A 5G wireless communication module parameter self-adaptive adjustment method and device

Abstract

The present application relates to the technical field of 5G communication, in particular to a 5G wireless communication module parameter adaptive adjustment method and device, comprising: allocating a communication window for each connected device according to the number of connected devices; obtaining SRS of each channel, setting the modulation order for each channel according to the SRS of each channel and the data to be transmitted of each channel; setting the corresponding transmission power for each channel according to the modulation order of each channel, sorting the channels according to the size of the transmission power of each channel, and determining the communication order in a communication period according to the sorting result. The present application establishes the correlation between the modulation order and the transmission power, and intelligently sorts the communication order according to the size of the transmission power, so that when the communication module transmits the data of different channels in turn, the transmission power can smoothly transition along the path with the smallest change, fundamentally avoiding the power jump that may be caused by improper channel scheduling order.

Description

Technical Field

[0001] This invention relates to the field of 5G communication technology, and in particular to a method and apparatus for adaptive adjustment of parameters of a 5G wireless communication module. Background Technology

[0002] During operation, the core "adaptive" task of a 5G communication module is to dynamically adjust the modulation and coding scheme (MCS) according to the constantly changing wireless environment. At the same time, it will also adjust the transmit power and resource quantity to achieve the best balance between speed and reliability.

[0003] Currently, the traditional standard solution employs Outer Loop Link Adaptation (OLLA) technology, which introduces a dynamically adjusted "offset" based on Channel Quality Indicator (CQI / SINR) feedback. If acknowledgment feedback is received, the offset is slightly increased to attempt a higher Mid-Segment Cross Section (MCS); if negative feedback is received, the offset is significantly reduced to rapidly improve reliability. Its advantages include simple implementation, maturity and stability, and effective compensation for channel feedback errors, stabilizing the block error rate (BRR) near the target value (e.g., 10%). However, it also suffers from slow response, insufficient adaptability to channel abrupt changes, poor flexibility, and the inability to maximize throughput in all scenarios with a fixed BRR target.

[0004] It is evident that how to efficiently perform adaptive parameter adjustment in 5G communication modules is a problem that needs to be solved. Summary of the Invention

[0005] Therefore, it is necessary to provide a method and apparatus for adaptive adjustment of 5G wireless communication module parameters to address the above-mentioned problems.

[0006] This invention is implemented as follows: a method for adaptive adjustment of 5G wireless communication module parameters, the method comprising: Each connected device is assigned a communication window based on the number of connected devices, so that the communication window of any connected device has a unique frequency band and a full-cycle time period. Obtain the SRS of each channel, and set the modulation order for each channel based on the SRS of each channel and the data to be transmitted in each channel; The corresponding transmit power is set for each channel according to the modulation order of each channel, the channels are sorted according to the transmit power of each channel, and the communication order within a communication cycle is determined according to the sorting result.

[0007] In one embodiment, the present invention provides a 5G wireless communication module parameter adaptive adjustment device, the 5G wireless communication module parameter adaptive adjustment device comprising: The allocation module is used to allocate a communication window to each connected device based on the number of connected devices, so that the communication window of any connected device has a unique frequency band and a full-cycle time period. The modulation order setting module is used to obtain the SRS of each channel and set the modulation order for each channel based on the SRS of each channel and the data to be transmitted in each channel. The sorting module is used to set the corresponding transmit power for each channel according to the modulation order of each channel, sort the channels according to the transmit power of each channel, and determine the communication order within a communication cycle based on the sorting result.

