Multi-user and multi-service resource sensing type scheduling method suitable for low-orbit satellite system

Abstract

The invention discloses a multi-user and multi-service resource sensing type scheduling method suitable for a low-orbit satellite system, and belongs to the field of satellite communication. The method comprises the following steps of: firstly, for a low-orbit satellite multi-beam communication scene, calculating the priority of each service in an initial time slot of a current scheduling period, and scheduling and distributing unoccupied PRBs (Physical Resource Blocks) in sequence after sequencing each user; calculating the throughput of each user; and entering the next time slot, and recalculating the throughput of each user until the cycle of the scheduling is ended. Constructing an optimization target to maximize the system throughput, and recording the total system throughput under the current weight combination; and finally, setting a learning rate and the maximum number of iterations, updating the weight, entering the next scheduling cycle, and recalculating the system throughput under the current weight combination. Traversing and screening out an optimal weight group which maximizes the throughput of the system, and combining and outputting the optimal weight group; according to the invention, the utilization efficiency of satellite resources is maximized, and the fairness of user services is ensured.

Description

Technical Field

[0001] The present invention belongs to the field of satellite communication, and particularly relates to a multi-user and multi-service resource-aware scheduling method applicable to a low-earth orbit satellite system. Background Art

[0002] According to different orbital altitudes, satellites are divided into geosynchronous earth orbit (GEO) satellites, medium earth orbit (MEO) satellites, and low earth orbit (LEO) satellites. Compared with GEO and MEO, due to its low orbital altitude, small signal transmission delay, low path loss, high spectral efficiency, and low manufacturing cost, LEO has better compatibility with the ground 5G communication network in the space-ground integrated network. However, the low-earth orbit satellite network mainly consists of small satellites, which means the scarcity of effective payload and spectrum resources. When the satellite serves a large number of users simultaneously, spectrum congestion is likely to occur, resulting in a decline in communication quality. To improve the utilization rate of on-board payload and allocate the limited resources of the satellite as needed, it is crucial to study resource scheduling strategies.

[0003] In a low-earth orbit satellite system, resource scheduling is a key technology for optimizing communication performance and ensuring user fairness. Traditional resource scheduling is mainly designed for terrestrial mobile communication networks and performs well in dealing with static or low-dynamic scenarios. However, in a low-earth orbit satellite system, due to the frequent change of network topology caused by the continuous movement of the satellite and the significant difference in user channel conditions caused by the wide coverage area, the performance of traditional scheduling schemes is severely limited. Especially when dealing with the resource allocation of edge users, it is often impossible to ensure both system efficiency and user fairness at the same time. At the same time, when dealing with diverse service requirements, there is a lack of a flexible priority adjustment mechanism, and the optimal allocation of system resources cannot be achieved.

[0004] In a low-earth orbit satellite system, the existing resource scheduling schemes mainly have the following technical difficulties:

[0005] First of all, users are distributed in different geographical locations and have diverse service requirements, including real-time service users sensitive to delay and non-real-time service users with higher throughput requirements. At the same time, users located at the edge of the satellite coverage area often face the problem of poor channel quality, while users in the central area have better channel conditions. This double complexity of user distribution and service requirements makes it difficult for traditional single-dimensional scheduling strategies to adapt.

[0006] Secondly, real-time service users require low latency and stable resource guarantees, and have extremely high requirements for the timeliness of resource allocation. Non-real-time service users are more concerned about system throughput, and expect to obtain more resources to improve transmission efficiency when channel conditions are good. There is a conflict between this differentiated service demand and limited system resources;

[0007] Thirdly, users at the edge of the satellite coverage area are easily marginalized in traditional scheduling algorithms dominated by channel quality due to poor channel quality, and often find it difficult to obtain fair service opportunities.

[0008] Therefore, how to design a resource scheduling solution that can comprehensively consider the user's geographical location and business type under limited system resources to achieve the joint optimization of system performance and user experience is a key issue that needs to be solved urgently. Summary of the invention

[0009] In view of the above problems, the present invention proposes a multi-user, multi-service resource-aware scheduling method suitable for low-orbit satellite systems, which maximizes the efficiency of on-board resource utilization and ensures the fairness of user services.

[0010] The multi-user, multi-service resource-aware scheduling method applicable to a low-orbit satellite system has the following specific steps:

[0011] Step 1: Build a low-orbit satellite multi-beam system as an application scenario;

[0012] The scenario includes several users and multiple satellites with different business needs. Each user corresponds to a business type. The service area is divided into multiple cells. The network control center is responsible for allocating resources in a unified manner and collecting and processing business demand information from each area.

