5G uplink coverage enhancement method

Abstract

The invention relates to a 5G uplink coverage enhancement method, and the method comprises the steps: collecting the data of a 5G network, including RSRP, SINR, TxPower, service type, uplink load, switching success times, switching failure times, transmission rate and block error rate, and dividing the data into a training set and a test set; a multi-dimensional threshold SUL trigger model based on the deep neural network is constructed, the training set is used for training the multi-dimensional threshold SUL trigger model, and multi-dimensional thresholds comprise an RSRP threshold Pth, a PL threshold and an SINR compensation threshold Sth; constructing a three-layer architecture comprising a global load sensing layer, a regional resource scheduling layer and a terminal dynamic adaptation layer, and improving a dynamic time slot matching algorithm; and inputting the test set into a trained multi-dimensional threshold SUL trigger model, and outputting parameter configuration of an uplink in combination with a dynamic time slot matching algorithm to enhance 5G uplink coverage. Compared with the prior art, the method has the advantages of reducing misjudgment of SUL switching, realizing uplink coverage enhancement and the like.

Description

Technical Field

[0001] This invention relates to the field of 5G mobile communication technology, and in particular to a method for enhancing 5G uplink coverage. Background Technology

[0002] While driving the development of communication technology, 5G mobile communication systems face the challenge of unbalanced uplink and downlink coverage, particularly in the latter. With the diversification of services, such as ultra-high-definition video communication, big data collection, and intelligent monitoring, higher demands are placed on uplink capacity and coverage. Mainstream commercial 5G frequency bands (such as 3.5GHz) mostly adopt Time Division Duplex (TDD) mode, and downlink capacity and coverage are greatly improved through the introduction of technologies such as Massive MIMO, leveraging precise beamforming and scanning. However, due to frequency band characteristics and the uneven uplink and downlink time slot ratio under TDD, the uplink and downlink coverage of C-Band TDD systems is unbalanced, and the uplink coverage and capacity of 3.5GHz 5G networks urgently need improvement. At the same time, with the development of mobile internet, IoT, and other services, the demand for massive data uploads is rapidly increasing, posing a severe challenge to 5G uplink performance. The high-frequency bands (such as Sub-6GHz and millimeter waves), flexible time slot ratios, and massive MIMO characteristics of 5G mobile networks exacerbate the uplink and downlink imbalance problem, specifically manifested as follows: 1. High transmission loss in high frequency bands: Millimeter wave path loss is about 18dB higher than that of Sub-6GHz (1km distance). 2. Imbalanced time slot allocation: A high downlink time slot ratio (e.g., 8:2) reduces the terminal's uplink transmission opportunities; 3. Massive MIMO gain difference: Base station multi-antenna beamforming improves downlink gain, but the number of terminal antennas is limited; 4. Increased demand for upstream services: Applications such as live video streaming require continuous, high-quality upstream coverage.

[0003] Existing solutions do not provide quantitative analysis of the differences in uplink and downlink coverage across different frequency bands, and lack systematic technical means to enhance uplink coverage.

[0004] A search revealed Chinese invention patent application publication number CN116056224A, which discloses an uplink coverage enhancement method in a 5G mobile communication system. The method involves: configuring the operating frequency band available to the terminal; performing link budgeting to determine the maximum allowable path loss; transmitting a probe signal in a high-frequency band to obtain the signal-to-noise ratio (SNR) and received power information, calculating the proportion of tap energy of the direct path to the total energy of all paths, and the path loss; the base station selecting an uplink data transmission frequency band based on the initial probe results and promptly feeding back the selection result to the user; tracking the uplink coverage status under high-frequency communication; and when the initial probe interval reaches one cycle, the user transmits a probe signal again, and the base station selects an uplink data transmission frequency band based on the probe status and feeds back to the user, allowing the user to switch frequency bands promptly. This existing patent application suffers from several drawbacks. It only uses path loss and SINR to trigger SUL, without considering SINR in conjunction with service type, leading to misjudgments in frequency band switching in complex scenarios and neglecting to consider service type.

[0005] How to enhance 5G uplink coverage and compensate for the imbalance between uplink and downlink coverage has become a technical problem that needs to be solved. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a 5G uplink coverage enhancement method.

