Uplink multi-carrier scheduling method, system and device based on 5G service characteristics and medium

Abstract

The invention discloses an uplink multi-carrier scheduling method, system and device based on 5G service characteristics and a medium, and the method comprises the following steps: building a millimeter wave network structure of a 5G eMBB scene, determining user parameters, building a scheduling priority model, and initializing parameters corresponding to a priority function; different time slot priority users of each subframe in the nth frame are calculated and obtained, scheduling resources are allocated to the user with the highest priority, and a priority function is based on a proportional fair scheduling algorithm; systems, devices, and media for implementing the method; according to the method, the priority function of the user is optimized by introducing the rate required by the user according to different application scenarios, and the user with relatively low demand and relatively good channel condition is preferentially considered; compared with a traditional polling scheduling algorithm and a standard proportional fairness algorithm, the method can meet the satisfaction of more users, can improve the throughput of the system, achieves balanced service distribution among the users, and meets the requirements of more users.

Description

Technical Field

[0001] The present invention belongs to the technical field of 5G system uplink resource scheduling, and in particular relates to an uplink multi-carrier scheduling method, system, device and medium based on 5G service characteristics. Background Art

[0002] With the commercialization of 5G mobile communication systems, wireless communication technology has been widely used and developed. To ensure the continuous evolution and prosperity of 5G technology, it is crucial to greatly enhance the efficiency of network deployment and operation of 5G systems. The wireless resource scheduling problem for the enhanced mobile broadband (Enhanced Mobile BroadBand, eMBB) scenario in 5G systems has always been an important research topic in the field of mobile communications. This paper discusses reasonable resource allocation schemes to ensure service latency and quality of service (QoS) requirements while improving system communication capacity and the number of device connections.

[0003] In the past twenty years, many scholars have proposed various scheduling strategies. These strategies can be classified according to their sensitivity to channels and QoS. They can be roughly divided into three types: strategies that are insensitive to channel changes, strategies that respond to channel changes to a certain extent but have low QoS requirements, and strategies that consider both channel status and QoS.

[0004] In the article “Providing quality of service over a shared wireless link”, M.ANDREWS et al. proposed an algorithm called Modified Largest Weight Delay First (M-LWDF). The main idea is to comprehensively consider the influence of two aspects, namely the channel conditions reported by users and the packet delay with the longest waiting time in the queue. Therefore, M-LWDF is committed to finding a balance between system throughput and fairness, while reducing network delay. In this research field, delay priority scheduling algorithm is an important research direction because it directly affects the performance of the communication system and user experience. Therefore, the proposed algorithm fills the gap that the previous algorithm ignores the delay problem when taking into account both throughput and fairness. B.SADIQ et al. in the article “Downlink scheduling for multiclass traffic in Two scheduling algorithms are proposed in "LTE": the exponential rule scheduling algorithm (EXP-rule, EXP) and the logarithmic rule scheduling algorithm (LOG-rule, LOG). Both algorithms use the user's channel conditions and the delay experienced by the data packet with the largest queuing delay as a reference. The difference between them is that the EXP algorithm calculates the delay parameters in an exponential manner, while the LOG algorithm calculates the delay parameters in a logarithmic form. These differentiated data flows will, in a sense, have a great impact on the sensitivity of the delay and the flexibility of the scheduling strategy.

[0005] Millimeter wave (mmWave) based networks have become increasingly common in recent years. However, mmWave channels are fragile and are susceptible to propagation and penetration losses, which causes changes in the signals received by the terminal and significantly affects the performance of 5G networks. Therefore, in mmWave based 5G networks, Radio Resource Management (RRM) is necessary to effectively utilize the valuable mmWave 60GHz frequency band resources. In the IEEE802.11ad standard that discusses wireless LAN scenarios, the access point (AP) is the node responsible for performing RRM operations such as terminal scheduling. The existing mmWave standards do not yet consider certain scheduling schemes for 5G networks. The following two articles discuss scheduling in mmWave based 5G networks. Ahmed M. Nor et al. give the performance of the standard proportional fair scheduling scheme in the article "Performance Evaluation of Proportional Fairness Scheduling in MmWave Network" and compare it with the performance of the polling scheduling algorithm. Jiteng Ma's article "Recent Results on Proportional Fair Scheduling for mmWave-based Industrial Wireless In this paper, an enhanced proportional fair scheduling algorithm is proposed in "Networks" to handle the fluctuating mmWave channels in industrial applications by increasing the priority of users with poor channel conditions.

