristics of the antenna array unit, a beam main lobe pointing angle sequence that matches the spatial distribution range of the user cluster is generated, so that the gain attenuation rate at the edge of the main lobe coverage area is consistent with the spatial gradient of the user cluster distribution density.

[0095] In this embodiment, the user cluster centroid motion trajectory function is a mathematical model that uses the three-dimensional motion direction angle, velocity attenuation coefficient, and distribution density of the user device cluster output by the vector prediction model to describe the movement trend of the user cluster. The time-varying weight factor is used to adjust the importance of user devices in different areas. Its value is negatively correlated with the regional signal quality attenuation rate of the device density heat map, that is, the area with worse signal quality has a greater weight. The dynamic offset error model evaluates the accuracy of beam pointing by comparing the position difference between the main lobe coverage center point and the user cluster centroid. The beam width adaptive adjustment rule dynamically adjusts the beam width based on the spatial discreteness of the user cluster distribution range and the preset coverage redundancy threshold.

[0096] In an embodiment of the present application, first, the system constructs a user cluster centroid motion trajectory function based on the data output by the vector prediction model, and uses a sliding time window mechanism to predict the discrete position sequence of the user cluster centroid within a preset time interval in the future. Then, combined with the beam width adaptive adjustment rule of the antenna array, a dynamic offset error model between the main lobe coverage center point and the user cluster centroid position is established. Then, by introducing an iterative optimization algorithm for the directional angle correction amount, the real-time error value output by the dynamic offset error model is used as the objective function. In each iteration, the correction amount is jointly calculated according to the gradient direction of the discretized position sequence and the antenna array steering delay parameter to obtain the optimal solution sequence of the beam pointing angle. Finally, the optimal solution sequence is input into the beam morphology synthesizer, and based on the reconfigurable directional pattern characteristics of the antenna array unit, a beam main lobe pointing angle sequence that matches the spatial distribution range of the user cluster is generated to ensure that the edge gain attenuation rate of the main lobe coverage area is consistent with the spatial gradient of the user cluster distribution density.

[0097] The following is a specific embodiment:

[0098] At a large-scale public event, a sensor network is deployed throughout the venue, monitoring the electromagnetic field strength, user device density, and its dynamics around 5G base stations in real time. It also records signal reflection paths from buildings and other obstacles. The system first extracts the three-dimensional motion direction angles, velocity attenuation coefficients, and distribution density of user device clusters from the dynamic environment perception matrix. It then constructs a trajectory function for the user cluster's centroid and uses a sliding time window mechanism to predict the discrete position sequence of the user cluster's centroid over the next few minutes. For example, at the event, the system detected a large number of users concentrated in a certain area and moving toward the stage. It then dynamically adjusts the mainlobe radiation direction of the beam to consistently cover the high-density user area. Simultaneously, the system incorporates the antenna array's adaptive beamwidth adjustment rules to model the dynamic offset error between the mainlobe coverage center and the user cluster's centroid. By introducing an iterative optimization algorithm for directional angle corrections, the system continuously optimizes the beam pointing angle. In each iteration, the correction is calculated based on the gradient direction of the discretized position sequence and the antenna array's steering delay parameters, resulting in the optimal solution sequence for the beam pointing angle. Finally, the optimal solution sequence is input into the beamform synthesizer. Based on the reconfigurable pattern characteristics of the antenna array unit, a beam mainlobe pointing angle sequence that matches the spatial distribution range of the user cluster is generated, ensuring that the gain attenuation rate at the edge of the mainlobe coverage area is consistent with the spatial gradient of the user cluster distribution density, thereby providing stable and efficient communication services in complex environments.

[0099] In order to further improve the accuracy and adaptability of interference suppression, in certain embodiments, the step 102 extracts the azimuth distribution and reflection intensity of the interference source based on the obstacle reflection profile data, constructs a spatial feature matrix of the interference source, and calculates the null area of the beam weight vector based on the spatial feature matrix and the motion direction angle and velocity attenuation coefficient of the user cluster in combination with a multi-constraint optimization algorithm, so that the central axis of the null area coincides with the azimuth angle of the interference source and the null width and the reflection intensity have a nonlinear mapping relationship, including:

[0100] Based on the obstacle reflection profile data, the azimuth distribution cluster of the interference source and the corresponding reflection intensity level are extracted through the arrival angle estimation and signal attenuation factor analysis of the multipath reflection path; according to the azimuth distribution cluster and the reflection intensity level, a spatial feature matrix is constructed, wherein the row vector of the spatial feature matrix represents the center of the azimuth distribution cluster, the column vector represents the nonlinear quantization interval of the reflection intensity level, and the element value of the spatial feature matrix is the cumulative value of the weighted reflection intensity of the number of interference sources in each cluster; combined with the motion direction angle and speed attenuation coefficient of the user cluster, the relative motion trajectory of the user cluster and the interference source is predicted, the dynamic azimuth offset model of the interference source is established, and the null center axis is calculated. Real-time correction amount; based on the spatial feature matrix and the dynamic offset model, a multi-objective optimization problem is constructed with the coincidence of the null center axis as a hard constraint and the nonlinear mapping of the null width and the reflection intensity as a soft constraint, and the feasible solution set of the beam weight vector is solved by the weighted least squares method, wherein the null width function is designed as a piecewise exponential function of the reflection intensity to adapt to the suppression range requirements under different interference intensities; the feasible solution set of the beam weight vector is input into the antenna array response controller, and a three-dimensional radiation suppression pattern of the null area is generated according to the array aperture phase distribution, and the conflict threshold between the null depth and the user cluster receiving signal quality is monitored in real time. When performance degradation is detected, the dynamic backtracking calibration of the beam weight vector is triggered.