[0008] The 5G wireless communication module parameter adaptive adjustment method provided in this invention achieves refined and intelligent management of communication resources by constructing a logical chain of "window allocation - parameter decision - sequence arrangement". Its core advantage lies in establishing a correlation between modulation order and transmit power, and intelligently sorting the communication sequence according to the transmit power. This allows the transmit power of the communication module to smoothly transition along the path of least change when transmitting data from different channels sequentially, fundamentally avoiding power jumps that may occur due to improper channel scheduling. This smooth power adjustment significantly reduces the nonlinear distortion of the power amplifier, reduces the reconfiguration overhead of the RF link, and makes the entire communication module's operation more stable and efficient, thereby greatly improving the overall performance and reliability of the 5G communication module in multi-connection, high-load scenarios. Attached Figure Description

[0009] Figure 1 A flowchart of a method for adaptive adjustment of 5G wireless communication module parameters provided in one embodiment of the present invention; Figure 2 This is a structural block diagram of a 5G wireless communication module parameter adaptive adjustment device provided in one embodiment of the present invention; Figure 3 This is a block diagram of the internal structure of a computer device in one embodiment. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0011] It is understood that the terms "first," "second," etc., used in this invention may be used to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are used only to distinguish one element from another.

[0012] like Figure 1 As shown, in one embodiment, the present invention proposes a method for adaptive adjustment of 5G wireless communication module parameters, which may specifically include the following steps: S1. Allocate a communication window to each connected device based on the number of connected devices, so that the communication window of any connected device has a unique frequency band and a full-cycle time period; S2. Obtain the SRS of each channel, and set the modulation order for each channel based on the SRS of each channel and the data to be transmitted in each channel. S3. Set the corresponding transmit power for each channel according to the modulation order of each channel, sort the channels according to the transmit power of each channel, and determine the communication order within a communication cycle according to the sorting result.

[0013] In this embodiment, the method aims to provide an intelligent parameter adaptive adjustment scheme for 5G communication scenarios to reduce power fluctuations and make the module operate more smoothly. It can be understood that this method is mainly implemented at the base station end. First, the communication module allocates a dedicated communication window to each device based on the total number of connected devices in the current network. This window is designed to have two unique attributes: an exclusive frequency band in the frequency domain and a time period covering the entire communication cycle in the time domain. This means that each device can transmit without conflict on its dedicated frequency band throughout the entire communication cycle, fundamentally avoiding co-channel interference and time slot contention, and creating conditions for subsequent order adjustments. Next, the communication module acquires the Sounding Reference Signal (SRS) for each communication channel. The SRS carries real-time information about the channel quality. The communication module comprehensively analyzes the channel conditions reflected by the SRS of each channel (such as signal-to-noise ratio and interference level) and the amount of data currently to be transmitted on that channel, dynamically setting the most suitable modulation order for each channel. For example, for channels with good quality and a large amount of data to be transmitted, high-order modulation such as 256QAM can be set to improve the transmission rate; for channels with poor quality or a small amount of data, low-order modulation such as QPSK is set to ensure transmission reliability. Finally, the communication module will reverse-engineer and set a corresponding transmit power based on the modulation order set for each channel in the previous step. Generally, higher-order modulation requires a higher signal-to-noise ratio and thus higher transmit power to ensure correct demodulation. In this invention, the modulation order and transmit power are in one-to-one correspondence; this is the module's default setting, and this invention does not involve changing this correspondence. The communication module will sort the channels according to their transmit power and determine the communication order of each channel within a communication cycle based on this sorting result. For example, sorting by transmit power from highest to lowest (or lowest to highest) ensures that the power changes from highest to lowest (or lowest to highest) during transmission, preventing power jumps and allowing for smooth power switching, reducing power loss and making the module more stable.

[0014] The 5G wireless communication module parameter adaptive adjustment method provided in this invention achieves refined and intelligent management of communication resources by constructing a logical chain of "window allocation - parameter decision - sequence arrangement". Its core advantage lies in establishing a correlation between modulation order and transmit power, and intelligently sorting the communication sequence according to the transmit power. This allows the transmit power of the communication module to smoothly transition along the path of least change when transmitting data from different channels sequentially, fundamentally avoiding power jumps that may occur due to improper channel scheduling. This smooth power adjustment significantly reduces the nonlinear distortion of the power amplifier, reduces the reconfiguration overhead of the RF link, and makes the entire communication module's operation more stable and efficient, thereby greatly improving the overall performance and reliability of the 5G communication module in multi-connection, high-load scenarios.