[0013] Step 2: For each user in the scenario, randomize the user's initial task queue and calculate the SINR of each user respectively;

[0014] The user's initial task queue Q j ,j=1,2,3,...,J; the total number of users is J;

[0015] Step 3: Use different modulation and coding modes (MCS) to draw a BLER-SINR curve of bit error rate and signal-to-noise ratio, and obtain the optimal MCS level according to the SINR of each user;

[0016] The specific acquisition method is: find the SINR threshold value of BLER<0.1 in the figure, compare the SINR calculated by each user with the threshold value, and the curve greater than the SINR is the MCS that meets the conditions;

[0017] Step 4: At the initial time slot of the current scheduling period, calculate the priorities of each service, and sort the users according to the priorities;

[0018] The service types are divided into: real-time services and non-real-time services;

[0019] The priority calculation formula for real-time services is:

[0020]

[0021] where w1 is the weight for controlling parameters such as delay and packet loss rate, w2 is the weight for controlling the geographical location factor, and the initial values are set manually. a j =-log(P j-loss ) / τ j , P j-loss represents the maximum allowable packet loss rate when user j transmits data, and τ j represents the tolerable delay threshold. r j (t) represents the instantaneous data transmission rate of user j's data in time slot t. R j (t) represents the average packet transmission rate of user j's data in time slot t, and W j (t) represents the waiting delay of user j's data in time slot t. q j (t) refers to the length of the remaining data queue in time slot t. L j (t) refers to the location factor introduced in time slot t, which changes dynamically at each scheduling moment; the calculation formula for this dynamically adjusted location factor is:

[0022]

[0023] where d refers to the straight-line distance between the user and the point where the satellite projects onto the Earth's surface, refers to the standard deviation of Gaussian attenuation, which determines the rate at which the priority decays with distance.

[0024] The priority calculation formula for non-real-time services is:

[0025]

[0026] Step 5: Schedule each user in the sorted order in turn, and allocate the unoccupied physical resource blocks PRBs to each user in order according to the channel quality;

[0027] B t =[rb1,rb2,rb3,....,rb b ,..,rb total represents the set of physical resource blocks in time slot t. If rb b takes the value of 1, it means that the PRB is occupied;

[0028] Step 6: Update the number of PRBs and the PRB occupancy of physical resource blocks, calculate the throughput of each user in combination with the optimal MCS level, and record the remaining transmission queue q j (t) of each user;

[0029] The throughput calculation formula of user j is expressed as: thj = N PRB ·n RE_per_PRB ·M q ·C q ;

[0030] Where N PRB represents the total number of PRBs allocated to the user; n RE_per_PRB represents the number of resource particles RE available for data transmission in each PRB; M q represents the modulation order of the MCS with index q selected; C q represents the coding rate of the MCS with index q selected.

[0031] Step 7: Enter the next time slot, return to Step 4, and recalculate the throughput of each user in this time slot until the scheduling cycle ends.

[0032] Step 8: Based on the throughput of each user recorded in each time slot, construct an optimization objective to maximize the system throughput, and record the total throughput of the system under the current weight combination w1 and w2;

[0033] The optimization objective is:

[0034]

[0035] T is the total number of time slots;

[0036] Constraint 1 means that the weight factors must add up to 1; Constraint 2 means that the bit error rate threshold is 0.1 in the BLER-SINR curve graph; ρ q represents the SINR required for the MCS with index q; Constraint 3 means that each PRB can only be occupied once in each time slot; Constraint 4 means that the number of PRBs allocated to all users in each time slot cannot exceed the total number of PRBs B in this time slot total,t ; Constraint 5 means that the number of PRBs allocated to each user in each time slot cannot be less than 0; Constraint 6 is the Jain fairness mechanism, indicating that the fairness constraint is at least 0.85, and N represents the number of scheduled users;

[0037] Step 9: Set the learning rate η and the maximum number of iterations N max , update the weight w1 t+1 = w1 t + η, enter the next scheduling cycle, return to Step 4, adjust the weight factor w1, and recalculate the system throughput under the current weight combination;

[0038] Step 10: Record the ownership weight combinations and their corresponding throughput values, traverse and filter to find the optimal weight combination that maximizes the system throughput, and output it;