[0007] The objective of this invention can be achieved through the following technical solutions: According to one aspect of the present invention, a 5G uplink coverage enhancement method is provided, the method comprising: Collect 5G network data, including RSRP, SINR, TxPower, service type, uplink load, number of successful handovers, number of failed handovers, transmission rate, and block error rate, and divide the data into training set and test set; A multi-dimensional threshold SUL triggering model based on a deep neural network is constructed. The deep neural network includes an input layer, three hidden layers, and an output layer. The dimensions of the input layer correspond to RSRP, SINR, path loss, and uplink load in the collected 5G network data. The parameter weights of each layer are optimized through the backpropagation algorithm. The multi-dimensional thresholds include RSRP threshold Pth, PL threshold Lth, and SINR compensation threshold Sth. A three-layer architecture consisting of a global load awareness layer, a regional resource scheduling layer, and a terminal dynamic adaptation layer is constructed, and the dynamic time slot allocation algorithm is improved. The test set is input into the trained multi-dimensional threshold SUL triggering model, and the improved dynamic time slot allocation algorithm is combined to output the uplink parameter configuration. The parameter configuration includes RSRP threshold Pth, SINR compensation threshold Sth, whether to trigger SUL access, and time slot configuration. The parameter configuration is used to enhance 5G uplink coverage.

[0008] As a preferred technical solution, the multi-dimensional threshold SUL trigger model dynamically adjusts the multi-dimensional threshold in the following manner: Based on the uplink load and service type association data in the training set, a threshold adjustment mapping table is established, and the mapping table contains the RSRP threshold Pth and SINR compensation threshold Sth corresponding to different uplink load intervals and different service types. The output result of the multi-dimensional threshold SUL trigger model is a binary decision value, where 1 indicates triggering SUL access, and 0 indicates exiting the SUL carrier or maintaining the original access mode.

[0009] As a preferred technical solution, when the UE uses the mid-high frequency band for uplink, if any of the following conditions is met and the duration is greater than the time delay value TTT, SUL carrier access is triggered: The first condition: RSRP ≤ RSRP threshold Pth and PL ≥ PL threshold Lth; The second condition: SINR ≤ SINR compensation threshold Sth.

[0010] As a preferred technical solution, when the UE uses the SUL carrier for uplink, if both of the following two conditions are met simultaneously, the UE exits the SUL carrier and returns to mid-high frequency band access: The third condition: RSRP > RSRP threshold Pth or PL < PL threshold Lth; The fourth condition: SINR > SINR compensation threshold Sth.

[0011] As a preferred technical solution, the multi-dimensional threshold SUL trigger model classifies the uplink load and implements different RSRP thresholds Pth for different levels of uplink load. Specifically: If the uplink load is less than or equal to the first uplink load UL1, the default RSRP threshold Pth is maintained; If the uplink load is between the first uplink load UL1 and the second uplink load UL2, the RSRP threshold Pth is increased by 3 dB; If the uplink load is greater than or equal to the second uplink load UL2, SUL is forcibly triggered and the limitation of the SINR compensation threshold Sth is ignored.

[0012] As a preferred technical solution, the SINR compensation threshold Sth is combined with the service type to implement differential triggering of SUL. Specifically: The uRLLC service corresponds to the first SINR compensation threshold; The eMBB service corresponds to the second SINR compensation threshold; The mMTC service corresponds to the third SINR compensation threshold; The first SINR compensation threshold is greater than the second SINR compensation threshold, which is greater than the third SINR compensation threshold.

[0013] As a preferred technical solution, the global load perception layer periodically monitors the uplink-to-downlink traffic ratio α of the entire network. When α is greater than the traffic ratio threshold, the uplink coverage enhancement mode is activated, and the time slot ratio is adjusted to 2DL:2UL. The aforementioned regional resource scheduling layer adds an additional UL timeslot in the SUL carrier coverage area of ​​the hotspot region; The terminal dynamic adaptation layer is bound to the service type output by the multi-dimensional threshold SUL triggering model, and the granularity of the time slot configuration is dynamically adjusted.

[0014] As a preferred technical solution, the granularity of the dynamically adjusted time slot configuration includes: For eMBB services: allocate 2 UL time slots and 1 flexible time slot consecutively; For uRLLC services: Reserve fixed UL time slots and emergency time slot request channels; For mMTC services, time-division multiplexing is used to allocate one UL time slot.

[0015] As a preferred technical solution, the method further includes dynamically adjusting the number of available UL time slots based on real-time SINR, specifically: , , in, SL actual This represents the actual number of uplink time slots. To round down; SL basic The base number of time slots is preset by the terminal dynamic adaptation layer according to the service type; SL borrow To dynamically borrow time slots, the actual number available is determined by the global load awareness layer; The SUL carrier SINR value reported by the UE in real time; The SINR compensation threshold Sth corresponding to the current service type. This represents the ideal upper limit of SINR for a SUL carrier. The link quality correction coefficients range from -1 to 1; β is the slot enhancement factor, dynamically mapped from the uplink load level; γ is the borrowed slot effectiveness coefficient, which is related to the region type; and δ is the BLER attenuation coefficient. This constitutes the link reliability correction term, where BLER is the block error rate reported by the UE in real time.

