Data packet scheduling determination method, device, equipment and storage medium

Abstract

The present application provides a method, apparatus, device and storage medium for determining data packet scheduling, including: determining constraints and an objective function, wherein the constraints are determined by the uncertainty of a wireless channel between a first terminal and a second terminal and packet parameters under a finite code length mechanism; and determining the optimal solution of the objective function and a URLLC packet scheduling scheme using a non-convex penalty method NCP and a continuous approximation algorithm SCA.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method, device, equipment and storage medium for determining data packet scheduling. Background Art

[0002] Driven by the practical needs of key businesses such as factory automation, autonomous driving, remote surgery, and touch Internet, ultra-reliable and low latency communication (URLLC) has become one of the research hotspots of B5G and 6G.

[0003] Due to the strict low latency requirements, only short packets can usually be transmitted in URLLC systems, which means that the traditional Shannon capacity is no longer applicable to characterize the maximum achievable rate under short packets. Secondly, due to the service characteristics of URLLC: short packet transmission, ultra-low latency and ultra-high reliability, the existing URLLC packet scheduling method has high algorithm complexity and poor robustness. Summary of the invention

[0004] The present application provides a method, apparatus, device and storage medium for determining data transmission by scheduling data packets, so as to achieve robust scheduling and resource allocation in the worst case.

[0005] In a first aspect, an embodiment of the present application provides a method for determining data packet scheduling, including:

[0006] Determining a constraint condition and an objective function, wherein the constraint condition is determined by uncertainty of a wireless channel between the first terminal and the second terminal and a packet parameter under a finite code length mechanism;

[0007] The non-convex penalty method NCP and the continuous approximation algorithm SCA are used to determine the optimal solution of the objective function and the URLLC packet scheduling scheme.

[0008] In a second aspect, an embodiment of the present application provides a data packet scheduling determination device, including:

[0009] A first determination module is configured to determine a constraint condition and an objective function, wherein the constraint condition is determined by uncertainty of a wireless channel between the first terminal and the second terminal and a packet parameter under a finite code length mechanism;

[0010] The second determination module is configured to determine the optimal solution of the objective function and the URLLC packet scheduling scheme using the non-convex penalty method NCP and the continuous approximation algorithm SCA.

[0011] In a third aspect, an embodiment of the present application provides a device, including:

[0012] one or more processors;

[0013] A memory for storing one or more programs;

[0014] When the one or more programs are executed by the one or more processors, the one or more processors implement any method provided in the embodiments of the present application.

[0015] In a fourth aspect, an embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the embodiments of the present application is implemented.

[0016] The data packet scheduling determination method, apparatus, device and storage medium provided in the embodiments of the present application determine constraints and objective functions, wherein the constraints are determined by the uncertainty of the wireless channel between the first terminal and the second terminal and the packet parameters under the finite code length mechanism; the optimal solution of the objective function and the URLLC packet scheduling scheme are determined by the non-convex penalty method NCP and the continuous approximation algorithm SCA, thereby achieving robust scheduling and resource allocation in the worst case.

[0017] With regard to the above embodiments and other aspects of the present application and their implementation, further description is provided in the accompanying drawings, detailed description and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of a method for determining data packet scheduling provided by an embodiment of the present application;

[0019] Figure 2 is the approximate V provided in the embodiment of the present application mnk Schematic diagram of accuracy when ≈1;

[0020] Figure 3 It is a structural diagram of a data packet scheduling determination device provided in an embodiment of the present application;

[0021] Figure 4 It is a structural schematic diagram of a device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present application more clear, the embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other arbitrarily without conflict.

[0023] The steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions. Also, although a logical sequence is shown in the flowchart, in some cases, the steps shown or described can be performed in a sequence different from that shown here.

[0024] The technical solution of the present application can be applied to a variety of mission-critical applications under OFDM systems, such as virtual reality, augmented reality, autonomous vehicles, telesurgery, haptics communication, intelligent transportation system (ITS), Industry 4.0, etc. As long as it is an OFDM system, the technical solution described in this application can be adopted.

[0025] In the embodiment of the present application, the base station may be a device that can communicate with a user terminal. The base station may be any device with wireless transceiver functions. Including but not limited to: base station NodeB, evolved base station eNodeB, base station (gNB) in 5G communication system, base station in future communication system, access node in WiFi system, wireless relay node, wireless backhaul node, etc. The base station may also be a wireless controller in the cloud radio access network (CRAN) scenario; the base station may also be a small station, transmission reference point (TRP), etc., which is not limited in the embodiment of the present application. In this application, a 5G base station is used as an example for explanation.

[0026] In the embodiments of the present application, the user terminal is a device with wireless transceiver function that can be deployed on land, including indoors or outdoors, handheld, wearable or vehicle-mounted; it can also be deployed on the water surface (such as ships, etc.); it can also be deployed in the air (such as airplanes, balloons and satellites, etc.). The user terminal can be a mobile phone, a tablet computer (Pad), a computer with wireless transceiver function, a virtual reality (VR) terminal, an augmented reality (AR) terminal, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, etc. The embodiments of the present application do not limit the application scenarios. The user terminal may also be sometimes referred to as a terminal, an access terminal, a UE unit, a UE station, a mobile station, a mobile station, a remote station, a remote terminal, a mobile device, a UE terminal, a wireless communication device, a UE agent or a UE device, etc. The embodiments of the present application are not limited.

[0027] The core of Industry 4.0 is to promote the transformation and upgrading of the real economy such as manufacturing and deep structural adjustment through informatization and intelligence. In order to achieve this goal, the future wireless communication system must have diversified performance to carry various emerging services, thereby promoting the deep integration of industrialization and informatization. In order to meet various potential needs, the 5G system proposes three types of application scenarios: enhanced mobile broadband (eMBB) provides high-speed data transmission, massive machine type communication (mMTC) provides massive connections to support IoT communication, and ultra-reliable low-latency communication URLLC realizes key services.

[0028] The core indicators of URLLC are information transmission reliability (packet error rate PER or packet error probability BLER) and end-to-end transmission delay. End-to-end delay includes air interface delay, queuing delay, calculation / processing delay and retransmission delay (if retransmission exists). Under the 3GPP framework, for general services, the core indicators of URLLC are defined as: the reliability of transmitting 32-byte packets within 1ms of user plane delay reaches 99.999%. User plane delay is defined as the one-way transmission delay of packets from the IP layer of the source node to the IP layer of the destination node. The strict requirements of ultra-low latency and ultra-high reliability pose severe challenges to the design of 5G and post-5G wireless communication systems. Key technologies for reducing latency include shortening the transmission time interval TTI, such as from 1ms in LTE to 0.125ms in the new 5G air interface; reducing the HARQ round-trip delay RTT. However, mini-slots are obtained by increasing the subcarrier spacing, and the number of resource blocks is reduced, which will increase the queuing delay of data packets. Technologies to improve reliability include retransmission HARQ-CC / HARQ-IR, multi-connection technology, network coding, etc. However, latency and reliability are a pair of contradictory indicators, and how to effectively compromise latency and reliability is also a hot topic of current research.

[0029] Driven by the practical needs of key businesses such as factory automation, autonomous driving, remote surgery, and touch Internet, ultra-reliable low-latency communication (URLLC) has become one of the research hotspots of B5G and 6G. Due to the strict low-latency requirements, only short packets can usually be transmitted in URLLC systems, which means that the traditional Shannon capacity is no longer suitable for describing the maximum achievable rate under short packets. Therefore, it is necessary to study the design of transmission schemes under the limited code length mechanism. Secondly, due to factors such as channel estimation error and quantization error, the transmitter cannot always know the perfect channel state information (CSI). It is more practical to study robust scheduling and resource allocation in the worst case.

[0030] Due to the service characteristics of URLLC: short packet transmission, ultra-low latency and ultra-high reliability, there are not many existing URLLC packet scheduling methods, and the algorithm complexity is high and the robustness is poor.

[0031] In one embodiment, the present application provides a method for determining data packet scheduling, such as Figure 1 As shown, the data packet scheduling determination method provided in this embodiment mainly includes steps S11 and S12.

[0032] S11, determining a constraint condition and an objective function, wherein the constraint condition is determined by uncertainty of a wireless channel between the first terminal and the second terminal and a packet parameter under a finite code length mechanism;

[0033] S12. Determine the optimal solution of the objective function and the URLLC packet scheduling scheme using the non-convex penalty method NCP and the continuous approximation algorithm SCA.

[0034] In an exemplary embodiment, determining the constraints and the objective function includes one or more of the following:

[0035] Determine the constraints and objective functions for a single-antenna, multi-user scenario;

[0036] Determine the constraints and objective functions for multi-antenna multi-user scenarios.

[0037] In this embodiment, the uncertainty of the wireless channel between the central controller and the robot in single-antenna multi-user scenarios and multi-antenna multi-user scenarios, the achievable rate of packets under a finite code length mechanism, and the delay, reliability and target bit number requirements of the packets are modeled. The objective function considered is the problem of minimizing the total weighted transmission power consumption of the controller, which is of concern to the industry.

[0038] The problem is transformed by using convex optimization techniques such as variable substitution, perspective transformation, and first-order Taylor approximation, and a non-convex penalty method NCP is proposed to solve it. norm constraint,design of robust URLLC packet scheduling design;

[0039] A continuous convex approximation SCA iterative algorithm based on NCP is proposed to solve the URLLC packet scheduling problem in this scenario, and the NCP method is compared with the classic weighted Methods for performance comparison.

