Communication resource optimization method for wireless network control system in low-altitude Internet of Things

Abstract

The invention discloses a communication resource optimization method for a wireless network control system in a low-altitude Internet of Things, and relates to the technical field of wireless communication resource allocation. The method comprises the following steps: constructing a communication resource coupling model of a wireless network control system in the low-altitude Internet of Things, wherein the communication resource coupling model comprises a communication model, a calculation time model and a unified energy constraint; based on the communication resource coupling model, constructing a resource joint optimization problem with minimization of the LQR control cost as a target by using a monotone mapping relation between the lower bound of the LQR control cost and an effective load; based on a condition that an uplink effective information load is equal to a downlink effective information load, namely, an effective load balance condition, a resource joint optimization problem is converted into a single-target optimization problem taking maximization of the uplink effective information load as a target; on the basis of an alternative optimization algorithm, a single-target optimization problem is decomposed into a time and bandwidth optimization sub-problem and a power allocation sub-problem, and an optimal resource allocation scheme is obtained through iterative solution.

Description

Technical Field

[0001] This invention relates to the field of wireless communication resource allocation technology, and more specifically to a method for optimizing communication resources in a wireless network control system in a low-altitude intelligent network. Background Technology

[0002] In high-risk industrial production sites such as nuclear facilities and chemical plants, utilizing unmanned aerial vehicles (UAVs) within the Low-Altitude Internet of Things (LAIoT) as aerial mobile nodes to interact in real-time with ground sensors and actuators, thereby enabling remote monitoring and intervention of the production process, has become a key technological approach. UAVs interact with ground equipment via wireless networks, constructing a closed-loop system integrating sensing, computing, and control. However, existing LIoT wireless network control systems often separate communication resource allocation from control algorithm design, optimizing only Quality of Service (QoS) indicators such as transmission rate and bit error rate, while neglecting their impact on system stability, linear quadratic regulator (LQR) costs, and other control performance aspects. This results in communication resource configuration failing to meet the actual needs of the control task.

[0003] Meanwhile, due to limitations in size and payload, the energy reserves of UAV onboard batteries are extremely limited. During operation, they not only consume energy for uplink and downlink wireless communication but also provide computational power for the onboard processor to execute complex control algorithms. Current communication resource allocation methods either ignore computational power consumption or optimize uplink and downlink communication links in isolation, failing to coordinate the scheduling of time, bandwidth, and power resources under the overall energy constraints encompassing communication, computation, and wireless power supply (WPT) energy consumption. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide a communication resource optimization method for a wireless network control system in a low-altitude intelligent network.

[0005] The technical solution of this invention is:

[0006] A method for optimizing communication resources in a wireless network control system within a low-altitude intelligent network (LAN) is disclosed. The LNA N ...

[0007] Construct a communication resource coupling model for a wireless network control system in a low-altitude intelligent network that includes a communication model, a computation time model, and a unified energy constraint;

[0008] Based on the aforementioned communication resource coupling model, and utilizing the monotonic mapping relationship between the lower bound of LQR control cost and payload, a joint resource optimization problem is constructed with the objective of minimizing LQR control cost.

[0009] Based on the condition that the effective information load of the uplink is equal to that of the effective information load of the downlink, i.e., the effective load balancing condition, the joint resource optimization problem is transformed into a single-objective optimization problem with the goal of maximizing the effective information load of the uplink.

[0010] Based on the alternating optimization algorithm, the single-objective optimization problem is decomposed into time and bandwidth optimization subproblems and power allocation subproblems, and the optimal resource allocation scheme is obtained through iterative solution.

[0011] Compared with the prior art, the present invention has the following beneficial effects:

[0012] This invention introduces a control-centric design philosophy, directly allocating communication resources with the goal of minimizing LQR control costs, thus avoiding the performance degradation caused by traditional methods that only focus on communication QoS. Simultaneously, this invention innovatively integrates finite code length transmission theory, airborne computing, and WPT energy consumption into a unified resource optimization framework, avoiding the problems of premature UAV battery depletion or communication resource imbalance caused by neglecting computational energy consumption. Furthermore, the alternating optimization algorithm designed based on payload balance conditions can transform complex non-convex optimization problems into easily solvable single-objective optimization problems, ensuring rapid convergence and real-time performance. It can adaptively adjust the communication resource allocation strategy according to the actual bottlenecks (bandwidth, power, or energy) of the wireless network control system in the low-altitude intelligent network. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of a wireless network control system in a low-altitude intelligent network.

