Heterogeneous network adaptive optimization method and system based on 5G RedCap module

Abstract

The invention belongs to the technical field of communication, and particularly relates to a heterogeneous network adaptive optimization method and system based on a 5G RedCap module, and the method comprises the steps: 1, calculating a node feature value of each 5G RedCap terminal node based on the obtained operation data of each 5G RedCap terminal node in a network; calculating the quantity of resources allocated to the 5G RedCap terminal node according to the node characteristic value; 2, each 5G RedCap terminal node adaptively adjusts the transmitting power, the modulation order and the coding rate of the 5G RedCap terminal node according to the resource quantity currently allocated to the 5G RedCap terminal node and the adjustment parameters; and 3, calculating the network performance, updating the adjustment parameters in combination with the set target network performance, and returning to the step 1. According to the method, the network throughput can be improved, the energy consumption management can be optimized, the bit error rate can be reduced, and the intelligent level and the self-adaptive capability of the system can be improved.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and specifically relates to a heterogeneous network adaptive optimization method and system based on a 5G RedCap module. Background Art

[0002] With the rapid development of 5G communication technology, low-power wide area network (LPWAN) and Internet of Things (IoT) applications have put forward higher requirements for wireless communication. In application scenarios such as smart manufacturing, smart cities, Internet of Vehicles, telemedicine, and environmental monitoring, devices are usually widely distributed and power consumption is limited. Therefore, a wireless communication solution with both efficient communication capabilities and low power consumption characteristics is required. As part of 3GPP standardization, 5G RedCap aims to provide low-cost, low-power, and moderate data rate 5G communication solutions for these applications. However, in actual applications, 5G RedCap terminals need to operate in complex heterogeneous network environments. Due to multiple factors such as resource allocation, channel quality, power management, and bit error rate optimization, existing network optimization methods still face many challenges.

[0003] In current wireless communication optimization methods, resource allocation strategies are mainly based on static or semi-dynamic scheduling mechanisms. For example, scheduling algorithms in LTE and 5G networks mainly rely on fixed scheduling strategies, such as channel state-based scheduling, fairness-based scheduling, etc. Although these methods perform well in high-throughput network environments, in 5G RedCap terminal scenarios, due to limited device power consumption, limited communication capabilities, and rapidly changing channel conditions, traditional resource allocation methods cannot effectively adapt to the individual characteristics of terminals. For example, in the 5GNR standard, time division multiple access (TDMA) and frequency division multiple access (FDMA) are used in combination to enable multi-user sharing of wireless resources, but these allocation methods are usually optimized based on channel state information (CSI) without fully considering the power consumption constraints and bit error rate requirements of the terminal. In addition, traditional scheduling algorithms often do not take into account the business needs of the terminal and cannot take into account the multiple optimization goals of low latency, high throughput, and low power consumption. Summary of the invention

[0004] In view of this, the main purpose of the present invention is to provide a heterogeneous network adaptive optimization method and system based on the 5G RedCap module, and by constructing a set of dynamic adaptive mechanisms for resource allocation, power management, modulation and coding adjustment, and network performance optimization, the 5G RedCap terminal can achieve high-efficiency, low-power consumption, and high-reliability communication capabilities in a complex wireless communication environment. The present invention proposes a resource allocation strategy based on eigenvalue calculation to ensure that resources are tilted towards efficient terminals while taking into account the basic needs of inefficient terminals; a power control method based on interference suppression and energy efficiency optimization to reduce the total energy consumption of the system and reduce co-channel interference; a modulation and coding adjustment strategy based on bit error rate optimization to dynamically select the optimal modulation method in combination with signal margin and channel quality; a parameter adaptive optimization mechanism based on target performance feedback to adjust the resource allocation priority, power allocation index, and signal margin in real time to ensure that the system can maintain optimal performance under different network conditions. Compared with traditional wireless optimization methods, the method of the present invention can improve network throughput, optimize energy consumption management, reduce bit error rate, and improve the intelligence level and adaptive ability of the system.

[0005] The technical solution adopted by the present invention is as follows: A heterogeneous network adaptive optimization method based on a 5G RedCap module, the method comprising: Step 1: Based on the acquired operation data of each 5G RedCap terminal node in the network, the node characteristic value of the 5GRedCap terminal node is calculated; according to the node characteristic value, the amount of resources allocated to the 5G RedCap terminal node is calculated; Step 2: Each 5G RedCap terminal node adaptively adjusts its own transmit power, modulation order, and coding rate according to the adjustment parameters based on the amount of resources currently allocated to it; Step 3: Calculate the network performance, and update the adjustment parameters based on the set target network performance, and return to step 1.

[0006] Furthermore, the operational data of the 5G RedCap terminal nodes obtained include: available bandwidth, transmit power, channel gain, total interference power, energy consumption rate, service delay requirements, service quality requirements and distance to the base station.

[0007] Furthermore, the adjustment parameters include: a resource allocation priority index, a power allocation index and a signal margin index; wherein the initial values ​​of the resource allocation priority index and the signal margin index are 2; and the initial value of the power allocation index is 1.

[0008] Furthermore, the node characteristic value of the 5G RedCap terminal node is calculated by the following formula: ; in, 5G RedCap terminal node The available bandwidth in MHz; 5G RedCap terminal node The transmission power, in mW; 5G RedCap terminal node The channel gain in dB; is the ambient noise power spectrum density, in mW / MHz; 5G RedCap terminal node The total interference power received, in mW; 5G RedCap terminal node The energy consumption rate, in mW; 5G RedCap terminal node The service delay requirement, in ms; 5G RedCap terminal node The service quality requirement ranges from 1 to 10; 5GRedCap terminal node The distance to the base station, in meters; is an integer subscript index.

[0009] Furthermore, the amount of resources allocated to the 5G RedCap terminal node is calculated by the following formula: ; in, For the moment Assigned to 5G RedCap terminal nodes The amount of resources, in RB; For the moment The total available resources of the system, in RB; Assigning priority indexes to resources; 5GRedCap terminal node The channel quality index is greater than 0 and less than 1; is the average channel quality of the network, with a value greater than 0 and less than 1; is the total number of 5G RedCap terminal nodes; is an integer subscript index.

[0010] Furthermore, the transmit power is adaptively adjusted using the following formula: ; in, 5G RedCap terminal node At the moment The transmission power; is the maximum allowed transmit power, in mW; The maximum resource allocation for a single 5G RedCap terminal node; is the power allocation index; For the moment The total interference power, in mW; It is the mean of the node characteristic values ​​of all 5G RedCap terminal nodes in the network.

[0011] Furthermore, the modulation order and coding rate are adaptively adjusted through the following formula: ; ; in, 5G RedCap terminal node At the moment The modulation order can be 2, 4, 16, 64 and 256; For the moment hour Endpoints The channel gain in dB; For the moment hour Endpoints The total interference power received, in mW; is the signal margin parameter; 5G RedCap terminal node At the moment The coding rate is greater than 0 and less than 1; is the target bit error rate; Indicates floor operation.

[0012] Furthermore, the network performance is calculated by the following formula: ; in, For the moment Network performance, in bps; 5G RedCap terminal node At the moment The actual bit error rate of 5G RedCap terminal node At the moment The end-to-end delay in milliseconds.

[0013] Furthermore, the adjustment parameters are updated according to the set target network performance through the following formula: ; ; ; in, assigning a priority index to the updated resource; is the updated power allocation index; is the updated signal margin parameter; To target network performance; For the moment The total network power consumption in mW; is the target power consumption in mW; is the maximum acceptable total power, in mW; For the moment The average bit error rate of the network; is the target bit error rate.

[0014] A heterogeneous network adaptive optimization system based on a 5G RedCap module, the system comprising: a data acquisition part, used to acquire the operating data of each 5G RedCap terminal node in the network; a feature extraction and resource allocation part, used to calculate the node characteristic value of the 5G RedCap terminal node based on the acquired operating data of each 5G RedCap terminal node in the network; and calculate the amount of resources allocated to the 5G RedCap terminal node according to the node characteristic value; a 5G RedCap terminal node network, comprising a plurality of 5G RedCap terminal nodes, each 5G RedCap terminal node adaptively adjusting its own transmission power, modulation order and coding rate according to the adjustment parameters based on the amount of resources currently allocated to itself; and a parameter updating part, used to calculate the network performance and update the adjustment parameters in combination with the set target network performance.