[0006] Although the schemes proposed in these works achieve good system performance because they allocate resources among users based on their channel quality; they ignore the impact of user data rate requirements, and therefore, users with lower requirements may be allocated resources beyond their needs because they only have good channel conditions, while other users face the problem of insufficient allocation of resources. Summary of the invention

[0007] In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to propose an uplink multi-carrier scheduling method based on 5G service characteristics, determine user parameters by establishing a millimeter wave network structure, and use the proportional fair scheduling algorithm based on 5G service characteristics in the scheduling priority model to calculate the priority of users to be scheduled, and allocate scheduling resources according to the priority of users in different time slots; the present invention can achieve reasonable and efficient allocation of scheduling resources, thereby improving the system throughput, user fairness and user satisfaction performance of the uplink of the 5G system.

[0008] To achieve the above object, the technical solution of the present invention includes the following:

[0009] An uplink multi-carrier scheduling method based on 5G service characteristics comprises the following steps:

[0010] S1. Establish the millimeter wave network structure for the 5G eMBB scenario and determine user parameters;

[0011] S2. According to the user parameters determined in step S1, a scheduling priority model is established, and the corresponding parameters of the priority function of the nth (n=1, 2, 3, ..., n) frame in the scheduling priority model are initialized;

[0012] S3, according to the parameters corresponding to the nth (n=1, 2, 3, ..., n) frame of the priority function initialized in step S2, the priority of the user to be scheduled in different time slots of each subframe in the nth (n=1, 2, 3, ..., n) frame is calculated using the priority function in step S2, and the user to be scheduled with the highest priority in different time slots of each subframe in the nth (n=1, 2, 3, ..., n) frame is obtained;

[0013] S4, respectively selecting the users to be scheduled with the highest priority in different time slots of each subframe of the nth (n=1, 2, 3, ..., n) frame described in step S3, and allocating scheduling resources to them in turn;

[0014] S5. Repeat steps S2 to S4 until all the users to be scheduled with the highest priority in different time slots of all subframes in the nth (n=1, 2, 3, ..., n) frame have completed scheduling resource allocation.

[0015] The millimeter wave network structure of the 5GeMBB scenario is an indoor millimeter wave propagation model based on Li-Fi and millimeter wave technology. The indoor millimeter wave propagation model uses distributed LED bulbs as Li-Fi atto-cells (small cells) and uses positioning technology to estimate the user's location, so as to use the millimeter wave transmit / receive beam that is expected to cover the user as the optimal beam pair of the indoor millimeter wave propagation model.

[0016] The user parameters in step S1 include: the length x, width y and height z of the indoor scene corresponding to the millimeter wave network structure, the height UE of the user equipment from the ground h , millimeter wave carrier frequency, millimeter wave bandwidth and beamwidth, the number of users waiting for scheduling k, the number of access points AP, the time slot length n, the subframe length fn, and the data rate required by users of different service types

[0017] The priority function in step S2 is a priority scheduling function based on a proportional fair scheduling algorithm, and the priority function calculation process is specifically as follows:

[0018] S2.1 Calculate the data rate r that can be achieved by user k in time slot n based on the user parameters described in step S1k (n), r k (n) The calculation formula is as follows:

[0019] r k (n) = η BW BW log2(1+η SNR SNR k (n))

[0020] Among them, η BW represents bandwidth efficiency, η SNR represents the signal-to-noise ratio (SNR) efficiency, BW represents the millimeter wave bandwidth, and SNR k (n) represents the received signal-to-noise ratio when user k is associated with the mmWave access point in time slot n;

[0021] S2.2 Based on the user parameters in step S1 and the data rate r in step S2.1 k (n), calculate the historical average rate R of user k before time slot n k (n), R k (n) The update follows the following formula:

[0022]

[0023] Among them, T c represents the update time window. The size of the time window reflects the user's tolerance for not being scheduled within a certain period of time. c The larger the value of T is, the longer the user is willing to wait until the channel condition improves. c The value of is equal to time slot n. At the first time slot, the historical average rate R of user k before the first time slot k (1) is 0;

[0024] S2.3 The data rate required by user k of different service types according to the user parameters in step S1 and the historical average rate R described in step S2.2 k (n), calculate the data rate required by user k in each time slot n

[0025] The update follows the following formula:

[0026]

[0027] Among them, R k (n) represents the historical average rate of user k before time slot n. In the first time slot, the historical average rate of user k before the first time slot is R k (1) is 0;

[0028] S2.4 The data rate required by user k of different service types according to step S2.3 Calculate the weight coefficient w k (n), w k (n) The formula is as follows:

[0029]

[0030] in, represents the maximum rate required by any relevant user in the priority function;

[0031] S2.5 r calculated according to steps S2.1, S2.2, and S2.4 k (n), R k (n), w k (n), calculate the priority function P of the user k to be scheduled k , P k The formula is as follows:

[0032]

[0033] Among them, η is a control parameter, and the value range of η is 0.1~1.

[0034] The corresponding parameters of the nth (n=1, 2, 3, ..., n) frame of the priority function initialized in step S2 are specifically the data rates required by users of different service types.

[0035] A system for uplink multi-carrier scheduling based on 5G service characteristics includes a millimeter wave network module, a scheduling priority calculation module, and a resource allocation module, wherein the millimeter wave network module is used to establish a millimeter wave network structure based on a 5G eMBB scenario, the scheduling priority calculation module includes a scheduling priority model for network users, the scheduling priority model determines the user with the highest priority in each time slot of each subframe in each frame through a priority function, and the resource allocation module allocates scheduling resources to the user with the highest priority in each time slot obtained by the scheduling priority calculation module.

[0036] A device for uplink multi-carrier scheduling based on 5G service characteristics, comprising:

[0037] Memory: used to store a computer program for implementing the above-mentioned uplink multi-carrier scheduling method based on 5G service characteristics;

[0038] Processor: used to implement the uplink multi-carrier scheduling method based on 5G service characteristics when executing the computer program.

[0039] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the uplink multi-carrier scheduling method based on 5G service characteristics is implemented.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. The present invention optimizes the user's priority function by introducing the user's required rate according to different user requirements for data rate in different application scenarios, thereby achieving an improvement in system throughput compared to traditional scheduling algorithms.

[0042] 2. The present invention gives priority to users with lower demands and better channel conditions. If the user's required rate is reached, they are prioritized according to their channel quality. Compared with traditional scheduling algorithms, this can ensure that more users are satisfied. That is, all users in the network have the opportunity to obtain services through 5G links and achieve a certain degree of satisfaction, but their opportunity depends not only on their channel conditions but also on their demands. Through simulation, in the case of 16 users, the present invention can achieve more than 84% user satisfaction compared with traditional polling scheduling algorithms and standard proportional fair scheduling algorithms.

[0043] 3. Compared with the traditional polling scheduling algorithm and the standard proportional fair scheduling algorithm, the present invention can achieve an improvement in system throughput while maintaining the same fairness among users; through simulation, while ensuring the same fairness among 16 users, compared with the traditional polling scheduling algorithm and the standard proportional fair scheduling algorithm, the present invention can achieve an improvement in system throughput of more than 300Mbps.