[0014] Figure 2 This is a flowchart of the communication resource optimization method for the wireless network control system in the low-altitude intelligent network of this embodiment;

[0015] Figure 3 This is a performance comparison chart of the method of the present invention and existing technologies under different resource allocation strategies;

[0016] Figure 4 This is a graph showing the effective information payload variation of the method of the present invention under different system total bandwidth conditions;

[0017] Figure 5 This is a schematic diagram illustrating the impact of computational complexity on communication resource allocation in the method of this invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The specific embodiments described herein are merely illustrative and are not intended to limit the scope of the invention.

[0019] The core ideas of this invention are: 1. Considering the non-asymptotic characteristics brought about by the finite code length transmission theory and the coupling effect of airborne computing and WPT, a low-altitude intelligent network communication resource coupling model is constructed, which includes a communication transmission model, an airborne computing time model, and a unified energy constraint; 2. Utilizing the monotonically inverse relationship between the lower bound of LQR control cost and the end-to-end effective information load, the "effective load balance condition" that the optimal solution must satisfy is derived, thereby eliminating the non-convex minimum operation in the objective function and transforming the problem of minimizing LQR control cost, which is difficult to solve directly, into a single-objective optimization problem with maximizing uplink effective load as the objective; 3. Based on the alternating optimization algorithm, the transformed single-objective optimization problem is decomposed into two sub-problems: time and bandwidth optimization and power allocation. The binary search method and monotonicity analysis are used to solve these sub-problems alternately and iteratively, respectively. Effective load convergence is used as the judgment criterion to output the optimal resource allocation scheme that satisfies the strict time delay and energy constraints of the system.

[0020] like Figure 1 As shown, this embodiment of the invention relates to a wireless network control system in a low-altitude intelligent network. The system includes a drone, sensors located in a hazardous area, and actuators. The drone, acting as an aerial node, receives status data collected by the sensors via an uplink and sends control commands to the actuators via a downlink, thereby forming a closed-loop control system.

[0021] Figure 2 This is a flowchart illustrating the communication resource optimization method for a wireless network control system in a low-altitude intelligent network provided in an embodiment of the present invention. Figure 2 As shown, the communication resource optimization method for the wireless network control system in the low-altitude intelligent network includes the following steps:

[0022] Step 1: Based on the finite code length transmission theory, airborne computing and WPT mechanism, construct a communication resource coupling model for the wireless network control system in the low-altitude intelligent network, which includes a communication model, a computation time model and a unified energy constraint.

[0023] Step 1.1: Establish a communication model based on the finite code length transmission theory;

[0024] In each control cycle Within, the uplink transmission time is defined as... and downlink transmission time is Total system bandwidth Divided into uplink bandwidth and downlink bandwidth And it satisfies the bandwidth constraint. .

[0025] Based on the finite code length transmission theory, maximum reachable payload models for the uplink and downlink are constructed respectively:

[0026] (1)

[0027] (2)

[0028] in, and These are the maximum reachable payload for the uplink and the maximum reachable payload for the downlink, respectively. and These are the code lengths for the uplink and downlink, respectively. and These are the preset packet error rates for the uplink and downlink, respectively. It is the inverse Q function.

[0029] The and These are the uplink Shannon channel capacity and the downlink Shannon channel capacity, respectively. and These are the uplink channel dispersion and the downlink channel dispersion, respectively. Based on the obtained uplink channel gain... and downlink channel gain The calculation formulas are as follows:

[0030] (3)

[0031] (4)

[0032] (5)

[0033] (6)

[0034] in, and These are the uplink transmit power and the downlink transmit power, respectively. This represents the channel noise power.

[0035] Step 1.2: Establish the computation time model for the UAV airborne controller;

[0036] Based on the computing frequency of the UAV airborne controller and the amount of data received via the uplink, a computing time model is constructed to quantify the time resource consumption of the airborne computing process:

[0037] (7)

[0038] in, For onboard computing time; The number of CPU cycles required to process each bit of data; This refers to the calculation frequency of the airborne controller; The information utility factor represents the proportion of effective information extracted from the raw sensor data.