[0040] In an exemplary embodiment, the packet parameters include one or more of the following:

[0041] The achievable rate of packets, the transmission delay of packets, the reliability of packets, and the target number of bits of packets.

[0042] In an exemplary embodiment, the objective function includes: the first terminal meets the transmission task requirements of all second terminals in the system The minimum weighted sum power required; a resource scheduling scheme for minimizing power consumption of the first terminal under non-perfect CSI and limited code length mechanism;

[0043] The transmission task requires The first terminal is at D k OFDM symbols with a packet error probability ε k Set the target data bit number to B k The control command packet is sent to the kth second terminal, K is the number of the second terminals, and k is any integer between 1 and K.

[0044] In an exemplary embodiment, the objective function in the single-antenna multi-user scenario is:

[0045]

[0046] Among them, p tot1 is the weighted sum of the transmission power required by the first terminal to satisfy all second terminals to complete the transmission task in the single-antenna multi-user scenario, K is the number of second terminals, M is the number of schedulable resource blocks RB, N is the number of schedulable OFDM symbols, φ mnk is the allocation identifier. If the nth OFDM symbol of the mth RB is allocated to the kth second terminal, then φ mnk =1,ω mnk is the weight of the kth second terminal when transmitting on the nth OFDM symbol in the mth RB, p mnk is the transmission power allocated to the kth second terminal in the nth OFDM symbol of the mth RB.

[0047] In an exemplary embodiment, the constraint conditions in the single-antenna multi-user scenario include one or more of the following:

[0048] The maximum number of received bits of the kth second terminal in the worst case is greater than or equal to the target number of data bits B of the kth second terminal. k ;

[0049] Any RB is allocated to at most one second terminal;

[0050] When the number of OFDM symbols allocated to the kth second terminal on the mth RB is greater than the target number of OFDM symbols D k When the assignment label φ mnk Set to zero; that is, exceed the delay requirement D of the kth second terminal k No resources are allocated.

[0051] The transmission power p allocated to the kth second terminal on the nth OFDM symbol in the mth RB is mnk is a non-negative power, and the transmission power p allocated to the kth second terminal on the nth OFDM symbol in the mth RB is mnk Less than or equal to the product of the assigned identifier and the maximum constrained power. mnk = 0, p mnk =0;φ mnk When =1, there is a maximum transmit power constraint.

[0052] In an exemplary embodiment, the constraint condition in the single-antenna multi-user scenario is:

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] Among them, R k is the maximum number of received bits of the kth second terminal in the worst case, B k is the target number of data bits for the kth second terminal, φ mnk is the allocation identifier. If the nth OFDM symbol of the mth RB is allocated to the kth second terminal, then φ mnk =1, K is the number of second terminals, D k is the delay requirement of the kth second terminal, p mnk is the transmission power allocated to the kth second terminal on the nth OFDM symbol in the mth RB, P max is the maximum constrained power.

[0059] In an exemplary embodiment, in the single-antenna multi-user scenario, the maximum number of received bits R of the kth second terminal in the worst case is k By assigning the identifier φ mnk , the worst received signal-to-noise ratio ρ of the kth robot mnk , and the wireless channel dispersion V mnk Sure.

[0060] In an exemplary embodiment, in the single-antenna multi-user scenario, the maximum number of received bits R of the kth second terminal in the worst case is k for:

[0061]

[0062] in, Q -1 (ε k )yes The inverse, V mnk is the wireless channel dispersion.

[0063] In an exemplary embodiment, in the single-antenna multi-user scenario, the worst received signal-to-noise ratio ρ of the kth robot is mnk for:

[0064]

[0065] Among them, α kis the large-scale channel gain of the kth robot, is the channel estimate of the first terminal and the kth second terminal transmitted on the nth OFDM symbol in the mth RB, e mnk represents the channel estimation error, δ mnk represents the boundary of the channel uncertainty region, σ 2 is the power of additive white Gaussian noise, p mnk is the transmission power allocated to the kth second terminal on the nth OFDM symbol in the mth RB. Through the equivalence problem

[0066]

[0067] st|e mnk |≤δ mnk .

[0068] Using the (Karush–Kuhn–Tucker) KKT condition, it is easy to obtain Therefore, the receiving SNR of the kth robot can be rewritten as

[0069] ρ mnk =g mnk p mnk , in

[0070] In the single-antenna multi-user scenario, consider a smart factory with 1 central controller and K robots. The controller and all robots are equipped with a single antenna. k OFDM symbols with a packet error probability ε k The number of data bits is B k The control command packet is sent to the kth robot (k=1,…,K). Assume that the controller knows the transmission task requirements of all robots According to the definition of 5G NR physical resource blocks in TS 38.211, the basic resource scheduling unit is a resource block RB, which consists of 12 consecutive subcarriers in the frequency domain and 1 OFDM symbol in the time domain. The number of schedulable resource blocks RBs is defined as M, and the number of schedulable OFDM symbols is defined as N. Because a packet can be transmitted on multiple RBs, a binary variable φ is defined mnk ∈{0,1} represents RB mapping. If the nth OFDM symbol of the mth RB is assigned to the kth robot, then φ mnk =1, otherwise φ mnk = 0. Assuming that each RB is assigned to at most one robot, then

[0071]

[0072] Assume a quasi-static block fading channel, that is, the channel remains unchanged in each coherent block, and the channel changes independently in different coherent blocks. However, due to practical limitations, such as CSI estimation error, quantization error, and CSI feedback delay, it is usually impossible to obtain perfect CSI on the transmitter side, especially for critical services. Because the transmission time interval TTI becomes very short in critical services, the time used for channel estimation is limited by the data transmission time and becomes very short and insufficient for accurate channel estimation. Here, a bounded CSI estimation error model is used to characterize the uncertainty of the channel. The actual channel transmitted by the controller and the k-th robot on the n-th OFDM symbol in the m-th RB is expressed as

[0073]

[0074] in represents the channel estimation, e mnk represents the channel estimation error, δ mnk represents the bound of the channel uncertainty region.

[0075] Under this bounded CSI estimation error model, the worst received signal-to-noise ratio SNR of the kth robot is expressed as

[0076]

[0077] where α k represents the large-scale channel gain of the kth robot, p mnk represents the transmit power allocated to the kth robot when transmitting on the nth OFDM symbol in the mth RB, σ 2 is the power of additive white Gaussian noise. It is easy to get by KKT condition Therefore, the receiving SNR of the kth robot can be rewritten as

[0078] ρ mnk =g mnk p mnk , (4)

[0079] in

[0080] Since the URLLC system has ultra-low delay requirements and usually transmits short packets, the traditional Shannon capacity formula is no longer applicable to characterize the maximum achievable rate of the URLLC system. Y.Polyanskiy et al. proposed a capacity formula for interference-free flat fading channels under a finite code length mechanism, which characterizes the relationship between transmission delay, decoding error probability and achievable rate. Later, this capacity formula was extended to other channels, including fading channels. Here, a joint coding scheme is considered, that is, each user's packet is jointly encoded on all called RBs. Therefore, the maximum number of received bits for the kth robot in the worst case can be approximately characterized as

[0081]

[0082] in Q -1 (·)yes The inverse, is the channel dispersion.

[0083] Note that the approximate V mnk ≈1, this approximation is accurate enough when the received SNR is higher than 3dB, and this condition almost holds in URLLC scenarios. Figure 2 As shown in the figure, the channel usage is 120 and the decoding error probability is ε = 10 -6 The achievable rate.

[0084] The questions of interest are: (1) How much power does the controller need to support all the robots in the system? transmission task; (2) How can the controller perform resource scheduling under the imperfect CSI and limited code length mechanism to minimize power consumption and maximize energy efficiency. Based on this, the optimization problem of robust scheduling and resource allocation of URLLC packets to minimize the total transmission power of the controller can be modeled as follows

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] (6b) ensures the target load requirement B of the kth robot k(k=1,…,K), constraints (6c) and (6d) require that any RB can be assigned at most one robot, constraint (6e) reflects the delay requirements of each robot, constraint (6f) is a non-negative power and maximum power constraint, and requires φ mnk = 0 when p mnk =0, symbol engineering practice.

[0092] Note that there is a product term φ in the objective function of problem (6) mnk p mnk , make the following transformations on constraints (6b) and (6f)

[0093]

[0094]

[0095] Therefore, the original problem (6) can be rewritten as

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] Lemma 1: If problem (9) is feasible, then the optimal solution to problem (6) is also the optimal solution to problem (9) and can be recovered by the solution to problem (9).

[0103] Proof: Note that due to the difference between constraints (6f) and (9f), the feasible set of problem (9) is larger than that of problem (6). Specifically, when φ mnk = 0, constraint (6f) means that p mnk = 0, and constraint (9f) implies that p mnk can take any value; when φ mnk = 1, constraints (6f) and (9f) are equivalent. Therefore, it can be concluded that if problem (9) is feasible, the optimal solution of problem (6) is also the optimal solution of problem (9), and can be recovered by the solution of problem (9). Specifically, based on the solution of problem (9), let φ mnk = p when 0 mnk = 0, the solution to the original problem (6) can be recovered.