[0044] In summary, compared with the prior art, the present invention can improve system throughput while maintaining the same fairness among users, and can meet the needs of more users. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a general flow chart for implementing the present invention;

[0046] Figure 2 It is a usage scenario diagram of the present invention;

[0047] Figure 3 This is a simulation result diagram of the system throughput of the first scenario of the first embodiment of the present invention when serving 8 users in total;

[0048] Figure 4 This is a simulation result diagram of user satisfaction in the first scenario of the first embodiment of the present invention when a total of 8 users are served;

[0049] Figure 5This is a simulation result diagram of user fairness in the first scenario of the first embodiment of the present invention when serving 8 users in total;

[0050] Figure 6 This is a simulation result diagram of the system throughput of the second scenario of the first embodiment of the present invention when serving 8 users in total;

[0051] Figure 7 This is a simulation result diagram of user satisfaction in the second scenario of the first embodiment of the present invention when a total of 8 users are served;

[0052] Figure 8 This is a simulation result diagram of user fairness in the second scenario of the first embodiment of the present invention when serving 8 users in total;

[0053] Fig. 9 This is a simulation result diagram of the system throughput of the first scenario of the second embodiment of the present invention when serving 16 users in total;

[0054] Fig.10 This is a simulation result diagram of user satisfaction in the first scenario of the second embodiment of the present invention when a total of 16 users are served;

[0055] Fig.11 This is a simulation result diagram of user fairness in the first scenario of the second embodiment of the present invention when a total of 16 users are served;

[0056] Fig.12 This is a simulation result diagram of the system throughput of the second scenario of the second embodiment of the present invention when serving 16 users in total;

[0057] Fig.13 This is a simulation result diagram of user satisfaction in the second scenario of the second embodiment of the present invention when a total of 16 users are served;

[0058] Fig.14 This is a simulation result diagram of user fairness in the second scenario of the second embodiment of the present invention when serving 16 users in total. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following Figures 1 to 14 The present invention is further described in detail with reference to the accompanying drawings and embodiments.

[0060] like Figure 1 As shown, the present invention is an uplink multi-carrier scheduling method based on 5G service characteristics, comprising the following steps:

[0061] An uplink multi-carrier scheduling method based on 5G service characteristics comprises the following steps:

[0062] S1. Establish the millimeter wave network structure of the 5GeMBB scenario and determine the user parameters; the user parameters specifically include: the length x, width y and height z of the indoor scene corresponding to the millimeter wave network structure, the height of the user equipment UE from the ground h , millimeter wave carrier frequency, millimeter wave bandwidth and beamwidth, the number of users waiting for scheduling k, the number of access points AP (the number of access points AP affects the stability and coverage of the network), the time slot length n, the subframe length fn, and the data rate required by users of different service types

[0063] S2. According to the user parameters determined in step S1, a scheduling priority model is established, and the corresponding parameters of the priority function nth (n=1, 2, 3, ..., n) frame in the scheduling priority model are initialized. The corresponding parameters specifically refer to the required data rates of users of different service types between different frames.

[0064] According to the data rate required by users of different business types To initialize the data rates required by different application users when requesting to associate to the mmWave access point.

[0065] The data rate required by users in each time slot, whose initial value in the first time slot is equal to the data rate required by users of different service types The update of its subsequent time slots follows the steps in step S2.3. The update formula of the value of k (n) is the historical average rate of user k before time slot n. Its initial value is the average value of the data rate required by users of different services. The update of its subsequent time slots follows the r in step S2.2. k Update formula for the value of (n).

[0066] S3, according to the parameters corresponding to the nth (n=1, 2, 3, ..., n) frame of the priority function initialized in step S2, the priority of the user to be scheduled in different time slots of each subframe in the nth (n=1, 2, 3, ..., n) frame is calculated using the priority function in step S2, and the user to be scheduled with the highest priority in different time slots of each subframe in the nth (n=1, 2, 3, ..., n) frame is obtained;

[0067] S4, respectively selecting the users to be scheduled with the highest priority in different time slots of each subframe of the nth (n=1, 2, 3, ..., n) frame described in step S3, and allocating scheduling resources to them in turn;

[0068] The detailed steps of the uplink resource scheduling transmission process are as follows:

[0069] (1) The user equipment (UE) sends an SRS signal to the base station so that the base station can perform subsequent measurements and allocations;

[0070] (2) The base station analyzes the received SRS signal and determines the corresponding physical resources;

[0071] (3) The user equipment receives the signaling instruction from the base station and completes the data upload according to the instruction. This process requires data transmission on the specified time and frequency resources according to the instruction of the base station. The user equipment needs to follow the scheduling arrangement of the base station to ensure that the data is uploaded on time and meets the requirements of the communication protocol;

[0072] (4) The base station receives and demodulates the user data based on the received DM-RS signal.