[0101] In this embodiment, the spatial characteristic matrix is a data structure that describes the characteristics of the interference source. Its row vectors represent the centers of the interference source azimuth distribution clusters, the column vectors represent the nonlinear quantization intervals of the reflection intensity levels, and the element values are the cumulative values of the reflection intensity weighted by the number of interference sources in each cluster. The arrival angle estimation of the multipath reflection path is used to determine the azimuth distribution clusters of the interference source, while the signal attenuation factor analysis evaluates the reflection intensity level. The dynamic azimuth offset model of the interference source is used to predict the relative motion trajectory between the user cluster and the interference source, and calculate the real-time correction amount of the null center axis. The multi-objective optimization problem solves the feasible solution set of the beam weight vector by the weighted least squares method to ensure effective coverage of the null area.

[0102] In an embodiment of the present application, first, the system extracts the azimuth distribution cluster and reflection intensity level of the interference source based on the obstacle reflection profile data through arrival angle estimation and signal attenuation factor analysis of the multipath reflection path. Then, a spatial feature matrix is constructed, in which the row vector represents the center of the azimuth distribution cluster, the column vector represents the nonlinear quantization interval of the reflection intensity level, and the element value is the cumulative value of the weighted reflection intensity of the number of interference sources in each cluster. Then, combined with the motion direction angle and velocity attenuation coefficient of the user cluster, the relative motion trajectory of the user cluster and the interference source is predicted, a dynamic offset model of the azimuth angle of the interference source is established, and the real-time correction amount of the null center axis is calculated. Based on the spatial feature matrix and the dynamic offset model, a multi-objective optimization problem is constructed with the coincidence of the null center axis as a hard constraint and the nonlinear mapping of the null width and reflection intensity as a soft constraint, and the feasible solution set of the beam weight vector is solved by the weighted least squares method. Finally, the feasible solution set of the beam weight vector is input into the antenna array response controller. A three-dimensional radiation suppression pattern of the null area is generated according to the array aperture phase distribution. The conflict threshold between the null depth and the quality of the user cluster received signal is monitored in real time. When performance degradation is detected, dynamic backtracking calibration of the beam weight vector is triggered.

[0103] The following is a specific embodiment:

[0104] At a large-scale public event, a sensor network is deployed throughout the venue, monitoring the electromagnetic field strength around 5G base stations, user device density, and its dynamics in real time. It also records signal reflection paths from buildings and other obstacles. The system first extracts the azimuth distribution clusters and reflection intensity levels of interference sources based on obstacle reflection profile data, estimating the angle of arrival of multipath reflection paths and analyzing signal attenuation factors. For example, at the event site, the system discovered multiple interference sources distributed at different azimuths, each with a different reflection intensity level. The system constructs a spatial feature matrix, in which row vectors represent the centers of interference source azimuth distribution clusters, column vectors represent nonlinear quantized intervals of reflection intensity levels, and the element values are the cumulative reflection intensities weighted by the number of interference sources within each cluster. Next, combining the motion direction angles and velocity attenuation coefficients of the user clusters, the system predicts the relative motion trajectories of the user clusters and the interference sources, establishes a dynamic azimuth offset model for the interference sources, and calculates real-time corrections to the null center axis. Based on the spatial characteristic matrix and dynamic offset model, the system constructs a multi-objective optimization problem with the hard constraint of null center axis coincidence and the soft constraint of nonlinear mapping between null width and reflection intensity. The feasible solution set of the beam weight vector is solved using the weighted least squares method. Ultimately, the feasible solution set of the beam weight vector is input into the antenna array response controller, which generates a three-dimensional radiation suppression pattern for the null area based on the array aperture phase distribution. The conflict threshold between the null depth and the received signal quality of the user cluster is monitored in real time. When performance degradation is detected, the system triggers dynamic back-calibration of the beam weight vector to ensure stable and efficient communication services in complex environments.

[0105] To further improve the accuracy and adaptability of beam coverage, in certain embodiments, step 102 uses a sliding time window mechanism to predict a discrete position sequence of the user cluster centroid within a preset time interval based on the user cluster centroid motion trajectory function, and combines the antenna array's beam width adaptive adjustment rule to establish a dynamic offset error model between the main lobe coverage center point and the user cluster centroid position. The beam width adaptive adjustment rule is dynamically calculated based on the spatial discreteness of the user cluster distribution range and a preset coverage redundancy threshold, and further includes:

[0106] Based on the user cluster centroid motion trajectory function, a sliding time window with a fixed step size is used to divide the trajectory segments, and the spatiotemporal dynamic evolution law is modeled by a neural differential equation in each window, and a discretized centroid position sequence within a preset time interval is output; the spatial dispersion index of the current user cluster distribution is calculated in real time, and the user position coordinates are dynamically grouped using a clustering algorithm, and the beam width adjustment coefficient is determined according to the ratio of the intra-class distance standard deviation of the clustering result to the preset coverage redundancy threshold; according to the beam width adjustment coefficient, the phase distribution of the excitation signal of the antenna array is reconstructed through a phase difference compensation algorithm to generate the main lobe beam width parameter that meets the current user distribution range, and the actual coverage area boundary coordinates are recorded; based on the discretized centroid position sequence, the main lobe beam width parameter and the actual coverage area boundary coordinates, a dynamic offset error model between the main lobe coverage center point and the predicted centroid position is established.