[0039] Step 1.3: Establish a unified energy constraint model that includes communication, computation time, and WPT energy consumption;

[0040] First, determine the computational power of the UAV onboard controller. This power is related to the calculated frequency. It is proportional to the cube of the power, expressed as .

[0041] Combined with the airborne computing time obtained in step 1.2 Calculate the onboard computing power consumption of UAVs :

[0042] (8)

[0043] Subsequently, based on the battery capacity limitations of the UAV, a system was constructed that includes uplink WPT power consumption, downlink transmission power consumption, and the aforementioned airborne computing power consumption. A unified energy constraint is applied to ensure that the total energy consumption of the system does not exceed the total energy budget for each control cycle.

[0044] (9)

[0045] in, Total energy budget for each control cycle; WPT (Power Transfer Inefficiency Factor) is used to characterize the path loss and conversion efficiency when a UAV wirelessly powers a sensor. The effective switching capacitor coefficient of the airborne processor; This indicates the WPT power consumption of the UAV to support uplink transmission; This indicates the downlink transmission power consumption.

[0046] Step 2: Based on the system model built in Step 1, utilize the monotonic mapping relationship between the lower bound of LQR control cost and payload to construct a joint resource optimization problem with the objective of minimizing LQR control cost;

[0047] Step 2.1: Define the end-to-end effective information payload Based on this load, the lower bound of LQR control cost is determined. The function expression;

[0048] First, define the end-to-end effective information payload. This represents the bottleneck value between the effective uplink information volume and the effective downlink payload, i.e.:

[0049] (10)

[0050] Subsequently, LQR (Lower Boundary of Control Cost) was introduced. This index serves as an evaluation metric for system control performance. Based on control theory, its relationship with the end-to-end effective information load is determined. The following functional relationship is satisfied:

[0051] (11)

[0052] in, This is a constant term unrelated to resource allocation; The state dimension of the controlled system; The state matrix; All are constant matrices determined by the system dynamics equations. This relationship indicates that minimizing the lower bound of LQR control costs... Equivalent to maximizing end-to-end effective information payload .

[0053] Step 2.2: Integrate the sub-models constructed in Step 1 and the objective function determined in Step 2.1 to construct a joint resource optimization problem.

[0054] To minimize the lower bound of LQR control cost To optimize the objective, the communication model in step 1.1, the computation time model in step 1.2, and the unified energy constraint model in step 1.3 are used as constraints to construct the following joint resource optimization problem:

[0055] Objective function:

[0056] (12)

[0057] Constraints:

[0058] (13)

[0059] (14)

[0060] (15)

[0061] (9)

[0062] (10)

[0063] Among them, the set of optimization variables .

[0064] Step 3: Based on the payload model constructed in Step 1.1, derive the payload balance condition by utilizing its monotonically increasing characteristics with respect to transmit power and bandwidth, and transform the joint resource optimization problem constructed in Step 2 into a single-objective optimization problem;

[0065] Step 3.1: Analyze the monotonicity of the payload model and determine that the optimal solution for resource allocation must satisfy the payload balance condition;

[0066] Analyze formulas (1) and (2) in step 1.1. Due to the uplink Shannon channel capacity... and downlink Shannon channel capacity Each according to its uplink transmit power and downlink transmit power Monotonically increasing, and uplink code length and downlink code length Each according to its uplink bandwidth and downlink bandwidth Monotonically increasing, therefore the maximum uplink achievable payload and downlink maximum reachable payload All of them are monotonically increasing functions of their corresponding transmit power and bandwidth.

[0067] Based on this monotonically increasing characteristic, it is derived that the optimal solution to the joint resource optimization problem must satisfy the condition that the effective information load of the uplink is equal to the effective information load of the downlink, i.e., the effective load balance condition:

[0068] (16)

[0069] Step 3.2: Based on the aforementioned payload balancing condition, the original joint resource optimization problem is transformed into a single-objective optimization problem aimed at maximizing the effective uplink information payload;

[0070] First, based on the monotonically decreasing relationship between LQR control cost and effective information payload described in step 2.1, the original problem of minimizing LQR control cost is equivalently transformed into the problem of maximizing effective information payload, i.e. .

