Efficient dynamic network adaptation method based on 5G RedCap module

Abstract

The invention belongs to the technical field of communication, and particularly relates to an efficient dynamic network adaptation method based on a 5G RedCap module, and the method comprises the steps: 1, obtaining and defining a 5G RedCap module parameter, a modulation coding mode parameter, a transmission parameter and a queue parameter; 2, according to the 5G RedCap module parameters, the modulation and coding mode parameters, the transmission parameters and the queue parameters obtained and defined in the step 1, multi-target multi-constraint dynamic optimization is carried out on resource block allocation, transmitting power and modulation and coding mode indexes of each 5G RedCap module; and 3, constructing a Lagrange function, setting an optimality criterion by means of a KKT condition, solving the multi-target multi-constraint dynamic optimization problem in the step 2, and carrying out dynamic network adaptation according to a result obtained after solving. The method is used for realizing optimal balance among low power consumption, high throughput and low time delay.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to an efficient dynamic network adaptation method based on a 5G RedCap module. Background Art

[0002] In recent years, with the rapid development of the Internet of Things and 5G technology, the demand for wireless communication of massive low-power devices has continued to increase. Traditional 5GNR technology is mainly aimed at high data rate and large bandwidth applications, and its design goals and performance indicators are not fully adapted to low-power, low-cost, and low-data-rate Internet of Things scenarios. To this end, the industry has proposed a 5G RedCap module, which has been simplified and optimized in hardware complexity, energy consumption, and transmission rate to meet the needs of being widely used in Internet of Things scenarios such as smart homes, industrial sensors, and environmental monitoring. In the prior art, the 5G RedCap module has been partially defined in the standardization organization, which can achieve basic data communication functions while reducing equipment costs and energy consumption. However, the existing 5G RedCap communication solution still has many shortcomings in network adaptation and resource scheduling, especially in a dynamic network environment. How to efficiently and in real time schedule resources is still a difficult problem to be solved.

[0003] Most existing wireless resource allocation methods rely on preset static scheduling strategies or simple priority queue mechanisms. These methods can achieve good results in scenarios where the network load is relatively stable and the channel conditions change little. However, in the 5G IoT environment, due to the obvious time-varying characteristics of wireless channel conditions and service traffic, traditional static allocation methods cannot respond to drastic changes in network status in a timely manner, resulting in insufficient resources or uneven resource utilization for some devices, which in turn affects the overall transmission performance and energy consumption of the system. Traditional methods usually cannot take into account throughput, transmission delay, energy consumption, and data transmission reliability at the same time. They often perform local optimization on a certain indicator at the expense of other key indicators, which is unacceptable for RedCap modules that require high real-time performance and limited energy consumption. Summary of the invention

[0004] In view of this, the main purpose of the present invention is to provide an efficient dynamic network adaptation method based on the 5G RedCap module, dynamically solve resource block allocation, transmit power control and modulation coding mode selection schemes in each time slot, and achieve an optimal balance between low power consumption, high throughput and low latency. Compared with traditional static or single-objective optimization methods, the present invention can adaptively adjust network parameters according to real-time channel status, equipment business requirements and queue dynamics, improve resource utilization, reduce power consumption waste, and reduce block error rate and transmission delay. In addition, the present invention adopts an efficient optimization solution method to ensure that the algorithm can converge quickly under limited computing resources, and supports cross-time slot optimization, so that the system can continue to maintain optimal communication performance in a time-varying channel environment.

[0005] The technical solution adopted by the present invention is as follows: An efficient dynamic network adaptation method based on a 5G RedCap module, the method comprising: Step 1: Obtain and define 5G RedCap module parameters, modulation and coding mode parameters, transmission parameters, and queue parameters; Step 2: Based on the 5G RedCap module parameters, modulation and coding mode parameters, transmission parameters and queue parameters obtained and defined in step 1, a multi-objective and multi-constraint dynamic optimization problem is constructed in each time slot to perform multi-objective and multi-constraint dynamic optimization on the resource block allocation, transmission power and modulation and coding mode index of each 5G RedCap module; Step 3: Construct the Lagrangian function and use the KKT condition to set the optimality criterion to solve the multi-objective and multi-constraint dynamic optimization problem in step 2, and perform dynamic network adaptation based on the results obtained after the solution.

[0006] Further, in step 1, obtaining and defining 5G RedCap module parameters specifically includes: assuming that there are 5GRedCap modules, denoted as ; Time is divided into discrete time slots ; The maximum number of resource blocks that can be allocated to each time slot is ; Each 5G RedCap in the time slot The resource block allocation is denoted as , and satisfies ;in, represents the set of integers greater than 0; let For the Modules in time slot The effective channel gain is For the Modules in time slot The transmission power must meet in is the upper limit of the transmission power; in step 1, obtaining and defining the modulation and coding mode parameters specifically includes: assuming that the optional modulation and coding mode set is ;make Indicates module In time slot The selected modulation and coding mode index; is the upper limit of the number of modulation and coding modes; the corresponding modulation order and coding rate are represented by two mapping functions respectively: and ;in ; for ; for .

[0007] Further, in step 1, obtaining and defining transmission parameters specifically includes: setting the time slot , module The instantaneous signal-to-noise ratio is expressed as ;in, is the noise power spectral density; is the bandwidth of each RB; for the selected modulation and coding mode When , the block error rate under ideal conditions is expressed as ;in It is a nonlinear function that decreases monotonically with the increase of SNR. Assuming that retransmission is ignored, the module The instantaneous throughput of ;in, is the symbol efficiency weight parameter, is the set value; in step 1, obtaining and defining the queue parameters specifically includes: for each module ,make Indicates time slot The amount of business entering the queue, let is the queue length; the queue length update equation is expressed as ; Assume that based on the small packet data transmission scenario, the end-to-end delay is closely related to the queue length, and the queue delay and Positively correlated, ;in, Indicates module The actual output rate is approximated, Indicates other fixed delays.

[0008] Furthermore, the multi-objective and multi-constraint dynamic optimization problems in step 2 include: minimizing the sum of the queuing delays or queue lengths of all 5G RedCap modules, minimizing the overall block error rate, maximizing the instantaneous throughput, and limiting the transmission power within a set power range.

[0009] Furthermore, the multi-objective multi-constraint dynamic optimization problem in step 2 is expressed as: ; Subject to the following conditions: ; ; ; ; ; in, ; is the upper limit of the block error rate.

[0010] Furthermore, in step 3, in order to solve the multi-objective multi-constraint dynamic optimization problem in step 2, let , is the Lagrange multiplier introduced, and is the set of Lagrange multipliers introduced, then the Lagrange function It is expressed by the following formula: ; In actual implementation, due to The function is not differentiable and needs to be relaxed or sliced. for The result of relaxation or fragmentation processing; is the first Lagrange multiplier, is the second Lagrange multiplier set The elements in The second Lagrange multiplier of the module; is the third Lagrange multiplier; is the fourth Lagrange multiplier set The elements in The fourth Lagrange multiplier of the module; is the queue length of the next time slot after update.

[0011] Furthermore, in step 3, the optimal solution of the Lagrangian function of the multi-objective multi-constraint dynamic optimization problem must meet the following conditions: ; And the following complementary relaxation and feasibility conditions are met: ; in, is the channel power.