[0107] In this embodiment, the user cluster centroid motion trajectory function is a mathematical model used to describe the movement trend of the user device cluster in three-dimensional space. It is constructed based on the user's motion direction angle, velocity attenuation coefficient and distribution density. The discretized centroid position sequence is a predicted position sequence of the user cluster in the future obtained by sliding time window and neural differential equation modeling. The beam width adjustment coefficient is a parameter calculated based on the spatial discreteness of the user cluster distribution and the preset coverage redundancy threshold, and is used to dynamically adjust the beam width of the antenna array. The main lobe beam width parameter refers to the width of the main lobe beam generated by the antenna array to ensure coverage of the current user distribution range.

[0108] In an embodiment of the present application, first, the system divides the trajectory segments into segments based on the user cluster centroid motion trajectory function using a sliding time window with a fixed step size, and models the spatiotemporal dynamic evolution law within each window through a neural differential equation, and outputs a discretized centroid position sequence within a preset time interval. Next, the system calculates the spatial dispersion index of the current user cluster distribution in real time, and dynamically groups the user position coordinates using a clustering algorithm. The beam width adjustment coefficient is determined based on the ratio of the intra-class distance standard deviation of the clustering result to the preset coverage redundancy threshold. Then, based on this coefficient, the phase distribution of the excitation signal of the antenna array is reconstructed through a phase difference compensation algorithm to generate the main lobe beam width parameters that meet the current user distribution range, and the actual coverage area boundary coordinates are recorded. Finally, based on the discretized centroid position sequence, the main lobe beam width parameters and the actual coverage area boundary coordinates, a dynamic offset error model between the main lobe coverage center point and the predicted centroid position is established to minimize the spatial offset error between the two.

[0109] The following is a specific embodiment:

[0110] At a large-scale public event, a sensor network is deployed throughout the venue, monitoring the electromagnetic field strength around 5G base stations, user device density, and its dynamics in real time. It also records signal reflection paths from buildings and other obstacles. The system first segments the trajectory using a sliding time window with a fixed step size based on the user cluster centroid trajectory function. Within each window, a neural differential equation models the spatiotemporal dynamics of the evolution, outputting a sequence of discretized centroid positions for the next few minutes. For example, at the event, the system detects a large number of users concentrating in a certain area and moving toward the stage. It then dynamically adjusts the mainlobe radiation direction of the beam to consistently cover the high-density user area. Simultaneously, the system calculates the spatial dispersion index of the current user cluster distribution in real time and dynamically groups user location coordinates using a clustering algorithm. The beamwidth adjustment coefficient is determined by the ratio of the standard deviation of the intra-cluster distances in the clustering results to a preset coverage redundancy threshold. Based on this coefficient, the system reconstructs the phase distribution of the excitation signal of the antenna array using a phase difference compensation algorithm, generating the mainlobe beamwidth parameters that meet the current user distribution range and recording the actual coverage area boundary coordinates. Ultimately, based on the sequence of discretized centroid positions, mainlobe beamwidth parameters, and the actual coverage area boundary coordinates, the system established a dynamic offset error model between the mainlobe coverage center and the predicted centroid position. This ensures that the beam coverage area always aligns with the centroid position of the user cluster, thereby providing stable and efficient communication services in complex environments. This approach not only improves the accuracy of beam coverage but also effectively reduces the risk of multipath interference and signal loss, improving user experience and service quality.

[0111] To further improve spectrum efficiency and multipath interference resistance, in some embodiments, the step 103 of reconstructing a subcarrier distribution pattern with non-uniform spacing and compensating for multipath phase based on the main lobe radiation direction and the null region in combination with real-time channel multipath phase characteristics includes:

[0112] Based on the coverage range of the main lobe radiation direction and the suppression angle of the null zone, the arrival angle distribution and phase delay characteristics of the multipath propagation path in the current channel environment are extracted to construct a multipath phase characteristic matrix; according to the multipath phase characteristic matrix, the phase offset of each subcarrier on the multipath propagation path is calculated, and combined with the user equipment distribution density within the main lobe coverage range, a non-uniform subcarrier spacing allocation strategy is generated; based on the non-uniform subcarrier spacing allocation strategy, a phase compensation function is constructed, and the initial phase offset of each subcarrier is iteratively adjusted by the phase offset, and based on the initial phase offset, the phase delay on the multipath propagation path is aligned at the receiving end; the non-uniform subcarrier spacing allocation strategy and the phase compensation function are input into an orthogonal frequency division multiplexing modulator to generate a pilot sequence that matches the current channel environment, and the subcarrier spacing and the parameters of the phase compensation function are corrected in real time based on the pilot sequence in combination with a closed-loop feedback mechanism.