[0073] S5. Repeat steps S2 to S4 until all the users to be scheduled with the highest priority in different time slots of all subframes in the nth (n=1, 2, 3, ..., n) frame have completed scheduling resource allocation.

[0074] The millimeter wave network structure of the 5GeMBB scenario is an indoor millimeter wave propagation model based on Li-Fi and millimeter wave technology. The model takes into account the beamforming of millimeter wave APs and user devices. By combining Li-Fi and millimeter wave technologies and using distributed LED bulbs as Li-Fi at to-cells (small cells), the received signal strength of distributed LED bulbs is collected, and the user's location is estimated using positioning technology; then, the millimeter wave transmit / receive beam that is expected to cover the user is selected, so that the best beam pair for establishing a millimeter wave link can be selected by only performing beam combination.

[0075] The priority function in step S2 is a priority scheduling function based on the proportional fair scheduling algorithm, which is improved by introducing the data rate required by each time slot user. The priority function calculation process is as follows:

[0076] S2.1 Calculate the data rate r that can be achieved by user k in time slot n based on the user parameters described in step S1 k (n), r k (n) The calculation formula is as follows:

[0077] r k (n) = η SW BW log2(1+η SNR SNR k (n))

[0078] Among them, η BW represents bandwidth efficiency, η SNRrepresents the signal-to-noise ratio (SNR) efficiency, BW represents the millimeter wave bandwidth, and SNR k (n) represents the received signal-to-noise ratio when user k is associated with the mmWave access point in time slot n;

[0079] S2.2 Based on the user parameters in step S1 and the data rate r in step S2.1 k (n), calculate the historical average rate R of user k before time slot n k (n), R k (n) The update follows the following formula:

[0080]

[0081] Among them, T c represents the update time window. The size of the time window reflects the user's tolerance for not being scheduled within a certain period of time. c The larger the value of T is, the longer the user is willing to wait until the channel condition improves. c The value of is equal to time slot n. At the first time slot, the historical average rate R of user k before the first time slot k (1) is 0;

[0082] S2.3 In 5G and subsequent networks, each user device has its own data rate requirement according to the service type requirements. The data rate required by user k of different service types in the user parameters in step S1 is

[0083] and the historical average rate R described in step S2.2 k (n), calculate the data rate required by user k in each time slot n The update follows the following formula:

[0084]

[0085] Among them, R k (n) represents the historical average rate of user k before time slot n. In the first time slot, the historical average rate of user k before the first time slot is R k (1) is 0, so R reqk (1) = R reqk ;

[0086] S2.4 The data rate required by user k of different service types according to step S2.3 Calculate the weight coefficient w k (n), w k (n) The formula is as follows:

[0087]

[0088] in, represents the maximum rate required by any relevant user in the priority function;

[0089] S2.5 r calculated according to steps S2.1, S2.2, and S2.4 k (n), R k (n), w k (n), calculate the priority function P of the user k to be scheduled k , P k The formula is as follows:

[0090]

[0091] Among them, η is a control parameter, and the value range of η is 0.1~1.

[0092] The corresponding parameters of the nth (n=1, 2, 3, ..., n) frame of the priority function initialized in step S2 are specifically the data rates required by users of different service types. That is, the rate required by different application users when requesting to associate to the millimeter wave access point, and the first time slot of each subframe of each frame must initialize the parameters (i.e. ), the data rate required by the user for each subframe and each time slot in the nth frame is the same value.

[0093] The effects of the present invention are further described with reference to the following examples:

[0094] like Figure 2 As shown, the present invention uses millimeter waves in the 60GHz frequency band for communication, and the studied 5GWLAN has architectures of different eMBB services, namely weak interaction VR, strong interaction VR, 4K ultra-high-definition video streaming and 8K ultra-high-definition video streaming. The WLAN consists of 1 millimeter-wave access point AP responsible for scheduling and k associated users; for the network, a medium research area with high traffic is assumed, in which the access point AP is hung in the center of the area, three-dimensional beamforming is only applicable to the access point AP side, and the user antenna is considered to be a quasi-omnidirectional antenna; before the scheduling process, the millimeter-wave access point AP collects user channel quality measurements during beamforming training and association, which can be used for scheduling of multi-user communication systems; in addition, the scheduler can provide users with a flexible number of time slots, thereby improving user throughput and fairness.