[0012] Furthermore, in step 3, the Lagrangian function corresponding to the multi-objective multi-constraint dynamic optimization problem is solved iteratively; in each iteration, the Lagrangian multiplier is updated by the following sub-gradient method to ensure constraint convergence: ; ; ; ; in, is the first update step length; is the second update step length; is the third update step length; is the fourth update step; is the updated first Lagrange multiplier; For the updated The second Lagrange multiplier of the module; is the updated third Lagrange multiplier; For the updated The fourth Lagrange multiplier of the module.

[0013] Furthermore, in the iterative solution, the update amplitude of all Lagrange multipliers is less than the set threshold When the maximum number of iterations is reached , then convergence is determined and the iterative solution ends; the solution that approximately satisfies the KKT condition is obtained. , and As in the current time slot The optimal strategy; when it comes to the next time slot , based on the new channel gain And the queue status Repeat the same process to achieve dynamic real-time adaptation.

[0014] By adopting the above technical scheme, the present invention produces the following beneficial effects: the present invention introduces a dynamic scheduling method based on an optimization algorithm in resource allocation. Traditional resource allocation schemes usually adopt a static or semi-static method, which is difficult to quickly respond to changes in wireless channel conditions and business loads, resulting in resource waste or system congestion. The present invention can dynamically adjust resource allocation in each communication time slot by constructing an optimization problem, so that the device can adaptively obtain reasonable resource block allocation according to the current channel quality and queue status. This dynamic resource scheduling method not only improves the overall throughput of the system, but also avoids individual devices from being in a state of insufficient resources for a long time, which helps to improve the fairness and stability of communication. Secondly, the present invention provides an intelligent optimization strategy in terms of transmit power control. In the prior art, the transmit power usually adopts a fixed value or a method based on simple feedback adjustment, which is difficult to achieve the best balance between energy consumption and communication quality. The present invention uses an optimization algorithm to adaptively adjust the transmit power with the goal of minimizing power consumption under the premise of ensuring communication reliability, so that the device can reduce energy consumption as much as possible while meeting communication requirements. Especially in scenarios where IoT devices have high requirements for endurance, the intelligent power control strategy of the present invention can effectively reduce unnecessary energy consumption, thereby increasing the service life of the equipment, reducing maintenance costs, and improving the energy efficiency of the overall system. Another important advantage of the present invention is the optimization of the modulation and coding mode. The modulation and coding mode of the wireless communication system directly affects the data transmission rate and bit error rate. Traditional adaptive modulation and coding schemes usually rely on simple threshold judgments and lack global optimization capabilities, which can easily lead to reduced transmission efficiency or high block error rates. In the optimization process, the present invention uses the modulation and coding mode as an adjustable parameter, and selects a suitable mode through an optimization algorithm to ensure that the data transmission rate is maximized while ensuring reliability. Especially in an environment where channel conditions are constantly changing, the present invention can dynamically adjust the modulation order and coding method, so that the device can always maintain the optimal communication strategy under different channel conditions, thereby improving the stability and efficiency of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A method flow chart of an efficient dynamic network adaptation method based on a 5G RedCap module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] 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.

[0017] 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.

[0018] Example 1: Reference Figure 1 , an efficient dynamic network adaptation method based on a 5G RedCap module, the method comprising: Step 1: Obtain and define 5G RedCap module parameters, modulation and coding mode parameters, transmission parameters, and queue parameters; When obtaining the parameters of the 5G RedCap module, its low power consumption characteristics, time division multiple access (TDD) scheduling mode, limited bandwidth resources, and its performance under different channel conditions must be considered. Since the 5G RedCap module is mainly used for low-cost, low-power IoT applications, its transmission rate is lower than that of traditional 5GNR user equipment. Therefore, when allocating network resources, it is necessary to clarify its maximum supported bandwidth, transmit power upper limit, optional modulation and coding mode, channel state feedback capability and other key indicators. The selection of these parameters must not only meet the hardware capabilities of the 5G RedCap module, but also be combined with the business needs of the actual application scenario to ensure the reasonable scheduling of network resources. For example, in some low-latency application scenarios, the RedCap module may need to operate in a higher modulation and coding mode to obtain a faster data transmission rate, while in power-sensitive scenarios, it may be necessary to reduce the transmit power or reduce the modulation complexity to extend the battery life of the device. The modulation and coding mode parameters are key factors affecting the communication quality of the 5G RedCap module. The modulation mode determines the number of bits that each symbol can carry, while the coding mode affects the bit error rate and signal robustness. In this method, it is necessary to first determine the modulation and coding set supported by the RedCap module, such as QPSK, 16QAM, 64QAM, etc., and dynamically select the optimal mode in combination with the actual channel status. In order to ensure the reliability of data transmission while taking into account network throughput, an adaptive modulation and coding (AMC) strategy is usually adopted to adjust the modulation and coding scheme according to the channel quality indicators (such as signal-to-interference-noise ratio SINR or channel gain) fed back by the RedCap module. For example, when the channel conditions are good, 64QAM high-order modulation can be used to increase the data rate, while when the channel quality is poor, the modulation order should be reduced and the coding redundancy should be increased to ensure the stability of data transmission. By obtaining and defining this parameter, the network adaptation method can flexibly adjust the modulation and coding mode of the RedCap module in different network environments, thereby achieving optimal resource utilization and performance optimization.

[0019] Transmission parameters involve the key performance indicators of the RedCap module on the wireless link, such as bandwidth allocation, subcarrier spacing, power control strategy, etc. In 5G networks, the transmission bandwidth of the RedCap module is usually subject to certain restrictions. Compared with traditional 5GNR devices, the RedCap module may only support part of the subcarrier bandwidth. Therefore, when adapting the network, its available bandwidth range must be clarified and reasonably allocated according to business needs. In addition, the transmit power is an important parameter that affects the network coverage and energy consumption. Since the RedCap module is usually used in low-power applications, its transmit power is much lower than that of ordinary 5G terminal devices. The network adaptation method needs to define a reasonable transmit power adjustment strategy to minimize power consumption while ensuring communication quality. For example, the transmit power can be dynamically adjusted according to the channel state information (CSI), and the transmit power can be reduced under good channel conditions to save energy consumption, while the power can be appropriately increased when the channel conditions are poor to ensure data transmission reliability. On the other hand, the uplink transmission of the 5GRedCap module is limited. Therefore, when allocating resources, it is necessary to combine the TDD frame structure to reasonably allocate the uplink and downlink time slot ratios to optimize data throughput. The definition of queue parameters involves the packet scheduling strategy, queue management mechanism and quality of service (QoS) requirements of the RedCap module. In 5G networks, different service types have different requirements for queue parameters. For example, low-latency applications need to ensure the rapid transmission of data packets, while high-throughput services pay more attention to bandwidth utilization. In the method of the present invention, the definition of queue parameters needs to include indicators such as packet arrival rate, queue length, and service priority. For example, in some industrial Internet of Things application scenarios, the RedCap module may need to transmit sensor data in real time. At this time, a shorter queue length should be defined to reduce queuing delay, while in non-real-time business scenarios (such as remote firmware updates), longer queues can be tolerated to improve bandwidth utilization. In addition, the queue parameters also need to be jointly optimized in combination with the modulation and coding mode and transmission power of the RedCap module to ensure the stability and efficiency of data transmission. In particular, when multiple RedCap modules share the same base station resources, it is necessary to define a queue fairness strategy to ensure the reasonable allocation of resources between multiple devices and avoid individual modules occupying too many resources and affecting the overall network performance.