[0015] By adopting the above technical scheme, the present invention produces the following beneficial effects: Traditional resource scheduling methods mainly rely on channel quality indication (CQI) or preset priority strategies. These methods are often difficult to achieve optimal resource allocation when facing multiple business requirements and dynamically changing network environments. The present invention constructs a characteristic value calculation model that comprehensively considers bandwidth, channel gain, power, interference, energy consumption, business requirements, service quality and terminal distance, so that resource allocation can be adaptively adjusted based on the actual communication capabilities and needs of the terminal. By introducing the resource allocation priority index, the method of the present invention ensures that when the network load is light, resources can be fairly allocated, and when the load is high, the system can give priority to guaranteeing the resource supply of high characteristic value terminals, thereby improving the overall network throughput and service quality. In the typical application scenario of 5G RedCap terminals, the terminals are usually limited by battery power, so power management is crucial. Traditional power control methods are mainly based on fixed signal-to-noise ratio (SNR) thresholds or adaptive adjustments based on target bit error rate (BER), but these methods often ignore the interference level in the network and the overall energy efficiency optimization goals. The method of the present invention introduces the resource allocation ratio, the total interference power and the eigenvalue normalization factor of the terminal in the power adjustment calculation, so that the transmission power of the terminal can not only adapt to the current resource allocation situation, but also can be dynamically adjusted according to the total interference situation of the network. Through this method, when the network load is low or the interference is large, the system can adaptively reduce the transmission power of the terminal, thereby reducing the total energy consumption of the system, and when the load is high and the interference is small, the power can be increased to improve the data throughput and ensure the communication quality. In wireless communication, high-order modulation can increase the data transmission rate, but the anti-interference ability is weak, while low-order modulation can enhance the signal stability, but it will reduce the throughput. The method of the present invention calculates the optimal modulation order based on the channel gain, power optimization result, signal margin parameter and total interference power of the terminal, ensuring that the terminal adopts high-order modulation to improve the throughput when the channel quality is good, and automatically reduces the order when the channel deteriorates or the interference increases, so as to improve the anti-interference ability of the signal. In addition, the present invention also proposes a dynamic coding rate adjustment strategy, which is optimized in combination with the target bit error rate, modulation order and resource allocation ratio, so that the terminal can reduce the coding rate when the bit error rate is high to enhance data reliability, and increase the coding rate when the bit error rate is low to improve data transmission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a method flow of a heterogeneous network adaptive optimization method based on a 5G RedCap module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] All features disclosed in this specification, or steps in all methods or processes disclosed, except mutually exclusive features and / or steps, can be combined in any manner.

[0018] Any feature disclosed in this specification (including any additional claims and abstract), unless otherwise stated, may be replaced by other alternative features that are equivalent or have similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

[0019] Example 1: Reference Figure 1 , a heterogeneous network adaptive optimization method based on a 5G RedCap module, the method comprising: Step 1: Based on the acquired operation data of each 5G RedCap terminal node in the network, the node characteristic value of the 5GRedCap terminal node is calculated; according to the node characteristic value, the amount of resources allocated to the 5G RedCap terminal node is calculated; In a heterogeneous network environment, there is an obvious imbalance in the distribution, business needs and channel conditions of 5G RedCap terminals. Some terminals may be in a better channel environment, with higher channel gain and less interference, while other terminals may be in a marginal area with poor channel conditions and more interference. This difference leads to uneven network performance. If a fixed resource allocation strategy or an allocation method based only on SNR is used according to the traditional method, some terminals will have low resource utilization, while other terminals will have insufficient resource allocation, thereby affecting the throughput, reliability and energy efficiency of the entire system. Therefore, the present invention first calculates the node characteristic value of each 5G RedCap terminal based on the acquired network operation data. This characteristic value is a characterization of the comprehensive capabilities of the terminal, including data in multiple dimensions such as channel quality, interference environment, business needs, and energy consumption.

[0020] The core idea of ​​calculating the node eigenvalue is to construct a comprehensive function to measure the communication capability of each terminal, and dynamically adjust the weight of the eigenvalue in combination with the current load of the network, so that the allocation process can give priority to the performance of high-efficiency terminals and take into account the basic communication needs of weak channel terminals. When calculating the eigenvalue, it is first necessary to obtain the basic operation data of each terminal from the network, including multiple parameters such as available bandwidth, transmission power, channel gain, total interference power, energy consumption rate, service delay requirement, service quality requirement and distance to the base station. These data jointly determine the communication capability of the terminal. For example, in the case of larger available bandwidth and stronger channel gain, the terminal can achieve a higher data rate, while if the terminal is subject to greater interference or higher energy consumption, it may affect the stability of its continuous operation. In addition, service delay requirement and service quality requirement are key indicators of user experience. For low-latency services, resource supply needs to be guaranteed more preferentially, while for general data transmission services, the priority can be appropriately reduced to ensure the balance of overall resource allocation. Through the comprehensive evaluation of these factors, the calculated node eigenvalue not only reflects the basic physical conditions of the terminal, but also includes the terminal's business requirements and resource utilization efficiency.

[0021] When calculating the eigenvalue, the present invention adopts a normalized calculation method based on channel capacity, energy efficiency, service quality and distance weighting. The core idea of ​​this method is that when calculating the throughput, it not only depends on the channel gain and bandwidth, but also combines the transmission power and interference situation, calculates the channel capacity through the Shannon formula, and uses this as the preliminary communication capability evaluation index of the terminal. Then, in order to take into account energy efficiency, the ratio of throughput to energy consumption is calculated to ensure that high-energy-efficiency terminals can obtain higher eigenvalues. In addition, considering the actual business needs, the eigenvalue further combines the business delay requirements, service quality requirements and the distance from the terminal to the base station. The business delay requirements and service quality requirements jointly determine the urgency of the terminal for resources, and the distance from the terminal to the base station affects the propagation loss and reception quality of the signal. Therefore, in the process of calculating the eigenvalue, these factors are used as weighting factors for correction to ensure that the eigenvalue finally calculated can truly reflect the adaptability of the terminal in the current network environment. Through the calculation of the eigenvalue, the system can rank all terminals, so as to allocate resources according to a certain strategy. In order to ensure fairness and improve the overall network throughput at the same time, the present invention adopts a resource allocation mechanism based on eigenvalue weighting. In the traditional static allocation mode, resources are evenly allocated to all terminals, or allocated according to a fixed scheduling strategy, while the method of the present invention allocates resources in proportion based on the characteristic value of each terminal, so that efficient terminals can obtain more resources, while inefficient terminals can still maintain basic communication capabilities. In the resource allocation process, a resource allocation priority index is introduced, which determines the weight of the characteristic value in the allocation process. By dynamically adjusting the priority index, the system can adaptively adjust the degree of resource inclination under different network load conditions. For example, when the network load is high, the priority index can be appropriately increased so that high characteristic value terminals obtain more resources, thereby improving the overall throughput; and when the network load is low, the priority index can be reduced so that low characteristic value terminals can also obtain more communication resources to improve the overall user experience. It is worth mentioning that when allocating resources, the present invention also introduces a channel quality correction factor to correct the problem of unbalanced resource allocation caused by fluctuations in channel status. This factor is calculated based on the ratio of the channel quality of the terminal to the average channel quality of the network. Its function is to ensure that the terminal can still obtain certain resources under poor channel conditions, avoiding the problem of complete interruption of communication due to poor channel conditions. In addition, in order to prevent resources from being completely occupied by a small number of high-characteristic value terminals, the present invention also adopts a normalized adjustment mechanism so that resource allocation has a certain upper limit, thereby ensuring that all terminals can obtain certain resources.