[0104] Next, we will solve problem (9). Introducing slack variables Replace φ mnk p mnk , problem (9) can be rewritten as

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111] The left side of constraint (10b) is in the form of the difference of concave functions (the first term on the left side can be judged to be a concave function by the concavity preservation of the perspective function), so constraint (10b) is a non-convex constraint. By making a first-order Taylor approximation, we can obtain its locally tight upper bound in is x k The value at the i-th iteration. Based on the first-order Taylor approximation, constraint (10b) can be transformed into the following convex constraint

[0112]

[0113] The existence of binary variables makes the problem difficult to solve. The solution is to first relax it to a continuous variable of [0,1]. Considering that any RB can be assigned to at most one robot, we add the constraint

[0114]

[0115] Guaranteed sparse solution, where Defined as φ mn =[φ mn1 ,…,φ mnK ] T .for Norm constraint, the classic method is to use weighted Specifically, in each iteration The norm is approximately the weighted norm, that is

[0116]

[0117] in is the diagonal weight matrix at the i-th iteration, and the diagonal elements are After each iteration, Updated to As the initial value of the next iteration, where ξ>0 is a small number. Therefore, by weighting Method, you can Approximately convex constraints Based on the above weighted Method, problem (10) can be approximated as the following convex problem

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] In this application, the non-convex penalty method NCP is used to deal with norm to obtain a sparse scheduling solution. The theory is as follows: for any vector It has at most one nonzero element if and only if

[0126] ‖x‖ a =‖x‖ b ,1≤a<b, (15)

[0127] ‖·‖ a ,‖·‖ b Respectively Norm, Norm. Without loss of generality, we have the following conclusions

[0128]

[0129] Where v>0 is the power exponent. The norm constraint (12) can be rewritten into the following equivalent form

[0130]

[0131] generally, This holds true for any a and b that satisfy 1≤a<b. Here we consider smoothness penalty a=1, b=2, v=2. In order to make φ mn There is at most one non-zero element, and a penalty term is added to the objective function

[0132]

[0133] Where λ>0 is the penalty factor. Note that the penalty function is in the form of a difference between convex functions. In order to make the objective function a convex function, Performing the first-order Taylor approximation, it becomes

[0134]

[0135] in Yes mn The value at the i-th iteration. Therefore, the penalty term can be approximated as

[0136]

[0137] Based on the above NCP method, we can get the following penalty convex problem:

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144] In one embodiment, in a single-antenna multi-user scenario, based on weighted The SCA iterative algorithm is as follows:

[0145] Algorithm 1: Reweighted based SCA Iterative Algorithm for SolvingProblem(6)

[0146] 1:Initialize iteration index i=1,power weightsω mnk ,feasible set initial weights in reweighted as tolerance ∈>0.

[0147] 2:Calculate for all k.

[0148] 3: Repeat

[0149] 4: Obtain by Solving(14) and restore

[0150] 5: Update

[0151] 6: Update

[0152] 7: Update

[0153] 8: Set i=i+1.

[0154] 9: Until

[0155] 10: Output φ mnk and for all m,n,k.

[0156] The SCA iterative algorithm based on NCP is as follows:

[0157] Algorithm 2: NCP based SCA Iterative Algorithm for Solving Problem(6)

[0158] 1: Initialize iteration index i=1, power weights ω mnk , feasible initial penalty λ 0 >1, η>1, tolerance ∈>0.

[0159] 2: Calculate for all k.

[0160] 3: Repeat

[0161] 4: Obtain by Solving(21) and restore

[0162] 5: Update

[0163] 6: Update

[0164] 7: Update λ (i) =ηλ (i-1) .

[0165] 8: Set i = i + 1.

[0166] 9: Until

[0167] 10:Outputφ mnk and for all m,n,k.

[0168] In an exemplary embodiment, the objective function in the multi-antenna multi-user scenario includes:

[0169]

[0170] Among them, p tot2 is the weighted sum of the transmission power required by the first terminal to satisfy all second terminals to complete the transmission task in a multi-antenna multi-user scenario, is a set of binary scheduling variables, is the set of transmit beamforming vectors, K is the number of second terminals, M is the number of schedulable resource blocks RB, N is the number of schedulable OFDM symbols, φ mnk is the allocation identifier. If the nth OFDM symbol of the mth RB is allocated to the kth second terminal, then φ mnk =1, ω mnk is the weight of the kth second terminal when transmitting on the nth OFDM symbol of the mth RB, represents the transmit beamforming vector w mnk of The square of the norm, that is, the transmit power of the transmit beam pointing to the kth second terminal on the nth OFDM symbol of the mth RB.

[0171] In an exemplary embodiment, the constraint conditions in the multi-antenna multi-user scenario include one or more of the following:

[0172] The maximum number of received bits of the kth second terminal in the worst case is greater than or equal to the target number of data bits B of the kth second terminal k ;

[0173] Any RB is allocated to at most one second terminal;

[0174] When the number of OFDM symbols allocated to the kth second terminal on the mth RB is greater than the target number of OFDM symbols D k When the assignment label φ mnk Set to zero; that is, exceed the delay requirement D of the kth second terminalk No resources are allocated.

[0175] The transmit beam power allocated to the kth second terminal on the nth OFDM symbol of the mth RB Less than or equal to the product of the assigned identifier and the maximum constrained power. mnk = 0, p mnk =0;φ mnk When =1, there is a maximum transmit power constraint.

[0176] In an exemplary embodiment, the constraint conditions in the multi-antenna multi-user scenario include one or more of the following:

[0177]

[0178]

[0179]

[0180]

[0181]

[0182] Among them, R k is the maximum number of received bits of the kth second terminal in the worst case, B k is the target number of data bits for the kth second terminal, φ mnk is the allocation identifier. If the nth OFDM symbol of the mth RB is allocated to the kth second terminal, then φ mnk =1, K is the number of second terminals, D k is the delay requirement of the kth second terminal, w mnk is the transmit beamforming vector assigned to the kth second terminal on the nth OFDM symbol in the mth RB, P max is the maximum constrained power.

[0183] In an exemplary embodiment, in the multi-antenna multi-user scenario, the maximum number of received bits R of the kth second terminal in the worst case is k for:

[0184]

[0185] in, Q -1 (ε k )yes The inverse, V mnk is the wireless channel dispersion.

[0186] In an exemplary embodiment, in the multi-antenna multi-user scenario, the worst received signal-to-noise ratio ρ of the kth robot is mnk for:

[0187]

[0188] Among them, e mnk Indicates that it is in a bounded set The channel estimation error in is the channel estimate transmitted by the first terminal and the kth second terminal on the nth OFDM symbol in the mth RB, δ mnk Represents the boundaries of the channel uncertainty region.

[0189] In an exemplary embodiment, the optimal solution of the objective function and the URLLC packet scheduling scheme are determined using a non-convex penalty method NCP and a continuous approximation algorithm SCA, including:

[0190] The constraint conditions and the objective function are processed by using variable substitution, perspective transformation, first-order Taylor approximation transformation, and NCP method to obtain a penalized convex problem;

[0191] Solving the penalty convex problem obtains the optimal solution of the objective function and the URLLC packet scheduling scheme.

[0192] In an exemplary embodiment, the constraint condition and the objective function are processed by variable substitution, perspective transformation, first-order Taylor approximation transformation, and NCP method to obtain a penalized convex problem, including:

[0193] The target bit number constraint condition is approximated as a convex constraint condition by using variable substitution, perspective transformation, and first-order Taylor approximation transformation;

[0194] Relax the binary indicator variable representing the resource block mapping and add -norm constraints to ensure the sparseness of each resource block call. Among them, the new constraints are Defined as φ mn =[φ mn1 ,…,φ mnK ] T ;

[0195] Use non-convex penalty method NCP to deal with norm constraint to obtain a sparse scheduling solution. Specifically, a penalty term is added to the objective function. Where λ>0 is the penalty factor.

[0196] The penalty term is in the form of a convex function-convex function, which is a non-convex function. By performing a first-order Taylor approximation on it, the non-convex penalty term can be approximated to a convex function. At this time, the objective function and all constraints are convex constraints, resulting in a penalized convex problem.

[0197] In a multi-antenna multi-user scenario, consider a smart factory with N t A central controller with 4 antennas and K robots equipped with single antennas. The controller should be located at D k OFDM symbols with a packet error probability ε k The number of data bits is B k The control command packet is sent to the kth robot (k=1,…,K). Assume that the controller knows the transmission task requirements of all robots According to the definition of 5G NR physical resource blocks in TS 38.211, the basic resource scheduling unit is a resource block RB, which consists of 12 consecutive subcarriers in the frequency domain and 1 OFDM symbol in the time domain. The number of schedulable resource blocks RBs is defined as M, and the number of schedulable OFDM symbols is defined as N. Because a packet can be transmitted on multiple RBs, a binary variable φ is defined mnk ∈{0,1} represents RB mapping. If the nth OFDM symbol of the mth RB is assigned to the kth robot, then φ mnk =1, otherwise φ mnk = 0. Assuming that each RB is assigned to at most one robot, then

[0198]

[0199] Assuming a quasi-static block fading channel, the received signal transmitted by the kth robot on the nth OFDM symbol in the mth RB is

[0200]

[0201] in is the channel when the controller and the k-th robot transmit on the n-th OFDM symbol of the m-th RB, is the transmit beamforming vector, d mnk is an independently normalized complex data signal, z mnk is zero mean with variance σ 2 cyclically symmetric complex Gaussian noise.

[0202] However, due to practical limitations such as channel mismatch, quantization error, etc., perfect CSI is usually not available at the central controller. Especially for critical services, the TTI is very short and the time for channel estimation is strictly limited. In this case, the channel h mnk Can be modeled as

[0203]

[0204] in represents the channel estimation, e mnk Indicates that it is in a bounded set The channel estimation error in .