[0020] Step 2: Based on the 5G RedCap module parameters, modulation and coding mode parameters, transmission parameters and queue parameters obtained and defined in step 1, a multi-objective and multi-constraint dynamic optimization problem is constructed in each time slot to perform multi-objective and multi-constraint dynamic optimization on the resource block allocation, transmission power and modulation and coding mode index of each 5G RedCap module; In this optimization process, the objective function of optimization must be clarified first. Since the communication requirements of 5G RedCap modules are usually subject to multiple constraints of device power consumption, channel quality and business requirements, the design of the objective function needs to reduce the transmission power consumption as much as possible while improving data throughput, and meet the requirements of latency and reliability in different business scenarios. Specifically, the system needs to dynamically calculate the instantaneous channel state of each RedCap module and evaluate its maximum achievable throughput in combination with the selection of modulation and coding mode. At the same time, considering that 5G RedCap modules usually have a low power budget, the optimization problem must also include power consumption constraints to ensure that the energy consumption of the device does not exceed the acceptable range. In addition, in some low-latency applications, latency needs to be taken as one of the optimization objectives to ensure the timeliness of data transmission. Therefore, this optimization problem is essentially a multi-objective optimization problem involving throughput, power consumption and latency, rather than a traditional single-objective power allocation or bandwidth allocation problem. In the process of constructing the optimization problem, constraints need to be introduced to ensure the feasibility of the optimization results. First, due to the limited bandwidth of the 5G RedCap module, the number of resource blocks available in each time slot is limited, and it must be ensured that the sum of resource block allocations of all RedCap modules does not exceed the total number of available resource blocks in the system. Secondly, the transmit power of the RedCap module is limited by hardware and network specifications. The transmit power of each module must be within its maximum allowable power range, while ensuring that reliable communication quality can be maintained under certain channel conditions. Thirdly, in terms of the selection of modulation and coding modes, each RedCap module can only select a specific modulation and coding mode under specific channel conditions. For example, 64QAM can be used to improve throughput when the channel quality is good, while only QPSK may be used to ensure reliability when the channel quality is poor. Therefore, the selection of modulation and coding modes must meet the applicability constraints under different channel conditions. In addition, in order to ensure the stability of the queue, it is necessary to ensure that the data in the queue does not accumulate indefinitely, that is, the average input rate and output rate balance of the service queue must be met to prevent serious data loss or excessive delays.

[0021] In the process of solving the optimization problem, due to the complexity of multiple objectives and multiple constraints, the traditional analytical solution is difficult to obtain directly, so it is usually necessary to use the optimization algorithm to solve it. A common solution method is to normalize multiple optimization objectives and merge them into a single objective function in a weighted manner so that they can be solved using standard optimization methods. For example, the three objectives of throughput maximization, power consumption minimization and delay minimization can be combined into a comprehensive utility function through appropriate weight parameters, and then solved using convex optimization, gradient descent or heuristic algorithms. In addition, the Pareto optimal solution method can also be adopted, that is, instead of merging multiple objectives into a single objective, a solution set with a reasonable trade-off between throughput, power consumption and delay is found to ensure flexible adaptation of different business needs. In the specific solution process, it is first necessary to use the channel state information and queue state information of the RedCap module to construct the utility function of the module in the current time slot, which characterizes the comprehensive performance of the module under a specific resource allocation scheme. Then, under the premise of ensuring that all constraints are met, find the resource block allocation, transmit power control and modulation coding mode index that optimizes the utility function. In the solution process, the Lagrange dual method can be used to convert the constrained optimization problem into an unconstrained problem, and the optimal solution can be gradually approached through iterative optimization methods. Specifically, by introducing Lagrange multipliers, resource block allocation constraints, power consumption constraints and queue stability constraints can be incorporated into the optimization process, and the value of the Lagrange multiplier can be adjusted at each iteration, so that the final solution can not only meet the constraints, but also obtain better optimization results on the multi-objective function.

[0022] Step 3: Construct the Lagrangian function and use the KKT condition to set the optimality criterion to solve the multi-objective and multi-constraint dynamic optimization problem in step 2, and perform dynamic network adaptation based on the results obtained after the solution.

[0023] In the process of constructing the Lagrangian function, it is necessary to combine the optimization objective function with the constraints so that the resource allocation of the RedCap module can meet the actual constraints of the system during the problem solving process. For example, in the network adaptation problem of the RedCap module, the core optimization variables include the allocation of resource blocks, the selection of modulation and coding modes, and the regulation of transmit power. The adjustment of each variable will affect the overall performance of the system. The allocation of resource blocks determines the bandwidth that each RedCap module can use, thereby affecting the throughput and delay; the selection of modulation and coding modes directly affects the transmission rate and bit error rate of the signal, and needs to be adaptively adjusted according to the channel status; and the setting of transmit power is related to the stability and energy consumption of communication, which must ensure that data can be successfully transmitted and avoid unnecessary energy consumption. Therefore, when constructing the optimization problem, it is first necessary to define a comprehensive utility function, which needs to balance between maximizing throughput, minimizing power consumption, and optimizing delay, and adjust the importance of different optimization objectives through appropriate weight factors. Subsequently, combined with the resource allocation constraints, power limit constraints, and optional range of modulation and coding modes of the RedCap module, these constraints are introduced into the optimization framework to ensure that the optimal solution is feasible in the actual network. In order to efficiently solve the optimization problem, it is necessary to use the Lagrange multiplier method to introduce the constraints into the optimization objective so that the problem can be solved by dual optimization. Specifically, by introducing Lagrange multipliers, the original constraint problem can be transformed into an unconstrained optimization problem, and the optimality condition is used to ensure that the final solution satisfies the constraints of the original problem. Since the network adaptation problem of the RedCap module involves multiple interrelated variables, it is difficult to directly solve the global optimal solution. Through the Lagrange dual method, the problem can be decomposed into multiple sub-problems, and different variables can be optimized separately, thereby reducing the computational complexity.

[0024] For example, when solving the optimal solution for resource block allocation and power control, we can first assume that the modulation and coding mode remains unchanged, and optimize the resource block allocation scheme so that each RedCap module obtains the optimal bandwidth utilization when the channel state allows; then, under the condition of a fixed resource block allocation scheme, further optimize the modulation and coding mode and the transmit power to ensure that the RedCap module operates in the optimal communication mode. Through this step-by-step optimization method, the computational complexity of the problem can be effectively reduced, so that the optimization solution can be completed in real time within each time slot. In the optimization process, the KKT condition plays the role of an optimality criterion. The KKT condition is a necessary optimality condition for nonlinear optimization problems. It stipulates that at the optimal solution of the constrained optimization problem, the gradient of the objective function must be consistent with the gradient direction of the constraint, and the Lagrange multiplier should satisfy the complementary relaxation condition. This means that when solving the resource allocation and power control problems of the RedCap module, it is necessary to ensure that the Lagrange multiplier of each constraint condition meets the non-negativity requirement, and for all inequality constraints, the Lagrange multiplier can only take a non-zero value when the constraint is tightly satisfied. This property is particularly important in the network adaptation optimization problem, because the power of the RedCap module is limited and the total number of resource blocks is also limited, so some devices may not be able to obtain sufficient resources, and the KKT condition can ensure the reasonable allocation of resources, so that devices with better channel conditions can obtain resources first, while devices with poor channel conditions may need to reduce power consumption or reduce data rate to maintain the overall stability of the system. By solving the KKT condition, the optimal resource allocation plan for the RedCap module in the current time slot can be obtained, and these results can be used for dynamic network adaptation. Since the 5G network environment has a strong time-varying characteristic, the optimal resource allocation plan needs to be recalculated in each time slot to ensure that the network adaptation can respond to changes in channel status and business needs in a timely manner. In the specific implementation, the iterative optimization method can be adopted, that is, in the initial state, the initial resource allocation plan is first set according to the optimization results of the previous time slot, and then the variables are gradually adjusted using gradient descent or convex optimization methods so that the Lagrangian function of the optimization problem converges to the optimal solution. When the optimization process converges, the optimal solution will be used to control the transmit power, modulation and coding mode of the RedCap module, and the allocation of resource blocks, and will be applied to the actual network in the current time slot. At the same time, in order to improve computing efficiency, reinforcement learning or data-driven prediction methods can be used to perform intelligent inference based on historical optimization results to reduce the complexity of each optimization calculation.