[0022] Step 2: Each 5G RedCap terminal node adaptively adjusts its own transmit power, modulation order, and coding rate according to the adjustment parameters based on the amount of resources currently allocated to it; In the communication process of 5G RedCap terminals, the choice of transmission power directly affects the signal coverage and communication quality, and also determines the energy consumption level of the system. However, if the terminal transmission power is too high, it will not only increase its own energy consumption, but also cause interference to other adjacent terminals, reducing the stability of the entire network. Therefore, in the method of the present invention, the terminal dynamically adjusts the transmission power according to the amount of resources allocated, so that it can reduce power consumption and interference as much as possible while meeting communication needs. Specifically, the adjustment of the transmission power comprehensively considers multiple factors, including the amount of resources allocated, the total interference level of the network, the characteristic value of the terminal, and the average characteristic value of all terminals in the network. Through such a dynamic power control mechanism, the terminal can appropriately increase the power when high resources are allocated to increase the data rate, and reduce the power when resources are tight to reduce energy consumption and reduce interference to other terminals. Unlike the traditional fixed power control strategy, the method of the present invention can be adaptively adjusted according to the real-time network conditions, making the entire system more energy-efficient and efficient, and improving resource utilization. In addition to the adjustment of the transmission power, the adaptive adjustment of the modulation order and the coding rate is also an important innovation of the present invention. In wireless communication systems, the modulation method determines the amount of information transmitted per unit time, while the coding rate affects the data's ability to resist bit errors. Higher-order modulation methods can increase the data transmission rate, but at the same time, they also require higher channel quality. If high-order modulation is still used when the channel conditions are poor, the bit error rate will rise sharply, resulting in data transmission failure. Therefore, the method of the present invention enables the terminal to adaptively select a suitable modulation order and coding rate based on the current channel state, transmission power and interference after obtaining resources. Specifically, the terminal calculates the current channel quality and determines the appropriate modulation order based on the current signal-to-noise ratio. For example, when the signal quality is good, the terminal can use a higher-order modulation method, such as 64-QAM or 256-QAM, to increase the data transmission rate; and when the channel conditions are poor, a low-order modulation, such as QPSK or BPSK, is used to improve the robustness of the signal and reduce the bit error rate. This dynamic modulation adjustment method ensures that the terminal can communicate in the best way under different channel environments, which not only ensures high throughput, but also reduces the probability of data transmission failure.

[0023] The adjustment of the coding rate further enhances the adaptive ability of the system. In wireless communication systems, the coding rate determines the data redundancy. A low coding rate means more error correction capability, but it will reduce the data payload ratio, while a high coding rate can improve data transmission efficiency, but it has higher requirements on channel quality. The method of the present invention enables the terminal to intelligently adjust the coding rate according to the current bit error rate, bit error rate target value and the amount of allocated resources, so as to achieve the best balance between improving the data transmission rate and ensuring reliability. Unlike the traditional fixed coding rate scheme, the method of the present invention can reduce the coding rate under low signal-to-noise ratio conditions to enhance the anti-interference ability, and increase the coding rate under high signal-to-noise ratio environment to maximize the data throughput. It is worth noting that the adaptive optimization method of the present invention does not simply adjust the power, modulation order and coding rate separately, but adopts a collaborative optimization strategy, that is, these three parameters are mutually influenced and dynamically adapted during the adjustment process. For example, when the terminal resources are sufficient, the system will appropriately increase the transmission power, and use a higher order modulation method and a higher coding rate to increase the data rate; when the network resources are tight or the channel quality is reduced, the system will reduce the power output, reduce interference, and use low-order modulation and low coding rate to ensure the stability of data transmission. This collaborative optimization method ensures that the terminal can communicate with the optimal configuration under different network conditions, thereby improving the adaptability and performance of the overall network. In addition, in order to achieve long-term stable optimization, the adaptive optimization method of the present invention is not a one-time adjustment, but a continuous optimization based on a dynamic feedback mechanism. After adjusting the transmission power, modulation order and coding rate, the terminal will re-evaluate its communication quality within a certain time interval, and further optimize the adjustment parameters in combination with the latest network status and bit error rate. This dynamic feedback mechanism enables the terminal to adapt to sudden changes in network status, such as sudden increases in interference, changes in network load, etc., thereby ensuring the stability and continuity of communication.

[0024] Step 3: Calculate the network performance, and update the adjustment parameters based on the set target network performance, and return to step 1.

[0025] In this step, the current network performance needs to be calculated first to evaluate whether the overall operation status of the system meets the target optimization direction. The calculation of network performance comprehensively considers multiple factors, including key indicators such as terminal throughput, modulation mode, coding rate, bit error rate, service delay and power consumption. Throughput reflects the efficiency of data transmission, while modulation mode and coding rate directly affect the transmission rate and data reliability; bit error rate determines the communication quality, and service delay is related to the real-time requirements of terminal tasks; at the same time, power consumption needs to be strictly controlled to ensure that the energy efficiency optimization goal of the system is achieved. These factors influence each other and jointly determine the overall performance of the network. In the process of network performance calculation, the present invention adopts a weighted evaluation mechanism so that the calculation results of different terminals can be unified into a standardized evaluation model, so as to better measure the comprehensive performance of the entire system and provide a reliable basis for subsequent optimization decisions. After the network performance calculation is completed, the system needs to compare the calculation results with the preset target network performance to determine whether the current optimization strategy has achieved the expected effect. The target network performance is set according to the system design requirements and business needs. It not only considers the goal of maximizing the overall throughput, but also takes into account the controllability of power consumption and the goal of minimizing the bit error rate. When the calculated actual network performance is lower than the target performance, the system needs to correct the current resource scheduling plan by adjusting the optimization parameters to improve the network operation status. If the actual performance is higher than the target value, it means that the current optimization strategy still has room for improvement in resource utilization efficiency. The system can appropriately adjust the optimization parameters to make it more energy-saving and efficient. Through this comparison process, the system can achieve adaptive adjustment of the optimization direction and gradually improve the overall network operation effect.

[0026] In the process of adjusting the optimization parameters, the present invention adopts a dynamic optimization strategy based on gradient feedback, so that the adjustment process has stronger adaptability and convergence. First, in terms of resource allocation optimization, the system will adjust the resource allocation priority according to the changes in network performance, so that efficient terminals can obtain more resources, while inefficient terminals are appropriately restricted to improve the overall throughput. At the same time, in terms of power control, the system will dynamically adjust the power allocation factor by analyzing the difference between the current total network power consumption and the target power consumption, so that the overall power utilization rate is more reasonable, which not only meets the communication needs, but also does not cause excessive energy consumption. In addition, in terms of signal modulation and coding optimization, the system will adjust the signal margin parameters by comparing the gap between the actual bit error rate and the target bit error rate to optimize the modulation mode and coding strategy of the terminal, so as to ensure that the optimal data transmission mode can be achieved under different channel conditions. Through the comprehensive adjustment of these optimization parameters, the system can always maintain the best matching state of resource allocation, power control and modulation and coding strategies in the case of a constantly changing network environment, thereby ensuring that the overall network performance is optimal. It is worth noting that the optimization parameter adjustment of the present invention is not completed in one go, but a closed-loop optimization mechanism with continuous feedback is adopted, so that the system can fine-tune the optimization direction at multiple times and finally converge to the optimal solution. In each iteration, the system calculates the new network performance, compares it with the target performance, and then adjusts the parameters based on the error size. As the optimization process continues, the network performance gradually approaches the target value and stabilizes in an optimal operating state after convergence. This closed-loop optimization mechanism enables the system to automatically adapt to complex heterogeneous network environments without manual intervention, greatly improving the intelligence level and adaptive ability of the system. In addition, since the adjustment of the optimization parameters is based on real-time feedback of the network status, the system also has the ability to adapt to sudden changes in the network environment. For example, when the interference in a certain area suddenly increases or the business needs of some terminals suddenly change, the system can quickly perceive this change and quickly restore the stability of the network through dynamic adjustment of the optimization parameters to ensure that the communication quality will not be seriously affected.

[0027] Example 2: The acquired operating data of the 5G RedCap terminal node includes: available bandwidth, transmit power, channel gain, total interference power, energy consumption rate, service delay requirement, service quality requirement and distance to the base station.