[0015] In one embodiment of the present invention, the step of allocating communication windows according to the number of connected devices, so that the communication window of any connected device has a unique frequency band and a full-cycle time period, includes: The bandwidth of a single device is obtained based on the total bandwidth of the frequency band and the number of currently connected devices. A unique frequency band is then allocated to each device based on the obtained bandwidth. Allocate a full-cycle time period as a communication time period for each connected device.

[0016] In this embodiment, the allocation method of the communication window is specifically defined, clarifying the specific method for achieving "unique frequency band and full-cycle time period". First, the communication module obtains the total available frequency band width, for example, 100MHz. Then, based on the number of currently connected devices, for example, 10 devices, the frequency band width allocated to each device is obtained through division, i.e., 100MHz / 10 = 10MHz. The communication module then allocates a non-overlapping independent frequency band with a width of 10MHz to each device, for example, device 1 uses 0-10MHz, device 2 uses 10-20MHz, and so on. At the same time, the communication module allocates a time period covering the entire communication cycle to each device. This means that within this communication cycle, each device can continuously transmit and receive data on its exclusive frequency band, without having to communicate only within a specific time slice as in traditional Time Division Multiple Access (TDMA). The purpose of this allocation method is to create conditions for freely adjusting the transmission time of each channel through a two-dimensional orthogonal resource allocation of "frequency division + full cycle".

[0017] In one embodiment of the present invention, setting the modulation order for each channel based on the SRS of each channel and the data to be transmitted in each channel includes: Select several parameters from the SRS and set weight coefficients for the selected parameters; Normalize the amount of data to be transmitted on all channels and set weighting coefficients for the amount of data to be transmitted; The score for each channel is calculated based on the selected parameters and their corresponding weighting coefficients, the normalized amount of data to be transmitted and its corresponding weighting coefficients. The modulation order is assigned to each channel based on the range in which the score falls.

[0018] In this embodiment, to achieve fine-grained setting of the modulation order, a decision-making mechanism based on multi-factor comprehensive scoring is introduced. First, several key parameters that best represent channel quality are selected from the large amount of channel state information contained in the SRS. For example, the Reference Signal Received Power (RSRP) and Signal-to-Interference-plus-Noise Ratio (SINR) can be selected. The communication module assigns a weighting coefficient to each selected parameter, such as setting the weight of RSRP to 0.3 and the weight of SINR to 0.7, to reflect the importance of different parameters in the decision-making process. Next, the communication module calculates the amount of data to be transmitted for each channel. Since the data amount of different channels may vary greatly, for example from 1KB to 1GB, normalization processing is required to map it to a uniform range, such as between 0 and 1. Simultaneously, a weighting coefficient, such as 0.4, is assigned to the normalized data amount. Finally, the communication module calculates a comprehensive score for each channel. Assuming channel A has a normalized RSRP of 0.8, a SINR of 0.9, and a normalized amount of data to be transmitted of 0.6, then its score = (0.8) 0.3) + (0.9 0.7) + (0.6 0.4) = 0.24 + 0.63 + 0.24 = 1.11. Finally, the communication module presets multiple scoring intervals, each corresponding to a modulation order. For example, the scoring interval [0, 0.8) corresponds to QPSK, [0.8, 1.2) corresponds to 16QAM, and [1.2, +∞) corresponds to 64QAM. Channel A has a score of 1.11, falling within the [0.8, 1.2) interval, therefore it is assigned 16QAM. The purpose of this mechanism is to comprehensively evaluate the channel's physical conditions and actual service requirements through quantitative methods, so that the selection of the modulation order no longer relies solely on a single indicator, but rather more intelligently matches the current communication scenario, thereby maximizing spectral efficiency while ensuring transmission reliability; it also lays the foundation for subsequently assigning the corresponding modulation order and ultimately the power based on the score.

[0019] In one embodiment of the present invention, the sorting of channels according to the transmit power of each channel includes: For the first cycle, the channels are sorted according to their transmit power, either from largest to smallest or from smallest to largest. For periods other than the first, the channels are sorted according to the relationship between the transmit power of the last channel in the previous period and the transmit power of each channel in the current period.