[0039] The advantages of the present invention are as follows:

[0040] A multi-user and multi-service resource-aware scheduling method applicable to a low-earth orbit satellite system according to the present invention comprehensively considers the differentiated service requirements of real-time services and non-real-time services, as well as the impact of the geographical distribution characteristics of users on resource allocation. Quantitatively analyze key factors such as user transmission rate, queue waiting duration, and geographical location, and establish a scientific priority sorting mechanism. The simulation results show that compared with the M-LWDF algorithm and the proportional fairness algorithm, the system throughput and user fairness of the system described in the present invention are significantly improved, and the packet loss rate is similar to the performance of the M-LWDF algorithm. Brief Description of the Drawings

[0041] Figure 1 is a flowchart of a multi-user and multi-service resource-aware scheduling method applicable to a low-earth orbit satellite system according to the present invention;

[0042] Figure 2 is a scenario diagram of a low-earth orbit satellite multi-beam system built according to the present invention;

[0043] Figure 3 is a BLER-SINR curve diagram drawn according to the present invention;

[0044] Figure 4 is a comparison diagram of the user fairness between the method described in the present invention and existing algorithms;

[0045] Figure 5 is a comparison diagram of the system throughput between the method described in the present invention and existing algorithms;

[0046] Figure 6 is a comparison diagram of the packet loss rate between the method described in the present invention and existing algorithms; Detailed Embodiment

[0047] The following further elaborates on the specific implementation method of the present invention in conjunction with the accompanying drawings.

[0048] The present invention proposes a multi - user, multi - service resource - aware scheduling method (A Multi - User Multi - Service Resource - Aware Scheduling Strategy for Low Earth Orbit Satellite Systems) for low - earth - orbit satellite systems. Aiming at the user - level resource allocation problem, considering the differentiated requirements of user services and the characteristics of the geographical distribution of users, it aims to maximize the resource utilization efficiency and improve the fairness of user services.

[0049] The present invention realizes the dynamic allocation of multi - users and multi - PRB resources through a resource - aware strategy. First, the optimization goal is determined to be maximizing the system throughput while ensuring service fairness. Second, based on the Maximum - Weighted Delay - First (M - LWDF) algorithm, a multi - dimensional resource - allocation priority criterion is established, and a priority function is designed. The system comprehensively evaluates the service requirements, queue status, channel quality, and geographical location of users, and completes each round of resource allocation based on the priority sequence. The allocation output result is the resource - acquisition plan for each user, and the system can further determine which users are provided with service resources in a certain time slot within its resource - allocation plan.

[0050] The multi - user, multi - service resource - aware scheduling method applicable to low - earth - orbit satellite systems is as Figure 1 shown, and the specific steps are as follows:

[0051] Step 1: Build a low - earth - orbit satellite multi - beam system as the application scenario;

[0052] As Figure 2 shown, the scenario includes several users with different service requirements and multiple satellites. The users are geographically distributed differently, and each user corresponds to a specific service type;

[0053] The service area is divided into multiple cells, and the network control center is responsible for uniformly allocating resources and collecting and processing the service - demand information of each area.

[0054] Step 2: For each user in the scenario, randomize the initial task queue of the user and calculate the SINR of each user respectively;

[0055] The initial task queue Q j , j = 1, 2, 3,..., J; the total number of users is J;

[0056] The path - loss model between the satellite and the ground station is:

[0057] PL = PL b + PL g + PL s + PL e (1)

[0058] PL b is the basic path loss, PL g is the atmospheric attenuation, PL s is the ionospheric attenuation, PL e is the penetration loss.

[0059] where the basic path loss is calculated as:

[0060] PL b = FSPL(d, f c ) + SF + CL(α, f c ) (2)

[0061] where, SF refers to the shadow fading, CL(α, f c ) refers to the cluster fading, FSPL(d, f c ) refers to the path loss in free space, and its calculation formula is:

[0062] FSPL(d, f c ) = 32.45 + 20log 10 (f c ) + 20log 10 (d) (3)

[0063] where, d refers to the transmission distance between the satellite and the user, f c refers to the carrier center frequency, with the unit of GHz.

[0064]

[0065] where R E represents the radius of the earth, and α represents the elevation angle of the user's position with respect to the satellite.