[0205] Under this bounded CSI estimation error model, if the packet of the kth robot is transmitted on the nth OFDM symbol of the mth RB, the worst received SNR of the kth robot is

[0206]

[0207] Typical characteristics of URLLC systems: short packet transmission, ultra-low latency, and ultra-high reliability. The Polyanskiy-Poor-Verdu bound is a tight Gaussian approximation that characterizes the maximum achievable rate of short packets under finite code length AWGN channel conditions. Later, this bound was extended to fading channels. Based on this capacity bound and joint coding scheme, the maximum number of received bits of the kth robot in the worst case can be approximately characterized as

[0208]

[0209] in Q -1 (·)yes The inverse, is the channel dispersion. Based on the following two considerations, the approximate method V is used mnk ≈1:(1)V mnk ≈1 is accurate enough when the received SNR is high enough (e.g. 3 dB). The URLLC scenario basically meets this condition, and this approximation method is used in many current research works. (2) This approximation is actually to find R k The lower bound of is a more stringent requirement.

[0210] The problem of concern is the robust resource scheduling and transmit beamforming design problem under the limited code length mechanism. The goal is to minimize the total controller transmit power under the non-perfect CSI and limited code length mechanism while ensuring all robot-specific QoS requirements. The optimization problem can be modeled as follows:

[0211]

[0212]

[0213]

[0214]

[0215]

[0216]

[0217] in is a set of binary scheduling variables, is the set of beamforming vectors. Constraint (6b) means that for all possible CSI errors, the number of data bits received by any robot k is R k To meet the target load requirement B k bits, constraints (6c) and (6d) require that any RB is assigned at most one robot, constraint (6e) reflects the delay requirements of each robot, and constraint (6f) is the maximum power constraint and guarantees that if φ mnk When =0, the power of the corresponding beamforming vector is 0.

[0218] Considering that in problem P1 the variable φ mnk and w mnk Coupled together, introducing a slack variable s mnk Replace φ mnk w mnk Therefore, the maximum number of received bits R k Can be rewritten as

[0219]

[0220] in as well as

[0221]

[0222] Therefore, the total transmission power of the central controller can be expressed as

[0223]

[0224] Based on the above transformation, problem P1 can be equivalently expressed as

[0225]

[0226]

[0227]

[0228]

[0229]

[0230]

[0231] in

[0232] Now the key challenge of Problem P2 is to solve the countless strictly non-convex constraints of (10b) and the binary variable constraints (10c).

[0233] First, for a given CSI estimation error set Constraint (10b) is difficult to handle. Note that exist Therefore, the standard solution is to introduce an auxiliary variable as The lower bound of is obtained by using S-procedure to convert the constraint into a linear inequality LMI constraint. The original problem is converted into an SDP (semidefinite program) problem by using methods such as SCA and SDR (semidefinite relaxation). However, a well-known problem with this method is that the complexity of solving the SDP problem is very high. In the present invention, the beamforming design is simplified to power control by utilizing the independence of the design of the beamforming vectors emitted by each robot. The details are given by Lemma 2. Lemma 2: The worst case in problem P2 is and optimal It can be given by the following formula

[0234]

[0235] and where p mnk ≥0 corresponds to beam s mnk The power,

[0236] Proof: It is easy to get from formula (7) exist is strictly monotonically increasing, then for any beam s mnk have Then the optimal beam for problem P2 can be designed to maximize the worst-case SNR, that is, for all m, n, k,

[0237]

[0238]

[0239] Based on the max-min property, there is

[0240]

[0241] Where (14a) is true if and only if , (14b) holds if and only if It is easy to conclude that problem (13) is and When the equality sign holds.

[0242] Therefore, consider the equivalent problem of P2

[0243]

[0244]

[0245]

[0246]

[0247]

[0248]

[0249] in is the collection of powers,

[0250] The left side of constraint (15b) is in the form of the difference of concave functions (the first term on the left side can be judged to be a concave function by the concavity of the perspective function), so constraint (15b) is a non-convex constraint. By making a first-order Taylor approximation, we can obtain its locally tight upper bound in Yes k The value at the i-th iteration. Based on the first-order Taylor approximation, constraint (15b) can be transformed into the following convex constraint

[0251]

[0252] Next, solve the binary variable The solution is to first relax it to a continuous variable of [0,1], and considering that any RB can be assigned to at most one robot, add the constraint

[0253]

[0254] Guaranteed sparse solution, where Defined as φ mn =[φ mn1 ,…,φ mnK ] T .for Norm constraint, the classic method is to use weighted Specifically, in each iteration The norm is approximately the weighted norm, that is

[0255]

[0256] in is the diagonal weight matrix at the i-th iteration, and the diagonal elements are After each iteration, Updated to As the initial value of the next iteration, where ξ>0 is a small number. Therefore, by weighting Method, you can Approximately convex constraints Based on the above weighted Method, the P3 problem can be approximated as the following convex problem

[0257]

[0258]

[0259]

[0260]

[0261]

[0262]

[0263]

[0264] In the present invention, a non-convex penalty method NCP is used to process norm to obtain a sparse scheduling solution. The theory is as follows: for any vector It has at most one nonzero element if and only if

[0265] ‖x‖ a =‖x‖ b ,1≤a<b, (20)

[0266] ‖·‖ a ,‖·‖ b Respectively Norm, Norm. Without loss of generality, we have the following conclusions

[0267]

[0268] Where v>0 is the power exponent. The norm constraint (17) can be rewritten into the following equivalent form:

[0269]

[0270] generally, This holds true for any a and b that satisfy 1≤a<b. Here we consider smoothness penalty a=1, b=2, v=2. In order to make φ mnThere is at most one non-zero element, and a penalty term is added to the objective function

[0271]

[0272] Where λ>0 is the penalty factor. Note that the penalty function is in the form of a difference between convex functions. In order to make the objective function a convex function, Performing the first-order Taylor approximation, it becomes

[0273]

[0274] in Yes mn The value at the i-th iteration. Therefore, the penalty term can be approximated as

[0275]

[0276] Based on the above NCP method, we can get the following penalty convex problem:

[0277]

[0278]

[0279]

[0280]

[0281]

[0282]

[0283] In one embodiment, in a multi-antenna multi-user scenario, based on weighted The SCA iterative algorithm is as follows:

[0284] Algorithm 1: Reweighted based SCA Iterative Algorithm for SolvingProblem P1

[0285] 1:Initialize iteration index i=1,power weightsω mnk ,feasible set initial weights in reweighted as tolerance ∈>0.

[0286] 2:Calculate for all k.

[0287] 3:Repeat

[0288] 4:Obtain {φ mnk , p mnk} by Solving P4-RL1 and restore

[0289] 5:Update

[0290] 6:Update

[0291] 7:Update

[0292] 8:Set i = i + 1.

[0293] 9:Until

[0294] 10:Output φ mnk and p mnk for all m, n, k.

[0295] The SCA iterative algorithm based on NCP is as follows:

[0296] Algorithm 2: NCP based SCA Iterative Algorithm for Solving Problem P1

[0297] 1: Initialize iteration index i = 1, power weights ω mnk , feasible initial penalty λ 0 >1, η>1, tolerance ∈>0.

[0298] 2: Calculate for all k.

[0299] 3: Repeat

[0300] 4: Obtain {φ mnk , p mnk} by Solving P4-NCP and restore

[0301] 5: Update

[0302] 6:Update

[0303] 7: Update λ (i) =ηλ (i-1) .

[0304] 8: Set i = i + 1.

[0305] 9: Until

[0306] 10:Outputφ mnk and p mnk for all m,n,k.

[0307] The embodiments of this application mainly provide a method for URLLC packet scheduling in vertical industries such as factory automation and autonomous driving. It includes: 1) Modeling the uncertainty of the wireless channel between the central controller and the robot in single-antenna multi-user scenarios and multi-antenna multi-user scenarios, the achievable rate of the packet under the limited code length mechanism, and the delay, reliability and target bit number requirements of the packet. The objective function considered is the total transmission power consumption minimization problem of the controller that the industry is concerned about; 2) Using convex optimization techniques such as variable substitution, perspective transformation, and first-order Taylor approximation to transform the problem, and propose a non-convex penalty method NCP solution norm constraint, and design a robust URLLC packet scheduling design scheme; 3) propose a continuous convex approximation SCA iterative algorithm based on NCP to solve the URLLC packet scheduling problem in this scenario, and compare the NCP method with the classic weighted The simulation results show that the SCA iterative algorithm based on NCP proposed in this application is superior to the weighted At the same time, the NCP method has good performance and wide applicability in the fields of sparse signal recovery and image processing.

[0308] In an applied embodiment, a URLLC packet scheduling method based on NCP in a single-antenna multi-user scenario is provided.

[0309] Consider a smart factory with 1 central controller and K robots. The controller and all robots are equipped with a single antenna. k OFDM symbols with a packet error probability ε k The number of data bits is B k The control command packet is sent to the kth robot (k=1,…,K). Assume that the controller knows the transmission task requirements of all robots According to the definition of 5G NR physical resource blocks in TS 38.211, the basic resource scheduling unit is a resource block RB, which consists of 12 consecutive subcarriers in the frequency domain and 1 OFDM symbol in the time domain. The number of schedulable resource blocks RBs is defined as M, and the number of schedulable OFDM symbols is defined as N. Because a packet can be transmitted on multiple RBs, a binary variable φ is defined mnk ∈{0,1} represents RB mapping. If the nth OFDM symbol of the mth RB is assigned to the kth robot, then φ mnk =1, otherwise φ mnk = 0. Assuming that each RB is assigned to at most one robot, then

[0310]

[0311] Assume a quasi-static block fading channel, that is, the channel remains unchanged in each coherent block, and the channel changes independently in different coherent blocks. However, due to practical limitations, such as CSI estimation error, quantization error, and CSI feedback delay, it is usually impossible to obtain perfect CSI on the transmitter side, especially for critical services. Because the transmission time interval TTI becomes very short in critical services, the time used for channel estimation is limited by the data transmission time and becomes very short and insufficient for accurate channel estimation. Here, a bounded CSI estimation error model is used to characterize the uncertainty of the channel. The actual channel transmitted by the controller and the k-th robot on the n-th OFDM symbol in the m-th RB is expressed as

[0312]

[0313] in represents the channel estimation, e mnk represents the channel estimation error, δ mnk represents the bound of the channel uncertainty region.

