A method and system for fair resource allocation among multiple users in a MIMO-URLLC scenario.

Abstract

This invention provides a method for fair resource allocation among multiple users in a MIMO-URLLC scenario, comprising: Step 1. Constructing a multi-user MIMO-URLLC communication system model; Step 2. Based on the multi-user MIMO-URLLC communication system model, constructing an original optimization problem; Step 3. Equivalently transforming the original optimization problem into a first optimization problem; Step 4. Given user throughput, transforming the first optimization problem into a second optimization problem concerning power; Step 5. Decomposing the second optimization problem into several sub-problems, obtaining the optimal solution for each sub-problem; simultaneously demonstrating the range of optimal user throughput values, and determining whether the power constraint is satisfied under the given user throughput condition. If not, the user throughput is re-given within the range of optimal user throughput values; if satisfied, the final optimal solution is obtained. This invention effectively solves a series of max-min problems related to fair resource allocation among multiple users, providing a new approach to solving such problems.

Description

Technical Field

[0001] This invention relates to the field of communication technology, specifically to a method and system for fair resource allocation among multiple users in a MIMO-URLLC scenario. Background Technology

[0002] Ultra-Reliable Low-Latency Communication (URLLC) is a core technology in modern wireless communication systems, playing a crucial role in 5G and future 6G systems. This technology aims to achieve millisecond-level or even sub-millisecond-level communication latency and extremely high transmission reliability, primarily targeting applications with extremely high latency and reliability requirements, such as industrial automation, telemedicine, and autonomous driving. Multiple-Input Multiple-Output (MIMO) technology significantly improves the transmission rate and system capacity of wireless links by configuring multiple antennas at both the transmitter and receiver to achieve spatial multiplexing and diversity gain. MU-MIMO (Multi-User Multiple-Input Multiple-Output) technology further extends MIMO, allowing base stations to transmit data to multiple users simultaneously, effectively improving spectrum utilization and overall system throughput.

[0003] While MU-MIMO brings higher efficiency to wireless communication in multi-user environments, it also presents significant challenges in resource allocation while ensuring system performance and user fairness. Therefore, this invention designs a resource allocation search algorithm for a certain type of MU-MIMO communication scenario under FBL (Finite Block Length) and URLLC conditions, effectively solving the communication resource allocation problem in this scenario while ensuring multi-user fairness and considering the achievable system performance and corresponding power allocation. Summary of the Invention

[0004] To address the issue of communication resource allocation among multiple users in ultra-reliable low-latency scenarios, this invention proposes a fair resource allocation search method for multiple users in MIMO-URLLC scenarios. The technical solution adopted in this invention is as follows:

[0005] In a first aspect, the present invention provides a method for fair resource allocation among multiple users in a MIMO-URLLC scenario, comprising:

[0006] Step 1. Construct a multi-user MIMO-URLLC communication system model;

[0007] Step 2. Based on the multi-user MIMO-URLLC communication system model, calculate the throughput of the single-user MIMO system, and construct the original optimization problem based on the throughput of the single-user MIMO system and the power constraints of the signal vector transmitted by the base station;

[0008] Step 3. By separating the independent variables, the original optimization problem is equivalently transformed into the first optimization problem by expressing the single-user throughput as a function of SINR;

[0009] Step 4. Given the user throughput, transform the first optimization problem into a second optimization problem concerning power;

[0010] Step 5. Divide the second optimization problem into several sub-problems, obtain the optimal solution for each sub-problem, and thus obtain the optimal solution for the second optimization problem; at the same time, prove the range of optimal user throughput, and determine whether the power constraint is satisfied under the given user throughput in step 4. If not, re-given the user throughput within the range of optimal user throughput, and return to step 4. If satisfied, obtain the final optimal solution.

