5G network optimization method, system and network equipment

Abstract

The invention relates to the technical field of network optimization, in particular to a 5G network optimization method and system and network equipment. The method comprises the following steps of: acquiring channel state information reported by terminal equipment and array antenna transmitting signal information through a 5G base station, calculating an optimal beam direction of an array antenna based on the channel state information, adjusting transmitting weight and power according to the optimal beam direction, monitoring a channel quality trend, dynamically updating transmitting parameters when negative correlation is formed, and transmitting the optimal beam direction of the array antenna according to the optimal beam direction of the array antenna. And finally, the array antenna is controlled to send the optimized 5G network signal to the terminal equipment, so that the network transmission efficiency and the signal quality are improved. By constructing a dynamic closed-loop optimization mechanism and combining channel state detection, beam direction selection and dynamic adjustment of transmitting weight and power, comprehensive improvement of 5G network transmission efficiency, signal coverage quality and adaptive capacity is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of network optimization, and in particular to a 5G network optimization method, system and network equipment. BACKGROUND

[0002] In the 5G network, array antennas and beamforming technology are widely used to improve the signal quality and transmission efficiency of the downlink. Early network optimization methods mainly rely on static configuration of base stations or beam direction and power allocation based on historical statistical data. Such methods have a lag in response in scenarios where the network environment changes rapidly, and cannot fully adapt to the dynamic changes of mobile terminals. In addition, some existing technologies only focus on a single indicator of signal-to-noise ratio or bit error rate, lack multi-dimensional analysis of channel quality, and are prone to misjudgment or unstable optimization effect. Further, some optimization schemes are rough in beam direction selection and can only adjust the overall beam direction, making it difficult to handle signal abnormalities and local attenuation at the array antenna unit level, resulting in uneven network coverage, reduced signal quality, and limited user experience. SUMMARY

[0003] Therefore, it is necessary to provide a 5G network optimization method, system and network equipment to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a 5G network optimization method is provided, the method comprising the following steps:

[0005] Step S1: acquiring, by a 5G base station, channel state information reported by a terminal device and transmission signal information of an array antenna;

[0006] Step S2: calculating an optimal beam direction of the array antenna in the 5G base station to the terminal device based on the channel state information; adjusting the transmission weight of the array antenna and determining the transmission power of the array antenna according to the optimal beam direction and the transmission signal information;

[0007] Step S3: detecting a channel quality trend of the channel state information, and triggering an update of the transmission weight and the transmission power when the channel quality trend is negatively correlated;

[0008] Step S4: controlling the array antenna to send a 5G network signal to the terminal device using the updated transmission weight and transmission power.

[0009] In this specification, a 5G network optimization system is provided for executing the above-mentioned 5G network optimization method, the 5G network optimization system comprising:

[0010] a signal acquisition module configured to acquire, by a 5G base station, channel state information reported by a terminal device and transmission signal information of an array antenna;

[0011] The power confirmation module is used for calculating the optimal beam direction of the array antenna to the terminal device in the 5G base station based on the channel state information; and adjusting the transmission weight of the array antenna and determining the transmission power of the array antenna according to the optimal beam direction and the transmission signal information;

[0012] The channel quality detection module is used for detecting the channel quality trend of the channel state information, and triggering the update of the transmission weight and the transmission power when the channel quality trend is negatively correlated;

[0013] The network optimization module is used for controlling the array antenna to send the 5G network signal to the terminal device by using the updated transmission weight and transmission power.

[0014] The application also provides a network device, which comprises a processor, a memory, a radio frequency transceiver module, a signal detection module and an optimization control interface module, and is electrically connected through a data bus, and is used for monitoring the 5G base station, the array antenna and the terminal device and performing the 5G network optimization method as described above.

[0015] The application has the following beneficial effects:

[0016] I. By acquiring the channel state information reported by the terminal device and the transmission signal information of the array antenna, the bidirectional combination of channel measurement and transmission control is realized, the network environment can be perceived in real time and the beam direction and power distribution can be dynamically optimized, compared with the traditional method depending on static parameters, the accuracy of channel state perception and the timeliness of network optimization are improved.

[0017] II. The application introduces the beam calculation data set and the beam pointing data set, the beam direction is screened by combining the direction stability coefficient and the attenuation amplitude through the abnormal fluctuation point analysis and the adjacent beam segment attenuation degree calculation, the abnormal interference can be effectively excluded and the precision and stability of the optimal beam direction determination are improved, so that the beam forming of the array antenna is more flexible and reliable.