[0020] In this embodiment, to optimize the dynamic adjustment of the communication order, the method employs different sorting strategies for the first communication cycle and subsequent cycles. In the first communication cycle, due to the lack of historical information and the fact that the initial transmit power can start from 0 (without involving power continuity with the previous cycle), the communication module uses a relatively simple sorting method, such as sorting by transmit power from highest to lowest. This means that the channel with the highest power (usually corresponding to the highest modulation order and largest data volume) will receive the highest communication priority and be scheduled to communicate at the beginning of the cycle. This helps to prioritize the busiest or most quality-critical channels. For cycles other than the first, the sorting logic is more intelligent. It no longer independently sorts the channels of the current cycle but references the sorting results of the previous cycle, specifically the transmit power of the last channel in the previous cycle. The communication module compares this "historical anchor point" with the power of all channels in the current cycle to determine the current sorting strategy. The purpose of this design is to enable a smooth transition between different cycles in the communication sequence, ensuring that the change in transmission power from the end of one cycle to the beginning of the next is kept to a minimum, thereby avoiding power jumps caused by abrupt changes in the sequencing logic and keeping the power amplifier of the module operating continuously and stably.

[0021] In one embodiment of the present invention, the step of sorting the channels according to the relationship between the transmit power of the last channel in the previous period and the transmit power of each channel in the current period includes: Determine whether the transmit power of the last channel in the previous cycle is greater than the transmit power of each channel in the current cycle. If so, arrange the channels in the current cycle in descending order of power. If not, determine whether the transmit power of the last channel in the previous cycle is less than the transmit power of each channel in the current cycle. If so, arrange the channels in the current cycle in ascending order of power. If not, predict the SRS of each channel in the next cycle, and obtain the power of each channel in the next cycle based on the predicted value. Sort the channels in the current cycle according to the predicted power.

[0022] In this embodiment, the sorting method for non-first cycles is elaborated in detail, introducing a composite decision-making logic based on historical "anchor points" and future predictions. The communication module first obtains the transmit power value of the last channel in the previous communication cycle, denoted as P_last_prev. Then, it compares this value with the transmit power of all channels in the current cycle. If P_last_prev is greater than the transmit power of all channels in the current cycle, it means that the "tail end" power value of the previous cycle is still at the highest level in the current cycle. To maintain the continuity of the sorting, the communication module arranges all channels in the current cycle in descending order of power. Conversely, if P_last_prev is less than the transmit power of all channels in the current cycle, it indicates that the tail end power of the previous cycle is the lowest in the current cycle, and the communication module arranges all channels in the current cycle in ascending order of power. If neither of the above two situations holds, that is, the value of P_last_prev falls within the range of the channel power values ​​in the current cycle, it indicates that the channel state has undergone complex changes, and simple ascending or descending order cannot perfectly connect the channels. At this point, the communication module activates a prediction mechanism to predict the SRS of each channel in the next cycle, thereby estimating their transmit power for the next cycle. Then, the communication module sorts the channels in the current cycle based on these predicted future power values. The purpose of this mechanism is to handle complex transition scenarios by introducing predictions of future states, ensuring that the sorting decisions consider not only the past and present but also future trends. This guarantees that the transmit power transitions between the current cycle and the next cycle with minimal change, ensuring smooth power changes over a longer time horizon.

[0023] In one embodiment of the present invention, the prediction of the SRS of each channel in the next cycle includes: Calculate the mean values ​​of several selected parameters in the SRS across each channel, and obtain the regression lines for each of the selected parameters from the obtained mean values; Calculate the average deviation of each parameter relative to its respective regression line over the most recent few detection periods, and use this average deviation as the total deviation for the next period. The deviation of each parameter in each channel is calculated separately, and the calculated deviation is normalized as a weighting coefficient. The prediction deviation of each parameter in each channel is obtained based on the obtained weighting coefficient and the total deviation. The predicted values ​​of each parameter in each channel are obtained from the prediction bias.