[0011] Furthermore, the multi-user MIMO-URLLC communication system model includes:

[0012] Transmitter: Equipped with a total of n t Base station with a single transmitting antenna;

[0013] Receiver: A total of m multi-antenna users: (u1, u2, ..., u m ),

[0014] The number of antennas corresponding to each user is (r1, r2, ..., r m And the total number of antennas is n. r n r ≤n t ;

[0015] The MIMO-URLLC communication system model supports URLLC services.

[0016] Furthermore, the original optimization problem is:

[0017] max p min 1≤l≤m R l (p)

[0018] sttr(P 2 Q * Q)≤p

[0019] Where max is the maximum value, p is the base station's transmit power, p is the power allocation vector, and tr(P) is the maximum value. 2 Q* Q) is the signal vector transmitted by the base station, R l (p) represents the throughput of the l-th multi-antenna user, and Q is the normalized precoding matrix. * P is the transpose of the normalized precoding matrix, and P is the power allocation matrix.

[0020] Furthermore, the throughput R of the l-th multi-antenna user l (p) is:

[0021]

[0022] Where, γ k Let U be the SINR of the k-th antenna, and k be the user u. l The k-th antenna, V l The number of streams is r l The channel dispersion at time n is the block length of FBL coding during transmission, ∈ is the maximum decoding error probability, and r l This represents the number of antennas corresponding to the l-th multi-antenna user.

[0023] Further, step 3 includes:

[0024] tr(P) 2 Q * Q) is denoted as:

[0025] By using ZF precoding and assuming that the throughput of each user is equal, the original optimization problem is equivalently transformed into the first optimization problem.

[0026] Among them, p1, p2, p i , They are respectively the 1st, 2nd, i, and nth. r The power allocated to the antenna, a1, a2, a i , They are respectively the 1st, 2nd, i, and nth. r The power factor allocated to the root antenna.

[0027] Furthermore, the first optimization problem is:

[0028] max p,t t

[0029] stt-R l (p)=0, (1≤l≤m)

[0030]

[0031] p i ≥σ 2 (1≤i≤n r )

[0032] Where, σ 2 This represents the normalized noise power.

[0033] Furthermore, in step 4, for a given user throughput t, the first optimization problem is transformed into a second optimization problem concerning power:

[0034]

[0035] stt-R l (p)=0, (1≤l≤m)

[0036] p i ≥σ 2 (1≤i≤n r )

[0037] The optimal solution to the second optimization problem is pt * Verify whether it satisfies the power constraint; if not, decrease the value of t and reconstruct the second optimization problem; if it satisfies, then p * =pt * , where p * The optimal solution to the power allocation matrix is ​​t and pt. * .

[0038] Furthermore, step 5, which involves breaking down the second optimization problem, includes: Let the power allocation for user u1 be... User u l The power allocation is The power weight corresponding to user u1 is User u l The power weight is

[0039] For a given t, construct m subproblems, where the l-th subproblem is:

[0040]

[0041] st tR l (p l )=0,(1≤l≤m)

[0042] pu i ≥σ 2 (1≤i≤r l )

[0043] Where, r l For user u l The number of receiving antennas; For user u l The 1st, 2nd, ..., r lThe power of the antenna; These respectively reflect user u l The 1st, 2nd, ..., r l The power weighting coefficient of the channel state at the root antenna.

[0044] Furthermore, the range of values ​​for the optimal user throughput demonstrated in step 5 includes:

[0045] The power is evenly distributed, that is, each transmit power is allocated p. eq for:

[0046]

[0047] At this point, the power pequ of each user after precoding l for:

[0048]

[0049] To determine the user's throughput, construct m subproblems with varying value ranges, where the l-th subproblem with varying value range is:

[0050]

[0051] pu i ≥σ 2 (1≤i≤r l )

[0052] Let Req be the optimal solution to the subproblem that takes m values ​​in each interval. l (p l ),(1≤l≤m)

[0053] The optimal solution's range is: [t] min ,t max ],in

[0054] t max =max{Req1,Req2,…,Req m},

[0055] t min =min{Req1,Req2,…,Req m},

[0056] Req1, Req2, ..., Req m Find the optimal solution to the subproblem for the 1st, 2nd, ..., mth value intervals respectively;

[0057] t max Using t0 as the initial value for the search, a binary search method is used to search for t within the interval. When the interval length is less than a set threshold, the minimum value of the interval is taken as the minimum value for t. *Approximate to .