[0071] Subsequently, the effective load balancing condition described in formula (16) is introduced as an equality constraint to eliminate the minimum operation in the objective function, and the above optimization problem is further transformed into maximizing the effective information load of the uplink. Single-objective optimization problem with objective:

[0072] Objective function:

[0073] (17)

[0074] Constraints:

[0075] (9)

[0076] (13)

[0077] (14)

[0078] (15)

[0079] (16)

[0080] Step 4: Based on the alternating optimization algorithm, the single-objective optimization problem constructed in Step 3 is decomposed into time and bandwidth optimization sub-problems and power allocation sub-problems, and the optimal resource allocation scheme is obtained by iterative solution;

[0081] Step 4.1: Initialize algorithm parameters and optimization variables;

[0082] Set convergence threshold and maximum number of iterations; initialize uplink transmit power. and downlink transmit power To meet the feasible value of the power constraint, initialize the uplink bandwidth. Downlink bandwidth Uplink transmission time and downlink transmission time .

[0083] Step 4.2: Fix the transmit power variable, solve the time and bandwidth optimization subproblem, and update the time and bandwidth variables;

[0084] In the In this iteration, the transmit power is fixed at the level updated in the previous round. and A binary search method is used to determine the maximum feasible effective information payload under the current power configuration. The specific process is as follows:

[0085] Set a lower bound for binary search Upper Realm This is the theoretical maximum value. In each round of binary search, let the candidate payload be... .

[0086] For this candidate information payload First, based on the payload balance condition, the required uplink payload is determined as follows: The downlink payload is Subsequently, by solving the quadratic equations corresponding to formulas (1) and (2) in step 1.1, the uplink code length required to implement the payload is directly calculated. and downlink code length :

[0087] (18)

[0088] (19)

[0089] in, , , and This is a constant calculated based on the current fixed transmit power.

[0090] Subsequently, the time minimization problem and the energy minimization problem were solved respectively using the Lagrange multiplier method, and the uplink bandwidth allocation that satisfies the time minimization objective was calculated. Uplink bandwidth allocation that meets the goal of minimizing energy consumption :

[0091] (20)

[0092] (twenty one)

[0093] Based on the above bandwidth allocation, the following feasibility determination is performed:

[0094] If based on The total time consumption calculated satisfies the delay constraint described in formula (13) in step 2.2, or is based on... If the calculated total energy consumption satisfies the unified energy budget constraint described in formula (9) in step 1.3, then the candidate information payload is determined. Feasible, update Otherwise, it is deemed infeasible and updated. .

[0095] Repeat the binary search process described above until convergence; the maximum feasible effective information payload obtained is... At this point, a feasibility check is performed again: if based on If the total computation time satisfies the latency constraint, then the system is determined to be currently constrained by latency, and the uplink bandwidth is updated. Otherwise, determine that the system is energy-constrained and update the uplink bandwidth. Downlink bandwidth updated to .

[0096] Finally, update the time variable based on the code length: uplink transmission time. Downlink transmission time .

[0097] Step 4.3: With fixed time and bandwidth variables, solve the power allocation subproblem and update the transmit power variable;

[0098] The fixed time and bandwidth variables are the values ​​updated in step 4.2. Using the payload balancing condition described in formula (16) in step 3.1, the downlink transmit power is established. Regarding uplink transmit power monotonic mapping function .

[0099] Specifically, the downlink payload is based on the formula (2) defined in step 1.1. With transmission power The monotonically increasing relationship can be solved numerically by inverse kinematics. Represented as The function. Combined with the equilibrium condition. The mapping function can be expressed as:

[0100] (twenty two)

[0101] This function indicates that for any given uplink transmit power... First, calculate the corresponding uplink payload. Then, the required downlink payload is determined based on the balance condition. Finally, the required downlink transmit power is obtained by solving formula (2). Through this mapping function, the original information about... and The bivariate optimization problem is transformed into a problem only concerning... The single-variable optimization problem.

[0102] Define power constraint function and energy constraint function as follows:

[0103] (twenty three)

[0104] (twenty four)

[0105] Given and All are about The monotonically increasing function is solved using a binary search algorithm. and equations The power-constrained solution is obtained. and energy-limited solutions .