[0018] III. In the channel quality detection, the change trends of the signal-to-noise ratio and the bit error rate are considered at the same time, the joint analysis and trend fitting are carried out based on the continuous time slot set, the real state of the channel can be more comprehensively reflected, the transmission weight and power update are triggered in time when the channel quality is negatively correlated, the dynamic adjustment of the array antenna is realized, and the accuracy and response speed of channel degradation identification are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 It is a step flowchart of a 5G network optimization method;

[0020] Figure 2 It is Figure 1 It is a detailed implementation step flowchart of step S2 in the method;

[0021] Figure 3 A scene schematic diagram of a 5G network optimization method of the present application;

[0022] Figure 4 A signal strength optimization dynamic comparison diagram of a 5G network optimization method of the present application;

[0023] The purposes, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0024] The technical method of the present application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0025] In addition, the accompanying drawings are only schematic diagrams of the present application, and are not necessarily drawn to scale. The same reference signs in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0026] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be referred to as a second element, and similarly a second element can be referred to as a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0027] To achieve the above-mentioned purposes, please refer to Figures 1 to 4 A 5G network optimization method, the method comprising the following steps:

[0028] Step S1: acquiring channel state information reported by a terminal device and transmission signal information of an array antenna through a 5G base station;

[0029] In one embodiment, the 5G base station first receives channel state information (CSI) through the uplink feedback channel of the terminal device. The CSI includes at least one or more of the following: reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-noise ratio (SNR), and channel quality indication (CQI). Within a preset feedback period (e.g., 5ms to 20ms), the terminal device estimates the channel characteristics based on the received downlink reference signal and reports the CSI to the 5G base station in real time through the uplink control channel.

[0030] Simultaneously, the 5G base station also collects the transmission signal information of its own array antennas. This transmission signal information includes parameters such as the transmission power, current phase, beam direction, and amplitude weighting coefficient of each antenna element. The base station obtains the transmission characteristic data of each antenna element in real time through the beamforming module of the array antenna and in conjunction with the digital precoding matrix.

[0031] Through the above acquisition methods, 5G base stations can simultaneously obtain CSI data fed back by terminal devices and transmission signal information from array antennas, thereby establishing a dynamic correspondence between uplink and downlink, and providing input basis for subsequent beam direction optimization and resource allocation.

[0032] It is important to note that in multi-user scenarios, 5G base stations can distinguish the CSI feedback of different terminal devices through user identifiers (such as C-RNTI) and compare the transmission signal characteristics of different antenna units to achieve parallel monitoring and management of the channel status of multiple users.

[0033] Step S2: Calculate the optimal beam direction of the array antenna for the terminal device in the 5G base station based on channel state information; adjust the transmission weight of the array antenna and determine the transmission power of the array antenna according to the optimal beam direction and transmission signal information;

[0034] In one embodiment, after obtaining the channel state information (CSI) reported by the terminal device, the 5G base station first performs a comprehensive analysis of the indicators such as signal-to-noise ratio (SNR), channel quality indication (CQI), and reference signal reception quality (RSRQ) in the CSI, and uses a preset beamforming algorithm (such as the minimum mean square error (MMSE) algorithm or the maximum signal-to-interference-plus-noise ratio (SINR) algorithm) to calculate the optimal beam direction of the array antenna for the target terminal device.

[0035] During the calculation process, the base station performs a fast Fourier transform on the amplitude and phase characteristics of each subcarrier in the CSI to obtain the angular characteristic distribution of the target terminal device in space. Based on the element spacing and arrangement of the array antenna, combined with the estimation results of the angle of arrival (AoA) and the angle of departure (AoD), the spatial orientation of the corresponding terminal device is locked.

[0036] Once the optimal beam direction is determined, the base station automatically adjusts the transmission weight of each element of the array antenna based on the transmitted signal information of each element (including the element current phase and amplitude weighting coefficient), so that the array antenna forms the maximum signal gain in the target direction, while suppressing the signal in non-target directions to reduce interference between multiple users.

[0037] While adjusting the transmit weights, the base station calculates and determines the transmit power of the array antenna based on the channel quality indication (CQI) and service type of the terminal device (such as high-definition video transmission, low-latency communication, or large-scale IoT access). If the terminal device is in a weak coverage edge area, the transmit power of the corresponding beam is increased; if the terminal device is in a good coverage area, the power is appropriately reduced to decrease energy consumption and interference. In this way, the base station can dynamically adjust the beam direction and transmit power of the array antenna, achieving precise beamforming and power control for single or multiple terminal devices, thereby improving link transmission quality and overall system capacity.

[0038] It is important to note that in multi-user concurrent scenarios, base stations can use beam scheduling mechanisms to allocate independent beam directions and power configurations to different terminals in different time slices or frequency domain resource blocks to avoid spatial resource conflicts.

[0039] Step S3: Detect the channel quality trend of the channel state information. When the channel quality trend is negatively correlated, the update of the transmit weight and transmit power is triggered.

[0040] In one embodiment, after the 5G base station completes the determination of the optimal beam direction and power configuration of the array antenna, it continuously detects the channel state information (CSI) reported by the terminal device and performs trend analysis on the key indicators in the CSI (such as signal-to-noise ratio SNR, channel quality indicator CQI, bit error rate BER, and reference signal reception quality RSRQ) in a time series.

[0041] During trend analysis, the base station smooths the CSI data for consecutive time slots, using moving averages or Kalman filtering to reduce the impact of instantaneous fluctuations, and calculates the rate of change of each indicator. When the channel quality shows a downward trend over time (i.e., the channel quality trend is negatively correlated with the time series), the system determines that the channel environment of the target terminal has deteriorated.

[0042] If the channel quality trend is determined to be negatively correlated, the base station triggers a dynamic optimization mechanism to recalculate the transmit weights of the array antennas. Specifically, based on the terminal angle drift reflected in the latest CSI data, the base station uses an adaptive beamforming algorithm to adjust the phase and amplitude weights of each array element to ensure that the main lobe of the beam can continuously be aligned with the spatial direction of the target terminal device.