[0113] In this embodiment, the multipath phase characteristic matrix is a data structure containing the arrival angle distribution and phase delay characteristics of multipath propagation paths, used to describe the multipath effect of the channel. The non-uniform subcarrier spacing allocation strategy dynamically adjusts the subcarrier spacing based on the user device distribution density and channel conditions, aiming to improve spectrum efficiency and multipath interference mitigation. The phase compensation function corrects for phase offsets caused by multipath effects, ensuring correct signal decoding at the receiving end. A closed-loop feedback mechanism continuously optimizes the subcarrier spacing and phase compensation function parameters using pilot sequences to adapt to channel variations.

[0114] In an embodiment of the present application, first, the system extracts the arrival angle distribution and phase delay characteristics of the multipath propagation path in the current channel environment based on the coverage range of the main lobe radiation direction and the suppression angle of the null zone, and constructs a multipath phase characteristic matrix. Then, the system calculates the phase offset of each subcarrier on the multipath propagation path according to the matrix, and generates a non-uniform subcarrier spacing allocation strategy in combination with the user equipment distribution density within the main lobe coverage range. Then, the system constructs a phase compensation function based on the strategy, and iteratively adjusts the initial phase offset of each subcarrier so that the phase delays on the multipath propagation path are aligned at the receiving end. Finally, the non-uniform subcarrier spacing allocation strategy and the phase compensation function are input into the orthogonal frequency division multiplexing modulator to generate a pilot sequence that matches the current channel environment, and the subcarrier spacing and the parameters of the phase compensation function are corrected in real time based on the pilot sequence combined with a closed-loop feedback mechanism to ensure that the system can dynamically adapt to channel changes.

[0115] The following is a specific embodiment:

[0116] At a large-scale public event, a sensor network is deployed throughout the venue, monitoring the electromagnetic field strength, user device density, and changes around 5G base stations in real time. It also records signal reflection paths from buildings and other obstacles. The system first extracts the arrival angle distribution and phase delay characteristics of the multipath propagation paths in the current channel environment based on the coverage of the mainlobe radiation direction and the suppression angle of the null region, and constructs a multipath phase characteristic matrix. For example, at the event, the system discovered that a large number of users were concentrated in a certain area and moving toward the stage. The system then dynamically adjusts the mainlobe radiation direction of the beam to consistently cover the high-density user area. Simultaneously, the system calculates the phase offset of each subcarrier along the multipath propagation path based on the multipath phase characteristic matrix. This, combined with the user device density within the mainlobe coverage area, generates a non-uniform subcarrier spacing allocation strategy. In high-density user areas, denser subcarrier spacing is used to improve spectral efficiency, while in low-density areas, the subcarrier spacing is increased to reduce multipath interference.

[0117] Next, the system constructs a phase compensation function based on the non-uniform subcarrier spacing allocation strategy, and iteratively adjusts the initial phase offset of each subcarrier so that the phase delays on the multipath propagation path are aligned at the receiving end. Finally, the system inputs the non-uniform subcarrier spacing allocation strategy and the phase compensation function into the orthogonal frequency division multiplexing modulator to generate a pilot sequence that matches the current channel environment. By correcting the parameters of the subcarrier spacing and phase compensation function in real time based on the pilot sequence combined with a closed-loop feedback mechanism, the system can dynamically adapt to channel changes, ensuring stable and efficient communication services even when the density and distribution of user devices change rapidly. This method not only improves spectrum efficiency and anti-multipath interference capabilities, but also significantly enhances the flexibility and stability of the system, providing a solid guarantee for efficient communication in complex dynamic environments.

[0118] To further improve the flexibility of resource allocation and service quality, in certain embodiments, step 104 combines the main lobe radiation direction, the subcarrier distribution pattern, and the physical layer structure to dynamically adjust the spatial multiplexing density of the time-frequency resource block based on service priority, including:

[0119] Based on the service delay sensitivity, bandwidth requirements and reliability level, a multi-dimensional weight vector is generated through the hierarchical analysis method, and a discrete service priority label is output and associated with the logical channel identifier of the physical layer structure; according to the real-time location distribution of the user cluster and the service priority label, a deep reinforcement learning algorithm is used to construct a beam pointing strategy network, output the main lobe radiation direction angle and the beamforming weight matrix, and record the beam switching delay parameters; based on the beamforming weight matrix and the service priority label, the subcarriers are divided into high-priority service dedicated clusters, low-priority shared clusters and protection intervals through an improved graph theory clustering algorithm, and a beam pointing strategy network is generated. A subcarrier resource block mapping relationship table is generated, and the interference suppression factor within each cluster is calculated; according to the subcarrier resource block mapping relationship table and the beam switching delay parameter, the time slot length and symbol interval are dynamically divided, and an asymmetric cyclic prefix mechanism is adopted to align the transmission window of the high-priority service-dedicated cluster, and the spatial multiplexing density of the time-frequency resource block is output; based on the spatial multiplexing density and the interference suppression factor, a multi-objective optimization algorithm is used to solve the modulation order and code rate combination that meets the service priority constraint, and a binding relationship configuration instruction between the antenna port and the resource block is generated, and the spatial multiplexing density is dynamically adjusted based on the binding relationship configuration instruction.