[0025] Example 2: In step 1, obtaining and defining 5G RedCap module parameters specifically includes: assuming that there are 5GRedCap modules, denoted as ; Time is divided into discrete time slots ; The maximum number of resource blocks that can be allocated to each time slot is ; Each 5G RedCap in the time slot The resource block allocation is denoted as , and satisfies ;in, represents the set of integers greater than 0; let For the Modules in time slot The effective channel gain is For the Modules in time slot The transmission power must meet in is the upper limit of the transmission power; in step 1, obtaining and defining the modulation and coding mode parameters specifically includes: assuming that the optional modulation and coding mode set is ;make Indicates module In time slot The selected modulation and coding mode index; is the upper limit of the number of modulation and coding modes; the corresponding modulation order and coding rate are represented by two mapping functions respectively: and ;in ; for ; for .

[0026] Specifically, in a 5G RedCap network environment, the number of devices is usually limited, so setting 5GRedCap devices and record them as a collection , is to clearly identify each RedCap device in the scenario where multiple devices share limited resources, and provide a basis for subsequent resource scheduling. Since the communication process of RedCap devices is based on a discrete scheduling mode of time slots, time is divided into discrete time slots This setting is in line with the basic characteristics of the 5GTDD (time division duplex) communication system, allowing the system to adjust the resource allocation strategy in each time slot according to the real-time channel status and service requirements, thereby improving communication efficiency. In terms of network resource allocation, the number of resource blocks available in each time slot is limited, with a maximum of , which reflects the maximum bandwidth resources that 5G RedCap devices can use. In the scheduling process of RedCap devices, each device In time slot The number of resource blocks that can be obtained within Indicates that, due to the limited nature of wireless resources, the sum of the resource block allocations of all RedCap devices cannot exceed the total amount of available resource blocks, that is, the constraint must be satisfied , ensuring that all devices share bandwidth resources fairly. In addition, each It can only take non-negative integer values ​​to ensure the discreteness of resource allocation, so that the actual network deployment can meet the scheduling requirements of the 5GNR physical layer.

[0027] In the description of channel status, each 5G RedCap device is in the time slot The channel quality in the network is usually uncertain and affected by dynamic environmental factors. To indicate the The effective channel gain of each device. Channel Gain Affected by multipath fading, shadow fading and path loss of the wireless channel, it is an important parameter that determines the data rate and transmission quality of the device. When the channel gain is high, the device can use a higher-order modulation and coding mode to improve the throughput, while when the channel gain is low, it is necessary to reduce the modulation order or increase the transmission power to ensure reliable communication. The transmission power of the RedCap device is used Indicates that its value must be within the allowed range and satisfy ,in is the upper limit of the maximum transmit power of the device. The setting of this constraint not only meets the requirements of low power operation of 5G RedCap devices, but also ensures that the transmit power does not exceed the hardware capabilities of the device, while avoiding excessive interference to the network. When defining the modulation and coding mode parameters, the impact of different modulation methods and coding rates on data transmission rate and bit error rate must be considered. RedCap devices usually support a limited number of modulation and coding modes, so the set of optional modulation and coding modes is set to ,in Indicates the maximum number of modulation and coding modes, and each device can The selected modulation coding mode index is In order to ensure that different devices can select the optimal modulation and coding mode under different channel conditions, the modulation order and coding rate of each mode are mapped by two mapping functions: and Given. Among them, Represents the modulation order of different modulation methods, and the possible values ​​are , corresponding to different modulation methods such as QPSK (2), 16QAM (4), 64QAM (6) and 256QAM (8), while It represents the coding rate of different coding schemes, which may be , these coding rates determine the redundancy and noise immunity of data transmission. For example, in the case of poor channel quality, a lower modulation order (such as QPSK) is usually selected with a higher coding redundancy (such as ) to ensure correct decoding of the data; when the channel quality is good, a higher modulation order (such as 64QAM or 256QAM) can be selected and redundancy can be reduced (such as or ) to improve data throughput. Therefore, in the subsequent network adaptation process, it is necessary to Dynamic adjustment of changes , to ensure the stability and efficiency of data transmission.

[0028] Embodiment 3: In step 1, obtaining and defining transmission parameters specifically includes: setting the time slot , module The instantaneous signal-to-noise ratio is expressed as ;in, is the noise power spectral density; is the bandwidth of each RB; for the selected modulation and coding mode When , the block error rate under ideal conditions is expressed as ;in It is a nonlinear function that decreases monotonically with the increase of SNR. Assuming that retransmission is ignored, the module The instantaneous throughput of ;in, is the symbol efficiency weight parameter, is the set value; in step 1, obtaining and defining the queue parameters specifically includes: for each module ,make Indicates time slot The amount of business entering the queue, let is the queue length; the queue length update equation is expressed as ; Assume that based on the small packet data transmission scenario, the end-to-end delay is closely related to the queue length, and the queue delay and Positively correlated, ;in, Indicates module The actual output rate is approximated, Indicates other fixed delays.

[0029] Specifically, in the time slot Department, RedCap module The instantaneous signal-to-noise ratio By transmission power , Channel Gain And the noise power spectral density jointly determined and subject to resource block allocation and bandwidth of a single resource block The instantaneous signal-to-noise ratio The calculation formula shows that under the same channel conditions, increasing the transmission power Or allocate more resource blocks The signal-to-noise ratio can be improved, thereby improving the communication quality. However, since the transmission power of the RedCap device is limited, how to optimize the signal-to-noise ratio under low power consumption conditions is a key issue that needs to be addressed during the dynamic network adaptation process. Especially in high interference or weak signal environments, the system needs to dynamically adjust the power control strategy and resource block allocation to maintain reasonable communication performance. During the data transmission process of the RedCap device, the block error rate (BLER) is an important indicator to measure data reliability. Block error rate Instantaneous signal-to-noise ratio and the selected modulation and coding mode The influence of Since the choice of modulation and coding mode directly affects the block error rate, when the channel state is good, a high-order modulation and coding mode can be selected to increase the data rate, while when the channel is poor, the modulation order needs to be reduced and the coding redundancy needs to be increased to reduce the block error rate. In this model, The function is a monotonically decreasing function, which means that as the signal-to-noise ratio increases, the block error rate decreases, thereby improving data reliability. However, in the case of a high block error rate, a large amount of retransmission overhead may be caused due to data transmission failure, thus affecting throughput and energy consumption. Therefore, how to balance the block error rate and data rate is an important part of dynamic optimization.