[0028] Available bandwidth is the total amount of radio resource blocks (RBs) that a terminal can use, which directly determines the data transmission capacity. In 5G RedCap terminals, since they usually adopt a low-power design, the allocated bandwidth is often narrow, and the effective use of bandwidth resources is crucial to improving the throughput of the terminal. In the method of the present invention, the available bandwidth is not only used to calculate the transmission capacity of the terminal, but also as part of the resource allocation weight to ensure that the bandwidth utilization is maximized. In addition, the transmit power is a key parameter that determines the signal strength, and its size directly affects the communication quality, energy consumption, and interference level to other terminals. Higher transmit power can enhance signal coverage, but will lead to higher power consumption and potential channel interference, so it is necessary to optimize and adjust it in combination with the resource allocation and interference level of the terminal. Channel gain measures the quality of the wireless channel at the location of the terminal, including factors such as path loss, multipath effect, and shadow fading. The higher the channel gain, the better the communication quality of the terminal at a lower power, so when allocating resources, terminals with higher channel gain can often achieve higher throughput. However, due to the diversity of deployment environments, the channel gain of 5G RedCap terminals may change dramatically over time and location. Therefore, the optimization method of the present invention needs to dynamically track the changes in channel gain and adjust the power and modulation mode of the terminal accordingly to adapt to different network conditions.

[0029] The total interference power is the total amount of external interference received by the terminal in the current network environment, including interference from multiple wireless signal sources such as other 5GRedCap terminals, traditional 5G terminals, Wi-Fi, and IoT devices. The size of the interference power determines the availability and bit error rate of the channel. If the interference is too large, the terminal needs to increase the transmission power or adopt a more robust modulation and coding method to ensure the communication quality. The method of the present invention monitors the interference power in real time and adjusts the resource allocation strategy in combination with the interference environment, so that the system can adaptively adjust the communication mode of the terminal to minimize the impact of interference. The energy consumption rate is one of the key performance indicators of the 5G RedCap terminal, especially in battery-powered IoT devices, where energy efficiency optimization is a key factor affecting the terminal life and sustainable operation. Unlike traditional 5G terminals, RedCap devices usually operate in low power consumption mode. Therefore, in the optimization process, power consumption control must be combined with throughput optimization to ensure communication performance while minimizing energy consumption. The method of the present invention performs dynamic power control and resource allocation based on the energy consumption rate, so that the terminal can complete the communication task at the lowest possible power level, thereby extending battery life and reducing energy consumption.

[0030] The service delay requirement is an important indicator for determining the priority of terminal communication tasks. In different application scenarios, the 5GRedCap terminal may have very different requirements for delay. For example, in industrial automation control systems, terminals require ultra-low latency communication to ensure the real-time nature of control instructions, while in applications such as environmental monitoring and smart cities, the latency requirements for data transmission are relatively loose. Therefore, the method of the present invention dynamically adjusts the resource allocation strategy based on the service delay requirement, so that high-latency sensitive services can be given priority, while low-latency demand services can be flexibly scheduled to improve the overall network utilization efficiency. The quality of service requirement (QoS) further refines the service priority of the terminal, and by setting different QoS levels, the system can perform differentiated resource management according to the service attributes of the terminal. For example, for services with high QoS requirements such as video surveillance and voice communication, the system will prioritize the allocation of more resources to ensure smooth data transmission, while for ordinary data transmission services, a lower QoS level can be used to save network resources. The method of the present invention calculates the resource allocation priority of the terminal based on the QoS requirement, and combines other network parameters for comprehensive optimization to achieve the best service quality management. The distance to the base station is a key parameter affecting signal propagation loss. Since wireless signals attenuate as the distance increases during propagation, terminals far from the base station often require higher transmission power to maintain communication quality, while terminals close to the base station can complete communication with lower power. Therefore, in the method of the present invention, the distance from the terminal to the base station is used as an important factor in optimization calculation. In the process of resource allocation and power control, the system will give priority to ensuring signal coverage of remote terminals while avoiding unnecessary power waste, thereby improving the energy efficiency of the entire system.

[0031] Embodiment 3: The adjustment parameters include: a resource allocation priority index, a power allocation index and a signal margin index; wherein the initial values ​​of the resource allocation priority index and the signal margin index are 2; and the initial value of the power allocation index is 1.

[0032] Specifically, the resource allocation priority index determines how the system calculates the weights of different terminals when allocating resources. Since the operating environment and business requirements of 5G RedCap terminals are different, when allocating resources, the system needs to determine the resource share that each terminal should obtain based on the characteristic value of the terminal. The method of the present invention introduces a resource allocation priority index so that terminals with higher characteristic values ​​can obtain more resources, while terminals with lower characteristic values ​​can still maintain basic communication capabilities. The advantage of this index control method is that it allows the system to dynamically adjust the degree of resource inclination under different network load conditions. For example, when the network load is light, the resource allocation priority index can be smaller, making the resource allocation more uniform to ensure that all terminals can obtain a good communication experience; and when the network load is high, the index can be increased so that high characteristic value terminals can obtain more resources, thereby maximizing the overall network throughput. The initial value set by the present invention is 2, which means that in the initial operation stage of the system, the allocation of resources will be weighted based on the square ratio of the terminal characteristic value, so that high characteristic value terminals can obtain more obvious resource advantages in the early optimization process, while not over-compressing the resource share of low characteristic value terminals.

[0033] The role of the power allocation index is to control the terminal's transmit power allocation strategy so that it can be dynamically adjusted according to the amount of resources allocated, the current network interference situation and the characteristic value of the terminal. In wireless communication systems, power management is a key factor affecting energy consumption, interference control and channel utilization. Traditional power control methods usually use a fixed threshold value or an adjustment method based on the signal-to-noise ratio, while the method of the present invention introduces a power allocation index to make the power adjustment process more flexible and intelligent. Specifically, the index controls the sensitivity of power adjustment, that is, the degree to which the terminal's transmit power changes with resource allocation. When the power allocation index is small, the terminal's power adjustment is relatively mild and can maintain a relatively stable power output, while when the index is large, the terminal's power adjustment amplitude is more obvious to adapt to larger network load fluctuations. The initial value set by the present invention is 1, which means that in the initial stage, the power adjustment is linear, that is, the terminal's transmit power is proportional to the amount of resource allocation, which can ensure that the power adjustment remains in a relatively stable state during the early optimization of the system and provide a good benchmark for subsequent adaptive optimization.

[0034] The signal margin index is an important parameter for controlling the modulation order and coding rate optimization of the terminal. Its core role is to ensure the stability of the signal quality while taking into account the maximization of the data rate. In a wireless communication system, the selection of modulation mode and coding rate directly affects the bit error rate and data throughput. A higher-order modulation mode can increase the rate, but has higher requirements on channel quality, while a lower-order modulation mode has better anti-interference ability, but the data rate is lower. Therefore, in the method of the present invention, the introduction of the signal margin index enables the system to dynamically adjust the signal quality strategy under different network conditions to ensure that the bit error rate remains within an acceptable range. Specifically, the index determines the sensitivity of the modulation mode and coding rate to the channel gain and transmit power. When the index is small, the modulation mode and coding rate of the terminal are adjusted slowly, and the system pays more attention to stability; when the index is large, the modulation mode and coding rate of the terminal are adjusted more sensitively, so as to quickly adapt to changes in channel conditions. The initial value set by the present invention is 2, which means that in the initial stage, the adjustment strength of the modulation and coding mode is moderate, which can not only ensure the stability of the system, but also provide a higher data rate when the channel conditions are good.

[0035] Embodiment 4: Calculate the node characteristic value of the 5G RedCap terminal node by the following formula: ; in, 5G RedCap terminal node The available bandwidth in MHz; 5G RedCap terminal node The transmission power, in mW; 5G RedCap terminal node The channel gain in dB; is the ambient noise power spectrum density, in mW / MHz; 5G RedCap terminal node The total interference power received, in mW; 5G RedCap terminal node The energy consumption rate, in mW; 5G RedCap terminal node The service delay requirement, in ms; 5G RedCap terminal node The service quality requirement ranges from 1 to 10; 5GRedCap terminal node The distance to the base station, in meters; is an integer subscript index.