[0066] In the communication link between a low-earth-orbit satellite and a ground user, the signal-to-noise plus interference ratio (SINR) is a core indicator for measuring signal quality, which directly affects the reliability of the link and the data transmission efficiency. SINR can be regarded as the ratio of the received effective signal power to the interference and noise power. Its essence is a description of the ability of the signal to be received and decoded after being affected by external environmental interference. The calculation formula for the signal-to-noise ratio SINR of the jth user is as follows;

[0067]

[0068] EIRP j refers to the effective isotropic radiated power of user j, is the antenna gain-to-noise temperature ratio of user j, k is the Boltzmann constant, equal to -228.6 dBW / K / Hz, B is the system bandwidth, and PL refers to the path loss.

[0069] Step 3: Use different modulation and coding schemes (MCS) to plot the BLER-SINR curve of the bit error rate versus the signal-to-noise ratio, and obtain the optimal MCS level according to the SINR of each user.

[0070] The specific acquisition method is as follows: Based on the existing link-level simulation platform, simulate the system performance of different modulation and coding strategies to obtain the standard BLER-SINR curve. As Figure 3 shown, find the SINR threshold value where BLER < 0.1 in the figure, compare the SINR calculated for each user with the threshold value, and the curve with a SINR greater than this value is the MCS that meets the conditions.

[0071] Step 4: At the initial time slot of the current scheduling period, calculate the priority of each service, and sort each user according to the priority.

[0072] To meet the differentiated requirements of different service types in the low-earth orbit satellite multi-beam scenario, achieve on-demand allocation, meet diverse requirements such as low latency and high bandwidth, and at the same time balance the quality of service of each user and achieve coverage consistency, the system performance is improved by means of reasonable resource scheduling. This embodiment considers adding considerations of service type and user location based on M-LWDF to dynamically adjust the priority order and resource allocation decision.

[0073] In the satellite system, based on the sensitivity of the service to latency, the service types are divided into two categories: real-time services and non-real-time services. M = 0 represents real-time services, and m = 1 represents non-real-time services.

[0074] For real-time services, the priority calculation formula is:

[0075]

[0076] where w1 is the weight for controlling parameters such as latency and packet loss rate, w2 is the weight for controlling the geographical location factor, and the initial values are set manually. a j =-log(P j-loss ) / τ j , P j-loss represents the maximum allowable packet loss rate when user j transmits data, and τ j represents the tolerable latency threshold. R j (t) represents the average rate of packet transmission of user j's data in time slot t, and W j (t) represents the waiting latency of user j's data in time slot t. q j (t) refers to the length of the remaining data queue in time slot t. r j (t) represents the instantaneous rate of data transmission of user j's data in time slot t, and the calculation formula is:

[0077]

[0078] Among them, indicates how many bits of data can be transmitted per OFDM symbol, which is determined by the MCS selected by the user. indicates the number of OFDM symbols included in each time slot, indicates the number of time slots included in each scheduling period, indicates the number of subcarriers included in each RB.

[0079] L j (t) refers to the position factor introduced in time slot t, which changes dynamically at each scheduling moment; the calculation formula for this dynamically adjusted position factor is: where d refers to the straight-line distance between the user and the point where the satellite projects onto the Earth's surface, refers to the standard deviation of Gaussian attenuation, which determines the rate at which the priority decays with distance. The smaller it is, the faster the attenuation, that is, the bulk of the priority is mainly distributed within a smaller radius distance; The larger it is, the slower the attenuation, that is, a certain amount of priority can be retained at a farther distance. For a low-Earth orbit satellite system with a large coverage area, a larger is designed according to the ranges of the edge region and the near-star region to control the attenuation.

[0080] The position factor is to adjust the priority of edge users or users who have not been served for a long time in a timely manner according to the recent service status of users (such as the quality of allocated resources, the duration of unserved time, etc.) during the resource allocation process, so as to find a balance between fairness and resource efficiency. L j (t) combines the user's position with the channel state, making the users at the sub-satellite point score higher in the priority calculation, so as to obtain resources preferentially. For edge users, due to their poor channel conditions but high cost of throughput improvement, by weakening the position factor L j (t), their priority is reduced to avoid occupying too many resources in resource allocation.

[0081] The specific adjustment principle is that for users who fail to meet the service requirements (such as edge points), their priority is appropriately increased according to the number of times of non-service. When resources are abundant, the needs of users at the sub-satellite point are preferentially guaranteed. However, if the users at the sub-satellite point occupy resources excessively many times and reduce the fairness for some edge users, the L j (t) of users at the sub-satellite point can be appropriately restricted. Considering the position factor ensures that users at the edge of the beam can also be scheduled.