[0016] As a preferred technical solution, the method further includes improving the uplink quality closed-loop control, including: The UE periodically reports the SINR and BLER of the SUL carrier; The base station dynamically adjusts the time slot allocation based on BLER: if BLER is greater than the first BLER threshold, one UL time slot is added and the MCS level is reduced by 1-2 levels; if BLER is less than the second BLER threshold, one UL time slot is reduced and the MCS level is increased by 1-3 levels, where the first BLER threshold is greater than the second BLER threshold. Based on historical records, the core network uses a linear regression algorithm to predict resource demand in the future. If there is no additional downlink resource demand in the future, it reserves UL time slot resources for two SUL carriers in advance to compensate for transmission loss in the uplink coverage edge area.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1) This invention trains a multi-dimensional threshold SUL triggering model using multi-dimensional 5G network data such as RSRP, SINR, PL, and service type. The three-layer architecture optimizes the dynamic time slot allocation, enabling multi-parameter collaborative decision-making and hierarchical resource scheduling. This not only breaks through the limitations of traditional single-parameter triggering, reducing the SUL handover misjudgment rate by more than 60%, but also specifically compensates for the uplink coverage shortcomings in the mid-to-high frequency bands of 5G, increasing the uplink coverage radius by 30%. At the same time, it is compatible with the differentiated needs of three types of core services, improving the uplink reliability of users in edge areas by more than 40%, effectively solving the core pain points of unbalanced uplink and downlink coverage and unstable transmission in complex scenarios in 5G networks.

[0018] 2) This invention constructs a multi-dimensional threshold SUL triggering model through a deep neural network, and establishes the correlation between load, service type and threshold through a threshold mapping table. This not only enhances the interpretability and ease of operation and maintenance of threshold adjustment, but also avoids ping-pong handover caused by fuzzy judgment through binary decision output, thereby improving the success rate of SUL carrier handover and significantly improving the accuracy and stability of SUL carrier triggering decision in high interference scenarios or complex network environments.

[0019] 3) The multi-dimensional threshold SUL triggering model of the present invention implements differentiated RSRP threshold Pth for different levels of uplink load, and differentiated SINR compensation threshold Sth in combination with service type, so that SUL triggering or exit can be quickly adjusted according to network changes and more flexibly adapted to the actual network situation.

[0020] 4) This invention improves the dynamic time slot allocation by constructing a three-layer structure, dynamically adjusts the uplink time slot for various situations, and achieves three-level linkage scheduling from the entire network load to terminal services to meet the needs of different services under different conditions.

[0021] 5) After SUL carrier access, this invention dynamically adjusts the uplink time slot of the SUL carrier through a nonlinear formula coupled with multi-dimensional parameters (SINR, BLER, load, area type, etc.). It matches the link quality through the SINR correction coefficient, suppresses excessive borrowing in high-error-rate scenarios through the BLER exponential term, and adapts to the network status through dynamic parameters (β, γ, SLborrow). This ensures that the time slot configuration is accurately matched with the real-time link, network load, and area characteristics, and builds an update linkage mechanism between UE, base station, and area to ensure the real-time performance and accuracy of time slot adjustment. At the same time, boundary constraints ensure the stability of the algorithm, solving the problems of poor adaptability and low resource utilization of traditional algorithms.

[0022] 6) This invention also improves the uplink closed-loop quality control by combining real-time BLER feedback and machine learning optimization to dynamically adjust the uplink time slot and MCS level, and reserve UL time slot resources of SUL carrier in advance to compensate for uplink coverage, thereby improving the uplink reliability of edge users by about 40%. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the 5G uplink coverage enhancement method of the present invention; Figure 2 This is a schematic diagram illustrating the principle of the multi-dimensional threshold SUL triggering model in this invention; Figure 3 This is a schematic diagram of the three-layer architecture of the dynamic time slot allocation algorithm in this invention; Figure 4 This is a schematic diagram of the uplink closed-loop quality control process in this invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] Example 1 This embodiment relates to a 5G uplink coverage enhancement method. This method quantitatively analyzes the uplink and downlink coverage differences between the Sub-6GHz and millimeter-wave bands, introduces spectrum enhancement technology and dynamic time slot allocation technology to enhance uplink coverage and compensate for the imbalance of 5G uplink and downlink coverage.

[0026] The following analysis of uplink and downlink coverage differences is based on the link budget model: Based on link budget formula R = L NLOS+ L other P TX + G TX - L PL (d), where, R The coverage radius is the maximum distance that the signal can effectively cover. P TX For transmission power, G TX For the transmit antenna gain, L PL Path loss is denoted by d, and the transmission distance between the base station and the terminal is denoted by d, in meters. L NLOS This is the margin for shadow fading.

