5G URLLC resource scheduling method for power service quality assurance

Abstract

The invention discloses a 5G URLLC resource scheduling method for power service quality assurance, which belongs to the technical field of network resource scheduling, and comprises the following steps: S1, constructing a system transmission model, and determining a system total throughput calculation formula; s2, constructing a system total throughput maximization target model, and determining constraint conditions; s3, simplifying a system total throughput maximization target model; s4, scheduling channel resources by dynamically updating the priority of the terminal; and S5, performing power distribution through a non-cooperative game algorithm based on a pricing mechanism. According to the method, the system throughput can be effectively improved while the fairness and the QoS of the power terminal are considered.

Description

Technical Field

[0001] The present invention relates to the technical field of network resource scheduling, and specifically to a 5G URLLC resource scheduling method for power service quality assurance. Background Art

[0002] Driven by the rapid growth of energy and electricity demand, power grid applications have put forward more stringent requirements for wireless networks. Power services such as precise load control, distribution automation, and inspection control require low-latency communication guarantees, while also expecting high-reliability transmission of key control information. Ultra-Reliable and Low Latency Communication (URLLC), one of the three major technologies of the fifth generation of mobile communications (5G), can well meet the differentiated requirements of various services for reliability, latency and other performance. URLLC technology combined with medium and low frequency band transmission can better reduce transmission losses and ensure good network coverage. However, with the continuous expansion of the scale of communication services, limited spectrum resources are becoming increasingly scarce. Reasonable allocation of network frequency and power resources, suppression of inter-cell interference under the differentiated quality of service (QoS) requirements of different services, and improvement of system reliability and transmission efficiency have become key technical issues for 5G to carry power services. Summary of the invention

[0003] In view of this, the present invention aims at the deficiencies in the prior art and provides a 5G URLLC resource scheduling method for power service quality assurance.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: a 5G URLLC resource scheduling method for power service quality assurance, comprising the following steps:

[0005] S1. Build a system transmission model and determine the system total throughput calculation formula;

[0006] S2. Construct a target model for maximizing the total throughput of the system and determine the constraints;

[0007] S3, simplify the system total throughput maximization objective model;

[0008] S4, scheduling channel resources by dynamically updating the priority of the terminal;

[0009] S5. Power allocation is performed through a non-cooperative game algorithm based on a pricing mechanism.

[0010] Furthermore, in S1, the system transmission model is:

[0011] The system consists of N different cellular cells, denoted as the set N = {n|n = 1, 2, ..., N}, each cell consists of a base station located at the center of the cell and K randomly distributed power business terminals, where the power business terminals are denoted as the set K = {k|k = 1, 2, ..., K}; each resource block in the cell can only be scheduled to one terminal in the scheduling time slot t, and the spectrum resources within the cell are orthogonal in the time domain; each cell has M resource blocks, denoted as M = {m|m = 1, 2, ..., M} and the number of resource blocks is less than the number of terminals in the cell, that is, M < K, and the spectrum bandwidth of the unit resource block is B;

[0012] The signal-to-interference-to-noise ratio of terminal k in cell n in resource block m is:

[0013]

[0014] in, is the channel gain of terminal k in cell n in resource block m, represents the downlink transmission power allocated to resource block m in cell n, σ 2 =N 0 B, N 0 is the noise unilateral power spectral density;

[0015] The maximum achievable throughput of cell n resource block m scheduled to the terminal is:

[0016]

[0017] in, n 0 The length of the transmission data packet. is the downlink decoding error probability;

[0018] The total throughput of terminal k in cell n is:

[0019]

[0020] in, is a decision variable of 0-1 planning, which indicates the mapping relationship between resource block m of cell n and terminal k, that is, It means that resource block m of cell n is allocated to terminal k, otherwise, It indicates that resource block m of cell n is not allocated to terminal k.