[0024] This embodiment provides a specific and operable SRS prediction method based on statistical and regression analysis of historical data. First, taking a parameter in the SRS (such as SINR) as an example, the communication module collects historical data points (time-value) for this parameter across various channels and calculates the mean for each channel (since each channel has its own SRS, the mean of the corresponding parameter across all channels is used to measure the overall trend). Based on these mean points, a regression line reflecting the overall trend of the parameter can be fitted using methods such as linear regression. Next, the communication module calculates the deviation between the measured value (mean) of the parameter and the corresponding value on the regression line in each of the most recent detection periods (e.g., the most recent 5 detection periods; note that the detection period refers to the detection period of SRS, not the communication period), and calculates the average of these deviations. This average is considered the "total deviation" (composed of all channels) for the next period. This total deviation represents the current overall trend. Then, the communication module needs to allocate this total deviation to each channel. It calculates the historical deviation of each channel relative to the regression line and normalizes these deviations to determine the weights for allocation. For example, if a channel consistently exhibits a large historical deviation, indicating significant fluctuations, it should receive a larger weight when allocating the total deviation. Finally, multiplying the total deviation by the total number of channels and then by the normalized weight of each channel yields the predicted deviation for each channel on that parameter. The predicted value of this parameter equals the value of the corresponding point on the regression line plus this predicted deviation. The purpose of this prediction process is to generate a statistically sound SRS prediction value that reflects individual differences by combining the overall trend (regression line) and individual historical fluctuation characteristics (deviation normalization weights). This provides a reliable data foundation for subsequent power prediction and ranking decisions, ultimately contributing to a smooth transition of power across multiple cycles.

[0025] In one embodiment of the present invention, the step of calculating the deviation of each parameter in each channel, normalizing the calculated deviation as a weighting coefficient, and obtaining the prediction deviation of each parameter in each channel based on the obtained weighting coefficient and the total deviation includes: Calculate the regression lines of the values ​​of several selected parameters in the SRS in each channel; The deviation of each parameter in each channel is obtained by calculating the average deviation of each parameter relative to its respective regression line in the most recent detection periods. The calculated deviation is normalized and used as the weighting coefficient; The prediction deviations of each parameter of each channel are calculated by back-calculating the obtained weighting coefficients and the total deviation.

[0026] In this embodiment, the calculation process of "prediction bias" is explained more clearly, clarifying the conversion logic from "historical bias" to "weighting coefficient" and then to "prediction bias". Continuing from the previous embodiment, the communication module first fits its own regression line for each parameter (such as SINR) on each channel. Then, for this parameter on each channel, it calculates the average deviation of its actual value from its own regression line over the most recent several periods. This average value quantifies the historical volatility of this parameter on that channel. Next, the communication module normalizes these calculated average deviations so that the sum of the average deviations of all channels is 1. The normalized value is the weighting coefficient for that channel when allocating the total bias. Finally, by multiplying the "total bias for the next period" calculated in the previous step by the total number of channels and then by the normalized weight for each channel, the prediction bias of this parameter on each channel can be calculated. The purpose of this process is to calculate the "total deviation" (macroeconomic trend) and the "historical fluctuation characteristics" (micro-level individual characteristics) separately, and then combine the two organically through normalized weighting coefficients. This allows the final prediction deviation to reflect both the overall trend and the unique historical behavior pattern of each channel, thereby enabling the predicted power to more accurately reflect the individual change trend of each channel, providing a more refined basis for ranking decisions, and further ensuring the smoothness of power adjustment.

[0027] In one embodiment of the present invention, obtaining the predicted values ​​of each parameter in each channel from the prediction bias includes: The predicted value of each parameter in each channel is equal to the sum of the corresponding value of that parameter on the regression line and the prediction deviation.

[0028] This embodiment presents a concise and easy-to-implement method for calculating the predicted parameter values. After obtaining the prediction deviation ΔP for each channel and each parameter through the aforementioned steps, the predicted value V_pred for the parameter in the next period can be obtained through a simple addition operation: V_pred = V_reg + ΔP. Here, V_reg is the value of the parameter at the current prediction time point on its regression line. For example, for the SINR parameter on channel 3, we predict its deviation in the next period to be +2dB, and according to its SINR regression line, the value at the corresponding time point in the next period is 15dB, so its predicted value is 17dB. The purpose of this step is to transform the abstract prediction deviation into a concrete parameter prediction value in the most direct and clear way, providing a clear numerical basis for subsequent power calculation and ranking decisions, ensuring the closed loop and executability of the entire prediction logic, and ultimately ensuring the accuracy of power prediction, thereby supporting a smooth ranking strategy based on future trends.