[0043] Meanwhile, the base station adjusts the transmit power of the array antenna in a timely manner based on the current CQI and service requirements of the terminal equipment: if channel degradation is detected to be mainly due to fading and increased interference, the transmit power is increased within the available power budget; if channel degradation is detected to be mainly due to phase shift, the transmit weight is optimized while maintaining the power at the original configuration level. Through the above process, the base station can update the beam direction and power allocation of the array antenna in a timely manner when the channel environment changes dynamically, thereby ensuring the link stability and service continuity of the terminal equipment.

[0044] It is important to note that when the system is in a multi-user parallel communication scenario, if a terminal triggers a transmit weight and power update, the base station will simultaneously check the channel status of other users sharing spectrum resources with it to avoid increasing the overall system interference due to single-user optimization.

[0045] Step S4: Use the updated transmit weight and transmit power to control the array antenna to send 5G network signals to the terminal device.

[0046] In one embodiment, after the base station completes the update of the transmit weights and transmit power of the array antennas, it enters the signal transmission phase. Specifically, the base station performs channel coding, modulation, and mapping processing on the data stream to be transmitted downlink through the physical layer signal processing unit to generate a downlink physical signal sequence.

[0047] Subsequently, the base station inputs the generated physical signal sequence into the digital precoding module of the array antenna. Using updated transmit weights, the signal is amplitude- and phase-weighted to form a directionally controllable precoded signal matrix. This signal matrix, after RF link conversion and power amplification, is then loaded onto each antenna element according to the updated transmit power allocation scheme. During this process, the array antenna uses spatial combining effects to ensure the main lobe direction aligns with the target terminal device, achieving high-gain directional transmission, while suppressing interference leakage in the sidelobe direction. The base station can thus enhance the signal-to-noise ratio of the signal received by the target terminal in multipath propagation and complex interference environments, ensuring the stability of data transmission.

[0048] Furthermore, this embodiment introduces a dynamic link monitoring mechanism during signal transmission. While transmitting 5G network signals, the base station periodically receives acknowledgment feedback (ACK / NACK) and real-time channel measurement reports from terminal devices. When an increase in bit error rate or data packet retransmission frequency is detected in the terminal feedback, the base station can immediately trigger the next round of transmission weight and power adjustment, thereby achieving closed-loop control. Through this method, the base station can achieve accurate signal transmission from the array antenna to the terminal device using the updated transmission weight and transmission power, effectively improving link quality and spectrum utilization.

[0049] It is important to note that when a base station transmits signals to multiple terminals simultaneously, it will assign independent transmission weights and power to different terminals based on their channel conditions and priorities, and use a multi-user precoding algorithm to superimpose signals in parallel to avoid mutual interference.

[0050] As an example of the present invention, reference is made to... Figure 2 As shown, step S2 in this example includes:

[0051] Step S21: Construct a first beam calculation dataset based on channel state information, wherein the first beam calculation dataset is used to characterize the channel gain distribution of each element of the array antenna in different directions;

[0052] Step S22: Determine the optimal beam direction of the array antenna for the terminal device based on the first beam calculation dataset, and generate a second beam pointing dataset corresponding to that direction;

[0053] Step S23: Calculate the transmission weight of each antenna element in the array antenna using the second beam pointing dataset and the transmitted signal information;

[0054] Step S24: Allocate and adjust the transmit power of the array antenna based on the transmit weight to obtain the transmit power of the array antenna.

[0055] In one embodiment, the base station constructs a first beamforming dataset based on the aforementioned channel state information. Specifically, using channel gain data of each element of the array antenna at different angles, a gain distribution matrix covering the entire sector is formed through directional scanning and interpolation. This gain distribution matrix characterizes the channel gain characteristics of each antenna element in different directions, thereby generating the first beamforming dataset. Based on the first beamforming dataset, the base station identifies the optimal beam direction corresponding to the target terminal. This process includes normalizing the gain values ​​in each direction, selecting the gain peak direction as a candidate beam direction, and then dynamically correcting it based on the geographical location and movement trend of the terminal device, ultimately generating a second beam pointing dataset corresponding to that direction.

[0056] The base station calculates the transmission weights of each antenna element in the array antenna by combining the second beam pointing dataset with the transmitted signal information. Specifically, this involves using a precoding algorithm (such as zero-forcing or minimum mean square error algorithm) to weight the transmitted signal in terms of amplitude and phase, obtaining complex weight coefficients for each antenna element. The magnitude of the weights determines the power allocation ratio, and the phase of the weights is used to ensure that the signals of each element form coherent superposition in the target direction.

[0057] The base station allocates and adjusts the transmit power of the array antenna based on the aforementioned transmit weights. Specifically, the base station first allocates power to each antenna element according to the total transmit power budget, and then fine-tunes the power of each element in conjunction with the transmit weights to achieve radiation characteristics that maximize the main lobe gain in the optimal beam direction. This results in the final transmit power configuration scheme for the array antenna, which is then loaded onto the antenna RF link to complete the power allocation adjustment. Through these steps, the base station can dynamically adapt the transmit direction and power distribution of the array antenna, enabling 5G signals to achieve optimal coverage and signal quality in complex multipath environments.