[0021] The total system throughput is the sum of the throughputs of all terminals in each downlink cell:

[0022]

[0023] Furthermore, in S2, the system total throughput maximization objective model is:

[0024]

[0025] Constraints include transmit power, latency, and reliability:

[0026]

[0027] Among them, the first constraint condition ensures that the sum of the power allocated to each cell resource block should not be higher than the maximum value of the downlink transmission power of the cell base station; the second constraint condition ensures that the rate of each terminal is a non-negative value; the third constraint condition ensures that each resource block can only be scheduled to one terminal in a time slot; the fourth constraint condition gives the range of the decoding error rate to ensure system reliability; the fifth constraint condition guarantees the scheduling delay of each power business terminal Meet the latency requirements of its business

[0028] Furthermore, in S3, the method of simplifying the system total throughput maximization objective model is: the terminals that reuse the same resource block between different cells are regarded as participants in the game, and appropriate game strategies are selected for these terminals to achieve a balanced power allocation of the resource block to maximize the throughput of the resource block; the maximization of the overall system throughput is satisfied by maximizing the throughput of all resource blocks. The simplified system total throughput maximization objective model is:

[0029]

[0030] Further, in S4, the method for scheduling channel resources by dynamically updating the priority of the terminal is:

[0031] (1) Set the rate of each power terminal to R 0 , as the initial rate;

[0032] (2) Scheduling time slot t 0 , each cell a independently performs channel resource scheduling. Calculate t 0 The average rate of each terminal b in the first W rounds of scheduling:

[0033] (3) Assuming that each terminal is scheduled to one resource block and the power is evenly distributed, calculate the expected instantaneous rate R of each terminal. exp (a,b,t 0 );

[0034] (4) Calculate terminal priority:

[0035] (5) Each cell is sorted in descending order according to the terminal priority, and the M resource blocks of the cell are sequentially scheduled to the M terminals with the highest priority;

[0036] t=t0 After the channel resource scheduling is completed, power allocation is performed.

[0037] Furthermore, in S5, the method for allocating power through a non-cooperative game algorithm based on a pricing mechanism is:

[0038] (a) Initialization: The M terminals allocated resource blocks in each cell equally obtain the total downlink transmission power of the cell base station, that is, Each base station feeds back the co-channel interference information it receives to the terminals in the cell;

[0039] (b) Each resource block performs power allocation game; for the terminal allocated to resource block m in cell n, Given the interference power vector of the last iteration Update downlink transmit power;

[0040] (c) Repeat step (b) to iterate and solve until the power allocated to resource block m converges to a fixed point: If the absolute value of the power difference between the two rounds of iterations is less than a very small threshold, it is considered to have converged to the equilibrium point, and the optimal pricing factor of the resource block is determined, and the power allocation of the resource block is completed;

[0041] (d) When all resource blocks in the system converge to a unique fixed point under the condition of deciding the corresponding optimal pricing factor, t 0 The time slot system power allocation ends; the scheduling time slot t 0 The system channel resource scheduling and power allocation are optimized step by step, and the channel resource scheduling of the next scheduling time slot is entered.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention considers the QoS requirements of different power service terminals and schedules channel resources by dynamically updating the priority of the terminal. Then, a non-cooperative game algorithm based on a pricing mechanism is introduced to optimize the power allocation strategy, reduce inter-cell interference, and improve communication reliability. The simulation results show that the algorithm converges quickly, improves the system throughput while ensuring certain system fairness and transmission reliability, and can reduce the average scheduling delay compared to the classic resource scheduling algorithm, meeting the QoS requirements of different power service terminals. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a performance comparison diagram of resource allocation algorithms in an embodiment of the present invention;

[0045] Figure 2 is a comparison chart of average scheduling delays of terminals of different service levels in an embodiment of the present invention;

[0046] Figure 34 is a comparison chart of system throughput under different reliability requirements in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0048] Example

[0049] A 5G URLLC resource scheduling method for power service quality assurance includes the following steps:

[0050] S1. Build a system transmission model and determine the system total throughput calculation formula;

[0051] S2. Construct a target model for maximizing the total throughput of the system and determine the constraints;

[0052] S3, simplify the system total throughput maximization objective model;

[0053] S4, scheduling channel resources by dynamically updating the priority of the terminal;

[0054] S5. Power allocation is performed through a non-cooperative game algorithm based on a pricing mechanism.

[0055] Specifically, in S1, the system transmission model is:

[0056] The system consists of N different cellular cells, denoted as the set N = {n|n = 1,2,…,N}. Each cell consists of a base station located at the center of the cell and K randomly distributed power business terminals, where the power business terminals are denoted as the set K = {k|k = 1,2,…,K}. Each resource block in the cell can only be scheduled to one terminal in the scheduling time slot t, and the spectrum resources within the cell are orthogonal in the time domain. However, different cells use the same frequency, so there is co-frequency interference between terminals assigned to the same resource block in each cell. Each cell has M resource blocks, denoted as M = {m|m = 1,2,…,M}, and the number of resource blocks is less than the number of terminals in the cell, that is, M < K, and the spectrum bandwidth of the unit resource block is B.