[0036] Specifically, in the optimization method of the present invention, the calculation of the terminal's characteristic value involves multiple core variables, which cover multiple aspects such as communication resources, channel status, power control, energy consumption management and business requirements. First, the bandwidth availability of the terminal is the core parameter that determines the data transmission rate, so the characteristic value calculation is directly related to the bandwidth. The system uses Shannon's formula to build a throughput model, where the channel gain And the terminal's transmit power jointly determine the channel capacity, while the interference power and ambient noise power This has a significant impact on the quality of signal reception. The channel capacity calculation method takes into account the impact of the signal-to-noise ratio and uses a logarithmic function to measure the bandwidth utilization efficiency, thereby ensuring that the eigenvalue can truly reflect the communication capability of the terminal. Under low signal-to-noise ratio conditions, this calculation method can effectively amplify the eigenvalues ​​of terminals with good channel gain, and in high interference environments, it can reasonably suppress the eigenvalues ​​of terminals that are severely interfered with, to ensure fairness and rationality in resource allocation. In addition, in order to optimize system energy efficiency, the present invention introduces the energy consumption rate in the eigenvalue calculation. as a weighting factor. A key challenge of wireless communication systems is power consumption management, especially for 5G RedCap terminals, one of whose design goals is to achieve energy-efficient operation. Therefore, if the eigenvalue is determined only based on throughput without considering the energy consumption of the terminal, the system may prioritize allocating a large amount of resources to high-power terminals, resulting in a decrease in overall energy efficiency. The method of the present invention introduces an energy consumption parameter into the denominator of the eigenvalue calculation formula, so that terminals with stronger data transmission capabilities per unit energy consumption can obtain higher priority, thereby guiding resources to high-energy-efficiency terminals. This method ensures a balance between the communication capability and energy efficiency of the terminal, so that the system can not only improve throughput, but also maximize overall network performance under a limited power budget. At the same time, the service delay requirement It is also one of the factors that the present invention focuses on in the calculation of eigenvalues. In wireless communication systems, different types of services have different requirements for delay. For example, ultra-low latency applications (such as industrial automation control and telemedicine) require end-to-end delay to be as low as possible, while general data services (such as file download and data synchronization) have a higher tolerance for delay. Traditional resource allocation methods often focus on maximizing throughput, while ignoring the diversity requirements of services, resulting in some delay-sensitive applications being unable to obtain sufficient resource guarantees. The optimization method of the present invention introduces service delay requirements when calculating eigenvalues, so that terminals with low delay requirements can obtain priority in the resource allocation process. Specifically, the delay requirement is calculated as a denominator, so that terminals with higher delay requirements have smaller eigenvalues, thereby being at a disadvantage in resource competition, while terminals with low delay requirements have larger eigenvalues ​​and can obtain more support in the resource allocation process. This mechanism ensures that network resources can be dynamically adjusted according to business needs, improving the business adaptability of the entire system.

[0037] In the method of the present invention, the quality of service requirement It is introduced as a weighting factor to further enhance the intelligence of resource allocation. In modern wireless communication networks, different business applications have different requirements for QoS (quality of service). For example, high-definition video streaming and voice calls require higher data rates, while IoT terminals and large-scale sensor networks have lower bandwidth requirements but require high reliability. Therefore, in the process of eigenvalue calculation, QoS requirements are used as weighting factors so that terminals with high QoS requirements can obtain larger eigenvalues ​​during the calculation process, which means that they will be given priority in resource allocation. This mechanism ensures that different types of services can be prioritized according to their own importance to meet the needs of diverse application scenarios, thereby improving user experience and improving the overall efficiency of the network. In addition, in order to ensure the fairness of resource allocation, the present invention takes into account the distance from the terminal to the base station during the eigenvalue calculation process. . Wireless signals will attenuate as the distance increases during the propagation process. Therefore, terminals far away from the base station usually require higher transmission power to maintain stable communication, while terminals close to the base station can complete communication with lower power. Traditional resource scheduling methods tend to prioritize resource allocation for terminals with better channel conditions, while ignoring the communication needs of long-distance terminals, resulting in the problem of uneven network coverage. The method of the present invention uses distance as a normalization factor when calculating eigenvalues, so that the eigenvalues ​​of terminals far away from the base station are appropriately reduced, while the eigenvalues ​​of close-range terminals are relatively high, thereby guiding the system to prioritize resource allocation for terminals close to the base station during resource allocation, while ensuring that long-distance terminals can still obtain basic communication capabilities. This mechanism improves the fairness of network resources, enables the entire system to achieve balanced resource utilization within the coverage range, and improves the overall stability and availability of the network.

[0038] Embodiment 5: The amount of resources allocated to the 5G RedCap terminal node is calculated by the following formula: ; in, For the moment Assigned to 5G RedCap terminal nodes The amount of resources, in RB; For the moment The total available resources of the system, in RB; Assigning priority indexes to resources; 5GRedCap terminal node The channel quality index is greater than 0 and less than 1; is the average channel quality of the network, with a value greater than 0 and less than 1; is the total number of 5G RedCap terminal nodes; is an integer subscript index.

[0039] Specifically, in the method of the present invention, the characteristic value of the 5G RedCap terminal It is the basis of resource allocation, which measures the comprehensive communication capability of each terminal, including multiple factors such as channel quality, energy consumption, and business needs. In order to ensure the reasonable allocation of resources, the present invention adopts a normalized allocation strategy based on eigenvalue weights, that is, the system calculates the eigenvalues ​​of all terminals and determines the proportion of resources that each terminal can obtain based on their relative sizes. Terminals with higher eigenvalues ​​indicate that they have better channel quality, higher energy efficiency, or greater business needs, so they should be given priority to obtain more resources, while terminals with lower eigenvalues ​​will be allocated fewer resources to ensure maximum resource utilization efficiency. In order to further optimize the flexibility of resource allocation, the present invention introduces the resource allocation priority index To adjust the influence of the characteristic value. The role of the priority index is to control the degree of resource inclination toward high characteristic value terminals. When the priority index is small, the distribution of resources is more even, and when the priority index is large, the resources will be more concentrated on high characteristic value terminals. The benefit of this design is that the system can dynamically adjust the resource inclination strategy according to the network load. For example, when the network load is light, the priority index can be lowered so that all terminals can obtain relatively balanced resources, thereby improving the overall user experience; when the network load is heavy, the priority index can be increased so that high characteristic value terminals can obtain more resources to maximize network throughput. This adjustable resource allocation mechanism enables the system to adapt to different network environments and achieve optimal scheduling under different load conditions.

[0040] In addition, in order to ensure that resource allocation not only considers the characteristic values ​​of the terminal, but also can adapt to the time-varying characteristics of the wireless channel, the method of the present invention introduces a channel quality correction factor when calculating the amount of resource allocation. Channel quality is a key factor affecting data transmission rate and bit error rate. There may be significant differences in channel quality between different terminals. Therefore, if channel quality is not considered, some terminals with poor channels may obtain more resources, but they cannot be used effectively, resulting in a waste of resources. In order to avoid this problem, the present invention adopts a normalized channel quality correction strategy, that is, when calculating the allocated resources, in addition to considering the characteristic values ​​of the terminal, its channel quality is also considered. and the average channel quality of the entire network Adjust. When the channel quality of the terminal is higher than the average level, its actual allocated resources will increase appropriately, and when the channel quality is lower than the average level, its allocated resources will decrease accordingly. The advantage of this mechanism is that it ensures that resources can be allocated preferentially to terminals that can use resources more efficiently, thereby improving the overall data throughput of the network and reducing resource waste caused by poor channel conditions. The resource allocation strategy of the present invention not only improves network throughput, but also has significant advantages in energy efficiency optimization. Since 5G RedCap terminals are usually low-power devices, when allocating resources, the system needs to ensure that high-efficiency terminals can obtain more resources, and the resource occupancy of low-efficiency terminals will not be excessively expanded. The method of the present invention uses the energy consumption factor in the characteristic value calculation, combined with the dynamic adjustment during resource allocation, so that terminals with stronger data transmission capabilities per unit energy consumption can obtain more resources. This energy efficiency-driven optimization method ensures efficient utilization of network resources and extends the terminal's battery life. This design is particularly suitable for scenarios such as the Internet of Things and intelligent manufacturing. In these scenarios, the energy consumption management of the terminal is crucial, and the resource optimization method of the present invention can minimize power consumption while ensuring communication quality, thereby improving the overall energy efficiency of the system.