[0058] It should be noted that in practical applications, to avoid interference from neighboring cells due to excessive power concentration, this embodiment can also set an upper limit constraint on the transmit power. For example, the transmit power of a single antenna element can be limited to between 0.05W and 0.5W, and the total transmit power can not exceed 10W, so as to balance communication quality and energy consumption control.

[0059] Preferably, step S22 includes the following steps:

[0060] Step S221: Divide the first beam calculation data into segments according to the preset beam interval to obtain beam segment data;

[0061] Step S222: For each beam segment, analyze the characteristics of its signal strength or gain change with the beam direction, and record the obvious attenuation or abnormal beam fluctuation points that appear in the beam segment.

[0062] Step S223: Determine the direction of abnormal fluctuations in the first beam calculation data based on obvious attenuation or abnormal beam fluctuation points;

[0063] Step S224: Determine the optimal beam direction of the array antenna for the terminal device by using the abnormal fluctuation direction, beam segment data, and beam attenuation degree of adjacent beam segment data, and generate a second beam pointing dataset corresponding to that direction.

[0064] In one embodiment, the base station first segments the first beam calculation data according to a preset beam interval. The beam interval can be set according to the beam coverage angle of the array antenna, for example, dividing it into beam segments every 1° to 5°. This ensures that the channel gain or signal strength corresponding to each beam segment remains relatively continuous, thereby generating beam segment data.

[0065] The signal strength or gain variation curves within each beamband are analyzed. By comparing the amplitude differences between adjacent sampling points, local extreme points of signal strength are extracted, and the presence of obvious attenuation points or abnormal fluctuation points within the beamband is detected. For example, when the signal strength at a sampling point drops by more than 6dB compared to the adjacent average value, this point is recorded as a beam attenuation point; when a short-term surge or drop in amplitude exceeds 3dB, it is recorded as an abnormal fluctuation point.

[0066] Based on the recorded attenuation points and abnormal fluctuation points, the corresponding abnormal fluctuation directions in the first beam calculation data are further determined. This direction represents the angle information of interference effects such as multipath reflection or obstruction in the channel environment. Combining the abnormal fluctuation direction, beam segment data, and the beam attenuation degree of adjacent beam segment data, a weighted judgment is performed to select the optimal beam direction of the array antenna. Specifically, if the beam segment corresponding to the direction of the target terminal has slight attenuation, but the attenuation degree of adjacent beam segments is more obvious, then the target beam direction is preferentially selected as the optimal beam direction. Finally, a second beam pointing dataset corresponding to the optimal beam direction is generated and used for subsequent weight and power allocation calculations.

[0067] Through the above processing, this embodiment not only considers the absolute strength of the main gain direction, but also introduces a comprehensive analysis of abnormal fluctuation points and attenuation trends, enabling the array antenna to accurately locate the direction of the terminal device in complex channel environments and improve the stability and reliability of beam pointing.

[0068] It should be noted that in practical applications, the significant attenuation threshold can be flexibly adjusted according to the base station deployment environment. For example, in an indoor cell environment, the attenuation judgment threshold can be set to 4dB to 5dB; while in an outdoor macro base station environment, to enhance anti-interference capabilities, the threshold can be increased to 6dB to 8dB to ensure the robustness of beam direction selection.

[0069] Preferably, determining the optimal beam direction of the array antenna for the terminal device by considering the abnormal fluctuation direction, beamband data, and beam attenuation levels of adjacent beamband data includes:

[0070] Based on the abnormal fluctuation direction of each beamband, determine the angle and position of the signal strength decrease and the amplitude change information;

[0071] The continuous region of signal strength decline within each beam segment is determined based on the angle of signal strength decline, beam segment data, and the degree of beam attenuation of adjacent beam segment data.

[0072] The attenuation amplitude of a beamband is determined by the amplitude variation information and the continuous region of signal strength decrease within each beamband.

[0073] The optimal beam direction for the terminal device is selected based on the attenuation amplitude of the beam band.

[0074] In one embodiment, based on the abnormal fluctuation direction of each beamband, the base station extracts the angular location of the signal strength decrease and the corresponding amplitude change information. For example, when the signal strength at a certain directional angle within a beamband decreases by more than 6 dB compared to the beamband average, this directional angle is marked as the decrease location, and the decrease amplitude is recorded. Based on the angular location of the signal strength decrease, combined with the changing trends of beamband data and adjacent beamband data, a continuous region of signal strength decrease within each beamband is identified. Specifically, if multiple adjacent directional angles all exhibit an attenuation trend and the attenuation amplitude continuously increases or remains above a set threshold, then the set of directional angles is determined as a continuous attenuation region.

[0075] Furthermore, the base station uses amplitude variation information and the angular span of the continuous attenuation region to calculate the overall attenuation amplitude of the beamband. For example, the average attenuation amplitude can be obtained by summing the attenuation values ​​at each point within the continuous region and dividing by the number of points in the region, or the attenuation characteristics of the beamband can be characterized by taking the maximum attenuation value in the region. Finally, the base station filters beambands based on their attenuation amplitude: if a beamband has the smallest attenuation amplitude and its azimuth angle matches the approximate position of the terminal device, then the center direction corresponding to that beamband is determined as the optimal beam direction for the array antenna. The generated optimal beam direction is further converted into a second beam pointing dataset for subsequent transmit weight and transmit power adjustments.