[0106] Since the objective function monotonically increases with power, the optimal solution should be taken as the upper bound of the feasible region. Therefore, by comparing the power-constrained solution with the energy-constrained solution, the optimal uplink transmit power for the current round is determined. :

[0107] (25)

[0108] The corresponding optimal downlink transmit power is .

[0109] Step 4.4: Determine the convergence of the algorithm and output the optimal resource allocation scheme.

[0110] Repeat the time and bandwidth variable updates described in step 4.2 and the transmit power variable updates described in step 4.3. In each iteration, record the current effective information payload. When the difference between the effective information payloads calculated in two consecutive iterations is less than a preset convergence threshold... When the algorithm converges, it is determined that the algorithm has converged, and the final optimal resource allocation scheme is output. .

[0111] In practical applications, after obtaining the optimal resource allocation scheme, the UAV onboard controller reads the optimal resource allocation scheme output in step 4. Configure the communication parameters of the drone and sensors, and execute uplink transmission, onboard computing and downlink transmission in sequence to complete the closed-loop control task.

[0112] Specifically, the UAV onboard controller reads the optimal resource allocation scheme output in step 4. Then, the drone first determines the optimal downlink transmit power. and optimal downlink bandwidth Configure its own radio frequency transmission module and send scheduling commands to the ground sensor via the control channel, instructing it to use the optimal uplink bandwidth. and optimal uplink transmit power To transmit data.

[0113] Subsequently, in the current control cycle Within this timeframe, the system executes tasks strictly according to the optimal time allocation scheme: within a duration of... During the uplink time slot, the UAV receives status data uploaded by the sensors; then, the onboard controller calculates the time... The calculation of control commands is completed within the time limit; finally, the time limit is [time value missing]. During the downlink time slot, control commands are sent to the ground actuator, thereby completing the remote closed-loop control of the controlled object.

[0114] The system performance prediction effect under different resource allocation strategies and system parameters is as follows: Figures 3 to 5 As shown. In Figure 3 Compared to random allocation, average allocation, and time minimization strategies, the method of this invention improves the effective information payload. Furthermore, compared to the power minimization strategy that only optimizes communication resources, the method of this invention achieves lower LQR control costs, demonstrating that the co-design of communication and computation effectively improves control performance. Figure 4 In this invention, as the total system bandwidth increases, the method can identify the transition of the system from a bandwidth-constrained region to an energy-constrained region, stabilizing the payload at the maximum value that satisfies the energy constraints. However, the time minimization strategy becomes infeasible due to violation of the energy constraints, as evidenced by the curve truncation after the bandwidth exceeds 1.4 MHz. Figure 5 As computational complexity increases, the total communication time allocated by the method of this invention shows a monotonically decreasing trend, indicating that the system actively compensates for the increase in computation time by compressing communication time. Therefore, the method of this invention can maximize the control performance of the system under strict energy and delay constraints by jointly optimizing communication, computation, and power resources, thereby avoiding control failure or energy depletion problems caused by unreasonable resource allocation.

[0115] It should be understood that, inspired by the technical concept of this invention, those skilled in the art can make various improvements or modifications based on the above content without departing from the scope of this invention, and these modifications still fall within the protection scope of this invention.

Claims

1. A method for optimizing communication resources in a wireless network control system of a low-altitude intelligent network, wherein the wireless network control system of the low-altitude intelligent network includes a drone, ground-based sensors and actuators, wherein the drone acts as an aerial node, receiving status data collected by the sensors via an uplink and sending control commands to the actuators via a downlink, thereby forming a closed-loop control system; characterized in that, The method includes: Construct a communication resource coupling model for a wireless network control system in a low-altitude intelligent network that includes a communication model, a computation time model, and a unified energy constraint; Based on the aforementioned communication resource coupling model, and utilizing the monotonic mapping relationship between the lower bound of LQR control cost and payload, a joint resource optimization problem is constructed with the objective of minimizing LQR control cost. Based on the condition that the effective information load of the uplink is equal to that of the effective information load of the downlink, i.e., the effective load balancing condition, the joint resource optimization problem is transformed into a single-objective optimization problem with the goal of maximizing the effective information load of the uplink. Based on the alternating optimization algorithm, the single-objective optimization problem is decomposed into time and bandwidth optimization subproblems and power allocation subproblems, and the optimal resource allocation scheme is obtained through iterative solution.