[0027] The commonly used mid-to-high frequency bands for 5G mobile communication include Sub-6GHz and millimeter wave bands. The differences in path loss are analyzed for each of the different frequency bands: Sub-6GHz (3.5GHz), path loss formula L PL =20log10(d)+20log10(f)+147.55 (unit: dB); where f=3.5GHz.

[0028] For millimeter waves (28GHz), the formula is the same as the path loss formula for Sub-6GHz (3.5GHz), with f=28GHz substituted.

[0029] After substituting the frequency band f into the path loss formula, the results show that within a distance of 1km, the path loss of 28GHz is about 18dB higher than that of 3.5GHz.

[0030] Analysis of the impact of time slot allocation on uplink and downlink coverage: Assuming downlink time slot allocation is 80%, uplink is 20%, base station transmit power is 43dBm, and terminal transmit power is 23dBm. Base station equivalent downlink power P DL :P DL =P TX ×η DL , where η DL P represents the time slot percentage. TX This refers to the base station's transmit power. The terminal's effective uplink power is limited, necessitating increased transmit power or optimized receive sensitivity.

[0031] Based on the above link budget calculations, in the Sub-6GHz band: the downlink coverage radius is approximately 1.5 times that of the uplink; in the millimeter-wave band: the downlink coverage radius is only 1 / 3 of that of the uplink.

[0032] To address the uplink / downlink coverage imbalance, spectrum enhancement and dynamic time slot allocation technologies are introduced. For example... Figure 1 The method includes the following steps: S1 collects historical data from the 5G network, including RSRP, SINR, TxPower, service type, uplink load ULLoad, number of successful handovers, number of failed handovers, transmission rate, and block error rate, and divides the data into training set and test set; S2. A multi-dimensional threshold SUL triggering model based on deep neural networks is constructed. Traditional solutions only use RSRP (-110dBm~-115dBm) or SINR as the switching threshold, ignoring the impact of other factors on actual transmission quality. This application adopts multi-dimensional decision factors to construct a multi-dimensional triggering space: Supplementary Uplink (SUL) technology opens up uplink spectrum in low-frequency bands (e.g., 700MHz) to compensate for coverage deficiencies in high-frequency bands. SUL is a technique that introduces low-frequency bands (e.g., Sub-6GHz) as supplementary uplink to address insufficient uplink coverage in high-frequency bands (e.g., millimeter waves). The high-frequency band (primary carrier) is used for downlink transmission and some uplink transmission, while the low-frequency band (supplementary carrier) is dedicated to uplink transmission. Utilizing the low propagation loss and strong penetration of low-frequency bands, uplink coverage is expanded.

[0033] S3 constructs a three-layer architecture of "global load awareness - regional resource scheduling - terminal dynamic adaptation" and improves the dynamic time slot allocation algorithm; S4 inputs real-time 5G network data into the trained multi-dimensional threshold SUL triggering model, and combines it with the dynamic time slot allocation algorithm to output uplink parameter configuration (including RSRP threshold Pth, SINR compensation threshold Sth, whether to trigger SUL access, and time slot configuration) to achieve uplink coverage enhancement.

[0034] Furthermore, in S2, the construction of a machine learning-based multi-dimensional threshold SUL triggering model includes: Path loss (PL) is defined as: PL = TxPower - RSRP, which reflects the degree of signal attenuation during propagation, where TxPower is the base station transmit power.

[0035] RSRP (Pth) definition: Reflects the absolute value of signal strength, using the SSB-RSRP measurement value defined by 3GPP, and setting the range value according to the frequency band characteristics (e.g., Pth is set to -108dBm for 3.5GNUL carrier).

[0036] RSRP threshold (Pth): -108dBm (3.5GHz NUL carrier), PL threshold (Lth): 130dB (corresponding to a coverage distance of approximately 2km at 3.5GHz).

[0037] Uplink load is categorized, and differentiated triggering strategies are implemented for different levels of uplink load, specifically: Low uplink load (≤ first uplink load UL1, e.g., 30%): Maintain the default trigger threshold; Uplink load (between the first uplink load UL1 and the second uplink load UL2, for example, 30%-50%): RSRP threshold increases by 3dB (e.g., Pth=-105dBm). High uplink load (≥ second uplink load UL2, e.g., 50%): Forcefully trigger SUL, and ignore the SINR compensation threshold Sth limit at this time.

[0038] The BLER feedback is also included in the multi-dimensional scope. Specifically, when the BLER of the UE is ≥15% for 3 consecutive TTIs, the uplink load level is automatically increased by one level to trigger the priority.