[0058] On the other hand, the present invention provides a multi-user fair resource allocation system in a MIMO-URLLC scenario, comprising:

[0059] The communication system model building module is used to build multi-user MIMO-URLLC communication system models.

[0060] The primal optimization problem construction module is used to calculate the throughput of a single-user MIMO system based on the multi-user MIMO-URLLC communication system model, and to construct the primal optimization problem based on the throughput of the single-user MIMO system and the power constraints of the signal vector transmitted by the base station.

[0061] The first optimization problem transformation module is used to convert the original optimization problem into the first optimization problem by separating the independent variables through expressing the single-user throughput as a function of SINR.

[0062] The second optimization problem transformation module is used to transform the first optimization problem into a second optimization problem concerning power, given the user throughput.

[0063] The optimal solution acquisition module is used to break down the second optimization problem into several sub-problems, obtain the optimal solution for each sub-problem, and thus obtain the optimal solution for the second optimization problem. At the same time, it verifies the range of optimal user throughput and determines whether the power constraint is satisfied under the given user throughput condition. If it is not satisfied, the user throughput is re-given within the range of optimal user throughput and the module is returned to the previous module. If it is satisfied, the final optimal solution is obtained.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] 1. Addressing the challenges of multi-user MMIO-URLLC communication scenarios, including the difficulty in model construction, the large number of variables involved, and the complexity of the solution process, this paper proposes a resource allocation search algorithm for a specific class of MU-MIMO communication scenarios under FBL and URLLC conditions. This algorithm, while ensuring multi-user fairness, considers the achievable system performance and corresponding power allocation, effectively solving the communication resource allocation problem in this scenario. Unlike traditional iterative algorithms based on SCA (Continuous Convex Approximation), this algorithm does not require convex approximation of the objective function. Furthermore, through problem reconstruction and dimensionality reduction, it exhibits lower complexity and can effectively solve a series of max-min problems, including but not limited to multi-user fair resource allocation, providing a new approach to solving such problems.

[0066] 2. This invention proposes a multi-user fair resource allocation search algorithm in MIMO-URLLC scenarios. The core of the invention lies in the following four aspects: First, under FBL and URLLC conditions, for a certain type of MU-MIMO communication scenario, while ensuring multi-user fairness, a resource allocation search algorithm is designed based on the achievable system performance and corresponding power allocation, effectively solving the communication resource allocation problem in this scenario; Second, by proposing a lemma and providing corresponding proof, the necessary conditions for a class of max-min problems to obtain local optimal solutions are demonstrated in detail, providing inspiration for finding local optimal solutions to similar problems; Third, by decomposing and reconstructing the problem, the dimensionality of the optimization variables is reduced, thereby greatly reducing the complexity of solving the problem; Fourth, unlike traditional iterative solving algorithms based on SCA (continuous convex approximation), this algorithm does not require convex approximation of the objective function and can effectively solve a series of max-min problems, including but not limited to multi-user fair resource allocation, providing a new approach to solving such problems. Attached Figure Description

[0067] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0068] Figure 1 This is a flowchart of an embodiment of the present invention.

[0069] Figure 2 This is a schematic diagram illustrating the solution process in an embodiment of the present invention. Detailed Implementation

[0070] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0071] Example 1

[0072] Combined with appendix Figure 1 The following is a flowchart of the optimization algorithm implementation of the present invention. The technical solution of the system in the following embodiment of the present invention is a multi-user fair resource allocation search method in the MIMO-URLLC scenario.