[0076] It is important to note that in practical applications, the threshold for determining signal strength degradation can be dynamically adjusted according to the deployment environment. For example, it can be set to 3 to 5 dB in indoor scenarios, while it can be increased to 6 to 8 dB in large-scale outdoor scenarios; the minimum angular span of the continuous attenuation region can be set to 2° to 5° to avoid misjudgments caused by occasional fluctuations.

[0077] Preferably, the optimal beam direction for the terminal device of the array antenna is selected based on the attenuation amplitude of the beam band, including:

[0078] The directional stability coefficient of the beamband data is calculated by analyzing the abnormal fluctuation direction of each beamband.

[0079] The directional stability coefficient and attenuation amplitude are used as signal quality indicators to weight the beamband data, and the beamband direction corresponding to the beamband data with the highest weight is marked as the optimal beam direction of the array antenna for the terminal device.

[0080] In one embodiment, for each beamband, a directional stability coefficient is calculated based on the direction of abnormal fluctuations and the signal strength fluctuations within the beamband. The directional stability coefficient can be obtained by statistically analyzing the variance of the signal strength curve within a certain beamband. When the variance is small and the fluctuations are stable, the stability coefficient is higher; when the variance is large or there are multiple abrupt change points, the stability coefficient is lower. Preferably, the directional stability coefficient ranges from 0 to 1, where 0 indicates extreme directional instability and 1 indicates complete directional stability.

[0081] Then, the base station obtains the attenuation magnitude of that beamband, that is, the degree to which the signal strength of that beamband decreases relative to neighboring beambands. For example, if the signal strength of a certain beamband is 5dB lower than the average of neighboring beambands, its attenuation magnitude is recorded as 5dB.

[0082] Next, the directional stability coefficient and attenuation amplitude are used together as signal quality indicators for the beamband. The base station performs weighted calculations using a weighted approach, for example: ;in As a signal quality indicator, The directional stability coefficient, The attenuation magnitude, and It is a weighting factor, and satisfies In practical applications, it can be The value is set to 0.6–0.8 to emphasize the importance of directional stability. Set to 0.2 to 0.4 to avoid the direction of excessive attenuation being selected as the optimal direction.

[0083] Finally, the base station sorts the signal quality indices of all beambands, selects the beamband direction corresponding to the beamband with the highest signal quality index, and marks it as the optimal beam direction for the array antenna to the terminal device. A second beam pointing dataset corresponding to this direction is then generated for subsequent transmit weighting and power allocation.

[0084] It is important to note that the threshold values ​​for directional stability coefficient and attenuation amplitude can be flexibly set in specific application scenarios. For example, in indoor environments with strong multipath interference, it is preferable to reduce the impact of attenuation amplitude. ≤ 0.3), to ensure that the selection result depends more on directional stability; while in open outdoor environments, the weight of the attenuation amplitude factor can be increased ( ≥ 0.4), to avoid incorrect pointing caused by weak beams at the far end.

[0085] Preferably, step S3 includes the following steps:

[0086] Step S31: Obtain the first channel quality trend data based on the channel state information of the terminal device in continuous time slots and the signal-to-noise ratio change of the channel state information in continuous time slots;

[0087] Step S32: Obtain the second channel quality trend data based on the changes in bit error rate within consecutive time slots of the terminal device;

[0088] Step S33: Perform joint analysis based on the first channel quality trend data and the second channel quality trend data to determine whether the channel quality trend of the channel state information is negatively correlated;

[0089] Step S34: When the channel quality trend is negatively correlated, the transmit weight and transmit power are updated and the array antenna is dynamically adjusted.

[0090] In one embodiment, channel state information of the terminal device within consecutive time slots, including received signal power and signal-to-noise ratio (SNR), is acquired. First channel quality trend data is generated by statistically analyzing the SNR data within consecutive time slots, such as calculating the moving average, maximum, and minimum value trends. This data describes the overall quality trend of the channel over time. A reasonable time window can be set to 20ms–100ms to cover a typical transport block time interval (TTI) and balance real-time performance with trend stability. The base station processes the data based on the bit error rate (BER) changes of the terminal device within consecutive time slots. By statistically analyzing the BER changes in each time slot, the BER increase / decrease trend is calculated to generate second channel quality trend data. The time window is consistent with the SNR to ensure that the two types of trend data can be compared synchronously.

[0091] The first channel quality trend data and the second channel quality trend data are jointly analyzed. The specific methods include: normalizing the two types of trend data to make their numerical scales consistent; calculating the correlation coefficient between the two types of trends; if the correlation coefficient is less than zero, the channel quality trends are determined to be negatively correlated; a negative correlation usually means that the signal power decreases while the bit error rate increases, or the signal power increases while the bit error rate decreases, reflecting that the channel has experienced instantaneous fading or interference.

[0092] When the channel quality trend is determined to be negatively correlated, the base station triggers an update of the array antenna transmit weights and transmit power. Specific operations include: recalculating the transmit weights of each antenna element based on the second beam pointing dataset; adjusting the transmit power distribution to optimize beam gain and enhance signal strength in the direction of the target terminal; and issuing commands through the controller to dynamically adjust the array antenna elements, thereby achieving optimized signal coverage for the terminal equipment.