[0314] Under this bounded CSI estimation error model, the worst received signal-to-noise ratio SNR of the kth robot is expressed as

[0315]

[0316] Among them, α k represents the large-scale channel gain of the kth robot, p mnk represents the transmit power allocated to the kth robot when transmitting on the nth OFDM symbol in the mth RB, σ 2 is the power of additive white Gaussian noise. It is easy to get by KKT condition Therefore, the receiving SNR of the kth robot can be rewritten as

[0317] ρ mnk =gmnk p mnk , (4)

[0318] in

[0319] Since the URLLC system has ultra-low delay requirements and usually transmits short packets, the traditional Shannon capacity formula is no longer applicable to characterize the maximum achievable rate of the URLLC system. Y.Polyanskiy et al. proposed a capacity formula for interference-free flat fading channels under a finite code length mechanism, which characterizes the relationship between transmission delay, decoding error probability and achievable rate. Later, this capacity formula was extended to other channels, including fading channels. Here, a joint coding scheme is considered, that is, each user's packet is jointly encoded on all called RBs. Therefore, the maximum number of received bits for the kth robot in the worst case can be approximately characterized as

[0320]

[0321] in Q -1 (·)yes The inverse, is the channel dispersion. Note that the approximate V mnk ≈1, this approximation is accurate enough when the received SNR is higher than 3dB, and this condition almost holds in the URLLC scenario.

[0322] The questions of interest are: (1) How much power does the controller need to support all the robots in the system? transmission task; (2) How can the controller perform resource scheduling under the imperfect CSI and limited code length mechanism to minimize power consumption and maximize energy efficiency. Based on this, the optimization problem of robust scheduling and resource allocation of URLLC packets to minimize the total transmission power of the controller can be modeled as follows

[0323]

[0324]

[0325]

[0326]

[0327]

[0328]

[0329] (6b) ensures the target load requirement B of the kth robot k(k=1,…,K), constraints (6c) and (6d) require that any RB can be assigned at most one robot, constraint (6e) reflects the delay requirements of each robot, constraint (6f) is a non-negative power and maximum power constraint, and requires φ mnk = 0 when p mnk =0, symbol engineering practice.

[0330] Note that there is a product term φ in the objective function of problem (6) mnk p mnk , make the following transformations on constraints (6b) and (6f)

[0331]

[0332]

[0333] Therefore, the original problem (6) can be rewritten as

[0334]

[0335]

[0336]

[0337]

[0338]

[0339]

[0340] Lemma 1: If problem (9) is feasible, then the optimal solution to problem (6) is also the optimal solution to problem (9) and can be recovered by the solution to problem (9).

[0341] Proof: Note that due to the difference between constraints (6f) and (9f), the feasible set of problem (9) is larger than that of problem (6). Specifically, when φ mnk = 0, constraint (6f) means that p mnk = 0, and constraint (9f) implies that p mnk can take any value; when φ mnk = 1, constraints (6f) and (9f) are equivalent. Therefore, it can be concluded that if problem (9) is feasible, the optimal solution of problem (6) is also the optimal solution of problem (9), and can be recovered by the solution of problem (9). Specifically, based on the solution of problem (9), let φ mnk = p when 0 mnk = 0, the solution to the original problem (6) can be recovered.

[0342] Next, we will solve problem (9). Introducing slack variables Replace φ mnk p mnk , problem (9) can be rewritten as

[0343]

[0344]

[0345]

[0346]

[0347]

[0348]

[0349] The left side of constraint (10b) is in the form of the difference of concave functions (the first term on the left side can be judged to be a concave function by the concavity preservation of the perspective function), so constraint (10b) is a non-convex constraint. By making a first-order Taylor approximation, we can obtain its locally tight upper bound in is x k The value at the i-th iteration. Based on the first-order Taylor approximation, constraint (10b) can be transformed into the following convex constraint

[0350]

[0351] The existence of binary variables makes the problem difficult to solve. The solution is to first relax it to a continuous variable of [0,1]. Considering that any RB can be assigned to at most one robot, we add the constraint

[0352]

[0353] Guaranteed sparse solution, where Defined as φ mn =[φ mn1 ,…,φ mnK ] T .for Norm constraint, the classic method is to use weighted Specifically, in each iteration The norm is approximately the weighted norm, that is

[0354]

[0355] in is the diagonal weight matrix at the i-th iteration, and the diagonal elements are After each iteration, Updated to As the initial value of the next iteration, where ζ>0 is a small number. Therefore, by weighting Method, you can Approximately convex constraints Based on the above weighted Method, problem (10) can be approximated as the following convex problem

[0356]

[0357]

[0358]

[0359]

[0360]

[0361]

[0362]

[0363] In the present invention, a non-convex penalty method NCP is used to process norm to obtain a sparse scheduling solution. The theory is as follows: for any vector It has at most one nonzero element if and only if

[0364] ‖x‖ a =‖x‖ b ,1≤a<b, (15)

[0365] ‖·‖ a ,‖·‖ b Respectively Norm, Norm. Without loss of generality, we have the following conclusions

[0366]

[0367] Where v>0 is the power exponent. The norm constraint (12) can be rewritten into the following equivalent form

[0368]

[0369] generally, This holds true for any a and b that satisfy 1≤a<b. Here we consider smoothness penalty a=1, b=2, v=2. In order to make φ mn There is at most one non-zero element, and a penalty term is added to the objective function

[0370]

[0371] Where λ>0 is the penalty factor. Note that the penalty function is in the form of a difference between convex functions. In order to make the objective function a convex function, Performing the first-order Taylor approximation, it becomes

[0372]

[0373] in Yes mn The value at the i-th iteration. Therefore, the penalty term can be approximated as

[0374]

[0375] Based on the above NCP method, we can get the following penalty convex problem:

[0376]

[0377]

[0378]

[0379]

[0380]

[0381]

[0382] Problem (21) is a standard convex optimization problem. We can use the existing CVX tool to write MATLAB code based on the algorithm to solve the problem (the code must follow the DCP criterion set). From this, we can derive the URLLC packet scheduling scheme in this scenario and the minimum transmission power required by the controller to meet the QoS requirements of all robots.

[0383] In an applied embodiment, a URLLC packet scheduling method based on NCP in a multi-antenna multi-user scenario is provided.

[0384] Consider a smart factory with N t A central controller with 4 antennas and K robots equipped with single antennas. The controller should be located at D k OFDM symbols with a packet error probability ε k The number of data bits is B k The control command packet is sent to the kth robot (k=1,…,K). Assume that the controller knows the transmission task requirements of all robots According to the definition of 5G NR physical resource blocks in TS38.211, the basic resource scheduling unit is a resource block RB, which consists of 12 consecutive subcarriers in the frequency domain and 1 OFDM symbol in the time domain. The number of schedulable resource blocks RBs is defined as M, and the number of schedulable OFDM symbols is defined as N. Because a packet can be transmitted on multiple RBs, a binary variable φ is defined mnk ∈{0,1} represents RB mapping. If the nth OFDM symbol of the mth RB is assigned to the kth robot, then φ mnk =1, otherwise φ mnk = 0. Assuming that each RB is assigned to at most one robot, then

[0385]

[0386] Assuming a quasi-static block fading channel, the received signal transmitted by the kth robot on the nth OFDM symbol in the mth RB is

[0387]

[0388] in is the channel when the controller and the k-th robot transmit on the n-th OFDM symbol of the m-th RB, is the transmit beamforming vector, d mnk is an independently normalized complex data signal, z mnk is zero mean with variance σ 2 cyclically symmetric complex Gaussian noise.

[0389] However, due to practical limitations such as channel mismatch, quantization error, etc., perfect CSI is usually not available at the central controller. Especially for critical services, the TTI is very short and the time for channel estimation is strictly limited. In this case, the channel h mnk Can be modeled as

[0390]

[0391] in represents the channel estimation, e mnk Indicates that it is in a bounded set The channel estimation error in .

[0392] Under this bounded CSI estimation error model, if the packet of the kth robot is transmitted on the nth OFDM symbol of the mth RB, the worst received SNR of the kth robot is

[0393]

[0394] Typical characteristics of URLLC systems: short packet transmission, ultra-low latency, and ultra-high reliability. The Polyanskiy-Poor-Verdu bound is a tight Gaussian approximation that characterizes the maximum achievable rate of short packets under finite code length AWGN channel conditions. Later, this bound was extended to fading channels. Based on this capacity bound and joint coding scheme, the maximum number of received bits of the kth robot in the worst case can be approximately characterized as

[0395]

[0396] in Q -1 (·)yes The inverse, is the channel dispersion. Based on the following two considerations, the approximate method V is used mnk ≈1:(1)V mnk ≈1 is accurate enough when the received SNR is high enough (e.g. 3 dB). The URLLC scenario basically meets this condition, and this approximation method is used in many current research works. (2) This approximation is actually to find R k The lower bound of is a more stringent requirement.