[0041] Embodiment 6: Adaptively adjust the transmit power of the device by the following formula: ; in, 5G RedCap terminal node At the moment The transmission power; is the maximum allowed transmit power, in mW; The maximum resource allocation for a single 5G RedCap terminal node; is the power allocation index; For the moment The total interference power, in mW; It is the mean of the node characteristic values ​​of all 5G RedCap terminal nodes in the network.

[0042] Specifically, in the method of the present invention, the transmit power of the terminal depends not only on its current resource allocation, but also on the power allocation index, the total interference power and the characteristic value of the terminal. First, the transmit power is closely related to the resource allocation ratio. When a terminal is allocated more resources, it usually undertakes a larger data transmission task, so it is necessary to increase the transmit power accordingly to ensure the stability of the data throughput. The method of the present invention introduces the resource allocation ratio, that is, , to control the amplitude of power adjustment. This ratio indicates the ratio of the amount of resources obtained by the current terminal to the maximum amount of resources allocated to a single terminal in the system, and the power allocation index It is used to control the influence of this ratio on power adjustment. When is small, the impact of resource allocation on power is weak, and when When the value is larger, the change in resource allocation will have a more significant impact on the transmit power. Through this mechanism, the system can flexibly adjust the power regulation strategy under different network load conditions to ensure that the terminal can appropriately increase the transmit power when more resources are available to improve throughput, and reduce power when resources are tight to reduce energy consumption. In addition, in order to control the total interference level in the network, the method of the present invention introduces the total interference power factor in the power calculation. In wireless communication systems, concurrent communication of multiple terminals may cause co-channel interference and reduce the stability of the entire system. Therefore, when allocating power, it is necessary to consider the overall interference level of the current network environment. When it is small, the factor is close to 1, indicating that the network environment is relatively clean, and the terminal can adjust the transmission power relatively freely. When it increases, this factor will reduce the terminal's transmit power, so that the power level of all terminals drops appropriately, thereby reducing the mutual interference problem within the system. This dynamic power adjustment mechanism based on total interference power ensures that the system can automatically reduce power in a high-interference environment, thereby improving the stability of the entire network and avoiding resource waste due to unnecessary high-power transmission.

[0043] In the method of the present invention, in addition to considering resource allocation and interference level, a terminal characteristic value normalization factor is also introduced. To ensure that the adjustment of the transmit power matches the comprehensive capabilities of the terminal. Terminal characteristic value It reflects the comprehensive communication capability of the terminal, including channel conditions, energy consumption management, business needs and other dimensions. In order to ensure the fairness of power allocation, the system calculates the average characteristic value of all terminals. , and normalized based on the mean. If the characteristic value of a terminal is higher than the average value, it means that it has better communication conditions in the current network environment. Therefore, when calculating the transmit power, it can obtain a higher weight, thereby increasing the transmit power to further optimize the data transmission capacity; and when the characteristic value of the terminal is lower than the average value, its transmit power will be appropriately suppressed to reduce unnecessary power consumption. This adaptive power adjustment strategy based on the terminal characteristic value ensures that high-quality terminals can make full use of their channel advantages and improve the throughput of the entire network. At the same time, it also ensures the fairness of resource allocation, so that weak channel terminals can still maintain a certain communication capability. Compared with traditional fixed power control or simple adjustment methods based on signal-to-noise ratio, the method of the present invention has significant innovation in intelligence, adaptability and energy efficiency optimization. Traditional methods usually use preset power adjustment rules, such as power control based on a fixed channel gain threshold, or adopt power adaptive adjustment based on a target bit error rate. However, these methods often ignore the resource allocation strategy, interference level and communication capability of the terminal itself in the network, resulting in a relatively single power adjustment strategy that is difficult to adapt to complex heterogeneous network environments. The method of the present invention adopts a multi-factor joint optimization strategy, which not only takes into account the resource allocation of the terminal, but also combines the network interference level and the characteristic value of the terminal, so that the power adjustment strategy is more accurate and can be dynamically optimized under different network conditions, thereby improving the communication efficiency of the overall network. In a specific application scenario, the method of the present invention can significantly improve the communication quality of the 5G RedCap terminal while effectively reducing energy consumption. For example, in an industrial Internet of Things environment, some 5G RedCap terminals may need to continuously transmit a large amount of data, while other terminals may only need to perform intermittent low-power communication. In this case, the adaptive power adjustment method of the present invention can ensure that data-intensive terminals obtain sufficient power support to ensure high-throughput transmission, while power constraints are imposed on low-power terminals to extend their battery life. In addition, in application scenarios such as smart cities and Internet of Vehicles, the network environment may change rapidly, such as fluctuations in base station loads, entry and exit of mobile terminals, etc. In these cases, the power adjustment method of the present invention can perceive changes in the network environment in real time, and dynamically adjust the transmission power of the terminal, so that the network can continue to maintain a stable and efficient operating state.

[0044] Embodiment 7: Adaptively adjust the modulation order and coding rate by the following formula: ; ; in, 5G RedCap terminal node At the moment The modulation order can be 2, 4, 16, 64 and 256; For the moment hour Endpoints The channel gain in dB; For the moment hour Endpoints The total interference power received, in mW; is the signal margin parameter; 5G RedCap terminal node At the moment The coding rate is greater than 0 and less than 1; is the target bit error rate; Indicates floor operation.

[0045] Specifically, in the process of calculating the modulation order, the system first needs to evaluate the transmission capacity of the current channel, that is, to calculate the optimal choice of modulation method based on the signal-to-noise ratio. The logarithmic calculation expression in the formula is derived from Shannon's theorem, which is used to describe the maximum information capacity of the channel. First, the transmission power It directly affects the signal-to-noise ratio at the receiving end. Higher transmit power can increase the strength of the received signal, thereby increasing the modulation order that can be used. Secondly, channel gain It reflects the transmission characteristics of the wireless channel. Terminals with higher channel gain can obtain higher signal strength under the same transmission power. Therefore, when calculating the modulation order, the system will give priority to terminals with higher channel gain, so that they can use higher-order modulation to improve data throughput. However, the wireless channel is not an ideal environment and is affected by environmental noise and interference from other terminals. Therefore, when calculating the modulation method, the environmental noise power spectrum density also needs to be considered. and the total interference power The ambient noise power determines the basic noise level of the channel, while the interference power depends on the impact of other devices in the network on the current terminal. If the noise and interference levels are high, the available signal-to-noise ratio of the channel will be reduced, thereby limiting the available modulation order. In order to compensate for these uncertainties, the method of the present invention introduces the signal margin parameter , this parameter is used to adjust the calculation of the modulation order so that the terminal will not switch the modulation mode frequently in a short period of time, but can remain stable within a reasonable signal-to-noise ratio range to reduce the frequent mode switching problem caused by channel fluctuations. Through this design, the terminal can use higher-order modulation when the channel conditions are good, and automatically reduce the order when the channel quality is poor or the interference is large, so as to improve communication reliability.