[0039] SINR compensation threshold (Sth): Characterizes the relative value of signal quality, defined as the difference between the uplink SINR of the NUL carrier and the noise floor, with a dynamic range of [-3dB, 5dB].

[0040] Furthermore, the SINR compensation threshold (Sth) is combined with the service type (TT) to implement differentiated triggering, specifically as follows: For uRLLC services (low latency services, such as industrial control and vehicle networking): SINR ≤ first SINR compensation threshold, such as 8dB (relax the threshold to ensure real-time performance); For eMBB services (high-throughput services, such as video uploads and cloud storage), SINR ≤ the second SINR compensation threshold, such as 5dB (default threshold). For mMTC services (massive machine-type communications, such as IoT sensors): SINR ≤ third SINR compensation threshold, such as 2dB (strict threshold, to save resources).

[0041] Triggering conditions: For UE service types that match and SINR ≤ the corresponding threshold, for example, industrial robots trigger SUL when SINR = 6dB, while ordinary users need SINR ≤ 5dB to trigger.

[0042] like Figure 2 When the UE meets any of the following conditions and the duration is greater than the time-to-trigger (TTT) value, the SUL carrier access procedure is triggered. This solves the misjudgment problem of traditional single threshold in high-interference or complex scenarios, while avoiding the ping-pong effect caused by signal abrupt changes. First condition: RSRP≤Pth and PL≥Lth; Second condition: SINR≤Sth.

[0043] If neither of the two conditions is met, the SUL carrier is discontinued and the system switches to mid-to-high frequency band access.

[0044] If a certain area is a high-attenuation, low-interference area, only RSRP and PL need to be triggered; if a certain area is a low-attenuation, high-interference area, only SINR needs to be triggered; if the UE has RSRP = -105dBm (higher than Pth) but PL = 135dB (higher than Lth) in a tall building obstruction area, SUL access will not be triggered. SUL access will only be triggered when RSRP continues to deteriorate and is worse than Pth, and PL exceeds Lth. The multi-dimensional threshold SUL triggering model employs a deep neural network (DNN) architecture, optimizing the parameter weights of each layer through backpropagation to dynamically adjust the triggering strategy. Input parameters: RSRP, SINR, PL, ULLoad; output decision: whether to trigger SUL access. The deep neural network consists of an input layer, three hidden layers, and an output layer. The dimensions of the input layer correspond to RSRP, SINR, path loss (PL), and uplink load (ULLoad) in historical data. The multi-dimensional thresholds include the RSRP threshold (Pth), PL threshold (Lth), and SINR compensation threshold (Sth).

[0045] Collect historical data (RSRP, SINR, TxPower, service type, ULLoad, number of successful handovers, number of failed handovers, transmission rate, and block error rate), divide the data into training set and test set, use the training set to train the multi-dimensional threshold SUL triggering model, and dynamically adjust Pth, Pth, and Sth to trigger SUL access. The deep neural network has 4 neurons in the input layer, 64, 32, and 16 neurons in the hidden layer, and 1 neuron in the output layer. The backpropagation algorithm uses the Adam optimizer with a learning rate dynamic range of 0.001-0.01.

[0046] The multi-dimensional threshold SUL triggering model dynamically adjusts the multi-dimensional thresholds in the following way: Based on the uplink load and service type association data in the training set, a threshold adjustment mapping table is established. The mapping table contains the RSRP threshold Pth and SINR compensation threshold Sth corresponding to different uplink load ranges and different service types. The output result is a binary decision value, where "1" indicates triggering SUL access and "0" indicates exiting the SUL carrier or maintaining the original access mode.

[0047] In S3, a three-layer architecture is constructed, consisting of a global load perception layer, a regional resource scheduling layer, and a terminal dynamic adaptation layer. The three layers work together in the following ways: the time slot allocation strategy output by the global load perception layer serves as the adjustment basis for the regional resource scheduling layer; the time slot supplementation result of the regional resource scheduling layer serves as the configuration basis for the terminal dynamic adaptation layer; and the dynamic time slot allocation algorithm is improved, including: Three-tier architecture, as follows Figure 3 As shown, it includes: a) Global load perception layer: Monitor the uplink and downlink traffic ratio (α) of the entire network every 1 second. When α ≥ the traffic ratio threshold (e.g., 40%), start the uplink coverage enhancement mode and generate a global time slot allocation strategy (e.g., adjust the default 3DL:1UL to 2DL:2UL).

[0048] b) Regional resource scheduling layer: Based on the MR measurement report, hotspot areas are divided (RSRP≤-105dBm and user density≥ density threshold (e.g., 150 households / square kilometer)). An additional UL time slot is added to the SUL carrier coverage area of ​​the hotspot area to form a local time slot ratio of 1DL:3UL.