[0073] The communication scenario model includes the following: the transmitter is a base station equipped with a total of t transmit antennas; the receiver has a total of m multi-antenna users (u1, u2, ..., u...). m ), where the number of receiving antennas corresponding to each user is (r1, r2, ..., r m The system has a total of r antennas; users are considered using a single time-frequency resource block, and it is assumed that r ≤ t; the system supports URLLC service, the block length of FBL encoding during transmission is n, and the maximum decoding error probability is ∈; it is assumed that the antennas at the base station are a uniform linear array, where the spatial correlation coefficient between adjacent antennas is ρ0; let the base station's transmit power be p, and the power allocation matrix be... Where, p i This represents the power allocated to the i-th antenna out of a total of r antennas at the receiver; the normalized precoding matrix is ​​denoted as . The signal vector Qx transmitted by the base station then satisfies the corresponding power constraints.

[0074] Combined with appendix Figure 1 The following is a flowchart illustrating the implementation of the optimization algorithm constructed in this invention. The specific technical solution of the system in the following embodiment is a multi-user fair resource allocation search algorithm in a MIMO-URLLC scenario, and the specific implementation steps are as follows:

[0075] Step 1. Construct a multi-user MIMO-URLLC communication system model;

[0076] The mathematical model for constructing a multi-user MIMO communication system, as described in step 1, specifically includes: the transmitter being a unit equipped with a total of n... t A base station with 1 transmitting antenna. The receiving end has a total of m multi-antenna users (u1, u2, ..., u...). m ), where the number of receiving antennas corresponding to each user is (r1, r2, ..., r m And the total number of antennas is n. r ,Right now

[0077]

[0078] This embodiment considers all users using a single time-frequency resource block, and assumes n r ≤n t The system supports URLLC service. The block length of FBL encoding during transmission is n, and the maximum decoding error probability is ∈. Assume the antennas at the base station are a uniform linear array.

[0079] Where the spatial correlation coefficient between adjacent antennas is ρ0, then the transmitter correlation matrix is...

[0080]

[0081] Wherein, the spatial correlation coefficient between the i-th antenna and the j-th antenna is... Correlation matrix Let the base station's transmit power be p, and the power allocation matrix be... Where, p i This indicates a total of n for the receiving end. r The power allocated to the i-th antenna among the antennas. Then the symbol vector transmitted at the transmitter can be represented as...

[0082] in The superscript T indicates the transpose of the matrix.

[0083] Let the normalized precoding matrix be . Then the signal vector Qx transmitted by the base station satisfies

[0084] Power constraints

[0085]

[0086] Here, the superscript * denotes the conjugate transpose of the matrix. Let denote the normalized channel vector between the base station and the i-th receiving antenna. Then there is

[0087]

[0088] in, E t Represents n t A dimensional identity matrix. At this point, the total distance from the base station to the receiver is n. r The channel matrix of the root antenna can be represented as:

[0089]

[0090] If we use regularized zero-forcing for precoding, then we have

[0091]

[0092] Here, δ is the adjustment coefficient, which is usually considered a constant. This model considers completely eliminating inter-user interference, so the adjustment coefficient δ in the above equation is set to 0. In this case, regularized zero-forcing precoding will degenerate into regular zero-forcing precoding. At this point, the signal vector at the receiver... It can be given by the following formula

[0093] y = H T Qx+w (7)

[0094] Among them, additive white Gaussian noise and The channel vector in formula (4) above is normalized by the noise power, where σ 2 This is the normalized noise power.