[0120] In this embodiment, a multidimensional weight vector is a data structure generated by the hierarchical analysis method, which is used to quantify indicators such as service delay sensitivity, bandwidth requirements and reliability level, and then generate discrete service priority labels. These labels are associated with the logical channel identifiers of the physical layer structure to facilitate subsequent resource scheduling. The beamforming weight matrix describes the phase distribution of the excitation signal of the antenna array and is used to generate the optimal beam direction and shape. The subcarrier resource block mapping relationship table defines how subcarriers are allocated to different service types, including high-priority service dedicated clusters, low-priority shared clusters and guard intervals. The interference suppression factor is used to evaluate the interference level within each cluster to help optimize resource allocation.

[0121] In an embodiment of the present application, first, the system generates a multidimensional weight vector through a hierarchical analysis method based on service delay sensitivity, bandwidth requirements and reliability level, outputs a discretized service priority label, and associates it with the logical channel identifier of the physical layer structure. Then, according to the real-time location distribution of the user cluster and the service priority label, a deep reinforcement learning algorithm is used to construct a beam pointing strategy network, output the main lobe radiation direction angle and the beamforming weight matrix, and record the beam switching delay parameters. Then, based on the beamforming weight matrix and the service priority label, the subcarriers are divided into different types of clusters through an improved graph theory clustering algorithm, a subcarrier resource block mapping relationship table is generated, and the interference suppression factor within each cluster is calculated. Next, according to the subcarrier resource block mapping relationship table and the beam switching delay parameters, the system dynamically divides the time slot length and symbol interval, uses an asymmetric cyclic prefix mechanism to align the transmission window of the high-priority service dedicated cluster, and outputs the spatial multiplexing density of the time-frequency resource block. Finally, based on the spatial multiplexing density and interference suppression factor, a multi-objective optimization algorithm is used to solve the modulation order and code rate combination that meets the service priority constraints, generate the binding relationship configuration instructions between the antenna port and the resource block, and dynamically adjust the spatial multiplexing density based on the instructions.

[0122] The following is a specific embodiment:

[0123] At a large-scale public event, a sensor network is deployed throughout the venue, monitoring electromagnetic field strength around 5G base stations, user device density, and its dynamics in real time. It also records signal reflection paths from buildings and other obstacles. The system first generates a multidimensional weight vector based on service latency sensitivity, bandwidth requirements, and reliability levels using the Analytic Hierarchy Process (AHP). It then outputs a discretized service priority label and associates it with the logical channel identifier of the physical layer structure. For example, at the event, the system identifies emergency rescue communications as high-priority services, while video streaming for ordinary users is medium-priority. Based on the real-time location distribution and service priority labels of user clusters, the system employs a deep reinforcement learning algorithm to construct a beam-steering strategy network. This network outputs the mainlobe radiation angle and beamforming weight matrix, and records beam switching delay parameters. For example, if the system detects a large number of users concentrating in a certain area and moving toward the stage, it dynamically adjusts the mainlobe radiation direction of the beam to maintain coverage of the high-priority user area. At the same time, based on the beamforming weight matrix and service priority labels, the system uses an improved graph-theoretic clustering algorithm to divide subcarriers into high-priority service-dedicated clusters, low-priority shared clusters, and guard intervals. It then generates a subcarrier resource block mapping table and calculates the interference suppression factor within each cluster. In high-priority user areas, dense subcarrier spacing is used to improve spectral efficiency, while in low-priority areas, subcarrier spacing is widened to reduce multipath interference. Next, based on the subcarrier resource block mapping table and beam switching delay parameters, the system dynamically divides the time slot length and symbol interval, uses an asymmetric cyclic prefix mechanism to align the transmission window of the high-priority service-dedicated cluster, and outputs the spatial multiplexing density of the time-frequency resource blocks. For example, for high-priority services such as emergency rescue communications, the system allocates more resource blocks and shortens the time slot length to ensure low latency and high reliability. For low-priority services, the system shares the remaining resource blocks to improve overall resource utilization. Finally, based on the spatial multiplexing density and interference suppression factor, a multi-objective optimization algorithm is used to determine the modulation order and code rate combination that satisfies service priority constraints. This generates configuration instructions for the binding relationship between antenna ports and resource blocks, and dynamically adjusts the spatial multiplexing density based on these instructions, thereby providing stable and efficient communication services in complex environments. This approach not only improves resource allocation flexibility but also significantly enhances the system's anti-interference capabilities and user experience.