[0029] In an embodiment of the present invention, sorting the channels of the current period according to the predicted power includes: Denote the number of channels with the current period power greater than the transmit power of the last channel in the previous period as N1, and the number of channels with the current period power less than the transmit power of the last channel in the previous period as N2; Denote the number of channels with the next period power greater than the transmit power of the last channel in the previous period as N3, and the number of channels with the next period power less than the transmit power of the last channel in the previous period as N4; Compare the magnitudes of N1 + N3 and N2 + N4. If N1 + N3 ≥ N2 + N4, sort the channels with the current period power less than the transmit power of the last channel in the previous period in ascending order of power. If N1 + N3 < N2 + N4, sort the channels with the current period power greater than the transmit power of the last channel in the previous period in descending order of power.

[0030] In this embodiment, a very specific sorting decision rule based on statistics is provided to handle the complex situation where the power at the end of the previous cycle intersects with the powers of all channels in the current cycle. First, the communication module counts two numbers: the number of channels N1 with a transmit power greater than P_last_prev in the current cycle, and the number of channels N2 with a transmit power less than P_last_prev. Then, using the predicted power for the next cycle, the communication module counts two more numbers: the number of channels N3 with a predicted power greater than P_last_prev in the next cycle, and the number of channels N4 with a predicted power less than P_last_prev. Next, the communication module compares the sum of N1 and N3 with the sum of N2 and N4. If N1 + N3 ≥ N2 + N4, this means that in the current and the next cycle, the total number of channels with a power greater than P_last_prev is not less than the total number of channels with a power less than it, that is, the "high-power" channels are in the majority. At this time, to reduce the power jump, the communication module arranges all the channels with a power less than P_last_prev in the current cycle (i.e., the "low-power" channels) in ascending order of power and places them at the front of the communication order. This can make the majority of the "high-power" channels be continuously arranged at the back, forming a smooth power transition. On the contrary, if N1 + N3 < N2 + N4, then the "low-power" channels are in the majority, and the communication module arranges all the channels with a power greater than P_last_prev in the current cycle in descending order of power and places them at the front. The purpose of this mechanism is to determine the sorting strategy in a statistically optimal way by comprehensively evaluating the current and future power distribution situations, so that the communication order can smoothly connect to the previous cycle and adapt to the current and future channel state trends at the same time, minimizing the adjustment range of the transmit power across cycles, thus ensuring that the transmit power of the module always evolves along the path with the smallest change amount, completely eliminating the power jump phenomenon, and making the module work in the most stable state.

[0031] As Figure 2 shown, in an embodiment of the present invention, a 5G wireless communication module parameter adaptive adjustment device is further provided, including: An allocation module, configured to allocate a communication window for each connected device according to the number of connected devices, so that the communication window of any connected device has a unique frequency band and a full-cycle time period; An order setting module, configured to obtain the SRS of each channel, and set the modulation order for each channel according to the SRS of each channel and the data to be transmitted on each channel; A sorting module, configured to set the corresponding transmit power for each channel according to the modulation order of each channel, sort the channels according to the magnitude of the transmit power of each channel, and determine the communication order within a communication cycle according to the sorting result.

[0032] In this embodiment, the functions of each module correspond one-to-one with the steps in the aforementioned method embodiments, providing specific structural support for realizing the adaptive adjustment method. Through modular design, the device is easily integrated into the communication module of a 5G base station or terminal device, and the low coupling between functional modules facilitates subsequent maintenance, upgrades, and functional expansion. For example, the scoring algorithm in the "order setting module" can be independently optimized to better adapt to new channel environments without modifying other modules.