[0082] The priority calculation formula for non-real-time services is:

[0083]

[0084] Step 5: Schedule each user in the sorted order, and sequentially allocate the unoccupied Physical Resource Blocks (PRBs) to each user according to the channel quality.

[0085] In this scenario, one PRB resource block can only be allocated to one user at the same moment, and one user can obtain multiple PRB resource blocks simultaneously. The system allocates resources to users according to the priority sorting. The user will select the resource block with the optimal channel quality from the available PRBs and mark it as occupied. Each round of allocation will update the resource allocation result, the unallocated resource list, the user queue status, and the user priority sequence. After several rounds of allocation, the resource allocation scheme for each user and the owner user of each PRB will be finally determined, and the many-to-many matching of users and resources will be finally completed.

[0086] B t =[rb1,rb2,rb3,....,rb b ,..,rb total represents the set of physical resource blocks in time slot t. If rb b takes the value of 1, it means that this PRB is occupied.

[0087] Step 6: Update the number of PRBs and the PRB occupancy of the physical resource blocks, calculate the throughput of each user in combination with the optimal MCS level, and record the remaining transmission queue q j (t) of each user.

[0088] The throughput calculation formula for user j is expressed as: thj = N PRB ·n RE_per_PRB ·M q ·C q ;

[0089] where N PRB represents the total number of PRBs allocated to the user; n RE_per_PRB represents the number of Resource Elements (REs) available for data transmission in each PRB. One resource element (RE) occupies one subcarrier in the frequency domain and one OFDM symbol in the time domain; M q represents the modulation order of the MCS with the selected index q, and C q represents the coding rate of the MCS with the selected index q.

[0090] Step 7: Enter the next time slot, return to Step 4, and recalculate the throughput of each user in this time slot until the scheduling cycle ends.

[0091] Step 8: Based on the throughput of each user recorded in each time slot, construct an optimization objective to maximize the system throughput, and record the total throughput of the system under the current weight combination w1 and w2.

[0092] To maximize the utilization of on-board resources and ensure the fairness of user services, the optimization objectives are as follows:

[0093]

[0094] T is the total time slot;

[0095] Constraint 1 means that the sum of the weight factors must be 1; Constraint 2 means that the bit error rate threshold is 0.1 in the BLER-SINR curve graph; ρ q represents the SINR required for the MCS with index q; Constraint 3 means that each PRB can only be occupied once in each time slot; Constraint 4 means that the number of PRBs allocated to all users in each time slot cannot exceed the total number of PRBs B in that time slot total,t ; Constraint 5 means that the number of PRBs allocated to each user in each time slot cannot be less than 0; Constraint 6 is the Jain fairness mechanism, indicating that the fairness constraint is at least 0.85, and N represents the number of scheduled users;

[0096] Step Nine: Set the learning rate η and the maximum number of iterations N max , update the weight w1 t+1 = w1 t + η, enter the next scheduling cycle, return to Step Four, adjust the weight factor w1, and recalculate the system throughput under the current weight combination;

[0097] Step Ten: Record all weight combinations and their corresponding throughput values, traverse and filter to find the optimal weight combination that maximizes the system throughput and output it.

[0098] As Figure 4 , Figure 5 and Figure 6 shown, it can be seen that the algorithm proposed by the present invention has the best performance. Figure 4 It shows that as the number of users increases, the resource competition intensifies, and the fairness of the algorithm proposed by the present invention decreases the least, showing the most stability. While the fairness of proportional fairness and M-LWDF decreases significantly. Therefore, the algorithm proposed by the present invention performs best in resource allocation and user fairness optimization. Figure 5 It shows that as the number of users increases, the throughput of the three algorithms generally shows an increasing trend, but the trend of the algorithm proposed by the present invention is the most obvious. It still maintains a high level when the number of users is 30, and the highest throughput is close to 2.6*10^4 bps. Figure 6 It shows that as the number of users increases, the packet loss rates of the three algorithms all increase, and the packet loss rate of the algorithm proposed by the present invention increases the least, indicating that it performs best in resource allocation and packet loss rate control.