[0057] The signal-to-interference-to-noise ratio of terminal k in cell n in resource block m is:

[0058]

[0059] in, is the channel gain of terminal k in cell n in resource block m, represents the downlink transmission power allocated to resource block m in cell n, σ 2 =N 0 B, N 0 is the noise unilateral power spectral density.

[0060] Common wireless systems study the efficient transmission of sufficiently long data packets based on information theory principles, while URLLC needs to transmit key instructions (usually short packets) with extremely low latency and extremely high reliability. The impact of transmission error rate on reliability cannot be ignored, so the Shannon capacity formula cannot fully reflect the URLLC transmission requirements. Assume that the bandwidth allocated to the transmission of each data packet is less than the system-related bandwidth. In a quasi-static flat fading channel, the channel state information is known to both the transmitter and the receiver. The maximum achievable throughput (in bit / s) when the cell n resource block m is scheduled to the terminal is:

[0061]

[0062] In order to 0 Under the condition of meeting the reliability requirements, the bit error rate is introduced into the traditional Shannon formula Measuring system reliability, considering the reliability of throughput and Shannon channel capacity Proportional decay. It is called channel dispersion, which indicates the random variation of the channel compared with the deterministic channel of the same capacity, and its upper bound is 1. URLLC scenarios require high SINR to ensure high reliability and low latency. k It can be approximated to 1. 0 The length of the transmission data packet. is the probability of downlink decoding error. In order to meet the low latency requirements of services in URLLC scenarios, the transmission process should avoid the delay caused by retransmission, so Setting a lower threshold reduces the probability of an error in the first transmission, given the packet length. It can be regarded as a constant term. The maximum achievable throughput of the terminal when the resource block m in cell n is scheduled is not concave in bandwidth and transmit power, so resource management will become more complicated. The present invention considers calculating the data rate under the specified error probability requirement (reliability requirement).

[0063] The total throughput of terminal k in cell n is:

[0064]

[0065] in, is a decision variable of 0-1 planning, which indicates the mapping relationship between resource block m of cell n and terminal k, that is, It means that resource block m of cell n is allocated to terminal k, otherwise, It indicates that resource block m of cell n is not allocated to terminal k.

[0066] The total system throughput is the sum of the throughputs of all terminals in each downlink cell:

[0067]

[0068] In S2, the system total throughput maximization objective model is:

[0069]

[0070] Constraints include transmit power, latency, and reliability:

[0071]

[0072] Among them, the first constraint condition ensures that the sum of the power allocated to each cell resource block should not be higher than the maximum value of the downlink transmission power of the cell base station; the second constraint condition ensures that the rate of each terminal is a non-negative value; the third constraint condition ensures that each resource block can only be scheduled to one terminal in a time slot; the fourth constraint condition gives the range of the decoding error rate to ensure system reliability; the fifth constraint condition guarantees the scheduling delay of each power business terminal Meet the latency requirements of its business

[0073] From the frequency perspective, there is co-frequency interference between power terminals in each cell that obtain the same resource block (RB). Since each terminal is selfish and hopes to obtain higher downlink transmission power to maximize its own throughput, this selfish behavior will cause greater interference to other cell terminals using the same resource block, thereby reducing the throughput of adjacent cell terminals. Therefore, the problem of seeking the maximum system throughput can be expressed as a game problem.

[0074] In S3, the method of simplifying the system total throughput maximization objective model is: the terminals that reuse the same resource block between different cells are regarded as participants in the game, and appropriate game strategies are selected for these terminals to achieve a balanced power allocation of the resource block to maximize the throughput of the resource block; the maximization of the overall system throughput is achieved by maximizing the throughput of all resource blocks. The simplified system total throughput maximization objective model is:

[0075]

[0076] The present invention proposes an improved proportional fairness algorithm based on scheduling delay requirements, which takes the scheduling delay requirements of each power terminal service, the real-time channel conditions of the terminal, and the average amount of resources obtained by the terminal as the criteria for determining the terminal priority. The priority of terminal k in cell n in scheduling time slot t is

[0077]

[0078] W is the number of scheduling time slots contained in a scheduling time window, z n,k (t) is the expected instantaneous rate of terminal k in cell n in scheduling time slot t, Z n,k (t) represents the actual rate of the terminal, It is the scheduling delay requirement of the terminal, indicating that the terminal service data is expected to be scheduled within the specified number of time slots. is the delay requirement weight of each terminal, and r is the system delay exponential factor.