[0030] Instantaneous throughput of RedCap devices It is calculated based on the block error rate, that is, the throughput depends not only on the number of allocated resource blocks and resource block bandwidth , and the modulation order and coding rate In the throughput estimation formula, reflects the success probability of effective data transmission, while It reflects the bit transmission capacity of different modulation and coding modes, and the final symbol efficiency weight parameter It is used to adjust the accuracy of throughput estimation. Due to the diverse business types of RedCap devices, throughput optimization needs to improve data transmission efficiency while ensuring reliable communication. For example, in low-latency applications, the system may choose a lower block error rate strategy to ensure that data can be transmitted as quickly as possible, while in high-throughput applications, it may tend to choose a higher modulation order and coding rate to increase the amount of data transmitted per unit time. In terms of obtaining and defining queue parameters, the data transmission of RedCap devices is scheduled based on business queues, so it is necessary to establish a reasonable queue model to describe the arrival and processing of data packets. In the time slot The amount of business entering the queue Indicates the data to be transmitted received by the current RedCap device, and the queue length It indicates the amount of data that has not been processed at the current time. The dynamic update equation of queue length shows that the queue length is composed of the queue state of the previous time slot, the new traffic volume of the current time slot, and the And the actual throughput During data transmission, if Greater than , which means that the amount of data entering the queue exceeds the processing capacity of the device. At this time, the queue length will increase, which may cause the data queuing delay to increase or even cause data loss. Less than , the device can clear the data in the queue in time, thereby reducing latency. Therefore, during dynamic network adaptation, it is necessary to ensure that the throughput of the RedCap device matches the service load to avoid data backlog or resource waste. Since 5G RedCap devices are usually used in small packet data transmission scenarios, the end-to-end latency is mainly affected by the queue length, and the queuing latency and There is a positive correlation. That is, when the queue length is long, the waiting time of the data in the queue will increase accordingly, resulting in an increase in end-to-end delay. In the case of low queue length, data can be processed and sent quickly, thereby reducing end-to-end delay. The establishment of this model shows that in the resource allocation optimization process of RedCap devices, we cannot only focus on throughput and energy consumption, but also must consider the impact of queuing delay. Especially in applications with high real-time requirements, the queue management strategy needs to be appropriately adjusted to ensure that data can be processed and transmitted within the time limit.

[0031] Example 4: The multi-objective and multi-constraint dynamic optimization problem in step 2 includes: minimizing the sum of the queuing delays or queue lengths of all 5G RedCap modules, minimizing the overall block error rate, maximizing the instantaneous throughput, and limiting the transmission power within a set power range.

[0032] Specifically, in this optimization problem, the primary optimization goal is to minimize the sum of the queue delay or queue length of all 5G RedCap devices. Queue length is an important indicator for measuring network load and data transmission efficiency. In delay-sensitive applications (such as industrial control and telemedicine), the data of RedCap devices must be processed and transmitted as quickly as possible, otherwise it may affect the real-time performance and reliability of the system. Therefore, in each time slot, It is necessary to optimize resource allocation so that the queue length of all RedCap devices is as small as possible to reduce data queuing delay. Determined by the dynamic update equation, affected by the service arrival rate and throughput When When the throughput is large enough, data can be quickly cleared from the queue, thereby reducing the queuing delay; conversely, if the throughput is insufficient, data will accumulate in the queue, resulting in increased queuing delay. Therefore, in the optimization problem, it is necessary to adjust the resource block allocation and modulation coding mode , in order to improve throughput, reduce queue length, and ultimately reduce queuing delay. The second optimization goal is to minimize the overall block error rate (BLER), that is, to ensure the reliability of data transmission. In a wireless channel environment, the block error rate Signal-to-noise ratio and the selected modulation and coding mode Jointly determined. A block error rate that is too high will lead to data packet transmission failure, which will increase the retransmission overhead, waste network resources, and may cause an increase in end-to-end delay. Therefore, in the optimization problem, it is necessary to dynamically adjust the selection of modulation and coding modes to reduce the block error rate as much as possible, while avoiding overly conservative modulation and coding schemes that lead to a decrease in throughput. For example, in a high signal-to-noise ratio environment, a higher-order modulation method (such as 64QAM or 256QAM) can be used, while in a low signal-to-noise ratio environment, the modulation order should be reduced and a stronger error correction coding scheme should be selected to improve the reliability of data transmission. The third optimization goal is to maximize the instantaneous throughput, that is, to maximize the data transmission rate of the RedCap device while ensuring the optimization of the block error rate and queuing delay. Instantaneous throughput Affected by several factors, including the allocated resource blocks ,bandwidth , modulation order and coding rate . In order to maximize throughput, the optimization algorithm needs to reasonably allocate resource blocks while ensuring a low block error rate, so that RedCap devices with high channel gain can obtain more resources, thereby improving overall throughput. However, since the transmit power of RedCap devices is limited and the system needs to ensure fairness, all resources cannot be allocated to a few devices. Instead, resource allocation needs to be balanced so that while the overall throughput is improved, the throughput requirements of each device can be met to a certain extent. Finally, the optimization problem needs to meet the transmit power constraint, that is, to ensure that the transmit power of all RedCap devices is within the set power range and meets The transmission power control not only affects the signal-to-noise ratio and block error rate, but also directly determines the energy consumption of the RedCap device. Therefore, in the optimization process, the transmission power must be reduced as much as possible while ensuring the throughput and data reliability to extend the device life. In particular, power consumption optimization is particularly important in battery-powered IoT devices. By dynamically adjusting the transmission power, the device only increases the power when needed, and reduces the power when the channel conditions are good or the data rate is low. This can effectively reduce unnecessary energy consumption and improve energy efficiency. Since there is a mutual constraint relationship between the above optimization objectives, for example, increasing the throughput may lead to an increase in the block error rate, reducing the block error rate may require additional redundant coding, resulting in a decrease in throughput, and reducing the queuing delay may require higher resource allocation. Therefore, this optimization problem belongs to a multi-objective optimization problem and needs to be solved by an appropriate optimization algorithm. In the actual solution process, the weighted summation method can be used, that is, different weights are assigned to different optimization objectives and they are combined into a comprehensive utility function, so that the optimization process can weigh different objectives. Another method is to use the Pareto optimal solution, that is, to find a set of optimal solutions so that the optimization of any one objective will not harm the optimization degree of other objectives. Through these optimization methods, a balance can be found between computational complexity and optimization effect, making dynamic network adaptation of 5G RedCap devices feasible in actual scenarios.

[0033] Example 5: The multi-objective multi-constraint dynamic optimization problem in step 2 is expressed as: ; Subject to the following conditions: ; ; ; ; ; in, ; is the upper limit of the block error rate.