[0049] c) Terminal Dynamic Adaptation Layer: For each SUL access UE, it is bound to the service type (eMBB / uRLLC / mMTC) output by the multi-dimensional threshold SUL triggering model, dynamically adjusting the granularity of time slot allocation, including: For eMBB services: allocate 2 consecutive UL time slots + 1 flexible time slot (configurable as UL); For uRLLC services: Reserve fixed UL time slots (10ms period) + emergency time slot request channel; For mMTC: Time Division Multiplexing (TDM) is used to allocate one UL time slot.

[0050] This invention innovatively provides a time slot enhancement algorithm based on SINR: after the SUL carrier is accessed, a time slot enhancement factor β (0≤β≤1) is introduced, and the number of available UL time slots is dynamically adjusted according to the real-time SINR, so that the UL time slots are better utilized. , , in, SL actual This represents the actual number of uplink time slots. To round down to ensure the number of time slots is an integer; SL basic The base time slot number is preset by the terminal dynamic adaptation layer according to the service type (2 for eMBB services, 1 for uRLLC services, and 1 for mMTC services). SL borrow To dynamically borrow time slots, the actual number available is determined by the global load awareness layer; The SUL carrier SINR value (unit: dB) reported by the UE in real time; The SINR compensation threshold Sth corresponding to the current service type. This represents the ideal upper limit of SINR for the SUL carrier in this frequency band. The link quality correction coefficient ranges from -1 to 1; β is the time slot enhancement factor, dynamically mapped by the uplink load level. β = 0.8 when uplink load ≤ 30%, β = 1.0 when uplink load < 30% < 50%, and β = 1.2 when uplink load ≥ 50%; γ is the borrowed time slot effectiveness coefficient, associated with the region type. γ = 1.0 for hotspot regions, γ = 0.7 for ordinary regions, and γ = 0.3 for edge regions; δ is the BLER attenuation coefficient. The region type is determined in real-time by the region resource scheduling layer based on the MR measurement report. The link reliability correction term is formed, where BLER is the block error rate reported by the UE in real time. The higher the BLER, the smaller the value of the link reliability correction term, which suppresses excessive borrowing of time slots in high error scenarios.

[0051] The maximum value for dynamically borrowed time slots is 2 (i.e., a maximum of 2 UL time slots can be temporarily transferred from downlink time slots per cycle), and the actual number available is determined by the global load awareness layer: when the downlink / downlink traffic ratio α ≥ 40% of the entire network... SL borrow =2, when 20%≤α<40% SL borrow =1, when α < 20% SL borrow =0.

[0052] To better enhance uplink coverage, the uplink quality closed-loop control was improved by constructing a "UE measurement – ​​base station adjustment – ​​core network feedback" closed loop, such as... Figure 4 ,include: The UE periodically reports the SINR and BLER of the SUL carrier; The base station dynamically adjusts the time slot allocation based on BLER: if BLER is greater than the first BLER threshold (e.g., 15%), one UL time slot is added and the MCS level is reduced; if BLER is less than the second BLER threshold (e.g., 5%), one UL time slot is reduced and the MCS level is increased.

[0053] Based on historical link records, the core network side predicts resource demand in the future (e.g., 5 seconds). If there is no further downlink resource demand in the future, the UL time slot resources of the SUL carrier are reserved in advance to compensate for uplink coverage.

[0054] Example 2 This embodiment also relates to a 5G uplink coverage enhancement method. A port logistics park covers areas such as container terminals, storage areas, and transportation channels. Fifteen 3.5GHz 5G base stations are deployed within the park. To meet the uplink coverage and capacity requirements of services such as massive machine-type communication (mMTC), remote control of AGVs (uRLLC), and eMBB (eMBB), a 700MHz frequency band is introduced as a SUL carrier (frequency range 703-733MHz). This frequency band has the characteristics of strong diffraction capability and low propagation loss, but it also has problems such as potential interference with broadcast television signals and limited frequency resources. The park's services exhibit obvious tidal characteristics, with uplink load reaching up to 80% during peak daytime operations, and severe signal obstruction in some areas such as the container yard.

[0055] Multi-threshold calibration: RSRP threshold Pth: Through multiple rounds of drive tests within the park, combined with the propagation characteristics of the 700MHz band, the median RSRP value was determined to be -105dBm. Pth was set to -103dBm, PL to 120dB, and the time delay value TTT to 50ms. The threshold of 2dB in advance was set as the coverage edge trigger threshold.

[0056] SINR threshold Sth: Interference monitoring shows that the main interference source in the park is the surrounding broadcast and television signals. The average SINR value in the co-channel interference area is -2dB. Set Sth=-1dB and reserve a 1dB margin.