[0033] In one embodiment of the present invention, a computer device is also provided, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the 5G wireless communication module parameter adaptive adjustment method according to any embodiment of the present invention.

[0034] Figure 3 An internal structural diagram of a computer device in one embodiment is shown. Figure 3 As shown, the computer device includes a processor, memory, network interface, input device, and display screen connected via a communication module bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores operating communication modules and may also store a computer program. When executed by the processor, this computer program enables the processor to implement the 5G wireless communication module parameter adaptive adjustment method provided in this embodiment of the invention. The internal memory may also store a computer program. When executed by the processor, this computer program enables the processor to implement the 5G wireless communication module parameter adaptive adjustment method provided in this embodiment of the invention. The display screen of the computer device can be a liquid crystal display screen or an e-ink display screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse, etc.

[0035] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0036] In one embodiment, the 5G wireless communication module parameter adaptive adjustment device provided in this invention can be implemented as a computer program, which can be implemented in the form of, for example... Figure 3 It runs on the computer device shown. The computer device's memory can store the various program modules that make up the device, for example, Figure 2The diagram shows the allocation module, order setting module, and sorting module. The computer program, comprised of these modules, causes the processor to execute the steps in the adaptive adjustment method for 5G wireless communication module parameters described in the various embodiments of the present invention.

[0037] In one embodiment, a computer device is provided, the computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, performs the following steps: S1. Allocate a communication window to each connected device based on the number of connected devices, so that the communication window of any connected device has a unique frequency band and a full-cycle time period; S2. Obtain the SRS of each channel, and set the modulation order for each channel based on the SRS of each channel and the data to be transmitted in each channel. S3. Set the corresponding transmit power for each channel according to the modulation order of each channel, sort the channels according to the transmit power of each channel, and determine the communication order within a communication cycle according to the sorting result.

[0038] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, causes the processor to perform the following steps: S1. Allocate a communication window to each connected device based on the number of connected devices, so that the communication window of any connected device has a unique frequency band and a full-cycle time period; S2. Obtain the SRS of each channel, and set the modulation order for each channel based on the SRS of each channel and the data to be transmitted in each channel. S3. Set the corresponding transmit power for each channel according to the modulation order of each channel, sort the channels according to the transmit power of each channel, and determine the communication order within a communication cycle according to the sorting result.

[0039] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0040] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0041] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0042] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for adaptive adjustment of parameters of a 5G wireless communication module, characterized in that, The adaptive adjustment method for 5G wireless communication module parameters includes: Each connected device is assigned a communication window based on the number of connected devices, so that the communication window of any connected device has a unique frequency band and a full-cycle time period. Obtain the SRS of each channel, and set the modulation order for each channel based on the SRS of each channel and the data to be transmitted in each channel; The corresponding transmit power is set for each channel according to the modulation order of each channel, the channels are sorted according to the transmit power of each channel, and the communication order within a communication cycle is determined according to the sorting result.

2. The adaptive adjustment method for 5G wireless communication module parameters according to claim 1, characterized in that, The method of allocating communication windows based on the number of connected devices, so that the communication window of any connected device has a unique frequency band and a full-cycle time period, includes: The bandwidth of a single device is obtained based on the total bandwidth of the frequency band and the number of currently connected devices. A unique frequency band is then allocated to each device based on the obtained bandwidth. Allocate a full-cycle time period as a communication time period for each connected device.

3. The adaptive adjustment method for 5G wireless communication module parameters according to claim 1, characterized in that, The step of setting the modulation order for each channel based on the SRS of each channel and the data to be transmitted on each channel includes: Select several parameters from the SRS and set weight coefficients for the selected parameters; Normalize the amount of data to be transmitted on all channels and set weighting coefficients for the amount of data to be transmitted; The score for each channel is calculated based on the selected parameters and their corresponding weighting coefficients, the normalized amount of data to be transmitted and its corresponding weighting coefficients. The modulation order is assigned to each channel based on the range in which the score falls.