[0095] Step 2. Based on the multi-user MIMO-URLLC communication system model, calculate the throughput of the single-user MIMO system, and construct the original optimization problem based on the throughput of the single-user MIMO system and the power constraints of the signal vector transmitted by the base station;

[0096] Based on the communication system model constructed in step 1, an optimization problem considering multi-user fairness and improving user throughput is derived, specifically including: Let the elements in the precoded channel matrix be... At this time, the k-th (1≤k≤n) position at the receiving end r The signal received by the antenna can be represented as

[0097]

[0098] Let the power allocation vector be... The interference signal for the k-th antenna is Then the SINR of the k-th antenna can be expressed as:

[0099]

[0100] The research object is adjusted to the l-th (1≤l≤m) multi-antenna user u l For this user, this communication model is equivalent to the transmitter having t antennas and the receiver having r antennas. l A single-user MIMO system with one antenna. Considering r l ≤n r ≤n t Therefore, the equivalent number of transmitted signal streams is r. l And for the k-th (k∈u) receiver l ) antennas, whose SINR is γ k The throughput of this single-user MIMO system can then be expressed as:

[0101]

[0102] Where ∈ represents the maximum decoding error probability during transmission, n represents the FBL coding block length, and Q -1 (·) represents a function The inverse function of V l The number of streams is r l Channel dispersion at time, i.e.

[0103]

[0104] Therefore, the original optimization problem is described as follows:

[0105]

[0106] Step 3. By separating the independent variables, the original optimization problem is equivalently transformed into the first optimization problem by expressing the single-user throughput as a function of SINR;

[0107] The argument is that, under certain conditions, the optimal solution is obtained when the throughput of all users is equal. Specifically, when the SINR of all users meets a certain condition (not less than 1) and the ZF precoding scheme is used, the conclusion that the optimal solution is obtained when the throughput of all users is equal holds true. The specific proof is as follows:

[0108] Let's first prove a lemma.

[0109] Lemma 1: For the form of

[0110] The optimization problem is denoted by its local optimum as (x * ,y * ,z * If it satisfies

[0111]

[0112] Then we have h(x) * ,y * ,z * ) = 0, and f(x) * )=g(y * ,z * ).

[0113] Proof: The original optimization problem can be rewritten as a standard optimization problem.

[0114]

[0115] Let the local optimal solution of this problem be (x * ,y * ,z * ,t * According to the KKT conditions, we have

[0116]

[0117] Therefore there is

[0118]

[0119] If λ3=0, considering f′(x) * )≠0 and We can obtain λ1=λ2=0, which contradicts λ1+λ2=1. Therefore, λ3≠0, that is, h(x * ,y * ,z *)=0. At this time, combining with equation (17), we know that λ1≠0 and λ2≠0, that is, f(x)=0. * )=g(y * ,z * )=t * .

[0120] When there are more objective functions, as long as the independent variables of each objective function are independent, it is easy to draw similar conclusions.

[0121] To express the original optimization problem (12) in a similar form, consider separating the independent variables by representing the user throughput as a function of SINR. Let tr(P 2 Q * Q) is denoted as Where a i If ≤0, then we have

[0122]

[0123] At this point, the original optimization problem (12) can be expressed as:

[0124]

[0125] Next, we will try to reconstruct its representation into the following form.

[0126]

[0127] in, And R l The independent variables between (Γ) are mutually independent. The specific reconstruction process is as follows:

[0128] Considering that the SINR of each receiving antenna satisfies

[0129]

[0130] To solve this system of equations, we can first multiply the denominator polynomial to the left side of the equation, then move all terms from the left side to the right side, transforming the original system of equations into a linear system of equations about p. Therefore, the algorithmic complexity for solving this system of equations is O(p). From equation (21), we can see that

[0131]

[0132] in, 1≤i≤n represents the expression with respect to (γ1,γ2,…,γ). n The polynomial of ). Let the polynomial inequality obtained by substituting into equation (18) be .

[0133]

[0134] Then there is

[0135]

[0136] Thus, the reconstruction of the original optimization problem (12) is complete. Combining Lemma 1, it is easy to see that for a specific local optimum, if the first-order information (derivatives / partial derivatives) of each objective function and constraint condition at that point are not zero, then the function values ​​of each objective function are equal when the optimum is obtained. It is now proven that when SINR satisfies certain conditions, the first-order information of the objective function is always not zero.