[0034] Specifically, in the objective function The first item Represents the sum of the queuing delays of all RedCap devices. One of the optimization goals is to minimize this value to reduce the waiting time of data in the network and ensure that delay-sensitive applications (such as industrial control, telemedicine, etc.) can run in a low-latency environment. and queue length positively correlated, and the dynamic change of queue length is determined by the update equation Therefore, in order to reduce the queuing delay, the throughput must be increased during the optimization process. , reducing the accumulation of data in the queue, allowing the device to quickly clear the queued data, thereby reducing end-to-end latency. The second term of the objective function Represents the sum of the squares of the total transmit power of all RedCap devices. The existence of this term indicates that the optimization goal is not only to control the transmit power, but also to minimize high-power transmission. Since RedCap devices are usually used in low-power IoT applications, reducing transmit power is an important means to improve device endurance. Therefore, this optimization problem not only maximizes throughput, but also needs to optimize transmit power allocation so that the device can transmit data with the lowest power consumption while meeting communication requirements. The introduction of the squared power term can effectively suppress high-power transmission, allowing the device to prioritize low-power operation mode during network adaptation, rather than indiscriminately increasing transmit power in exchange for increased throughput. The third term of the objective function Represents the negative value of the throughput of all RedCap devices. Minimizing this term is actually equivalent to maximizing the total throughput. Resource block allocation , Modulation and Coding Mode , signal-to-noise ratio Block Error Rate In order to improve the throughput, the optimization process needs to reasonably allocate resource blocks so that devices with better channel quality can obtain more bandwidth, and dynamically adjust the modulation and coding mode to select the appropriate modulation order and coding rate to increase the data rate while ensuring a low block error rate. The constraints of the optimization problem ensure the reasonable allocation of network resources. First, the constraints Ensure that the total number of resource blocks allocated to all RedCap devices does not exceed the available resources of the system to avoid resource allocation overload. The transmission power range of the device is specified to ensure that the transmission power of each device does not exceed the upper limit allowed by the hardware, avoiding excessive power consumption or interference with other users. It is stipulated that resource block allocation must be a non-negative integer and modulation coding mode must belong to a limited optional set to ensure that the optimization result is feasible in the actual network. Queue length update equation It reflects the dynamic changes of data traffic in the queue, ensuring that the queue status of each device can be adjusted according to the current service arrival rate. and throughput During the optimization process, a balance needs to be struck between maximizing throughput and minimizing queue length to ensure that the device can process data in a timely manner and avoid increased latency caused by long queues. Finally, the block error rate constraint It stipulates that the block error rate of each device must be lower than the preset maximum allowable block error rate , ensuring the reliability of data transmission. Since the block error rate is affected by the signal-to-noise ratio and the modulation coding mode, it is necessary to dynamically adjust the modulation order and coding rate during the optimization process to maximize the throughput while ensuring data reliability. The introduction of this constraint can prevent the system from selecting too high a modulation mode in the process of pursuing high throughput, which may lead to data transmission failure, thereby ensuring the robustness of the network and the quality of communication.

[0035] Example 6: In step 3, in order to solve the multi-objective multi-constraint dynamic optimization problem in step 2, let , is the Lagrange multiplier introduced, and is the set of Lagrange multipliers introduced, then the Lagrange function It is expressed by the following formula: ; In actual implementation, due to The function is not differentiable and needs to be relaxed or sliced. for The result of relaxation or fragmentation processing; is the first Lagrange multiplier, is the second Lagrange multiplier set The elements in The second Lagrange multiplier of the module; is the third Lagrange multiplier; is the fourth Lagrange multiplier set The elements in The fourth Lagrange multiplier of the module; is the queue length of the next time slot after update.

[0036] Specifically, the optimization objective function is essentially composed of three parts, namely the sum of queuing delays, the power consumption of all RedCap devices, and the negative value of throughput. Among them, queuing delay is directly related to the data transmission efficiency and end-to-end delay. Especially in low-latency application scenarios, ensuring low queuing delay is a basic requirement for network adaptation. The square term of the transmission power is used to constrain energy consumption, so that the RedCap device does not consume too much power during data transmission, which is in line with the characteristics of low-power IoT devices. In addition, the goal of maximizing throughput is introduced into the optimization goal by taking a negative value, so that the optimization process can achieve the highest possible data rate while ensuring that power consumption and delay are controlled. Through the weighted summation of these three goals, the core goal of the optimization problem is clarified, that is, how the RedCap device can find the optimal balance between throughput, delay and power consumption under limited resources. In terms of constraints, it is first necessary to ensure that the total allocation of resource blocks for all RedCap devices does not exceed the number of resource blocks available in the system, so the Lagrange multiplier is introduced to handle this constraint. When the total amount of resources requested by all devices is less than the system capacity, The value of tends to zero, and when the resource demand exceeds the system capacity, This mechanism can adaptively adjust resource allocation, so that RedCap devices can dynamically adjust according to their own network needs when sharing limited bandwidth, avoiding resource waste and preventing individual devices from being completely unable to transmit data due to resource shortages.

[0037] Secondly, the transmission power constraint is introduced by introducing the Lagrange multiplier Since the transmit power of each RedCap device is limited by hardware and network standards, it is necessary to ensure that the transmit power of all devices does not exceed the maximum allowed value during the optimization process. When the transmission power of a device approaches the upper limit, the corresponding The value will increase, thus further constraining the power during the optimization process, prompting the device to reduce power or improve data transmission efficiency through better modulation and coding methods. This method can effectively prevent the device from increasing power without restraint to improve the signal-to-noise ratio, but instead prompt it to find a better resource allocation strategy under the given power limit to improve power efficiency. In terms of block error rate control, the Lagrange multiplier is introduced To ensure that the data reliability of the RedCap device does not exceed the block error rate threshold Since the block error rate changes with the signal-to-noise ratio, the system needs to ensure that the block error rate remains within a reasonable range during the optimization process. If the block error rate of a device is close to the upper limit, the Lagrange multiplier will increase, making the optimization process tend to choose a more conservative modulation and coding mode, such as reducing the modulation order or increasing the coding redundancy, thereby improving the success rate of data transmission. On the contrary, if the block error rate is much lower than the threshold, The error rate may approach zero, at which point the RedCap device can select a higher-order modulation method to increase throughput. This dynamic constraint method for the block error rate ensures that the system strikes a balance between reliability and data rate, allowing the RedCap device to maintain a low block error rate without wasting network resources due to overly conservative modulation methods. In terms of data queue management, the system needs to ensure that the data queue of each RedCap device does not accumulate indefinitely, so a queue dynamic constraint is adopted and the Lagrange multiplier is introduced. The update relationship of the queue length determines the service rate of the device in the current time slot. Is it sufficient to absorb the new business volume entering the queue? If the device throughput is insufficient, the queue length will increase, resulting in an increase in queuing delay. The role of is to suppress the situation where the queue is too long, so that the optimization solution tends to increase the throughput Or reduce the rate at which services enter the queue to avoid long-term data retention and improve transmission efficiency. Since the queue length update equation contains non-smooth Functions need to be relaxed or sharded during actual calculations so that the optimization problem can be solved using standard gradient optimization methods. In this way, the system can ensure that the data queue of the RedCap device is always maintained within a reasonable range, preventing long-term network congestion, while optimizing resource utilization, increasing throughput while maintaining low queuing delays.

[0038] Example 7: In step 3, the optimal solution of the Lagrangian function of the multi-objective multi-constraint dynamic optimization problem must meet the following conditions: ; And the following complementary relaxation and feasibility conditions are met: ; in, is the channel power.