[0095] In the first embodiment, the millimeter wave network structure of the 5GeMBB scenario includes 8 users (ie, user k=8), and the 8 users are divided into weak interaction VR (user required data rate 120Mbps), strong interactive VR (data rate required by users 200Mbps), 4K ultra-high-definition video streaming (user required data rate 150Mbps) and 8K UHD video streaming (user required data rate 300Mbps) four types of services can be associated with an access point AP in the same time period. Assume that the length x, width y, and height z of the indoor scene are 20m, 20m, and 5m respectively, and the height of the user equipment UE h The carrier frequency of the millimeter wave is 60 GHz, the bandwidth of the millimeter wave is 2.16 GHz, the beam width is 20°, the number of users k=8, the subframe length fn=5, and the time slot length n=800 in each subframe. The above parameters are brought into the millimeter wave network structure to set the initial parameters, and then the priority function is used to calculate the priority of each user to be scheduled.

[0096] Embodiment 1 is divided into two scenarios. In the first scenario, it is assumed that all applications will be required with the same probability (i.e., the probability given to the user is 0.25); in the second scenario, it is assumed that the demand probabilities of weakly interactive VR and 4K services are 0.375, and the demand probabilities of strongly interactive VR and 8K services are 0.125, respectively.

[0097] In order to verify the performance of the uplink multi-carrier scheduling method based on 5G service characteristics, three indicators, namely system throughput, user fairness and user satisfaction, are introduced for evaluation:

[0098] ① System throughput calculation:

[0099] The system throughput is defined as the sum of the throughputs of all users participating in the scheduling in the system. The throughput of each user participating in the scheduling is equal to its achievable data rate. Considering the millimeter wave transmission link model, the achievable data rate of user k in time slot n is r k (n).

[0100] ② Calculation of user satisfaction:

[0101] User satisfaction usually refers to the user's overall feeling and satisfaction with his communication experience. It is an important performance measurement indicator in eMBB application scenarios. Providing user satisfaction means allocating enough resource blocks to the user to ensure that its throughput is equal to or higher than the required rate. The satisfaction of user k, S k As shown below:

[0102]

[0103] Among them, R k represents the actual system throughput, Represents the rate required by the user. In this paper, R k The value cannot exceed

[0104] ③User fairness calculation:

[0105] In the case of network congestion or limited resources, resource allocation should try to meet fairness. The present invention adopts Jain's fairness index FI as a measure to describe fairness between users. The FI formula is as follows:

[0106]

[0107] like Figures 3 to 8 As shown, in the first and second scenarios, the present invention can improve about 1.34 Gbps and 1.60 Gbps respectively; in the first scenario, the present invention meets the needs of about 99% of network users, and in the second scenario, the present invention meets the needs of about 99% of network users; in the first scenario, the user fairness index of the present invention is 0.9991, and in the second scenario, the user fairness index of the present invention is 0.9829; after simulation, the system throughput, user satisfaction and user fairness of the uplink multi-carrier scheduling algorithm based on service characteristics in the two scenarios of Example 1 are better than the traditional polling scheduling algorithm and the standard proportional fair scheduling algorithm.

[0108] In Example 2, the millimeter wave network structure of the 5G eMBB scenario includes 16 users, divided into weak interaction VR (user required data rate 120Mbps), strong interactive VR (data rate required by users 200Mbps), 4K ultra-high-definition video streaming (user required data rate 150Mbps) and 8K UHD video streaming (user required data rate 300Mbps) four service types can be associated with one access point in the same time period, and the other parameters are the same as in Example 1; Example 2 is also divided into two scenarios. In the first scenario, it is assumed that all applications will be required with the same probability (that is, the probability given to the user is 0.25); in the second scenario, it is assumed that the demand probabilities of weak interaction VR and 4K services are 0.375, and the demand probabilities of strong interaction VR and 8K services are 0.125 respectively; in order to verify the performance of the uplink multi-carrier scheduling method based on 5G service characteristics at this time, the three indicators of system throughput, user fairness and user satisfaction that are the same as in Example 1 are also introduced for evaluation.