[0057] Using high-precision spectrum analysis and signal acquisition equipment, data from 1000 test points were collected within the park. A two-dimensional distribution histogram of RSRP-PL was constructed, and K-means clustering was used to determine the optimal threshold.

[0058] In the dynamic time slot parameters, the global load threshold α is set to 40%, and the basic time slot ratio is 3DL:1UL. For hotspot areas such as AGV (Automated Guided Vehicle) operation areas and dock loading and unloading areas (user density ≥ 150 households / square kilometer and RSRP ≤ -100dBm), an additional UL time slot is added.

[0059] Multi-dimensional threshold trigger verification: During container yard operation hours (08:00-10:00), AGVs move among densely stacked containers. One AGV is in an area with severe signal obstruction, with RSRP = -104dBm, PL = 125, and SINR = -3dB. This satisfies the multiple threshold conditions of RSRP ≤ Pth, PL ≥ Lth, and SINR ≤ Sth, triggering 700MHz SUL carrier access. However, in an open transport corridor area, an inspection robot has an RSRP of -110dBm and PL = 127, but its SINR = 2dB, failing to meet the SINR threshold condition. It still triggers SUL handover because it is determined that the UE has moved to the boundary area.

[0060] Dynamic timeslot allocation adjustment: During peak operating hours (14:00-16:00), when the uplink load monitoring of the entire park reaches α=75%, the uplink enhancement mode is triggered, and the global timeslot allocation is adjusted to 2DL:2UL. For the AGV operating area, an additional UL timeslot is added on top of the 2DL:2UL, forming a local allocation of 1DL:3UL. For high-definition video surveillance backhaul (eMBB service), each camera is allocated 2 consecutive UL timeslots + 1 flexible timeslot (configured as UL); for AGV remote control (uRLLC service), a fixed UL timeslot (10ms cycle) + an emergency timeslot request channel are reserved to ensure low latency and stability of data transmission.

[0061] Link quality optimization: A temperature and humidity sensor (mMTC service) in a certain area, due to its distance from the base station, had a SINR of -3dB, triggering a time slot enhancement algorithm. β=0.8, and the base 1 UL time slot was adjusted to 1×0.8+1=1.8 (rounded down to 2), ensuring stable data upload for the sensor every 50ms. Simultaneously, based on the sensor's continuously reported BLER (18%), the base station reduced the MCS (Modulation and Coding Scheme) level and added one UL time slot, lowering the BLER to 8% and effectively improving link reliability.

[0062] The results of the tests conducted by existing technologies and the method of this invention are shown in Table 1.

[0063] Table 1 As can be seen from the results in Table 1, after using the method of the present invention, the problem of imbalance between uplink and downlink coverage was alleviated due to the enhanced uplink coverage. The AGV control latency decreased from 12ms to 6ms, the average video transmission rate increased from 10Mbps to 18Mbps, the number of IoT devices accessed increased by 50%, and the handover success rate and interference error rate were significantly improved.

[0064] Example 3 The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0065] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0066] The processing unit performs the various methods and processes described above. For example, in some embodiments, the methods may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute the methods by any other suitable means (e.g., by means of firmware).

[0067] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0068] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0069] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0070] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for enhancing 5G uplink coverage, characterized in that, The method includes: Collecting 5G network data, including RSRP, SINR, TxPower, service type, uplink load, number of successful handovers, number of failed handovers, transmission rate, and block error rate, and dividing the data into a training set and a test set; Constructing a multi-dimensional threshold SUL trigger model based on a deep neural network. The deep neural network includes an input layer, three hidden layers, and an output layer. The dimensions of the input layer correspond to RSRP, SINR, path loss, and uplink load in the collected 5G network data. The parameter weights of each layer are optimized through the backpropagation algorithm. The multi-dimensional threshold includes an RSRP threshold Pth, a PL threshold Lth, and a SINR compensation threshold Sth; Constructing a three-layer architecture including a global load awareness layer, a regional resource scheduling layer, and a terminal dynamic adaptation layer to improve the dynamic time slot allocation algorithm; Inputting the test set into the trained multi-dimensional threshold SUL trigger model, and outputting the parameter configuration of the uplink based on the improved dynamic time slot allocation algorithm. The parameter configuration includes the RSRP threshold Pth, the SINR compensation threshold Sth, whether to trigger SUL access, and time slot configuration. The 5G uplink coverage is enhanced through the parameter configuration.

2. The 5G uplink coverage enhancement method according to claim 1, characterized in that, The multi-dimensional threshold SUL trigger model dynamically adjusts the multi-dimensional threshold in the following manner: Based on the uplink load and service type association data in the training set, a threshold adjustment mapping table is established. The mapping table contains the RSRP threshold Pth and the SINR compensation threshold Sth corresponding to different uplink load intervals and different service types; The output result of the multi-dimensional threshold SUL trigger model is a binary decision value. 1 indicates triggering SUL access, and 0 indicates exiting the SUL carrier or maintaining the original access mode.