[0397] The problem of concern is the robust resource scheduling and transmit beamforming design problem under the limited code length mechanism. The goal is to minimize the total controller transmit power under the non-perfect CSI and limited code length mechanism while ensuring all robot-specific QoS requirements. The optimization problem can be modeled as follows:

[0398]

[0399]

[0400]

[0401]

[0402]

[0403]

[0404] in is a set of binary scheduling variables, is the set of beamforming vectors. Constraint (6b) means that for all possible CSI errors, the number of data bits received by any robot k is R k To meet the target load requirement B kbits, constraints (6c) and (6d) require that any RB is assigned at most one robot, constraint (6e) reflects the delay requirements of each robot, and constraint (6f) is the maximum power constraint and guarantees that if φ mnk When =0, the power of the corresponding beamforming vector is 0.

[0405] Considering that in problem P1 the variable φ mnk and w mnk Coupled together, introducing a slack variable s mnk Replace φ mnk w mnk Therefore, the maximum number of received bits R k Can be rewritten as

[0406]

[0407] in as well as

[0408]

[0409] Therefore, the total transmission power of the central controller can be expressed as

[0410]

[0411] Based on the above transformation, problem P1 can be equivalently expressed as

[0412]

[0413]

[0414]

[0415]

[0416]

[0417]

[0418] in

[0419] Now the key challenge of Problem P2 is to solve the countless strictly non-convex constraints of (10b) and the binary variable constraints (10c).

[0420] First, for a given CSI estimation error set Constraint (10b) is difficult to handle. Note that exist Therefore, the standard solution is to introduce an auxiliary variable as The lower bound of is obtained by using S-procedure to convert the constraint into a linear inequality LMI constraint. The original problem is converted into an SDP (semidefinite program) problem by using methods such as SCA and SDR (semidefinite relaxation). However, a well-known problem with this method is that the complexity of solving the SDP problem is very high. In the present invention, the beamforming design is simplified to power control by utilizing the independence of the design of the beamforming vectors emitted by each robot. The details are given by Lemma 2. Lemma 2: The worst case in problem P2 is and optimal It can be given by the following formula

[0421]

[0422] and

[0423] where p mnk ≥0 corresponds to beam s mnk The power,

[0424] Proof: It is easy to get from formula (7) exist is strictly monotonically increasing, then for any beam s mnk have Then the optimal beam for problem P2 can be designed to maximize the worst-case SNR, that is, for all m, n, k,

[0425]

[0426]

[0427] Based on the max-min property, there is

[0428]

[0429] Where (14a) is true if and only if , (14b) holds if and only if It is easy to conclude that problem (13) is and When the equality sign holds.

[0430] Therefore, consider the equivalent problem of P2

[0431]

[0432]

[0433]

[0434]

[0435]

[0436]

[0437] in is the collection of powers,

[0438] The left side of constraint (15b) is in the form of the difference of concave functions (the first term on the left side can be judged to be a concave function by the concavity of the perspective function), so constraint (15b) is a non-convex constraint. By making a first-order Taylor approximation, we can obtain its locally tight upper bound in Yes k The value at the i-th iteration. Based on the first-order Taylor approximation, constraint (15b) can be transformed into the following convex constraint

[0439]

[0440] Next, solve the binary variable The solution is to first relax it to a continuous variable of [0,1], and considering that any RB can be assigned to at most one robot, add the constraint

[0441]

[0442] Guaranteed sparse solution, where Defined as φ mn =[φ mn1 ,…,φ mnK ] T .for Norm constraint, the classic method is to use weighted Specifically, in each iteration The norm is approximately the weighted norm, that is

[0443]

[0444] in is the diagonal weight matrix at the i-th iteration, and the diagonal elements are After each iteration, Updated to As the initial value of the next iteration, where ξ>0 is a small number. Therefore, by weighting Method, you can Approximately convex constraints Based on the above weighted Method, the P3 problem can be approximated as the following convex problem

[0445]

[0446]

[0447]

[0448]

[0449]

[0450]

[0451]

[0452] In the present invention, a non-convex penalty method NCP is used to process norm to obtain a sparse scheduling solution. The theory is as follows: for any vector It has at most one nonzero element if and only if

[0453] ‖x‖ a =‖x‖ b ,1≤a<b, (20)

[0454] ‖·‖ a ,‖·‖ b Respectively Norm, Norm. Without loss of generality, we have the following conclusions

[0455]

[0456] Where v>0 is the power exponent. The norm constraint (17) can be rewritten into the following equivalent form:

[0457]

[0458] generally, This holds true for any a and b that satisfy 1≤a<b. Here we consider smoothness penalty a=1, b=2, v=2. In order to make φ mn There is at most one non-zero element, and a penalty term is added to the objective function

[0459]

[0460] Where λ>0 is the penalty factor. Note that the penalty function is in the form of a difference between convex functions. In order to make the objective function a convex function, Performing the first-order Taylor approximation, it becomes

[0461]

[0462] in Yes mn The value at the i-th iteration. Therefore, the penalty term can be approximated as

[0463]

[0464] Based on the above NCP method, we can get the following penalty convex problem:

[0465]

[0466]

[0467]

[0468]

[0469]

[0470]

[0471] Problem P4-NCP is a standard convex optimization problem. We can use the existing CVX tool to write MATLAB code based on the algorithm to solve the problem (the code must follow the DCP criterion set). From this, we can derive the URLLC packet scheduling solution in this scenario and the minimum transmission power required by the controller to meet the QoS requirements of all robots.

[0472] It can be seen from the technical solution provided by the above examples of the present invention combined with the simulation results that the URLLC packet scheduling method based on NCP proposed in the examples of the present invention is superior to the classic weighted The algorithm is used to efficiently obtain the URLLC packet scheduling scheme in the corresponding scenario and the minimum transmission power required for the controller to meet the QoS requirements of all robots. It can be well adapted to scenarios with URLLC requirements, such as augmented reality, remote surgery, industrial Internet of Things, and autonomous driving.

[0473] In one embodiment, the present application provides a data packet scheduling determination device, such as Figure 3 As shown, the data packet scheduling determination device provided in this embodiment mainly includes a first determination module 31 and a second determination module 32 .

[0474] A first determination module 31 is configured to determine a constraint condition and an objective function, wherein the constraint condition is determined by the uncertainty of the wireless channel between the first terminal and the second terminal and the packet parameters under the limited code length mechanism;

[0475] The second determination module 32 is configured to determine the optimal solution of the objective function and the URLLC packet scheduling scheme using the non-convex penalty method NCP and the continuous approximation algorithm SCA.

[0476] In an exemplary embodiment, determining the constraints and the objective function includes one or more of the following:

[0477] Determine the constraints and objective functions for single-antenna multi-user scenarios;

[0478] Determine the constraints and objective functions for multi-antenna multi-user scenarios.

[0479] In an exemplary embodiment, the packet parameters include one or more of the following:

[0480] The achievable rate of packets, the transmission delay of packets, the reliability of packets, and the target number of bits of packets.

[0481] In an exemplary embodiment, the objective function includes: the first terminal meets the transmission task requirements of all second terminals in the system The minimum weighted sum power required; a resource scheduling scheme for minimizing power consumption of the first terminal under non-perfect CSI and limited code length mechanism;

[0482] The transmission task requires The first terminal is at D k OFDM symbols with a packet error probability ε k Set the target data bit number to B k The control command packet is sent to the kth second terminal, K is the number of the second terminals, and k is any integer between 1 and K.

[0483] In an exemplary embodiment, the objective function in the single-antenna multi-user scenario is:

[0484]

[0485] Among them, p tot1 is the weighted sum of the transmission power required by the first terminal to satisfy all second terminals to complete the transmission task in the single-antenna multi-user scenario, K is the number of second terminals, M is the number of schedulable resource blocks RB, N is the number of schedulable OFDM symbols, φ mnk is the allocation identifier. If the nth OFDM symbol of the mth RB is allocated to the kth second terminal, then φ mnk =1,ω mnk is the weight of the kth second terminal when transmitting on the nth OFDM symbol in the mth RB, p mnkis the transmission power allocated to the kth second terminal in the nth OFDM symbol of the mth RB.

[0486] In an exemplary embodiment, the constraint conditions in the single-antenna multi-user scenario include one or more of the following:

[0487] The maximum number of received bits of the kth second terminal in the worst case is greater than or equal to the target number of data bits B of the kth second terminal. k ;

[0488] Any RB is allocated to at most one second terminal;

[0489] When the number of OFDM symbols allocated to the kth second terminal on the mth RB is greater than the target number of OFDM symbols D k When the assignment label φ mnk Set to zero;

[0490] The transmission power p allocated to the kth second terminal on the nth OFDM symbol in the mth RB is mnk is a non-negative power, and the transmission power p allocated to the kth second terminal on the nth OFDM symbol in the mth RB is mnk Less than or equal to the product of the allocation identifier and the maximum constrained power.

[0491] In an exemplary embodiment, the constraint condition in the single-antenna multi-user scenario is:

[0492]

[0493]

[0494]

[0495]

[0496]

[0497] Among them, R k is the maximum number of received bits of the kth second terminal in the worst case, B k is the target number of data bits for the kth second terminal, φ mnk is the allocation identifier. If the nth OFDM symbol of the mth RB is allocated to the kth second terminal, then φ mnk =1, K is the number of second terminals, D k is the delay requirement of the kth second terminal, p mnk is the transmission power allocated to the kth second terminal on the nth OFDM symbol in the mth RB, P max is the maximum constrained power.