[0124] Figure 2 A structural diagram of a 5G communication system based on environment adaptation is provided for an embodiment of the present application, such as Figure 2 As shown, the system includes:

[0125] Generation module 21, used to generate a dynamic environment perception matrix by integrating electromagnetic field intensity distribution, terminal density heat map and obstacle reflection data in real time through distributed sensor nodes;

[0126] Establishing module 22, configured to establish a beam pointing model of the user equipment cluster motion trajectory based on the dynamic environment perception matrix, dynamically adjust the main lobe radiation direction of the beam according to the beam pointing model and create a null area pointing to the interference source;

[0127] A reconstruction module 23 is configured to reconstruct a subcarrier distribution pattern with non-uniform spacing and compensate for the multipath phase according to the main lobe radiation direction and the null region in combination with the multipath phase characteristics of the real-time channel;

[0128] An adjustment module 24 is configured to combine the main lobe radiation direction, the subcarrier distribution pattern, and the physical layer structure to dynamically adjust the spatial multiplexing density of the time-frequency resource block based on service priority;

[0129] The optimization module 25 is used to iteratively optimize the collaborative matching threshold of the main lobe radiation direction, the subcarrier distribution and the spatial multiplexing density through closed-loop interaction between the forward channel response characteristics and the reverse control instructions.

[0130] Figure 2 The 5G communication system based on environment adaptation can be performed Figure 1 The implementation principle and technical effects of the 5G communication method based on environmental adaptation described in the illustrated embodiment will not be repeated here. The specific manner in which each module and unit performs operations in the 5G communication system based on environmental adaptation in the above embodiment has been described in detail in the embodiment of the method and will not be elaborated here.

[0131] In one possible design, Figure 2 A 5G communication system based on environment adaptation in the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0132] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0133] The processing component 32 is as follows Figure 1 The embodiment provides a 5G communication method based on environment adaptation.

[0134] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0135] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0136] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0137] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0138] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0139] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0140] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 A 5G communication method based on environment adaptation in the illustrated embodiment.

[0141] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0142] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0143] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A 5G communication method based on environment adaptation, characterized in that: include: Generate a dynamic environment perception matrix by integrating electromagnetic field intensity distribution, terminal density heat map and obstacle reflection data in real time through distributed sensor nodes; Establishing a beam pointing model of the user equipment cluster motion trajectory based on the dynamic environment perception matrix, dynamically adjusting the main lobe radiation direction of the beam according to the beam pointing model and creating a null area pointing to the interference source; Reconstructing a subcarrier distribution pattern with non-uniform spacing and compensating for multipath phase based on the main lobe radiation direction and the null region in combination with real-time channel multipath phase characteristics, wherein the subcarrier distribution pattern dynamically adjusts the subcarrier spacing based on user equipment distribution density and channel conditions; Combining the main lobe radiation direction, the subcarrier distribution pattern, and the physical layer structure, dynamically adjusting the spatial multiplexing density of the time-frequency resource block based on service priority, where the spatial multiplexing density is the number of frequency resources shared by different users in the same time period; Iteratively optimizing the collaborative matching threshold of the main lobe radiation direction, the subcarrier distribution, and the spatial multiplexing density through closed-loop interaction between forward channel response characteristics and reverse control instructions; The step of establishing a beam pointing model of a user equipment cluster motion trajectory based on the dynamic environment perception matrix, dynamically adjusting a main lobe radiation direction of a beam according to the beam pointing model, and creating a null area pointing to an interference source includes: Separating the spatial distribution characteristics of the user device cluster and the spatial location parameters of the interference source from the dynamic environment perception matrix, and constructing a vector prediction model of the user cluster's motion trajectory based on the time-domain gradient change of the user device density heat map to calculate the motion direction angle and velocity attenuation coefficient of the user cluster in three-dimensional space; Based on the vector prediction model and the motion direction angle and velocity attenuation coefficient of the user cluster, a dynamic matching mechanism is established between the main lobe radiation direction and the motion trajectory of the user cluster. By minimizing the spatial offset error between the main lobe coverage center point and the user cluster mass center, a beam main lobe pointing angle sequence that is synchronously updated with the user cluster movement is generated; Extracting the azimuth distribution and reflection intensity of the interference source based on obstacle reflection profile data, constructing a spatial feature matrix of the interference source, and calculating the null area of the beam weight vector based on the spatial feature matrix and the motion direction angle and velocity attenuation coefficient of the user cluster in combination with a multi-constraint optimization algorithm, so that the central axis of the null area coincides with the azimuth of the interference source and the null width has a nonlinear mapping relationship with the reflection intensity; The beam main lobe pointing angle sequence and the null area are input into the beamforming parameter fusion device. By dynamically balancing the conflicting goals of maximizing the main lobe gain and the null suppression strength, a shaping weight vector set that jointly controls the radiation direction of the beam main lobe pointing angle sequence and the distribution of the null area is generated and loaded into the antenna array unit in real time.