[0039] Specifically, in the optimization process, the optimal solution of the Lagrangian function requires that its first-order partial derivative is zero, that is, the transmission power , Resource Block Allocation and modulation coding mode Find the partial derivative and set it equal to zero. This condition ensures that at the optimal point of the optimization problem, the gradient direction of the objective function matches the gradient direction of the constraint condition, thereby finding the optimal solution. It shows that for the power control of RedCap equipment, the optimal solution should be to allocate power to reduce energy consumption as much as possible while satisfying the block error rate constraint, while ensuring that the signal quality is not lower than the required level. The optimality condition of resource block allocation is specified to ensure that RedCap devices can obtain sufficient bandwidth to maintain high throughput when resources are limited. This ensures that the selection of modulation and coding mode achieves the highest data transmission efficiency under the premise of controlled block error rate. In the further complementary relaxation condition, the optimization problem introduces the activation of inequality constraints. The core idea of ​​the complementary relaxation condition is that when the constraint does not reach the boundary, the corresponding Lagrange multiplier should be zero, and when the constraint reaches the boundary, the Lagrange multiplier should be a non-zero value to prevent further violation of the constraint. In the given complementary relaxation relationship, the block error rate control constraint and the queue length update constraint on the right side of the expression work together to ensure that the system will not cause too high block error rate or too much data accumulation in the queue due to over-optimization of throughput, thereby affecting the end-to-end delay. The power control constraint and the resource block allocation constraint on the left side work together to ensure that the RedCap device will not exceed the maximum power, and the total amount of resource blocks of all devices will not exceed the available resources of the system.

[0040] In the specific mathematical expression of the complementary relaxation condition, The introduction of the operation enables the optimization strategies of different RedCap devices under different channel conditions to be adaptively adjusted. It reflects the degree of resource allocation tension. If the system resources are tight, The value of will be larger, prompting the optimization process to reduce unnecessary resource usage. When resources are more abundant, may approach zero, allowing the system to adjust resource allocation more freely. As for the block error rate control term, This part reflects the dynamic relationship between channel gain, power level and block error rate, that is, when the channel quality is poor or the power is limited, the optimization strategy should tend to choose low-order modulation to reduce the block error rate, while when the channel quality is good, a higher-order modulation coding mode can be selected to improve the throughput. On the other hand, the queue dynamic constraint term in the complementary relaxation condition By limiting changes to ensure that the system does not cause long-term data backlogs due to insufficient resources of the RedCap device. If the queue length exceeds the acceptable range, will increase, prompting the optimization algorithm to adjust resource allocation in the direction of increasing throughput. If the queue is in a stable state, This dynamic adjustment mechanism enables the RedCap device to flexibly adjust resource allocation strategies under different network load conditions to ensure optimal throughput and latency.

[0041] Embodiment 8: In step 3, the Lagrangian function corresponding to the multi-objective multi-constraint dynamic optimization problem is solved by iteration; in each iteration, the Lagrangian multiplier is updated by the following subgradient method to ensure constraint convergence: ; ; ; ; in, is the first update step length; is the second update step length; is the third update step length; is the fourth update step; is the updated first Lagrange multiplier; For the updated The second Lagrange multiplier of the module; is the updated third Lagrange multiplier; For the updated The fourth Lagrange multiplier of the module.

[0042] Specifically, in this optimization framework, the dual problem of the Lagrangian function needs to use Lagrangian multipliers to characterize the impact of different constraints on the optimization target. In each iteration, the Lagrangian multiplier will be adjusted according to the degree of constraint violation, so that the final optimization solution satisfies all constraints and strikes a balance between throughput maximization, power consumption minimization, and latency optimization. Specifically, the subgradient method adjusts the resource block allocation constraint multiplier , Transmit power constraint multiplier , block error rate constraint multiplier and the queue stability constraint multiplier , gradually approaching the optimal solution. First, for the resource block allocation constraint, update the formula Reflects the degree of system resource tension. If the current time slot The total number of resource blocks allocated by all RedCap devices in the ,but will increase, so in the next iteration, the optimization algorithm will reduce the resource block allocation of some devices, making the system resource usage more balanced. On the contrary, if the resource allocation is less than ,but This mechanism ensures the dynamic allocation of resource blocks, which can give priority to the needs of high-priority devices when resources are tight, and reasonably allocate them to all devices when resources are sufficient, thereby improving the overall throughput.

[0043] Secondly, in terms of transmit power constraints, update the formula Ensures that the RedCap device does not exceed the maximum allowed transmit power If the transmit power of a device is too high, causing it to exceed the allowed threshold, then will increase, making the optimization process more inclined to reduce the transmission power of the device, reduce energy consumption, and avoid interference with other devices. On the contrary, if the transmission power of the device still has room for optimization, This dynamic adjustment mechanism ensures that the RedCap device always operates within a reasonable power-constrained range, without over-consuming battery energy or affecting the reliability of data transmission due to insufficient power. In terms of block error rate control, the updated formula Keep the block error rate within the allowed range. If the block error rate of a RedCap device exceeds the maximum allowed value ,but The value of will increase, making the optimization process more inclined to reduce the modulation order or increase the coding redundancy in the next iteration to reduce the block error rate and improve data reliability. ,but The block error rate can be reduced as much as possible, allowing the system to appropriately adopt a higher-order modulation and coding scheme to improve throughput. This dynamic adjustment mechanism of the block error rate ensures that the RedCap device can adaptively adjust the modulation and coding mode under different channel conditions to balance the data rate and transmission reliability.

[0044] In terms of queue stability control, the update formula Responsible for controlling the length of the data queue to ensure that the device does not cause a long queue backlog due to insufficient throughput. If the queue length grows too fast, it means that the current throughput cannot meet the business needs, then will increase, making the optimization process more inclined to increase the resource block allocation of the device or optimize the modulation and coding mode to improve the throughput. If the queue length is small, then The system can appropriately reduce the resource allocation of the device and free up bandwidth for other devices. This mechanism ensures the dynamic balance of data traffic, ensuring that the system will not cause a sharp increase in end-to-end delay due to excessive queue length, while improving the overall resource utilization of the network. Since the subgradient optimization method belongs to the inexact gradient descent method, its core idea is to use the step size parameter Control the update speed of the Lagrange multiplier to ensure that the optimization process converges to the optimal solution. In actual implementation, these step size parameters can be adaptively adjusted according to historical update conditions. For example, an adaptive step size strategy is used to reduce the step size when the optimization result fluctuates greatly to prevent oscillation, and when the optimization process tends to be stable, the step size is appropriately increased to speed up the convergence speed. In addition, in the actual calculation process, the step size parameter may be affected by the dynamic changes of channel status, data traffic, and network load. Therefore, it can be combined with reinforcement learning or prediction models to make the step size adjustment more intelligent and adaptive.

[0045] Example 9: In the iterative solution, the update amplitude of all Lagrange multipliers is less than the set threshold When the maximum number of iterations is reached , then convergence is determined and the iterative solution ends; the solution that approximately satisfies the KKT condition is obtained. , and As in the current time slot The optimal strategy; when it comes to the next time slot , based on the new channel gain And the queue status Repeat the same process to achieve dynamic real-time adaptation.