[0137] Lemma 2: When SINR is not less than 1, i.e. not less than 0 dB, the first (partial) derivative of the user throughput $R$ is always greater than 0.

[0138] Proof: When the number of user antennas is k, we have

[0139]

[0140] Typically, the value of ∈ is 10. -5 Up to 10 -9 The encoding block length n is typically in the tens or hundreds, or even larger. If we denote...

[0141]

[0142] We can then assume a < 1. Let a be an abbreviation.

[0143]

[0144] Then the function f(x1,x2,…,x) k ) and R(x1,x2,…,x k The monotonicity of x1, x2, ..., x2 is exactly the same; it suffices to prove that x1≥1, x2≥1, ..., x2 is the same. k ≥1 At this point, when x1≥1, x2≥1,…,x k When ≥1, there is

[0145]

[0146] Similarly, we can see that x1≥1, x2≥1,…,x k ≥1 Therefore SINR

[0147] When all are not less than 1, the first-order partial derivatives of R are always greater than 0.

[0148] Furthermore, we consider the constraint condition g(γ1,γ2,…,γ) nThe first-order information of γ is crucial. Unlike the objective function, this function does not have a uniform form across different problems, making it difficult to analyze the properties of its derivative. However, in some cases, such as when ZF precoding is used, interference between users is completely eliminated. In this case, the transformation between γ and p becomes a linear transformation, making the constraint g a linear constraint, and its first-order derivative is never zero. In summary, when SINR is not less than 1 and ZF precoding is used, the optimal solution to this max-min problem is always obtained when the throughput of each user is equal.

[0149] Based on the above conclusions, the original optimization problem in step 2 is equivalently transformed into the first...

[0150] An optimization problem is proposed that can be solved using a one-dimensional search. The specific process is as follows: Taking the ZF precoding scheme as an example. Based on the assumption that the SINR of each antenna is not less than 1, p should satisfy...

[0151] p i ≥σ 2 ,(1≤i≤r) (29)

[0152] The first optimization problem can be refactored into the following form:

[0153]

[0154] Step 4. Given the user throughput, transform the first optimization problem into a second optimization problem concerning power;

[0155] To solve the above problem, we can choose a large number t0 (t0 ≥ t). * Using t as the initial value for the search, we search from largest to smallest. For a given t, we construct the following second optimization problem:

[0156]

[0157] Let the optimal solution to this second optimization problem be pt. * Verify whether it satisfies the power constraint. If not, decrease the value of t and reconstruct the subproblem; if it satisfies, then p * =pt * The optimal solution to the optimization problem (30) is determined by t and pt at that time. * Together they form a whole.

[0158] Step 5. Divide the second optimization problem into several sub-problems, obtain the optimal solution for each sub-problem, and thus obtain the optimal solution for the second optimization problem; at the same time, prove the range of optimal user throughput, and determine whether the power constraint is satisfied under the given user throughput in step 4. If not, re-given the user throughput within the range of optimal user throughput, and return to step 4. If satisfied, obtain the final optimal solution.

[0159] The second optimization problem in step 4 is decomposed to achieve variable dimensionality reduction. The specific process is as follows: Let the power allocation of user u1 be... User u l The power allocation is Then there is

[0160]

[0161] and Let the power weight corresponding to user u1 be... User u l The power weight is Then there is

[0162]

[0163] At this time, there is R l (p)=R l (p l This holds true. For a given t, construct m subproblems, where the l-th subproblem is:

[0164]

[0165] and Verify whether it satisfies the power constraint. If not, decrease the value of t and reconstruct the subproblem; if it satisfies, then p * =pt * The optimal solution to the optimization problem (30) is determined by t and pt at that time. * Together they form a whole. Through the above method, this invention decomposes an r-dimensional subproblem into m subproblems of lower dimensions, thereby simplifying the algorithm.