2. The method according to claim 1, characterized in that The method includes establishing a dynamic matching mechanism between the main lobe radiation direction and the user cluster motion trajectory based on the vector prediction model and the motion direction angle and velocity attenuation coefficient of the user cluster, and generating a beam main lobe pointing angle sequence that is synchronously updated with the user cluster motion by minimizing the spatial offset error between the main lobe coverage center point and the user cluster mass center. The method includes: Based on the three-dimensional motion direction angle, velocity attenuation coefficient, and distribution density of the user device cluster output by the vector prediction model, a user cluster centroid motion trajectory function weighted by a time-varying weight factor is constructed. The weight factor is negatively correlated with the regional signal quality attenuation rate of the device density heat map. Based on the user cluster centroid motion trajectory function, a sliding time window mechanism is used to predict the discrete position sequence of the user cluster centroid within a preset time interval. Combined with the antenna array's beamwidth adaptive adjustment rule, a dynamic offset error model is established between the mainlobe coverage center point and the user cluster centroid position. The beamwidth adaptive adjustment rule is dynamically calculated based on the spatial discreteness of the user cluster distribution range and a preset coverage redundancy threshold. An iterative optimization algorithm for azimuth angle corrections is introduced, with the real-time error value output by the dynamic offset error model as the objective function. In each iteration, the correction is jointly calculated based on the gradient direction of the discretized position sequence and the antenna array steering delay parameter to obtain the optimal solution sequence for the beam pointing angle. The optimal solution sequence is input into the beamform synthesizer. Based on the reconfigurable pattern characteristics of the antenna array unit, a beam mainlobe pointing angle sequence that matches the spatial distribution range of the user cluster is generated, so that the gain attenuation rate at the edge of the mainlobe coverage area is consistent with the spatial gradient of the user cluster distribution density.

3. The method according to claim 1, characterized in that The method includes extracting the azimuth distribution and reflection intensity of the interference source based on the obstacle reflection profile data, constructing a spatial feature matrix of the interference source, and calculating the null area of the beam weight vector based on the spatial feature matrix and the motion direction angle and velocity attenuation coefficient of the user cluster in combination with a multi-constraint optimization algorithm, so that the central axis of the null area coincides with the azimuth of the interference source and the null width and the reflection intensity have a nonlinear mapping relationship, including: Based on the obstacle reflection profile data, the azimuth distribution cluster of the interference source and the corresponding reflection intensity level are extracted by estimating the arrival angle of the multipath reflection path and analyzing the signal attenuation factor; Constructing a spatial feature matrix based on the azimuth distribution clusters and the reflection intensity levels, wherein the row vectors of the spatial feature matrix represent the centers of the azimuth distribution clusters, the column vectors represent the nonlinear quantization intervals of the reflection intensity levels, and the element values of the spatial feature matrix are the cumulative values of the reflection intensities weighted by the number of interference sources in each cluster; Combining the motion direction angle and velocity attenuation coefficient of the user cluster, predicting the relative motion trajectory of the user cluster and the interference source, establishing a dynamic azimuth offset model of the interference source, and calculating the real-time correction value of the null center axis; Based on the spatial feature matrix and the dynamic offset model, a multi-objective optimization problem is constructed with the coincidence of the null center axis as a hard constraint and the nonlinear mapping between the null width and the reflection intensity as a soft constraint. The feasible solution set of the beam weight vector is solved by the weighted least squares method, where the null width function is designed as a piecewise exponential function of the reflection intensity to adapt to the suppression range requirements under different interference intensities; The feasible solution set of the beam weight vector is input into the antenna array response controller, and a three-dimensional radiation suppression pattern of the null area is generated according to the array aperture phase distribution. The conflict threshold between the null depth and the quality of the user cluster received signal is monitored in real time. When performance degradation is detected, dynamic backtracking calibration of the beam weight vector is triggered.

4. The method according to claim 2, characterized in that The method uses a sliding time window mechanism to predict the discrete position sequence of the user cluster centroid within a preset time interval based on the user cluster centroid motion trajectory function, and combines the beam width adaptive adjustment rule of the antenna array to establish a dynamic offset error model between the main lobe coverage center point and the user cluster centroid position. The beam width adaptive adjustment rule is dynamically calculated based on the spatial discreteness of the user cluster distribution range and a preset coverage redundancy threshold, including: Based on the user cluster centroid motion trajectory function, a sliding time window with a fixed step size is used to divide the trajectory segments. In each window, a neural differential equation is used to model the spatiotemporal dynamic evolution law, and a discretized centroid position sequence within a preset time interval is output; Calculate the spatial dispersion index of the current user cluster distribution in real time, use a clustering algorithm to dynamically group user location coordinates, and determine the beamwidth adjustment coefficient based on the ratio of the standard deviation of the intra-cluster distance of the clustering result to the preset coverage redundancy threshold; According to the beam width adjustment coefficient, the phase distribution of the excitation signal of the antenna array is reconstructed through a phase difference compensation algorithm to generate a main lobe beam width parameter that meets the current user distribution range, and the actual coverage area boundary coordinates are recorded; Based on the discretized centroid position sequence, the main lobe beamwidth parameter and the actual coverage area boundary coordinates, a dynamic offset error model between the main lobe coverage center point and the predicted centroid position is established.

5. The method according to claim 1, wherein The reconstructing a subcarrier distribution pattern with non-uniform spacing and compensating for multipath phase according to the main lobe radiation direction and the null region in combination with real-time channel multipath phase characteristics includes: Based on the coverage range of the main lobe radiation direction and the suppression angle of the null area, the arrival angle distribution and phase delay characteristics of the multipath propagation path in the current channel environment are extracted to construct a multipath phase characteristic matrix; Calculating the phase offset of each subcarrier on the multipath propagation path according to the multipath phase characteristic matrix, and generating a non-uniform subcarrier spacing allocation strategy based on the user equipment distribution density within the main lobe coverage area; Based on the non-uniform subcarrier spacing allocation strategy, a phase compensation function is constructed, and an initial phase offset of each subcarrier is iteratively adjusted by the phase offset, so that the phase delays on the multipath propagation path are aligned at the receiving end based on the initial phase offset; The non-uniform subcarrier spacing allocation strategy and the phase compensation function are input into an orthogonal frequency division multiplexing modulator to generate a pilot sequence matching the current channel environment, and the subcarrier spacing and the parameters of the phase compensation function are corrected in real time based on the pilot sequence in combination with a closed-loop feedback mechanism.