[0093] Through this embodiment, the base station can respond to changes in channel quality in real time, especially when the channel quality is negatively correlated, and make rapid adjustments, thereby solving the problem of untimely response to instantaneous channel fading and interference in the prior art, and improving the reliability and stability of signal transmission.

[0094] It should be noted that in high-mobility scenarios, the time window can be appropriately shortened to 10ms to 30ms to improve the sensitivity of trend detection; in low-speed movement or static scenarios, it can be appropriately extended to 100ms to 200ms to reduce misjudgment of trends due to short-term fluctuations.

[0095] Preferred methods for obtaining continuous time slots include:

[0096] Within the preset scheduling period of the 5G base station, multiple adjacent time slot data are extracted from the channel state information reported by the terminal device. The time slot data is the result of the terminal device measuring and feeding back the downlink at different time intervals.

[0097] The extracted adjacent time slot data are arranged in chronological order to form a continuous time slot set, which is then used as a continuous time slot.

[0098] In one embodiment, the base station extracts multiple adjacent time slot data from the channel state information reported by the terminal device within a preset scheduling period, such as a downlink scheduling period of 1 millisecond to 10 milliseconds. Each time slot data contains downlink measurements taken by the terminal device within that time interval, such as received signal power, signal-to-noise ratio, and bit error rate.

[0099] The extracted adjacent time slot data are arranged in chronological order to form a continuous time slot set. This continuous time slot set can include several consecutive TTIs (Transmission Time Slots), such as 20 to 100 slots, to cover short-term channel fluctuations while ensuring the reliability of trend analysis. The continuous time slot set is then synchronized to ensure the accuracy of the time labels for each time slot data, and abnormal time slots caused by reporting delays or missing data are removed, resulting in a complete continuous time slot set. This set serves as the input data for subsequent channel quality trend calculations.

[0100] It should be noted that in high-speed mobile scenarios, the number of consecutive time slots can be appropriately reduced to minimize interference from outdated information; in static or low-speed scenarios, the number of consecutive time slots can be appropriately increased to improve the stability of trend analysis.

[0101] Preferably, step S33 includes:

[0102] By performing trend fitting on the first channel quality trend data, the direction of signal-to-noise ratio change can be obtained;

[0103] By performing trend fitting on the second channel quality trend data, the direction and magnitude of the bit error rate change can be obtained;

[0104] By comparing the direction of signal-to-noise ratio (SNR) change with the direction of bit error rate (BER) change, when the direction of SNR change is opposite to the direction of BER change and the magnitude of BER change exceeds a preset threshold, it is determined that the channel quality trend of the channel state information is negatively correlated.

[0105] In one embodiment, trend fitting is performed on the first channel quality trend data. For example, based on the signal-to-noise ratio (SNR) data within continuous time slots, a linear regression or moving average method is used to fit the SNR change trend over time to obtain the direction of SNR change (increasing or decreasing).

[0106] Trend fitting is performed on the second channel quality trend data. Based on the bit error rate data within continuous time slots, a similar method is used to fit the trend of bit error rate change over time, obtaining the direction and magnitude of bit error rate change (e.g., percentage or absolute value change).

[0107] The direction of signal-to-noise ratio (SNR) change is compared with the direction of bit error rate (BER) change. If the SNR decreases over time while the BER increases over time, and the magnitude of the BER change exceeds a preset threshold (e.g., the BER increases by more than 0.5% to 2%), then the channel quality trend of the channel state information is determined to be negatively correlated.

[0108] In another embodiment, it should be noted that the preset threshold can be adjusted according to different communication scenarios. For example, in environments with high-speed movement or severe multipath fading, the bit error rate threshold can be set to 1% to 2% to avoid misjudgment; in stationary or low-speed scenarios, it can be set to 0.5% to 1%. When the direction of signal-to-noise ratio change is not completely opposite to the direction of bit error rate change, or the magnitude of bit error rate change does not exceed the threshold, it is determined that the channel quality trend is not negatively correlated to prevent frequent triggering of array antenna adjustments.

[0109] Of particular importance, determining the direction of abnormal fluctuations in the first beam calculation data based on significantly attenuated or abnormal beam fluctuation points also includes:

[0110] The first beam calculation data is subjected to amplitude normalization processing to generate an amplitude normalization sequence;

[0111] Perform sliding window statistics on the amplitude normalized sequence to identify beam points with obvious amplitude decay or abnormal jumps, and generate a set of candidate points for abnormal fluctuations.

[0112] Perform adjacent point direction vector analysis on the set of candidate points for abnormal fluctuations, calculate the local beam change direction of each candidate point, and generate local abnormal direction data;

[0113] Based on the local anomaly direction data, the first beam calculation data is continuously drifted and identified. If the local anomaly directions of multiple consecutive candidate points show a consistent trend, then the direction is marked as an abnormal fluctuation direction. If the directions are inconsistent, they are temporarily retained as anomaly directions to be confirmed, and preliminary judgment data of abnormal fluctuation directions is generated.