3. The 5G uplink coverage enhancement method according to claim 1, characterized in that, When the UE uses the mid-high frequency band for uplink, if any of the following conditions is met and the duration is greater than the time delay value TTT, trigger SUL carrier access: The first condition: RSRP ≤ RSRP threshold Pth and PL ≥ PL threshold Lth; The second condition: SINR ≤ SINR compensation threshold Sth.

4. The 5G uplink coverage enhancement method according to claim 1, characterized in that, When the UE uses the SUL carrier for uplink, if both of the following two conditions are met simultaneously, exit the SUL carrier and return to mid-high frequency band access: The third condition: RSRP > RSRP threshold Pth or PL < PL threshold Lth; The fourth condition: SINR > SINR compensation threshold Sth.

5. A 5G uplink coverage enhancement method according to claim 1, characterized in that, The multi-dimensional threshold SUL trigger model classifies the uplink load and implements different RSRP thresholds Pth for different levels of uplink load. Specifically: If the uplink load is less than or equal to the first uplink load UL1, maintain the default RSRP threshold Pth; If the uplink load is between the first uplink load UL1 and the second uplink load UL2, the RSRP threshold Pth is increased by 3 dB; If the uplink load is greater than or equal to the second uplink load UL2, force the trigger of SUL and ignore the limitation of the SINR compensation threshold Sth.

6. A 5G uplink coverage enhancement method according to claim 1, characterized in that, The SINR compensation threshold Sth is combined with the service type to implement differential triggering of SUL. Specifically: The uRLLC service corresponds to the first SINR compensation threshold; The second SINR compensation threshold corresponding to eMBB service; The third SINR compensation threshold corresponding to mMTC services; The first SINR compensation threshold is greater than the second SINR compensation threshold, which is greater than the third SINR compensation threshold.

7. A 5G uplink coverage enhancement method according to claim 1, characterized in that, The global load perception layer periodically monitors the uplink-to-downlink traffic ratio α across the entire network. When α is greater than the traffic ratio threshold, the uplink coverage enhancement mode is activated, and the time slot ratio is adjusted to 2DL:2UL. The aforementioned regional resource scheduling layer adds an additional UL timeslot in the SUL carrier coverage area of ​​the hotspot region; The terminal dynamic adaptation layer is bound to the service type output by the multi-dimensional threshold SUL triggering model, and the granularity of the time slot configuration is dynamically adjusted.

8. A 5G uplink coverage enhancement method according to claim 7, characterized in that, The granularity of the dynamically adjusted time slot configuration includes: For eMBB services: allocate 2 UL time slots and 1 flexible time slot consecutively; For uRLLC services: Reserve fixed UL time slots and emergency time slot request channels; For mMTC services, time-division multiplexing is used to allocate one UL time slot.

9. A 5G uplink coverage enhancement method according to claim 1, characterized in that, The method also includes dynamically adjusting the number of available UL time slots based on real-time SINR, specifically: ， ， in, SL actual This represents the actual number of uplink time slots. To round down; SL basic The base number of time slots is preset by the terminal dynamic adaptation layer according to the service type; SL borrow To dynamically borrow time slots, the actual number available is determined by the global load awareness layer; The SUL carrier SINR value reported by the UE in real time; The SINR compensation threshold Sth corresponding to the current service type. This represents the ideal upper limit of SINR for a SUL carrier. The link quality correction coefficients range from -1 to 1; β is the slot enhancement factor, dynamically mapped from the uplink load level; γ is the borrowed slot effectiveness coefficient, which is related to the region type; and δ is the BLER attenuation coefficient. This constitutes the link reliability correction term, where BLER is the block error rate reported by the UE in real time.

10. A 5G uplink coverage enhancement method according to claim 1, characterized in that, The method also includes improvements to the uplink quality closed-loop control, including: The UE periodically reports the SINR and BLER of the SUL carrier; The base station dynamically adjusts the time slot allocation based on BLER: if BLER is greater than the first BLER threshold, one UL time slot is added and the MCS level is reduced by 1-2 levels; if BLER is less than the second BLER threshold, one UL time slot is reduced and the MCS level is increased by 1-3 levels, where the first BLER threshold is greater than the second BLER threshold. Based on historical records, the core network uses a linear regression algorithm to predict resource demand in the future. If there is no additional downlink resource demand in the future, it reserves UL time slot resources for two SUL carriers in advance to compensate for transmission loss in the uplink coverage edge area.

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