[0046] In the process of calculating the coding rate, the method of the present invention not only considers the influence of the modulation order on the data throughput, but also combines the target bit error rate As well as resource allocation, the terminal can optimize the coding method and improve the overall efficiency of the system under the premise of ensuring reliable data transmission. First, the coding rate is calculated using the logarithmic function of the modulation order, that is, , which means that the higher the modulation order, the higher the upper limit of the coding rate. The reason is that high-order modulation can carry more information, so under the same bandwidth and time resources, a higher coding rate can be used to increase the data transmission rate. However, high-order modulation usually increases the bit error rate, so when calculating the coding rate, it needs to be corrected in combination with the target bit error rate. Multiply A bit error rate compensation term is formed, that is, when the bit error rate requirement is strict, this term will constrain the coding rate to avoid the increase of data errors caused by high-order modulation. At the same time, this term also ensures that the coding rate will not be too high when the channel quality is poor, so as to reduce the bit error rate and improve the reliability of data transmission. In addition, in order to ensure the rational use of resources, the calculation of the coding rate also introduces the resource allocation ratio . This ratio is used to measure the relationship between the amount of resources currently obtained by the terminal and the maximum amount of resources allocated to a single terminal, which means that when the terminal obtains more resources, it can use a higher coding rate to transmit data at a higher rate, and when the resource allocation is less, the coding rate will be appropriately reduced to improve the stability of data transmission. This dynamic adjustment method ensures that the system can maximize the throughput under limited resources, while avoiding the increase in bit error rate due to insufficient resource allocation. Compared with the traditional fixed modulation and coding scheme, the method of the present invention provides an adaptive strategy based on multi-factor dynamic optimization, so that the terminal can flexibly adjust the modulation order and coding rate according to the real-time channel environment and network resource allocation. Traditional wireless communication systems usually adopt a modulation and coding adjustment method with a preset threshold, that is, the modulation method and coding rate are selected according to the fixed interval of the signal-to-noise ratio. However, this method often lacks sufficient adaptability in a complex heterogeneous network environment and is difficult to meet the rapidly changing channel requirements. The method of the present invention not only takes into account the signal-to-noise ratio, but also combines the signal margin, bit error rate target and resource allocation, so that the system can adaptively optimize the modulation and coding strategy under different loads and interference levels, improve communication efficiency, and ensure the reliability of data transmission. In actual application scenarios, the adaptive modulation and coding method of the present invention can significantly improve the communication efficiency of 5G RedCap terminals. For example, in industrial automation applications, different sensors and control devices may have different communication requirements. The method of the present invention can ensure that low-latency and high-reliability control devices use low-order modulation and low coding rates to improve the stability of data transmission, while for devices that transmit large amounts of data, they can be dynamically adjusted to high-order modulation and high coding rates to maximize data throughput. In the Internet of Vehicles environment, the method of the present invention can reduce the modulation order when the vehicle is moving at high speed to reduce the increase in bit error rate caused by rapid channel changes, and automatically increase the modulation order in a low-speed or stationary environment to improve data transmission efficiency. This intelligent modulation and coding adaptive strategy enables the 5G RedCap terminal to adapt to different channel environments and provide efficient and reliable communication services in complex wireless network environments.

[0047] Embodiment 8: Calculate network performance using the following formula: ; in, For the moment Network performance, in bps; 5G RedCap terminal node At the moment The actual bit error rate of 5G RedCap terminal node At the moment The end-to-end delay in milliseconds.

[0048] Specifically, in this formula, network performance The throughput is determined by the transmission capability, signal quality, latency constraints, and power control of all 5G RedCap terminals. First, the throughput is determined by the resource allocation of each terminal. , modulation order And the coding rate Joint decision. Reflects the radio resource blocks (RBs) obtained by each terminal, It appears in logarithmic form, which means that the higher the modulation order, the greater the throughput per unit resource, and the coding rate The ratio of effective data transmission is further corrected. The joint calculation method of modulation order and coding rate ensures that the system can flexibly adjust the data transmission rate under different channel environments and improve the overall network throughput. In order to further optimize the communication reliability of the system, the method of the present invention introduces a bit error rate compensation factor when calculating network performance. In wireless communication systems, the bit error rate It directly reflects the error probability of data transmission. If the bit error rate is high, more retransmissions are required, resulting in a decrease in system throughput. Therefore, the network performance calculation uses As an adjustment factor, it weakens the contribution of high bit error rate terminals and encourages low bit error rate terminals to take on more data transmission tasks. As the denominator, ensure that low-latency terminals have a higher weight in the network performance calculation. This means that when a terminal has a high latency, even if its throughput is large, it will not make too much contribution to the overall network performance, avoiding the impact of some high-latency devices on the real-time requirements of the entire system. This calculation method enables the network to strike a balance between maximizing throughput and optimizing low latency.

[0049] In addition, in order to optimize the power utilization efficiency of the system, the method of the present invention introduces a power normalization factor in the network performance calculation. . Since 5G RedCap terminals are usually low-power devices, the system must consider energy consumption when evaluating network performance. This calculation method ensures that when the terminal's transmit power is high, its contribution to network performance will be appropriately weakened to avoid excessive energy consumption by high-power terminals, thereby improving the energy efficiency of the overall network. On the contrary, if a terminal can maintain good communication quality at a lower transmit power, its weight in the network performance calculation will be higher, encouraging the system to give priority to high-efficiency terminals when allocating resources. This energy-efficiency-based normalization strategy enables the network to reduce overall power consumption while maintaining high throughput, thereby improving the sustainability of long-term operation. Compared with traditional single throughput optimization methods, the method of the present invention has significant advantages in intelligence, adaptability, and multi-objective optimization capabilities. Traditional network performance evaluation is usually based only on throughput calculation, that is, directly summing up the resource utilization of all terminals, but this type of method ignores the effects of bit error rate, latency, and power consumption, resulting in network optimization strategies that are difficult to adapt to complex heterogeneous environments. The method of the present invention not only considers the resource utilization of each terminal through multi-parameter joint optimization, but also combines bit error rate compensation, delay control and power consumption optimization, so that the system can achieve optimal performance in different application scenarios. For example, in a high-delay-sensitive industrial control scenario, the method can automatically reduce the contribution of high-delay devices, so that the system can give priority to serving low-delay tasks and improve the real-time performance of task completion; while in a low-power IoT environment, the method can optimize energy efficiency, so that battery-powered terminals can communicate efficiently at lower power and extend the life of the equipment. In practical applications, the method of the present invention can significantly improve the communication efficiency of 5G RedCap terminals. For example, in an intelligent transportation system, the method can give priority to ensuring the low-delay requirements of key communication tasks, ensuring that the Internet of Vehicles equipment obtains sufficient resource support during data exchange, while avoiding excessive transmission of high-bit error rate data and reducing communication errors. In smart factories and telemedicine scenarios, the method can optimize power usage, so that sensor devices can maintain high-quality data communication in low-power mode, and improve the energy efficiency ratio of the system. In addition, in a large-scale Internet of Things environment, the method of the present invention can dynamically adjust the resource allocation strategy so that terminals in different application scenarios can obtain the optimal communication experience, thereby improving the resource utilization and data transmission stability of the entire 5G RedCap network.

[0050] Embodiment 9: The adjustment parameters are updated according to the set target network performance through the following formula: ; ; ; in, assigning a priority index to the updated resource; is the updated power allocation index; is the updated signal margin parameter; To target network performance; For the moment The total network power consumption in mW; is the target power consumption in mW; is the maximum acceptable total power, in mW; For the moment The average bit error rate of the network; is the target bit error rate.

[0051] Specifically, in this method, the resource allocation priority index Directly affects the resource allocation tilt of each terminal in the network. By observing the current network performance and the target performance The system can dynamically adjust the index to optimize resource utilization. When the performance is lower than the target, it means that the overall resource utilization efficiency of the system may be insufficient. Therefore, this method calculates the rate of change of network performance over time. The rate of change is used to determine the optimization direction. The rate of change is used to determine the speed of adjustment, so that when the performance improvement trend is faster, the system appropriately reduces the parameter adjustment range, and when the improvement is slower, the adjustment strength is increased. Then, the system calculates the error ratio between the current performance and the target performance. Calculate the adjustment direction to ensure that the parameters are adjusted in the optimal direction. This design allows the resource allocation priority index to be flexibly adjusted according to the network conditions, so that high-characteristic value terminals can obtain higher priority weights in resource competition, thereby improving system throughput. In terms of power control, this method adjusts the power allocation index To optimize the overall power consumption. Since 5G RedCap terminals usually operate in low power consumption mode, power consumption must be strictly controlled to ensure sustainable operation of the terminal. The method of the present invention calculates the total power consumption of the network Relative to target power consumption The power allocation strategy is adjusted dynamically according to the deviation. When the total power consumption exceeds the target value, the power allocation index In order to prevent excessive power adjustment from affecting communication quality, the system normalizes the adjustment range and introduces the maximum acceptable total power. As a constraint, the power adjustment process is smoother. In addition, this adjustment method ensures that the system automatically reduces power consumption when the load is low, and maintains sufficient transmission power when the load is high to maintain reliable communication quality. In terms of signal modulation optimization, the method of the present invention adjusts the signal margin parameter To optimize the bit error rate control strategy. Since the wireless channel is affected by many factors, the bit error rate The signal margin may fluctuate over time, so the system needs to adaptively adjust the signal margin so that the terminal can dynamically select the optimal modulation order and coding rate. When the actual bit error rate is higher than the target value, the system appropriately increases the signal margin. , so that the terminal is more inclined to use low-order modulation and low coding rate to reduce the bit error rate; when the bit error rate is low, the system reduces the signal margin, so that the terminal can use higher-order modulation and higher coding rate to improve data throughput. In this way, the method of the present invention maximizes the data transmission capacity of the system while ensuring the communication quality.