[0114] In one embodiment, the signal gain of each beam point in the first beam calculation data is normalized to generate an amplitude normalization sequence, so that the sequence values ​​are uniformly mapped to the range of 0 to 1, eliminating the absolute gain difference between different beams. The amplitude normalization sequence is then subjected to sliding statistics with a preset window size (e.g., 5 to 10 beam points), calculating the mean and standard deviation of the gain within the window, identifying beam points with significant amplitude attenuation (e.g., more than 20% below the window mean) or abnormal jumps (gain change of more than 10% between adjacent points), and generating a set of candidate points for abnormal fluctuations.

[0115] Adjacent point direction vector analysis is performed on the candidate point set for abnormal fluctuations to calculate the local beam change direction (gain increase or decrease trend) of each candidate point, generating local abnormal direction data. Based on the local abnormal direction data, the trend of local abnormal directions of multiple consecutive candidate points is judged: if the local abnormal directions of multiple consecutive candidate points (e.g., 3 to 5 consecutive beam points) show a consistent trend, then the direction is marked as an abnormal fluctuation direction; if the directions are inconsistent, they are temporarily retained as abnormal directions to be confirmed, generating preliminary judgment data for abnormal fluctuation directions.

[0116] It should be noted that the sliding window size, amplitude attenuation threshold, and number of consecutive points can be adjusted according to the array antenna beam density and environmental multipath conditions. For example, arrays with smaller beam spacing can use a smaller window to increase sensitivity. At the same time, for abnormal directions to be confirmed, data from adjacent beam segments and historical beam change trends can be combined for further verification to avoid misjudging interference points as abnormal directions.

[0117] Of particular importance, step S32 also includes:

[0118] The bit error rate data within consecutive time slots of the terminal device is sorted by time series to generate an ordered bit error rate sequence.

[0119] Sliding window statistics are performed on the ordered bit error rate sequence to calculate the mean and standard deviation of the bit error rate within each window, generating local bit error rate trend data;

[0120] Differential processing is performed on the local bit error rate trend data to obtain the instantaneous increase or decrease trend of bit error rate changes;

[0121] The bit error rate of sudden spikes in the instantaneous increase and decrease trend is filtered, and the trend change of the bit error rate of sudden spikes is suppressed to obtain the second channel quality trend data.

[0122] In one embodiment, the bit error rate (BER) data within consecutive time slots of the terminal device is processed to generate second channel quality trend data for joint analysis. The BER data reported by the terminal device within consecutive time slots is sorted by timestamp to generate an ordered BER sequence {BER1, BER2, ..., BERN}, ensuring that the data order reflects the actual channel change trend. The ordered BER sequence is then subjected to sliding calculation with a preset window length (e.g., 3-5 time slots), and the mean BER and standard deviation σBER within each window are calculated to obtain local BER trend data {LBER1, LBER2, ..., LBERM}, reflecting the local variation characteristics of the channel BER. The local BER trend data is then subjected to first-order differencing to calculate the instantaneous BER increase / decrease trend.

[0123] ;

[0124] Positive values ​​indicate an increase in the bit error rate (BER), while negative values ​​indicate a decrease, thus obtaining the instantaneous direction and magnitude of the BER change. Abnormal spikes appearing in the instantaneous increase or decrease trend (e.g., points where the instantaneous change exceeds the mean ± 3σ) are filtered out, and their impact is suppressed through smoothing or moving average processing. Generate the smoothed bit error rate trend, which is the final second channel quality trend data.

[0125] It is important to note that the window length, spike threshold, and smoothing coefficient should be adjusted in conjunction with the time slot length and channel fluctuation characteristics of the 5G system. For example, the shorter the time slot interval, the smaller the window can be used to retain rapidly changing information. Spike suppression is to avoid misjudging the channel quality trend caused by transient interference (such as noise or short-term burst fading) while preserving the true trend of signal change.

[0126] This specification provides a 5G network optimization system for performing the above-described 5G network optimization method. The 5G network optimization system includes:

[0127] The signal acquisition module is used to acquire channel status information and transmitted signal information of the array antenna reported by the terminal device through the 5G base station;

[0128] The power confirmation module is used to calculate the optimal beam direction of the array antenna for the terminal device in the 5G base station based on channel state information; adjust the transmission weight of the array antenna and determine the transmission power of the array antenna according to the optimal beam direction and transmission signal information;

[0129] The channel quality detection module is used to detect the channel quality trend of channel state information. When the channel quality trend is negatively correlated, it triggers the update of transmission weight and transmission power.

[0130] The network optimization module is used to control the array antenna to send 5G network signals to the terminal device using the updated transmit weight and transmit power.

[0131] The present invention also provides a network device, including a processor, a memory, a radio frequency transceiver module, a signal detection module, and an optimization control interface module, and is electrically connected through a data bus, for monitoring 5G base stations, array antennas, and terminal equipment and executing the 5G network optimization method described above.

[0132] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0133] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A 5G network optimization method, characterized in that, The process, which applies to 5G base stations, array antennas, and terminal devices, includes the following steps: Step S1: Obtain the channel status information and the transmission signal information of the array antenna reported by the terminal device through the 5G base station; Step S2: Calculate the optimal beam direction of the array antenna for the terminal device in the 5G base station based on channel state information; adjust the transmission weight of the array antenna and determine the transmission power of the array antenna according to the optimal beam direction and transmission signal information; Step S3: Detect the channel quality trend of the channel state information. When the channel quality trend is negatively correlated, the update of the transmit weight and transmit power is triggered. Step S4: Use the updated transmit weight and transmit power to control the array antenna to send 5G network signals to the terminal device.