Claims

1. A multi-user, multi-service resource-aware scheduling method applicable to a low-orbit satellite system, characterized in that: The specific steps are as follows: Step 1: Build a low-orbit satellite multi-beam system as an application scenario; Step 2: For each user in the scenario, randomize the user's initial task queue and calculate the SINR of each user respectively; The user's initial task queue Q j ,j=1,2,3,...,J; the total number of users is J; Step 3: Use different modulation and coding modes (MCS) to draw a BLER-SINR curve of bit error rate and signal-to-noise ratio, and obtain the optimal MCS level according to the SINR of each user; Step 4: In the initial time slot of the current scheduling cycle, the priority of each service is calculated, and each user is sorted according to the priority; Business types are divided into: real-time business and non-real-time business; The real-time service priority calculation formula is: Among them, w1 is the weight of controlling parameters such as delay and packet loss rate, w2 is the weight of controlling geographical location factors, and the initial value is set manually. j = -log(P j-loss ) / τ j , P j-loss represents the maximum packet loss rate allowed for user j when transmitting data, τ j represents the tolerable delay threshold, r j (t) represents the instantaneous data transmission rate of user j in time slot t, R j (t) represents the average rate of data packet transmission of user j in time slot t, W j (t) represents the waiting delay of user j’s data in time slot t, q j (t) refers to the length of the remaining data queue in time slot t, L j (t) refers to the position factor introduced in time slot t; The priority calculation formula for non-real-time services is: Step 5: Schedule each user in the sorted order, and allocate unoccupied physical resource blocks (PRBs) to each user in order according to channel quality; B t =[rb1,rb2,rb3,....,rb b ,..,rb total ] represents the set of physical resource blocks in time slot t, if rb b If the value is 1, it means that the PRB is occupied; Step 6: Update the number of PRBs and PRB occupancy of the physical resource block, calculate the throughput of each user in combination with the optimal MCS level, and record the remaining queue q of each user to be transmitted. j (t); The throughput calculation formula for user j is expressed as: thj = N PRB ·n RE_per_PRB ·M q ·C q ; Where N PRB Indicates the total number of PRBs allocated to the user; n RE_per_PRB Indicates the number of resource elements RE available for data transmission in each PRB; M q Indicates the modulation order of the MCS with the selected index q, C q represents the coding rate of the MCS with the selected index q; Step 7: Enter the next time slot, return to step 4, and recalculate the throughput of each user in the time slot until the scheduling cycle ends; Step 8: According to the throughput of each user recorded in each time slot, the optimization goal is to maximize the system throughput, and the total throughput of the system under the current weight combination w1 and w2 is recorded; Step 9: Set the learning rate η and the maximum number of iterations N max , update the weight w1 t+1 =w1 t +η, enter the next scheduling cycle, return to step 4, adjust the weight factor w1, and recalculate the system throughput under the current weight combination; Step 10: Record all weight combinations and their corresponding throughput values, traverse and filter out the optimal weight combination that maximizes the system throughput, and output it.

2. The scheduling method according to claim 1, characterized in that: In step 1, the scenario includes several users and multiple satellites with different service requirements, and each user corresponds to a service type; the service area is divided into multiple cells, and the network control center is responsible for uniformly allocating resources and collecting and processing service demand information of each area.

3. The scheduling method according to claim 1, characterized in that: The specific acquisition method of step three is: find the SINR threshold value of BLER<0.1 in the BLER-SINR curve diagram, compare the SINR calculated by each user with the threshold value, and the curve with a SINR greater than the SINR is the MCS that meets the conditions.

4. The scheduling method according to claim 1, characterized in that: In step 4, the position factor L j (t) changes dynamically at each scheduling moment, and the calculation formula is: Where d refers to the straight-line distance between the user and the point projected by the satellite onto the surface of the earth. Refers to the standard deviation of the Gaussian falloff, which determines how fast the priority decays with distance.

5. The scheduling method according to claim 1, characterized in that: The optimization goal of step eight is: T is the total time slot; Constraint 1 means that the sum of weight factors must be 1; Constraint 2 means that the bit error rate threshold in the BLER-SINR curve is 0.1; ρ q represents the required SINR of the MCS with index q; constraint 3 indicates that each PRB can only be occupied once in each time slot; constraint 4 indicates that the number of PRBs allocated to all users in each time slot cannot exceed the total number of PRBs in the time slot B total,t ; Constraint 5 indicates that the number of PRBs allocated to each user in each time slot cannot be less than 0; Constraint 6 is the Jain fairness mechanism, which means that the minimum fairness constraint is 0.85, and N represents the number of scheduled users.

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