2. The communication resource optimization method according to claim 1, characterized in that, The construction of the communication resource coupling model of the wireless network control system in the low-altitude intelligent network includes the following steps: Step 1.1: Establish a communication model based on the finite code length transmission theory; In each control cycle Within, the uplink transmission time is defined as... and downlink transmission time is Total system bandwidth Divided into uplink bandwidth and downlink bandwidth And it satisfies the bandwidth constraint. ; Based on the finite code length transmission theory, maximum reachable payload models for the uplink and downlink are constructed respectively: (1) (2) in, and These are the maximum reachable payload for the uplink and the maximum reachable payload for the downlink, respectively. and These are the code lengths for the uplink and downlink, respectively. and These are the preset packet error rates for the uplink and downlink, respectively. It is the inverse Q function; The and These are the uplink Shannon channel capacity and the downlink Shannon channel capacity, respectively. and These are the channel dispersions of the uplink and downlink, respectively; based on the obtained uplink channel gain... and downlink channel gain The calculation formulas are as follows: (3) (4) (5) (6) in, and These are the transmit powers for the uplink and downlink, respectively; Channel noise power; Step 1.2: Based on the UAV airborne controller's computing frequency and the amount of data received via the uplink, establish a computing time model for the UAV airborne controller to quantify the time resource consumption of the airborne computing process. (7) in, For onboard computing time; The number of CPU cycles required to process each bit of data; This refers to the calculation frequency of the airborne controller; The information utility factor represents the proportion of effective information extracted from the raw sensor data. Step 1.3: Establish a unified energy constraint model that includes communication, computation time, and WPT energy consumption; First, calculate the onboard computing power consumption of the UAV. : (8) in The calculated power for the UAV airborne controller; The effective switching capacitor coefficient of the airborne processor; Subsequently, based on the battery capacity limitations of the UAV, a system was constructed that includes uplink WPT power consumption, downlink transmission power consumption, and... A unified energy constraint is applied to ensure that the total energy consumption of the system does not exceed the total energy budget for each control cycle. (9) in, Total energy budget for each control cycle; WPT (Power Transfer Inefficiency Factor) is used to characterize the path loss and conversion efficiency when a UAV wirelessly powers a sensor. This indicates the WPT power consumption of the UAV in supporting uplink transmission; This indicates the downlink transmission power consumption.

3. The communication resource optimization method according to claim 2, characterized in that, The construction of the joint resource optimization problem with the objective of minimizing LQR control cost includes the following steps: Step 2.1: Define the end-to-end effective information payload Based on this load, the lower bound of LQR control cost is determined. The function expression; First, define the end-to-end effective information payload. This represents the bottleneck value between the effective uplink information volume and the effective downlink payload, i.e.: (10) Subsequently, LQR (Lower Boundary of Control Cost) was introduced. As an evaluation index for system control performance; based on control theory, this index is determined in relation to the end-to-end effective information load. The following functional relationship is satisfied: (11) in, This is a constant term unrelated to resource allocation; The state dimension of the controlled system; The state matrix; All are constant matrices determined by the system dynamics equations; this relationship indicates that minimizing the lower bound of LQR control cost... Equivalent to maximizing end-to-end effective information payload ; Step 2.2: Minimize the lower bound of LQR control cost To optimize the objective, the communication model in step 1.1, the computation time model in step 1.2, and the unified energy constraint model in step 1.3 are used as constraints to construct the following joint resource optimization problem: Objective function: (12) Constraints: (13) (14) (15) (9), (10) Among them, the set of optimization variables .

4. The communication resource optimization method according to claim 3, characterized in that, The process of transforming the joint resource optimization problem into a single-objective optimization problem includes the following steps: Step 3.1: From equations (1) and (2), it can be seen that the maximum achievable uplink payload is... and downlink maximum reachable payload All of these are monotonically increasing functions of their corresponding transmit power and bandwidth. Therefore, the optimal solution to the joint resource optimization problem must satisfy the condition that the effective information payload of the uplink is equal to the effective information payload of the downlink, i.e., the effective payload balance condition. (16) Step 3.2: Based on the aforementioned payload balancing condition, the original joint resource optimization problem is transformed into a single-objective optimization problem aimed at maximizing the effective uplink information payload; First, based on the monotonically decreasing relationship between LQR control cost and effective information payload described in step 2.1, the original problem of minimizing LQR control cost is equivalently transformed into the problem of maximizing effective information payload, i.e. ; Subsequently, the effective load balancing condition shown in formula (16) is introduced as an equality constraint to eliminate the minimum operation in the objective function, transforming the joint resource optimization problem into maximizing the effective uplink information load. Single-objective optimization problem with objective: Objective function: (17) Constraints: Formula (9), (13), (14), (15), (16).