[0079] In S4, the specific steps of scheduling channel resources by dynamically updating the priority of the terminal are:

[0080] (1) Set the rate of each power terminal to R 0 , as the initial rate;

[0081] (2) Scheduling time slot t 0 , each cell a independently performs channel resource scheduling. Calculate t 0 The average rate of each terminal b in the first W rounds of scheduling:

[0082] (3) Assuming that each terminal is scheduled to one resource block and the power is evenly distributed, calculate the expected instantaneous rate R of each terminal. exp (a,b,t 0 );

[0083] (4) Calculate terminal priority:

[0084] (5) Each cell is sorted in descending order according to the terminal priority, and the M resource blocks of the cell are sequentially scheduled to the M terminals with the highest priority;

[0085] t=t 0 After the channel resource scheduling is completed, power allocation is performed.

[0086] After the terminals in each cell are prioritized and channel resources are allocated, power allocation is required for the terminals that obtain channel resources so that the throughput of each resource block is maximized, thereby maximizing the total system throughput, which is the sum of the throughputs of all terminals scheduled to the same resource block. The simplified objective function is:

[0087]

[0088] in, The downlink throughput of the terminal scheduled for resource block m in cell n. Considering the problem of co-channel interference in multiple cells, the optimal solution to the problem is a high-order derivative optimization problem with extremely high computational complexity and may not have a solution. Therefore, this project introduces non-cooperative games, and regards the power allocation problem of each resource block as an independent non-cooperative game process. In non-cooperative games, each terminal is selfish as a participant in the game, and chooses a power strategy with the goal of maximizing its own utility. Therefore, a pricing mechanism is introduced so that the terminal needs to consider its own utility and the corresponding price when choosing a power strategy, that is, the impact of co-channel interference generated by itself, to achieve power control and avoid vicious competition.

[0089] Let the game process be represented by G m =[N,{P m},{U m}],in, is the terminal set scheduled by resource block m in each cell, is the power strategy set, U m is the terminal net utility set.

[0090] The power strategy space of terminal n can be expressed as is a non-negative number; and That is, all resource blocks in the cell are evenly divided to obtain the maximum transmission power of the base station. The strategy space is a closed bounded convex set.

[0091] The terminal net utility function is defined as the difference between the terminal's utility function and its pricing function:

[0092]

[0093] Among them, at a given packet length can be regarded as a constant term, that is Pricing Function is the pricing factor, which indicates the price that the terminal should pay to obtain unit downlink transmission power. m In the game, each terminal aims to maximize its own net utility. The optimal power solution of each terminal, that is, the best response of the non-cooperative power game, is

[0094] b) Best response solution and Nash equilibrium

[0095] Let the first-order derivative of the terminal net utility be 0, that is, have to

[0096]

[0097] It can be proved that the monotonicity of the terminal net utility function is that it increases first and then decreases, and takes the maximum value when the first-order derivative is 0. In practical applications, the transmission power is required to meet rate The range of terminal pricing factors is shown in the following formula. The upper limit of the pricing factor range of the terminals scheduled by each resource block is taken as the intersection, and the lower limit is taken as the union, and the reasonable range of pricing factors for the resource block can be obtained. The optimal pricing factor of each resource block in each time slot is determined based on the corresponding system performance.