[0046] Specifically, in the efficient dynamic network adaptation method based on the 5G RedCap module, the core of Example 9 is to iteratively solve the Lagrangian function so that the system can dynamically optimize resource allocation, transmission power and modulation coding mode in each time slot, and based on the convergence judgment mechanism, ensure that the calculation converges to the optimal or suboptimal solution within a limited time, and realize adaptive adjustment across time slots. Since 5G RedCap devices usually run in a low-power, low-bandwidth, and strictly resource-constrained network environment, the optimization process must take into account computing efficiency, network resource utilization, and real-time performance to ensure that in each time slot A feasible solution is obtained quickly within 1 second and in the next time slot When the optimization is performed again based on the new channel state, the system can operate stably for a long time and adapt to the dynamic changes of the wireless network. In this method, the core of solving the multi-objective and multi-constraint dynamic optimization problem is the Lagrangian dual optimization method, in which subgradient optimization is used to gradually adjust the resource block allocation, transmit power and modulation coding mode to achieve a balance between throughput maximization, power consumption minimization, block error rate control and delay optimization. However, due to the dynamic changes in the network environment of the RedCap device, the constraints of the optimization problem (such as channel status, business load and network resource availability) are also constantly changing, so it must converge within a limited time to ensure that the optimization calculation does not occupy too many system resources. To this end, this method sets two types of convergence judgment conditions, namely whether the update amplitude of the Lagrangian multiplier is small enough, or whether the optimization process reaches the maximum number of iterations. When the update amplitude of all Lagrangian multipliers is less than the set threshold , indicating that the optimization results have basically converged, and further calculation will not bring significant performance improvement. At this time, the optimization can be terminated and the current time slot is output. If the optimization process has not converged, but the maximum number of iterations has been reached , the calculation is also terminated and the current suboptimal solution is output to ensure that the system can respond in real time without falling into endless calculation. This mechanism ensures the rational use of computing resources, so that the optimization solution of the RedCap device can be completed in each time slot without affecting the scheduling of subsequent time slots. In terms of dynamic adaptation across time slots in the optimization process, the optimization process of each time slot not only depends on the current channel status and service queue status, but also affects the next time slot. Therefore, in the time slot The system needs to be based on the new channel gain and queue status The same optimization process is re-executed to ensure that resource allocation can adapt to the latest network status. This time slot iterative optimization strategy enables the RedCap device to continuously adapt to the dynamic changes of the wireless channel and optimize resources according to different business needs, ultimately achieving long-term stable communication performance. Since the changes in the wireless channel are usually gradual, this optimization method can ensure that the optimization solution of each time slot will not differ too much from the result of the previous time slot, thereby avoiding system instability and improving energy efficiency and throughput in long-term operation.

[0047] 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. An efficient dynamic network adaptation method based on 5G RedCap module, characterized in that: The method comprises: Step 1: Obtain and define 5G RedCap module parameters, modulation and coding mode parameters, transmission parameters, and queue parameters; Step 2: Based on the 5G RedCap module parameters, modulation and coding mode parameters, transmission parameters and queue parameters obtained and defined in step 1, a multi-objective and multi-constraint dynamic optimization problem is constructed in each time slot to perform multi-objective and multi-constraint dynamic optimization on the resource block allocation, transmission power and modulation and coding mode index of each 5G RedCap module; Step 3: Construct the Lagrangian function and use the KKT condition to set the optimality criterion to solve the multi-objective and multi-constraint dynamic optimization problem in step 2, and perform dynamic network adaptation based on the results obtained after the solution.

2. The efficient dynamic network adaptation method based on the 5G RedCap module according to claim 1, characterized in that: In step 1, obtaining and defining the 5G RedCap module parameters specifically includes: 5G RedCap modules, denoted as ; Time is divided into discrete time slots ; The maximum number of resource blocks that can be allocated to each time slot is ; Each 5G RedCap in the time slot The resource block allocation is denoted as , and satisfies ;in, represents the set of integers greater than 0; let For the Modules in time slot The effective channel gain is For the Modules in time slot The transmission power must meet in is the upper limit of the transmission power; in step 1, obtaining and defining the modulation and coding mode parameters specifically includes: assuming that the optional modulation and coding mode set is ;make Indicates module In time slot The selected modulation and coding mode index; is the upper limit of the number of modulation and coding modes; the corresponding modulation order and coding rate are represented by two mapping functions respectively: and ;in ; for ; for .

3. The efficient dynamic network adaptation method based on the 5G RedCap module as claimed in claim 2, characterized in that: In step 1, obtaining and defining transmission parameters specifically includes: setting the time slot , module The instantaneous signal-to-noise ratio is expressed as ;in, is the noise power spectral density; is the bandwidth of each RB; for the selected modulation and coding mode When , the block error rate under ideal conditions is expressed as ;in It is a nonlinear function that decreases monotonically with the increase of SNR. Assuming that retransmission is ignored, the module The instantaneous throughput of ;in, is the symbol efficiency weight parameter, is the set value; in step 1, obtaining and defining the queue parameters specifically includes: for each module ,make Indicates time slot The amount of business entering the queue, let is the queue length; the queue length update equation is expressed as ; Assume that based on the small packet data transmission scenario, the end-to-end delay is closely related to the queue length, and the queue delay and Positively correlated, ;in, Indicates module The actual output rate is approximated, Indicates other fixed delays.

4. The efficient dynamic network adaptation method based on the 5G RedCap module as claimed in claim 3, characterized in that: The multi-objective and multi-constraint dynamic optimization problems in step 2 include: minimizing the sum of the queuing delays or queue lengths of all 5G RedCap modules, minimizing the overall block error rate, maximizing the instantaneous throughput, and limiting the transmit power within a set power range.

5. The efficient dynamic network adaptation method based on the 5G RedCap module as claimed in claim 4, characterized in that: The multi-objective multi-constraint dynamic optimization problem in step 2 is expressed as: ； Subject to the following conditions: ； ; ; ; ; in, ; is the upper limit of the block error rate.

6. The efficient dynamic network adaptation method based on the 5G RedCap module as claimed in claim 5, characterized in that: In step 3, in order to solve the multi-objective multi-constraint dynamic optimization problem in step 2, let , is the Lagrange multiplier introduced, and is the set of Lagrange multipliers introduced, then the Lagrange function It is expressed by the following formula: ； In actual implementation, due to The function is not differentiable and needs to be relaxed or sliced. for The result of relaxation or fragmentation processing; is the first Lagrange multiplier, is the second Lagrange multiplier set The elements in The second Lagrange multiplier of the module; is the third Lagrange multiplier; is the fourth Lagrange multiplier set The elements in The fourth Lagrange multiplier of the module; is the queue length of the next time slot after update.

7. The efficient dynamic network adaptation method based on the 5G RedCap module as claimed in claim 6, characterized in that: In step 3, the optimal solution of the Lagrangian function of the multi-objective multi-constraint dynamic optimization problem must meet the following conditions: ； And the following complementary relaxation and feasibility conditions are met: ； in, is the channel power.

8. The efficient dynamic network adaptation method based on the 5G RedCap module as claimed in claim 7, characterized in that: In step 3, the Lagrangian function corresponding to the multi-objective multi-constraint dynamic optimization problem is solved iteratively; in each iteration, the Lagrangian multiplier is updated by the following subgradient method to ensure constraint convergence: ； ； ； ； in, is the first update step length; is the second update step length; is the third update step length; is the fourth update step length; is the updated first Lagrange multiplier; For the updated The second Lagrange multiplier of the module; is the updated third Lagrange multiplier; For the updated The fourth Lagrange multiplier of the module.

9. The efficient dynamic network adaptation method based on the 5G RedCap module as claimed in claim 8, characterized in that: In the iterative solution, the update amplitude of all Lagrange multipliers is less than the set threshold When the maximum number of iterations is reached , then convergence is determined and the iterative solution ends; the solution that approximately satisfies the KKT condition is obtained. , and As in the current time slot The optimal strategy; when it comes to the next time slot , based on the new channel gain And the queue status Repeat the same process to achieve dynamic real-time adaptation.

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