5. The communication resource optimization method according to claim 4, characterized in that, The method involves decomposing the single-objective optimization problem into time and bandwidth optimization sub-problems and power allocation sub-problems, and obtaining the optimal resource allocation scheme through iterative solutions. Step 4.1: Initialize algorithm parameters and optimization variables; Set convergence threshold and maximum number of iterations; initialize uplink transmit power. and downlink transmit power To meet the feasible value of the power constraint, initialize the uplink bandwidth. Downlink bandwidth Uplink transmission time and downlink transmission time ; Step 4.2: Fix the transmit power variable, solve the time and bandwidth optimization subproblem, and update the time and bandwidth variables; In the In this iteration, the transmit power is fixed at the level updated in the previous round. and The maximum feasible effective information payload under the current power configuration is determined using a binary search method. The specific process is as follows: Set a lower bound for binary search Upper Realm This is the theoretical maximum value; In each round of binary search, let the candidate payload ; For this candidate information payload First, based on the payload balance condition, the required uplink payload is determined as follows: The downlink payload is Subsequently, the uplink code length required to realize the payload is directly calculated by solving equations (1) and (2). and downlink code length : (18) (19) in, , , and These are constants calculated based on the current fixed transmit power; Subsequently, the uplink bandwidth allocation that satisfies the time minimization objective was calculated based on the Lagrange multiplier method. Uplink bandwidth allocation that meets the goal of minimizing energy consumption : (20) (21) Based on the above bandwidth allocation, the following feasibility determination is performed: If based on The total time consumption of the calculation satisfies the delay constraint shown in equation (13), or is based on If the total energy consumption calculated by the formula satisfies the unified energy budget constraint shown in equation (9), then the candidate information payload is determined. Feasible, update Otherwise, it is deemed infeasible and updated. ; Repeat the binary search process described above until convergence; the maximum feasible effective information payload obtained is... At this point, a feasibility assessment is performed again: if based on If the total computation time satisfies the latency constraint, then the system is determined to be currently constrained by latency, and the uplink bandwidth is updated. Otherwise, determine that the system is energy-constrained and update the uplink bandwidth. Downlink bandwidth updated to ; Finally, update the time variable based on the code length: uplink transmission time. Downlink transmission time ; Step 4.3: Based on the fixed time and bandwidth variables updated in Step 4.2, solve the power allocation subproblem and update the transmit power variable; Downlink payload defined based on equation (2) With transmission power The monotonically increasing relationship can be solved numerically by inverse kinematics. Represented as The function, combined with the payload balance condition shown in equation (16), establishes the downlink transmit power. Regarding uplink transmit power Monotonic mapping function: (22) This function indicates that for any given uplink transmit power... First, calculate the corresponding uplink payload. Then, the required downlink payload is determined based on the balance condition. Finally, the required downlink transmit power is obtained by solving formula (2). Through this mapping function, the original information about... and The bivariate optimization problem is transformed into a problem only concerning... A single-variable optimization problem; Define power constraint function and energy constraint function as follows: (23) (24) Given and All are about The monotonically increasing function is solved using a binary search algorithm. and equations The power-constrained solution is obtained. and energy-limited solutions ; Since the objective function monotonically increases with power, the optimal solution should be taken as the upper bound of the feasible region. Therefore, by comparing the power-constrained solution with the energy-constrained solution, the optimal uplink transmit power for the current round is determined. : (25) The corresponding optimal downlink transmit power is ; Step 4.4: Repeat steps 4.2 and 4.3, recording the current effective information payload in each iteration; when the difference between the effective information payloads calculated in two consecutive iterations is less than the preset convergence threshold... When the algorithm converges, it is determined that the algorithm has converged, and the final optimal resource allocation scheme is output. .

Images