[0098]

[0099] Therefore, game G m The optimal power response of each terminal is

[0100]

[0101] In S5, the specific steps of power allocation through the non-cooperative game algorithm based on the pricing mechanism are:

[0102] (a) Initialization: The M terminals allocated resource blocks in each cell equally obtain the total downlink transmission power of the cell base station, that is, Each base station feeds back the co-channel interference information it receives to the terminals in the cell;

[0103] (b) Each resource block performs power allocation game; for the terminal allocated to resource block m in cell n, Given the interference power vector of the last iteration Update downlink transmit power;

[0104] (c) Repeat step (b) to iterate and solve until the power allocated to resource block m converges to a fixed point: If the absolute value of the power difference between the two rounds of iterations is less than a very small threshold, it is considered to have converged to the equilibrium point, and the optimal pricing factor of the resource block is determined, and the power allocation of the resource block is completed;

[0105] (d) When all resource blocks in the system converge to a unique fixed point under the condition of deciding the corresponding optimal pricing factor, t 0 The time slot system power allocation ends; the scheduling time slot t 0 The system channel resource scheduling and power allocation are optimized step by step, and the channel resource scheduling of the next scheduling time slot is entered.

[0106] The present invention and other algorithms are used to simulate the performance assurance of 5G network carrying differentiated power services. The results are as follows: Figures 1 to 3 shown.

[0107] Depend on Figure 1 It can be seen that the system throughput basically follows: MAXCI-AVE>algorithm of the present invention>

[0108] PF-AVE>RR-AVE>αPF-AVE; in terms of system fairness: αPF-AVE>RR-AVE>

[0109] PF-AVE>the algorithm of the present invention>MAXCI-AVE. The algorithm of the present invention (here r Taking 1) into account the different channel conditions of each terminal and the amount of resources it has obtained, the transmission power is allocated through a non-cooperative game algorithm, the interference between cells is reduced, and the throughput of the system is significantly increased. Since the algorithm proposed in the present invention sets different scheduling priorities according to the differentiated QoS requirements of the power terminal, it sacrifices the fairness of the system to a certain extent compared with the traditional PF-AVE algorithm, but compared with the MAXCI-AVE algorithm with the best throughput performance, the system fairness factor is higher. Overall, the algorithm proposed in the present invention can effectively improve the system throughput while taking into account fairness and QoS of power terminals.

[0110] Figure 2 In the figure, each group of bars represents the average scheduling delay of different algorithms under different numbers of cell terminals, which are the algorithm of the present invention (the delay index factor is r=1, 2, 3 respectively), PF-AVE algorithm and αPF-AVE algorithm. Bars of different colors represent different delay service levels, and the total height of the bars represents the sum of the average delays of terminals at each level. In addition, under the maximum carrier-to-interference ratio static scheduling, terminals with good channel conditions can always obtain channel resources, while terminals with poor channel conditions cannot obtain scheduling, and the scheduling waiting delay cannot be calculated; the polling algorithm does not distinguish between the service level and channel conditions of the terminal, and the average scheduling delay is the same. Both of them are not included in the comparison range of the above figure.

[0111] Figure 3 The comparison of URLLC system throughput with different reliability requirements shows that as the reliability requirements of the system increase, that is, the required transmission error probability decreases, the URLLC system throughput decreases, indicating that the data rate and reliability of ultra-short packet transmission are closely related. And as the number of cell terminals increases, the system throughput decreases in order to ensure the fairness of scheduling so that all terminals can obtain channel resource scheduling.

[0112] The simulation results show that the resource allocation mechanism of this project has made a suitable compromise between system throughput and fairness, improving the system throughput on the basis of ensuring a certain degree of scheduling fairness; corresponding scheduling can be made for the different scheduling delay requirements of terminals of different service levels in the power business to meet a variety of business needs. Therefore, the resource scheduling algorithm proposed in this invention has certain advantages in the application scenario of ensuring the communication quality of power grid business.

[0113] The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art can still modify or make equivalent substitutions to the specific implementations of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention are within the scope of protection of the claims of the present invention.

Claims

1. A 5G URLLC resource scheduling method for power service quality assurance, characterized in that: The following steps are involved: S1. Build a system transmission model and determine the system total throughput calculation formula; S2. Construct a target model for maximizing the total throughput of the system and determine the constraints; S3, simplify the system total throughput maximization objective model; S4, scheduling channel resources by dynamically updating the priority of the terminal; S5. Power allocation is performed through a non-cooperative game algorithm based on a pricing mechanism.