[0109] like Figures 9 to 14As shown, in the first and second scenarios, the present invention can improve about 0.33Gbps and 0.46Gbps respectively; in the first scenario, the present invention meets the needs of about 84% of network users, and in the second scenario, the present invention meets the needs of about 95% of network users; the user fairness index of the present invention in the first scenario is 0.9349, and the user fairness index of the present invention in the second scenario is 0.9817; after simulation, the system throughput and user satisfaction of the uplink multi-carrier scheduling algorithm based on service characteristics in the two scenarios of Example 2 are better than the traditional polling scheduling algorithm and the standard proportional fair scheduling algorithm; although the fairness of the uplink multi-carrier scheduling algorithm based on service characteristics is slightly lower than that of the other two traditional algorithms in the first scenario, it still maintains high user fairness; and in the second scenario which is closer to reality, the fairness is slightly higher than that of the other two traditional algorithms, from which it can be seen that the performance of the method of the present invention is superior.

[0110] A system for uplink multi-carrier scheduling based on 5G service characteristics, including a millimeter wave network module, a scheduling priority calculation module, and a resource allocation module, wherein the millimeter wave network module is used to establish a millimeter wave network structure based on a 5G eMBB scenario and set network user parameters in the millimeter wave network structure, the scheduling priority calculation module includes a scheduling priority model for network users, the scheduling priority model determines the user with the highest priority in each time slot of each subframe in each frame through a priority function, and the data rate required by the user in the priority function The resource allocation module allocates scheduling resources to the highest priority user in each time slot obtained by the millimeter wave network module.

[0111] A device for uplink multi-carrier scheduling based on 5G service characteristics, comprising:

[0112] Memory: used to store a computer program for implementing the above-mentioned uplink multi-carrier scheduling method based on 5G service characteristics;

[0113] Processor: used to implement the uplink multi-carrier scheduling method based on 5G service characteristics when executing the computer program.

[0114] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the uplink multi-carrier scheduling method based on 5G service characteristics is implemented.

[0115] A computer-readable storage medium includes: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0116] Working principle of the present invention:

[0117] In the present invention, considering that each user equipment has its own data rate requirement for different applications, an uplink multi-carrier scheduling method based on 5G service characteristics is proposed. On the basis of the standard proportional fair scheduling algorithm, the ratio of the rate required by different services to the maximum rate required in the current scenario is introduced to optimize the priority function, thereby ensuring high user satisfaction and user fairness while achieving an improvement in system throughput.

Claims

1. An uplink multi-carrier scheduling method based on 5G service characteristics, characterized in that: The following steps are involved: S1. Establish the millimeter wave network structure for the 5G eMBB scenario and determine user parameters; S2. According to the user parameters determined in step S1, a scheduling priority model is established, and the corresponding parameters of the priority function of the nth (n=1, 2, 3, ..., n) frame in the scheduling priority model are initialized; S3, according to the parameters corresponding to the nth (n=1, 2, 3, ..., n) frame of the priority function initialized in step S2, the priority of the user to be scheduled in different time slots of each subframe in the nth (n=1, 2, 3, ..., n) frame is calculated using the priority function in step S2, and the user to be scheduled with the highest priority in different time slots of each subframe in the nth (n=1, 2, 3, ..., n) frame is obtained; S4, respectively selecting the users to be scheduled with the highest priority in different time slots of each subframe of the nth (n=1, 2, 3, ..., n) frame described in step S3, and allocating scheduling resources to them in turn; S5. Repeat steps S2 to S4 until all the users to be scheduled with the highest priority in different time slots of all subframes in the nth (n=1, 2, 3, ..., n) frame have completed scheduling resource allocation.