[0052] Compared with the traditional static parameter adjustment method, the method of the present invention has significant advantages in adaptive optimization, feedback learning and dynamic adjustment capabilities. Traditional methods usually use fixed power control and modulation strategies, which are difficult to adapt to the dynamic changes of the network environment, resulting in limited system optimization capabilities. The method of the present invention adopts an adaptive parameter adjustment strategy based on target performance feedback, so that the system can autonomously optimize resource allocation, power management and signal quality control under different network conditions. For example, in a high-load scenario, the system can automatically adjust the resource allocation strategy to improve the priority index. The system can tilt towards high-characteristic terminals to improve the overall throughput; and when energy consumption is limited, the system can reduce the power allocation index , reducing the power consumption of the terminal, allowing the system to maintain normal operation in low-power mode. In addition, in an environment with large interference, the system can appropriately adjust the signal margin , ensure that the bit error rate is within an acceptable range and optimize communication stability. In actual application scenarios, the method of the present invention can significantly improve the communication quality and energy efficiency of 5G RedCap terminals. For example, in a large-scale Internet of Things network, the method can automatically optimize the resource allocation strategy when the number of devices increases significantly, so that high-efficiency devices obtain more priority resource allocation, and low-power devices can maintain communication under limited resources. In applications that are sensitive to bit error rates, such as industrial automation and telemedicine, the method can dynamically adjust the signal margin to ensure that the communication quality of critical tasks is not affected by environmental interference. In addition, in scenarios such as smart transportation and smart cities, the method can optimize power management, so that the terminal automatically reduces the transmission power when the communication demand is low, prolongs the device's battery life, and provides sufficient communication capabilities in high-demand scenarios to meet data transmission needs.

[0053] Embodiment 10: A heterogeneous network adaptive optimization system based on a 5G RedCap module, the system comprising: a data acquisition part, for acquiring the operating data of each 5G RedCap terminal node in the network; a feature extraction and resource allocation part, for calculating the node characteristic value of the 5G RedCap terminal node based on the acquired operating data of each 5G RedCap terminal node in the network; and calculating the amount of resources allocated to the 5G RedCap terminal node according to the node characteristic value; a 5GRedCap terminal node network, comprising a plurality of 5G RedCap terminal nodes, each 5G RedCap terminal node adaptively adjusting its own transmission power, modulation order and coding rate according to the adjustment parameters based on the amount of resources currently allocated to itself; a parameter updating part, for calculating the network performance, and updating the adjustment parameters in combination with the set target network performance.

[0054] Although the specific embodiments of the present invention are described above, it should be understood by those skilled in the art that these specific embodiments are only illustrative, and those skilled in the art may omit, replace, and change the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, merging the above method steps so as to perform substantially the same functions in substantially the same manner to achieve substantially the same results is within the scope of the present invention. Therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. A heterogeneous network adaptive optimization method based on 5G RedCap module, characterized in that: The method comprises: Step 1: Based on the acquired operation data of each 5G RedCap terminal node in the network, the node characteristic value of the 5G RedCap terminal node is calculated; according to the node characteristic value, the amount of resources allocated to the 5G RedCap terminal node is calculated; Step 2: Each 5G RedCap terminal node adaptively adjusts its own transmit power, modulation order, and coding rate according to the adjustment parameters based on the amount of resources currently allocated to it; Step 3: Calculate the network performance, and update the adjustment parameters based on the set target network performance, and return to step 1.

2. The heterogeneous network adaptive optimization method based on the 5G RedCap module according to claim 1, characterized in that: The operational data of the 5G RedCap terminal nodes obtained include: available bandwidth, transmit power, channel gain, total interference power, energy consumption rate, service delay requirements, service quality requirements and distance to the base station.

3. The heterogeneous network adaptive optimization method based on the 5G RedCap module as claimed in claim 2, characterized in that: The adjustment parameters include: a resource allocation priority index, a power allocation index and a signal margin index; wherein the initial values ​​of the resource allocation priority index and the signal margin index are 2; and the initial value of the power allocation index is 1.

4. The heterogeneous network adaptive optimization method based on the 5G RedCap module as claimed in claim 3 is characterized in that: The node characteristic value of the 5G RedCap terminal node is calculated using the following formula: ； in, 5G RedCap terminal node The available bandwidth in MHz; 5G RedCap terminal node The transmission power, in mW; 5G RedCap terminal node The channel gain, in dB; is the ambient noise power spectrum density, in mW / MHz; 5G RedCap terminal node The total interference power received, in mW; 5G RedCap terminal node The energy consumption rate, in mW; 5G RedCap terminal node The service delay requirement, in ms; 5G RedCap terminal node The service quality requirement ranges from 1 to 10; 5GRedCap terminal node The distance to the base station, in meters; is an integer subscript index.

5. The heterogeneous network adaptive optimization method based on the 5G RedCap module as claimed in claim 4, characterized in that: The amount of resources allocated to the 5G RedCap terminal node is calculated using the following formula: ； in, For the moment Assigned to 5G RedCap terminal nodes The amount of resources, in RB; For the moment The total available resources of the system, in RB; Assigning priority indexes to resources; 5G RedCap terminal node The channel quality index is greater than 0 and less than 1; is the average channel quality of the network, with a value greater than 0 and less than 1; is the total number of 5G RedCap terminal nodes; is an integer subscript index.

6. The heterogeneous network adaptive optimization method based on the 5G RedCap module as claimed in claim 5, characterized in that: Adaptively adjust its own transmit power through the following formula: ； in, 5G RedCap terminal node At the moment The transmission power; is the maximum allowed transmit power, in mW; The maximum resource allocation for a single 5G RedCap terminal node; is the power allocation index; For the moment The total interference power, in mW; It is the mean of the node characteristic values ​​of all 5G RedCap terminal nodes in the network.

7. The heterogeneous network adaptive optimization method based on the 5G RedCap module as claimed in claim 6, characterized in that: Adaptively adjust its modulation order and coding rate through the following formula: ； ； in, 5G RedCap terminal node At the moment The modulation order can be 2, 4, 16, 64 and 256; For the moment hour Endpoints The channel gain, in dB; For the moment hour Endpoints The total interference power received, in mW; is the signal margin parameter; 5G RedCap terminal node At the moment The coding rate is greater than 0 and less than 1; is the target bit error rate; Indicates floor operation.

8. The heterogeneous network adaptive optimization method based on the 5G RedCap module as claimed in claim 7, characterized in that: The network performance is calculated using the following formula: ； in, For the moment Network performance, in bps; 5G RedCap terminal node At the moment The actual bit error rate of 5G RedCap terminal node At the moment The end-to-end delay in milliseconds.

9. The heterogeneous network adaptive optimization method based on the 5G RedCap module as claimed in claim 8, characterized in that: The adjustment parameters are updated according to the set target network performance through the following formula: ； ； ； in, assigning a priority index to the updated resource; is the updated power allocation index; is the updated signal margin parameter; To target network performance; For the moment The total network power consumption in mW; is the target power consumption in mW; is the maximum acceptable total power, in mW; For the moment The average bit error rate of the network; is the target bit error rate.

10. A heterogeneous network adaptive optimization system based on a 5G RedCap module for implementing the method described in any one of claims 1 to 9, characterized in that: The system includes: a data acquisition part, which is used to acquire the operating data of each 5G RedCap terminal node in the network; a feature extraction and resource allocation part, which is used to calculate the node characteristic value of the 5G RedCap terminal node based on the acquired operating data of each 5G RedCap terminal node in the network; and calculate the amount of resources allocated to the 5G RedCap terminal node according to the node characteristic value; a 5G RedCap terminal node network, including multiple 5G RedCap terminal nodes, each 5G RedCap terminal node adaptively adjusts its own transmission power, modulation order and coding rate according to the adjustment parameters based on the current amount of resources allocated to itself; a parameter updating part, which is used to calculate the network performance and update the adjustment parameters in combination with the set target network performance.

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