6. The method according to claim 1, characterized in that The step of combining the main lobe radiation direction, the subcarrier distribution pattern, and the physical layer structure to dynamically adjust the spatial multiplexing density of the time-frequency resource block based on service priority includes: Based on service delay sensitivity, bandwidth requirements, and reliability levels, a multidimensional weight vector is generated through the analytic hierarchy process, which outputs a discrete service priority label and associates it with the logical channel identifier of the physical layer structure. Based on the real-time location distribution of user clusters and the service priority labels, a deep reinforcement learning algorithm is used to build a beam pointing strategy network, output the main lobe radiation direction angle and beamforming weight matrix, and record the beam switching delay parameters; Based on the beamforming weight matrix and the service priority label, the subcarriers are divided into a high-priority service dedicated cluster, a low-priority shared cluster, and a guard interval using an improved graph theory clustering algorithm, a subcarrier resource block mapping relationship table is generated, and an interference suppression factor within each cluster is calculated; Dynamically divide the time slot length and symbol interval according to the subcarrier resource block mapping relationship table and the beam switching delay parameter, adopt an asymmetric cyclic prefix mechanism to align the transmission window of the high-priority service dedicated cluster, and output the spatial multiplexing density of the time-frequency resource block; Based on the spatial multiplexing density and the interference suppression factor, a multi-objective optimization algorithm is used to solve the modulation order and code rate combination that meets the service priority constraint, and a binding relationship configuration instruction between the antenna port and the resource block is generated. The spatial multiplexing density is dynamically adjusted based on the binding relationship configuration instruction.

7. A 5G communication system based on environmental adaptation, characterized in that: include: A generation module is used to generate a dynamic environment perception matrix by integrating electromagnetic field intensity distribution, terminal density heat map and obstacle reflection data in real time through distributed sensor nodes; An establishment module is used to establish a beam pointing model of the user equipment cluster motion trajectory based on the dynamic environment perception matrix, dynamically adjust the main lobe radiation direction of the beam according to the beam pointing model, and create a null area pointing to the interference source; a reconstruction module, configured to reconstruct a subcarrier distribution pattern with non-uniform spacing and compensate for the multipath phase based on the main lobe radiation direction and the null region in combination with real-time channel multipath phase characteristics, wherein the subcarrier distribution pattern is a method of dynamically adjusting the subcarrier spacing based on user equipment distribution density and channel conditions; an adjustment module, configured to combine the main lobe radiation direction, the subcarrier distribution pattern, and the physical layer structure to dynamically adjust the spatial multiplexing density of the time-frequency resource block based on service priority, where the spatial multiplexing density is the number of frequency resources shared by different users in the same time period; an optimization module, configured to iteratively optimize the collaborative matching threshold of the main lobe radiation direction, the subcarrier distribution, and the spatial multiplexing density through closed-loop interaction between forward channel response characteristics and reverse control instructions; The step of establishing a beam pointing model of a user equipment cluster motion trajectory based on the dynamic environment perception matrix, dynamically adjusting a main lobe radiation direction of a beam according to the beam pointing model, and creating a null area pointing to an interference source includes: Separating the spatial distribution characteristics of the user device cluster and the spatial location parameters of the interference source from the dynamic environment perception matrix, and constructing a vector prediction model of the user cluster's motion trajectory based on the time-domain gradient change of the user device density heat map to calculate the motion direction angle and velocity attenuation coefficient of the user cluster in three-dimensional space; Based on the vector prediction model and the motion direction angle and velocity attenuation coefficient of the user cluster, a dynamic matching mechanism is established between the main lobe radiation direction and the motion trajectory of the user cluster. By minimizing the spatial offset error between the main lobe coverage center point and the user cluster mass center, a beam main lobe pointing angle sequence that is synchronously updated with the user cluster movement is generated; Extracting the azimuth distribution and reflection intensity of the interference source based on obstacle reflection profile data, constructing a spatial feature matrix of the interference source, and calculating the null area of the beam weight vector based on the spatial feature matrix and the motion direction angle and velocity attenuation coefficient of the user cluster in combination with a multi-constraint optimization algorithm, so that the central axis of the null area coincides with the azimuth of the interference source and the null width has a nonlinear mapping relationship with the reflection intensity; The beam main lobe pointing angle sequence and the null area are input into the beamforming parameter fusion device. By dynamically balancing the conflicting goals of maximizing the main lobe gain and the null suppression strength, a shaping weight vector set that jointly controls the radiation direction of the beam main lobe pointing angle sequence and the distribution of the null area is generated and loaded into the antenna array unit in real time.

8. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a 5G communication method based on environment adaptation as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, an environment-adaptive 5G communication method according to any one of claims 1 to 6 is implemented.

Images