[0498] In an exemplary embodiment, in the single-antenna multi-user scenario, the maximum number of received bits R of the kth second terminal in the worst case is k By assigning the identifier φ mnk , the worst received signal-to-noise ratio ρ of the kth robot mnk , and the wireless channel dispersion V mnk Sure.

[0499] In an exemplary embodiment, in the single-antenna multi-user scenario, the maximum number of received bits R of the kth second terminal in the worst case is k for:

[0500]

[0501] in, Q -1 (ε k )yes The inverse, V mnk is the wireless channel dispersion.

[0502] In an exemplary embodiment, in the single-antenna multi-user scenario, the worst received signal-to-noise ratio ρ of the kth robot is mnk for:

[0503]

[0504] Among them, α k is the large-scale channel gain of the k-th robot, is the channel estimate of the first terminal and the kth second terminal transmitted on the nth OFDM symbol in the mth RB, e mnk represents the channel estimation error, δ mnk represents the boundary of the channel uncertainty region, σ 2 is the power of additive white Gaussian noise, p mnk is the transmission power allocated to the kth second terminal on the nth OFDM symbol in the mth RB. Through the equivalence problem

[0505]

[0506] st|e mnk |≤δ mnk .

[0507] Using the KKT condition, it is easy to get Therefore, the receiving SNR of the kth robot can be rewritten as

[0508] ρ mnk=g mnk p mnk ,

[0509] in

[0510] In an exemplary embodiment, the objective function in the multi-antenna multi-user scenario includes:

[0511]

[0512] Among them, p tot2 is the weighted sum of the transmission power required by the first terminal to satisfy all second terminals to complete the transmission task in a multi-antenna multi-user scenario, is a set of binary scheduling variables, is the set of transmit beamforming vectors, K is the number of second terminals, M is the number of schedulable resource blocks RB, N is the number of schedulable OFDM symbols, φ mnk is the allocation identifier. If the nth OFDM symbol of the mth RB is allocated to the kth second terminal, then φ mnk =1, ω mnk is the weight of the kth second terminal when transmitting on the nth OFDM symbol of the mth RB, represents the transmit beamforming vector w mnk of -The square of the norm, that is, the transmit power of the transmit beam directed to the kth second terminal on the nth OFDM symbol of the mth RB.

[0513] In an exemplary embodiment, the constraint conditions in the multi-antenna multi-user scenario include one or more of the following:

[0514] The maximum number of received bits of the kth second terminal in the worst case is greater than or equal to the target number of data bits B of the kth second terminal. k ;

[0515] Any RB is allocated to at most one second terminal;

[0516] When the number of OFDM symbols allocated to the kth second terminal on the mth RB is greater than the target number of OFDM symbols D k When the assignment label φ mnk Set to zero;

[0517] The transmit beam power allocated to the kth second terminal on the nth OFDM symbol of the mth RB Less than or equal to the product of the allocation identifier and the maximum constrained power.

[0518] In an exemplary embodiment, the constraint conditions in the multi-antenna multi-user scenario include one or more of the following:

[0519]

[0520]

[0521]

[0522]

[0523]

[0524] Among them, R k is the maximum number of received bits of the kth second terminal in the worst case, B k is the target number of data bits for the kth second terminal, φ mnk is the allocation identifier. If the nth OFDM symbol of the mth RB is allocated to the kth second terminal, then φ mnk =1, K is the number of second terminals, D k is the delay requirement of the kth second terminal, w mnk is the transmit beamforming vector assigned to the kth second terminal on the nth OFDM symbol in the mth RB, P max is the maximum constrained power.

[0525] In an exemplary embodiment, in the multi-antenna multi-user scenario, the maximum number of received bits R of the kth second terminal in the worst case is k for:

[0526]

[0527] in, Q -1 (ε k )yes The inverse, V mnk is the wireless channel dispersion.

[0528] In an exemplary embodiment, in the multi-antenna multi-user scenario, the worst received signal-to-noise ratio ρ of the kth robot is mnk for:

[0529]

[0530] Among them, e mnk Indicates that it is in a bounded set The channel estimation error in is the channel estimate transmitted by the first terminal and the kth second terminal on the nth OFDM symbol in the mth RB, δ mnkRepresents the boundaries of the channel uncertainty region.

[0531] In an exemplary embodiment, the optimal solution of the objective function and the URLLC packet scheduling scheme are determined using a non-convex penalty method NCP and a continuous approximation algorithm SCA, including:

[0532] The constraint conditions and the objective function are processed by using variable substitution, perspective transformation, first-order Taylor approximation transformation, and NCP method to obtain a penalized convex problem;

[0533] Solving the penalty convex problem obtains the optimal solution of the objective function and the URLLC packet scheduling scheme.

[0534] In an exemplary embodiment, the constraint condition and the objective function are processed by variable substitution, perspective transformation, first-order Taylor approximation transformation, and NCP method to obtain a penalized convex problem, including:

[0535] The target bit number constraint condition is approximated as a convex constraint condition by using variable substitution, perspective transformation, and first-order Taylor approximation transformation;

[0536] Relax the binary indicator variable representing the resource block mapping and add -norm constraints to ensure the sparseness of each resource block call. Among them, the new constraints are Defined as φ mn =[φ mn1 ,…,φ mnK ] T ;

[0537] Use non-convex penalty method NCP to deal with -norm constraint to obtain sparse scheduling solution, specifically, adding penalty term in the objective function Where λ>0 is the penalty factor.

[0538] The penalty term is in the form of a convex function-convex function, which is a non-convex function. By performing a first-order Taylor approximation on it, the non-convex penalty term can be approximated to a convex function. At this time, the objective function and all constraints are convex constraints, resulting in a penalized convex problem.

[0539] The data packet scheduling determination device provided in this embodiment can execute the data packet scheduling determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method. For technical details not described in detail in this embodiment, please refer to the data packet scheduling determination method provided in any embodiment of the present invention.

[0540] It is worth noting that in the embodiment of the above-mentioned data packet scheduling determination device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application.

[0541] The present application also provides a device, Figure 4 is a schematic diagram of the structure of a device provided in an embodiment of the present application, such as Figure 4 As shown, the device includes a processor 41, a memory 42, an input device 43, an output device 44 and a communication device 45; the number of processors 41 in the device can be one or more. Figure 4 A processor 41 is taken as an example; the processor 41, memory 42, input device 43 and output device 44 in the device can be connected by a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0542] The memory 42 is a computer-readable storage medium that can be used to store software programs, computer executable programs, and modules. The processor 41 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 42, that is, implementing any method provided in the embodiments of the present application.

[0543] The memory 42 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the device, etc. In addition, the memory 42 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 42 may further include a memory remotely arranged relative to the processor 41, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0544] The input device 43 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 44 may include a display device such as a display screen.

[0545] The communication device 45 may include a receiver and a transmitter. The communication device 45 is configured to perform information transmission and reception communication according to the control of the processor 41.

[0546] In an exemplary embodiment, the present application also provides a storage medium containing computer executable instructions, wherein the computer executable instructions are used to perform a data packet scheduling determination method when executed by a computer processor, including:

[0547] Determining a constraint condition and an objective function, wherein the constraint condition is determined by uncertainty of a wireless channel between the first terminal and the second terminal and a packet parameter under a finite code length mechanism;

[0548] The non-convex penalty method NCP and the continuous approximation algorithm SCA are used to determine the optimal solution of the objective function and the URLLC packet scheduling scheme.

[0549] Of course, the storage medium containing computer executable instructions provided in the embodiment of the present application, whose computer executable instructions are not limited to the method operations described above, can also execute related operations in the data packet scheduling determination method provided in any embodiment of the present application.

[0550] Through the above description of the implementation method, the technicians in the relevant field can clearly understand that the present application can be implemented with the help of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application can be essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0551] The above description is merely an exemplary embodiment of the present application and is not intended to limit the protection scope of the present application.

[0552] It will be appreciated by those skilled in the art that the term user terminal covers any suitable type of wireless user equipment, such as a mobile phone, a portable data processing device, a portable web browser or a vehicle-mounted mobile station.

[0553] In general, various embodiments of the present application can be implemented in hardware or dedicated circuits, software, logic or any combination thereof. For example, some aspects can be implemented in hardware, while other aspects can be implemented in firmware or software that can be executed by a controller, microprocessor or other computing device, although the present application is not limited thereto.

[0554] Embodiments of the present application may be implemented by executing computer program instructions by a data processor of a mobile device, for example in a processor entity, or by hardware, or by a combination of software and hardware. The computer program instructions may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages.

[0555] The block diagram of any logic flow in the accompanying drawings of the present application can represent program steps, or can represent interconnected logic circuits, modules and functions, or can represent a combination of program steps and logic circuits, modules and functions. The computer program can be stored on a memory. The memory can have any type suitable for the local technical environment and can be implemented using any suitable data storage technology, such as but not limited to read-only memory (ROM), random access memory (RAM), optical memory device and system (digital versatile disc DVD or CD disc), etc. Computer-readable media may include non-transient storage media. The data processor can be any type suitable for the local technical environment, such as but not limited to a general-purpose computer, a special-purpose computer, a microprocessor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a programmable logic device (FGPA) and a processor based on a multi-core processor architecture.

[0556] By way of exemplary and non-limiting examples, a detailed description of exemplary embodiments of the present application has been provided above. However, various modifications and adjustments to the above embodiments will be apparent to those skilled in the art when considered in conjunction with the accompanying drawings and claims, but will not depart from the scope of the present invention. Therefore, the proper scope of the present invention will be determined according to the claims.