2. The uplink multi-carrier scheduling method based on 5G service characteristics according to claim 1, characterized in that: The millimeter wave network structure of the 5GeMBB scenario is an indoor millimeter wave propagation model based on Li-Fi and millimeter wave technology. The indoor millimeter wave propagation model uses distributed LED bulbs as Li-Fi atto-cells and uses positioning technology to estimate the user's location, with the millimeter wave transmit / receive beam expected to cover the user as the optimal beam pair of the indoor millimeter wave propagation model.

3. The uplink multi-carrier scheduling method based on 5G service characteristics according to claim 1, characterized in that: The user parameters in step S1 include: the length x, width y and height z of the indoor scene corresponding to the millimeter wave network structure, the height UE of the user equipment from the ground h , millimeter wave carrier frequency, millimeter wave bandwidth and beamwidth, the number of users waiting for scheduling k, the number of access points AP, the time slot length n, the subframe length fn, and the data rate R required by users of different service types reqk .

4. The uplink multi-carrier scheduling method based on 5G service characteristics according to claim 1, characterized in that: The priority function in step S2 is a priority scheduling function based on a proportional fair scheduling algorithm, and the priority function calculation process is specifically as follows: S2.1 Calculate the data rate r that can be achieved by user k in time slot n based on the user parameters described in step S1 k (n), r k (n) The calculation formula is as follows: r k (n)=η BW BW log2(1+η SNR SNR k (n)) Among them, η BW represents bandwidth efficiency, η SNR represents the signal-to-noise ratio (SNR) efficiency, BW represents the millimeter wave bandwidth, SNR k (n) represents the received signal-to-noise ratio when user k is associated with the mmWave access point in time slot n; S2.2 Based on the user parameters in step S1 and the data rate r in step S2.1 k (n), calculate the historical average rate R of user k before time slot n k (n), R k (n) The update follows the following formula: Among them, T c represents the update time window. The size of the time window reflects the user's tolerance for not being scheduled within a certain period of time. c The larger the value of T is, the longer the user is willing to wait until the channel condition improves. c The value of is equal to time slot n. At the first time slot, the historical average rate R of user k before the first time slot k (1) is 0; S2.3 The data rate R required by user k of different service types according to the user parameters in step S1 reqk and the historical average rate R described in step S2.2 k (n), calculate the data rate R required by user k in each time slot n reqk (n), R reqk (n) The update follows the following formula: R reqk (n)=R reqk -R k (n) Among them, R k (n) represents the historical average rate of user k before time slot n. In the first time slot, the historical average rate of user k before the first time slot is R k (1) is 0; S2.4 The data rate R required by user k of different service types according to step S2.3 reqk (n), calculate the weight coefficient w k (n), w k (n) The formula is as follows: in, represents the maximum rate required by any relevant user in the priority function; S2.5 r calculated according to steps S2.1, S2.2, and S2.4 k (n), R k (n), w k (n), calculate the priority function P of the user k to be scheduled k , P k The formula is as follows: Among them, η is a control parameter, and the value range of η is 0.1~1.

5. The uplink multi-carrier scheduling method based on 5G service characteristics according to claim 3, characterized in that: The corresponding parameters of the nth (n=1, 2, 3, ..., n) frame of the priority function initialized in step S2 are specifically the data rates R required by users of different service types. reqk .

6. A system for uplink multi-carrier scheduling based on 5G service characteristics, characterized in that: It includes a millimeter wave network module, a scheduling priority calculation module, and a resource allocation module. The millimeter wave network module is used to establish a millimeter wave network structure based on the 5G eMBB scenario. The scheduling priority calculation module contains a scheduling priority model for network users. The scheduling priority model determines the user with the highest priority in each time slot of each subframe in each frame through a priority function. The resource allocation module allocates scheduling resources to the user with the highest priority in each time slot obtained by the scheduling priority calculation module.

7. A device for uplink multi-carrier scheduling based on 5G service characteristics, characterized in that: include: Memory: used to store a computer program for implementing the uplink multi-carrier scheduling method based on 5G service characteristics as described in any one of claims 1 to 5; Processor: used to implement the uplink multi-carrier scheduling method based on 5G service characteristics as described in any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the uplink multi-carrier scheduling method based on 5G service characteristics as described in any one of claims 1-5.

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