4. The adaptive adjustment method for 5G wireless communication module parameters according to claim 1, characterized in that, The process of sorting the channels according to their transmit power includes: For the first cycle, the channels are sorted according to their transmit power, either from largest to smallest or from smallest to largest. For periods other than the first, the channels are sorted according to the relationship between the transmit power of the last channel in the previous period and the transmit power of each channel in the current period.

5. The adaptive adjustment method for 5G wireless communication module parameters according to claim 4, characterized in that, The process of sorting channels based on the relationship between the transmit power of the last channel in the previous period and the transmit power of each channel in the current period includes: Determine whether the transmit power of the last channel in the previous cycle is greater than the transmit power of each channel in the current cycle. If so, arrange the channels in the current cycle in descending order of power. If not, determine whether the transmit power of the last channel in the previous cycle is less than the transmit power of each channel in the current cycle. If so, arrange the channels in the current cycle in ascending order of power. If not, predict the SRS of each channel in the next cycle, and obtain the power of each channel in the next cycle based on the predicted value. Sort the channels in the current cycle according to the predicted power.

6. The adaptive adjustment method for 5G wireless communication module parameters according to claim 5, characterized in that, The prediction of the SRS for each channel in the next cycle includes: Calculate the mean values ​​of several selected parameters in the SRS across each channel, and obtain the regression lines for each of the selected parameters from the obtained mean values; Calculate the average deviation of each parameter relative to its respective regression line over the most recent few detection periods, and use this average deviation as the total deviation for the next period. The deviation of each parameter in each channel is calculated separately, and the calculated deviation is normalized as a weighting coefficient. The prediction deviation of each parameter in each channel is obtained based on the obtained weighting coefficient and the total deviation. Predictive values of each parameter in each channel are obtained from the prediction deviation.

7. The adaptive adjustment method for 5G wireless communication module parameters according to claim 6, characterized in that, The method for separately calculating the deviation of each parameter in each channel, normalizing the calculated deviation as a weight coefficient, and obtaining the prediction deviation of each parameter in each channel based on the obtained weight coefficient and the total deviation includes: Calculating the regression line of the values of several selected parameters in the SRS in each channel; Separately calculating the mean deviation of each parameter relative to its respective regression line in the most recent several detection periods to obtain the deviation of each parameter in each channel; Normalizing the calculated deviation as a weight coefficient; Back-calculating the prediction deviation of each parameter in each channel based on the obtained weight coefficient and the total deviation.

8. The adaptive adjustment method for 5G wireless communication module parameters according to claim 6, characterized in that, The method for obtaining the predictive value of each parameter in each channel from the prediction deviation includes: The predictive value of each parameter in each channel is equal to the sum of the corresponding value on the regression line of the parameter and the prediction deviation.

9. The adaptive adjustment method for 5G wireless communication module parameters according to claim 5, characterized in that, The method for sorting the channels in the current period according to the predicted power includes: Recording the number of channels with a power greater than the transmission power of the last channel in the previous period in the current period as N1, and the number of channels with a power less than the transmission power of the last channel in the previous period in the current period as N2; Recording the number of channels with a power greater than the transmission power of the last channel in the previous period in the next period as N3, and the number of channels with a power less than the transmission power of the last channel in the previous period in the next period as N4; Comparing the magnitudes of N1 + N3 and N2 + N4. If N1 + N3 ≥ N2 + N4, arranging the channels with a power less than the transmission power of the last channel in the previous period in the current period in ascending order of power. If N1 + N3 < N2 + N4, arranging the channels with a power greater than the transmission power of the last channel in the previous period in the current period in descending order of power.

10. A 5G wireless communication module parameter adaptive adjustment device, characterized in that, The 5G wireless communication module parameter adaptive adjustment device includes: An allocation module, configured to allocate a communication window for each connected device according to the number of connected devices, so that the communication window of any connected device has a unique frequency band and a full-cycle time period; An order setting module, configured to obtain the SRS of each channel, and set the modulation order for each channel according to the SRS of each channel and the data to be transmitted in each channel; A sorting module, configured to set the corresponding transmission power for each channel according to the modulation order of each channel, sort the channels according to the magnitudes of the transmission powers of each channel, and determine the communication order within a communication cycle according to the sorting result.

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