Claims

1. A method for determining data packet scheduling, characterized in that: include: Determining a constraint condition and an objective function, wherein the constraint condition is determined by uncertainty of a wireless channel between the first terminal and the second terminal and a packet parameter under a finite code length mechanism; Determine the optimal solution of the objective function and the URLLC packet scheduling scheme using a non-convex penalty method NCP and a continuous approximation algorithm SCA; The method of using the non-convex penalty method NCP and the continuous approximation algorithm SCA to determine the optimal solution of the objective function and the URLLC packet scheduling scheme includes: The constraint conditions and the objective function are processed by using variable substitution, perspective transformation, first-order Taylor approximation transformation, and NCP method to obtain a penalized convex problem; Solving the penalty convex problem obtains the optimal solution of the objective function and the URLLC packet scheduling scheme.

2. The method according to claim 1, characterized in that Determine constraints and objective functions, including one or more of the following: Determine the constraints and objective functions for single-antenna multi-user scenarios; Determine the constraints and objective functions for multi-antenna multi-user scenarios.

3. The method according to claim 1 or 2, characterized in that: The packet parameters include one or more of the following: The achievable rate of packets, the transmission delay of packets, the reliability of packets, and the target number of bits of packets.

4. The method according to claim 1, characterized in that: The objective function includes: the first terminal meets the transmission task requirements of all second terminals in the system The minimum weighted sum power required; a resource scheduling scheme for minimizing power consumption of the first terminal under non-perfect CSI and limited code length mechanism; The transmission task requires The first terminal is at D k OFDM symbols with a packet error probability ε k Set the target data bit number to B k The control command packet is sent to the kth second terminal, K is the number of the second terminals, and k is any integer between 1 and K.

5. The method according to claim 2, characterized in that: The objective function in the single-antenna multi-user scenario is: Among them, p tot1 is the weighted sum of the transmission power required by the first terminal to satisfy all second terminals to complete the transmission task in the single-antenna multi-user scenario, K is the number of second terminals, M is the number of schedulable resource blocks RB, N is the number of schedulable OFDM symbols, φ mnk is the allocation identifier. If the nth OFDM symbol of the mth RB is allocated to the kth second terminal, then φ mnk =1,ω mnk is the weight of the kth second terminal when transmitting on the nth OFDM symbol in the mth RB, p mnk is the transmission power allocated to the kth second terminal in the nth OFDM symbol of the mth RB.

6. The method according to claim 2, characterized in that The constraints in the single-antenna multi-user scenario include one or more of the following: The maximum number of received bits of the kth second terminal in the worst case is greater than or equal to the target number of data bits B of the kth second terminal. k ; Any RB is allocated to at most one second terminal; When the number of OFDM symbols allocated to the kth second terminal on the mth RB is greater than the target number of OFDM symbols D k When the assignment label φ mnk Set to zero; The transmission power p allocated to the kth second terminal on the nth OFDM symbol in the mth RB is mnk is a non-negative power, and the transmission power p allocated to the kth second terminal on the nth OFDM symbol in the mth RB is mnk Less than or equal to the product of the allocation identifier and the maximum constrained power.

7. The method according to claim 6, characterized in that Mathematical constraints for the single-antenna multi-user scenario: Among them, R k is the maximum number of received bits of the kth second terminal in the worst case, B k is the target number of data bits for the kth second terminal, φ mnk is the allocation identifier. If the nth OFDM symbol of the mth RB is allocated to the kth second terminal, then φ mnk =1, K is the number of second terminals, D k is the delay requirement of the kth second terminal, p mnk is the transmission power allocated to the kth second terminal on the nth OFDM symbol in the mth RB, P max is the maximum constrained power.

8. The method according to claim 6 or 7, characterized in that: In the single-antenna multi-user scenario, the maximum number of received bits R of the kth second terminal in the worst case k By assigning the identifier φ mnk , the worst received signal-to-noise ratio ρ of the kth robot mnk , and the wireless channel dispersion V mnk Sure.

9. The method according to claim 6 or 7, characterized in that: In the single-antenna multi-user scenario, the maximum number of received bits R of the kth second terminal in the worst case k for: in, Q -1 (ε k )yes The inverse, V mnk is the wireless channel dispersion.

10. The method according to claim 9, characterized in that In the single-antenna multi-user scenario, the worst received signal-to-noise ratio ρ of the kth robot mnk for: Among them, α k is the large-scale channel gain of the kth robot, is the channel estimate transmitted by the first terminal and the kth second terminal on the nth OFDM symbol in the mth RB, e mnk represents the channel estimation error, δ mnk represents the boundary of the channel uncertainty region, σ 2 is the power of additive white Gaussian noise, p mnk is the transmit power allocated to the kth second terminal on the nth OFDM symbol in the mth RB; Optimal Through the equivalence problem s.t.|e mnk |≤δ mnk . Using the KKT condition, we can get Therefore, the receiving SNR of the kth robot can be rewritten as r mnk =g mnk p mnk , in 11. The method according to claim 2, characterized in that The objective function in the multi-antenna multi-user scenario includes: Among them, p tot2 is the weighted sum of the transmission power required by the first terminal to satisfy all second terminals to complete the transmission task in a multi-antenna multi-user scenario, is a set of binary scheduling variables, is the set of transmit beamforming vectors, K is the number of second terminals, M is the number of schedulable resource blocks RB, N is the number of schedulable OFDM symbols, φ mnk is the allocation identifier. If the nth OFDM symbol of the mth RB is allocated to the kth second terminal, then φ mnk =1,ω mnk is the weight of the kth second terminal when transmitting on the nth OFDM symbol of the mth RB, represents the transmit beamforming vector w mnk The square of the e2-norm of , that is, the transmit power of the transmit beam pointing to the k-th second terminal on the n-th OFDM symbol of the m-th RB.

12. The method according to claim 2, characterized in that: The constraints in the multi-antenna multi-user scenario include one or more of the following: The maximum number of received bits of the kth second terminal in the worst case is greater than or equal to the target number of data bits B of the kth second terminal. k ; Any RB is allocated to at most one second terminal; When the number of OFDM symbols allocated to the kth second terminal on the mth RB is greater than the target number of OFDM symbols D k When the assignment label φ mnk Set to zero; The transmit beam power allocated to the kth second terminal on the nth OFDM symbol of the mth RB Less than or equal to the product of the allocation identifier and the maximum constrained power.

13. The method according to claim 11, characterized in that The constraints in the multi-antenna multi-user scenario include one or more of the following: Among them, R k is the maximum number of received bits of the kth second terminal in the worst case, B k is the target number of data bits for the kth second terminal, φ mnk is the allocation identifier. If the nth OFDM symbol of the mth RB is allocated to the kth second terminal, then φ mnk =1, K is the number of second terminals, D k is the delay requirement of the kth second terminal, w mnk is the transmit beamforming vector assigned to the kth second terminal on the nth OFDM symbol in the mth RB, P max is the maximum constrained power.

14. The method according to claim 12 or 13, characterized in that In the multi-antenna multi-user scenario, the maximum number of received bits R of the kth second terminal in the worst case k for: in, Q -1 (ε k )yes The inverse, V mnk is the wireless channel dispersion.

15. The method according to claim 14, characterized in that In the multi-antenna multi-user scenario, the worst received signal-to-noise ratio ρ of the kth robot mnk for: Among them, e mnk Indicates that it is in a bounded set The channel estimation error in is the channel estimate transmitted by the first terminal and the kth second terminal on the nth OFDM symbol in the mth RB, δ mnk Represents the boundaries of the channel uncertainty region.

16. The method according to claim 1, characterized in that The constraint conditions and the objective function are processed by using variable substitution, perspective transformation, first-order Taylor approximation transformation, and NCP method to obtain a penalized convex problem, including: The target bit number constraint condition is approximated as a convex constraint condition by using variable substitution, perspective transformation, and first-order Taylor approximation transformation; The binary indicator variable representing the resource block mapping is relaxed, and an e0-norm constraint is added to ensure the sparsity of each resource block call; the newly added constraint is Defined as φ mn =[φ mn1 ,…,φ mnK ] T ; The non-convex penalty method NCP is used to deal with the e0-norm constraint to obtain a sparse scheduling solution, and a penalty term is added to the objective function. Where λ>0 is the penalty factor; The penalty term is in the form of a convex function-convex function, which is a non-convex function. A first-order Taylor approximation is performed on it, and the non-convex penalty term is approximated as a convex function. At this time, the objective function and all constraints are convex constraints, resulting in a penalized convex problem.

17. A data packet scheduling determination device, characterized in that: include: A first determination module is configured to determine a constraint condition and an objective function, wherein the constraint condition is determined by uncertainty of a wireless channel between the first terminal and the second terminal and a packet parameter under a finite code length mechanism; A second determination module is configured to determine the optimal solution of the objective function and the URLLC packet scheduling scheme using a non-convex penalty method NCP and a continuous approximation algorithm SCA; The method of determining the optimal solution of the objective function and the URLLC packet scheduling scheme by using the non-convex penalty method NCP and the continuous approximation algorithm SCA includes: The constraint conditions and the objective function are processed by using variable substitution, perspective transformation, first-order Taylor approximation transformation, and NCP method to obtain a penalized convex problem; Solving the penalty convex problem obtains the optimal solution of the objective function and the URLLC packet scheduling scheme.

18. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 16.

19. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 16 is implemented.

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