2. The 5G URLLC resource scheduling method for power service quality assurance according to claim 1 is characterized in that: In S1, the system transmission model is: The system consists of N different cellular cells, denoted as the set N = {n|n = 1, 2, ..., N}, each cell consists of a base station located at the center of the cell and K randomly distributed power business terminals, where the power business terminals are denoted as the set K = {k|k = 1, 2, ..., K}; each resource block in the cell can only be scheduled to one terminal in the scheduling time slot t, and the spectrum resources within the cell are orthogonal in the time domain; each cell has M resource blocks, denoted as M = {m|m = 1, 2, ..., M} and the number of resource blocks is less than the number of terminals in the cell, that is, M < K, and the spectrum bandwidth of the unit resource block is B; The signal-to-interference-to-noise ratio of terminal k in cell n in resource block m is: in, is the channel gain of terminal k in cell n in resource block m, represents the downlink transmission power allocated to resource block m in cell n, σ 2 =N0B, N0 is the noise unilateral power spectrum density; The maximum achievable throughput of cell n resource block m scheduled to the terminal is: in, n0 is the length of the transmission data packet, is the downlink decoding error probability; The total throughput of terminal k in cell n is: in, is a decision variable of 0-1 planning, which indicates the mapping relationship between resource block m of cell n and terminal k, that is, It means that resource block m of cell n is allocated to terminal k, otherwise, It indicates that resource block m of cell n is not allocated to terminal k. The total system throughput is the sum of the throughputs of all terminals in each downlink cell:

3. The 5G URLLC resource scheduling method for power service quality assurance according to claim 2 is characterized in that: In S2, the system total throughput maximization objective model is: Constraints include transmit power, latency, and reliability: Among them, the first constraint condition ensures that the sum of the power allocated to each cell resource block should not be higher than the maximum value of the downlink transmission power of the cell base station; the second constraint condition ensures that the rate of each terminal is a non-negative value; the third constraint condition ensures that each resource block can only be scheduled to one terminal in a time slot; the fourth constraint condition gives the range of the decoding error rate to ensure system reliability; the fifth constraint condition guarantees the scheduling delay of each power business terminal Meet the latency requirements of its business 4. The 5G URLLC resource scheduling method for power service quality assurance according to claim 3 is characterized in that: In S3, the method of simplifying the system total throughput maximization objective model is: the terminals that reuse the same resource block between different cells are regarded as participants in the game, and appropriate game strategies are selected for these terminals to achieve a balanced power allocation of the resource block to maximize the throughput of the resource block; the maximization of the overall system throughput is achieved by maximizing the throughput of all resource blocks. The simplified system total throughput maximization objective model is:

5. The 5G URLLC resource scheduling method for power service quality assurance according to claim 4 is characterized in that: In S4, the method for scheduling channel resources by dynamically updating the priority of the terminal is: (1) Set the rate of each power terminal to R0 as the initial rate; (2) In the scheduling time slot t0, each cell a independently performs channel resource scheduling. Calculate the average rate of each terminal b in the W rounds of scheduling before t0: (3) Assuming that each terminal is scheduled to one resource block and the power is evenly distributed, calculate the expected instantaneous rate R of each terminal. exp (a,b,t0); (4) Calculate terminal priority: (5) Each cell is sorted in descending order according to the terminal priority, and the M resource blocks of the cell are sequentially scheduled to the M terminals with the highest priority; The channel resource scheduling at t=t0 is completed and power allocation is performed.

6. The 5G URLLC resource scheduling method for power service quality assurance according to claim 5 is characterized in that: In S5, the method for allocating power through a non-cooperative game algorithm based on a pricing mechanism is: (a) Initialization: The M terminals allocated resource blocks in each cell equally obtain the total downlink transmission power of the cell base station, that is, Each base station feeds back the co-channel interference information it receives to the terminals in the cell; (b) Each resource block performs power allocation game separately; For a terminal in cell n that is allocated resource block m Given the interference power vector of the last iteration Update downlink transmit power; (c) Repeat step (b) to iterate and solve until the power allocated to resource block m converges to a fixed point: If the absolute value of the power difference between the two rounds of iterations is less than a very small threshold, it is considered to have converged to the equilibrium point, and the optimal pricing factor of the resource block is determined, and the power allocation of the resource block is completed; (d) When all resource blocks of the system converge to a unique fixed point under the condition of deciding the corresponding optimal pricing factor, the system power allocation of time slot t0 ends; the system channel resource scheduling and power allocation of scheduling time slot t0 are optimized step by step, and the channel resource scheduling of the next scheduling time slot is entered.

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