2. The 5G network optimization method according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Construct a first beam calculation dataset based on channel state information, wherein the first beam calculation dataset is used to characterize the channel gain distribution of each element of the array antenna in different directions; Step S22: Determine the optimal beam direction of the array antenna for the terminal device based on the first beam calculation dataset, and generate a second beam pointing dataset corresponding to that direction; Step S23: Calculate the transmission weight of each antenna element in the array antenna using the second beam pointing dataset and the transmitted signal information; Step S24: Allocate and adjust the transmit power of the array antenna based on the transmit weight to obtain the transmit power of the array antenna.

3. The 5G network optimization method according to claim 2, characterized in that, Step S22 includes the following steps: Step S221: Divide the first beam calculation data into segments according to the preset beam interval to obtain beam segment data; Step S222: For each beam segment, analyze the characteristics of its signal strength or gain variation with the beam direction, and record any obvious attenuation or abnormal beam fluctuation points that occur within the beam segment. Step S223: Determine the direction of abnormal fluctuations in the first beam calculation data based on obvious attenuation or abnormal beam fluctuation points; Step S224: Determine the optimal beam direction of the array antenna for the terminal device by using the abnormal fluctuation direction, beam segment data, and beam attenuation degree of adjacent beam segment data, and generate a second beam pointing dataset corresponding to that direction.

4. The 5G network optimization method according to claim 3, characterized in that, The optimal beam direction of the array antenna for the terminal device is determined by analyzing the direction of abnormal fluctuations, beamband data, and beam attenuation levels in adjacent beamband data. Based on the abnormal fluctuation direction of each beamband, determine the angle and position of the signal strength decrease and the amplitude change information; The continuous region of signal strength decline within each beam segment is determined based on the angle of signal strength decline, beam segment data, and the degree of beam attenuation of adjacent beam segment data. The attenuation amplitude of a beamband is determined by the amplitude variation information and the continuous region of signal strength decrease within each beamband. The optimal beam direction for the terminal device is selected based on the attenuation amplitude of the beam band.

5. The 5G network optimization method according to claim 4, characterized in that, The optimal beam direction for the terminal device of the array antenna is selected based on the attenuation amplitude of the beam band, including: The directional stability coefficient of the beamband data is calculated by analyzing the abnormal fluctuation direction of each beamband. The directional stability coefficient and attenuation amplitude are used as signal quality indicators to weight the beamband data, and the beamband direction corresponding to the beamband data with the highest weight is marked as the optimal beam direction of the array antenna for the terminal device.

6. The 5G network optimization method according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Obtain the first channel quality trend data based on the channel state information of the terminal device in continuous time slots and the signal-to-noise ratio change of the channel state information in continuous time slots; Step S32: Obtain the second channel quality trend data based on the changes in bit error rate within consecutive time slots of the terminal device; Step S33: Perform joint analysis based on the first channel quality trend data and the second channel quality trend data to determine whether the channel quality trend of the channel state information is negatively correlated; Step S34: When the channel quality trend is negatively correlated, the transmit weight and transmit power are updated and the array antenna is dynamically adjusted.

7. The 5G network optimization method according to claim 6, characterized in that, Methods for obtaining continuous time slots include: Within the preset scheduling period of the 5G base station, multiple adjacent time slot data are extracted from the channel state information reported by the terminal device. The time slot data is the result of the terminal device measuring and feeding back the downlink at different time intervals. The extracted adjacent time slot data are arranged in chronological order to form a continuous time slot set, which is then used as a continuous time slot.

8. The 5G network optimization method according to claim 6, characterized in that, Step S33 includes: By performing trend fitting on the first channel quality trend data, the direction of signal-to-noise ratio change can be obtained; By performing trend fitting on the second channel quality trend data, the direction and magnitude of the bit error rate change can be obtained; By comparing the direction of signal-to-noise ratio (SNR) change with the direction of bit error rate (BER) change, when the direction of SNR change is opposite to the direction of BER change and the magnitude of BER change exceeds a preset threshold, it is determined that the channel quality trend of the channel state information is negatively correlated.

9. A 5G network optimization system, characterized in that, The 5G network optimization system, used to perform the 5G network optimization method as described in claim 1, comprises: The signal acquisition module is used to acquire channel status information and transmitted signal information of the array antenna reported by the terminal device through the 5G base station; The power confirmation module is used to calculate the optimal beam direction of the array antenna for the terminal device in the 5G base station based on channel state information; adjust the transmission weight of the array antenna and determine the transmission power of the array antenna according to the optimal beam direction and transmission signal information; The channel quality detection module is used to detect the channel quality trend of channel state information. When the channel quality trend is negatively correlated, it triggers the update of transmission weight and transmission power. The network optimization module is used to control the array antenna to send 5G network signals to the terminal device using the updated transmit weight and transmit power.

10. A network device, characterized in that, It includes a processor, a memory, a radio frequency transceiver module, a signal detection module, and an optimization control interface module, and is electrically connected via a data bus. It is used to monitor 5G base stations, array antennas, and terminal equipment and to execute the 5G network optimization method according to any one of claims 1-8.

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