[0166] The optimal solution's value range is defined, and the specific steps are as follows: First, the power is evenly distributed, that is, the transmission power is evenly allocated to each value.

[0167]

[0168] Let the power of each user after precoding be .

[0169]

[0170] To address the user's throughput, construct m interval subproblems, where the l-th interval subproblem is:

[0171]

[0172] Similar to the subproblem above, this subproblem can also be viewed as n r The existence of the intersection point between a 3D hyperplane and a high-dimensional surface is a problem, therefore the optimal solution must be obtained at the endpoint or tangent point of the feasible region.

[0173] Therefore, by simply calculating the objective function values ​​at the endpoints of the feasible region and the tangent points of the surface, the optimal solution to the problem can be easily obtained. Let the optimal solutions to the m subproblems be denoted as...

[0174] Req l (p l ),(1≤l≤m) (38)

[0175] make

[0176]

[0177] It is easy to see by contradiction that t must exist. min ≤t * ≤t max Therefore, t can be used as a reference. max The initial value t0 is used as the search value. Furthermore, considering the interval [t...] min ,t max Finding the maximum value of t that satisfies certain conditions in a given context is a one-dimensional search problem. The algorithm can be further optimized using one-dimensional search methods. For example... Figure 2 As shown, in this embodiment, a binary search method is used to search for t within the interval. When the interval length is less than a set threshold, the minimum value of the interval is taken as the minimum value for t. * The approximation allows the optimal solution obtained by this method to gradually approach the theoretical optimal solution from below.

[0178] It should be noted that, depending on the implementation needs, the various steps described in this application can be broken down into more steps, or two or more steps or parts of the steps can be combined into new steps to achieve the purpose of this invention.

[0179] Although this invention makes frequent use of terms such as MIMO and SINR, the possibility of using other terms is not excluded. These terms are used merely for the convenience of describing and explaining the essence of this invention; interpreting them as any additional limitation would contradict the spirit of this invention.

[0180] Example 2

[0181] This embodiment provides a fair resource allocation system for multiple users in a MIMO-URLLC scenario, including:

[0182] The communication system model construction module is used to construct a multi-user MIMO-URLLC communication system model; the primordial optimization problem construction module is used to calculate the throughput of a single-user MIMO system based on the multi-user MIMO-URLLC communication system model, and to construct a primordial optimization problem based on the throughput of the single-user MIMO system and the power constraints of the signal vector transmitted by the base station.

[0183] The first optimization problem transformation module is used to convert the original optimization problem into the first optimization problem by separating the independent variables through expressing the single-user throughput as a function of SINR.

[0184] The second optimization problem transformation module is used to transform the first optimization problem into a second optimization problem concerning power, given the user throughput.

[0185] The optimal solution acquisition module is used to break down the second optimization problem into several sub-problems, obtain the optimal solution for each sub-problem, and thus obtain the optimal solution for the second optimization problem. At the same time, it verifies the range of optimal user throughput and determines whether the power constraint is satisfied under the given user throughput condition. If it is not satisfied, the user throughput is re-given within the range of optimal user throughput and the module is returned to the previous module. If it is satisfied, the final optimal solution is obtained.

[0186] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

[0187] It should be understood that any parts not described in detail in this specification belong to the prior art. It should also be understood that the above description of preferred embodiments is quite detailed, but this should not be construed as limiting the scope of protection of this invention. Those skilled in the art, under the guidance of this invention, can make substitutions or modifications without departing from the scope of protection of the claims, all of which fall within the scope of protection of this invention. The scope of protection of this invention should be determined by the appended claims.

Claims

1. A method for fair resource allocation among multiple users in a MIMO-URLLC scenario, characterized in that, include: Step 1. Construct a multi-user MIMO-URLLC communication system model; Step 2. Based on the multi-user MIMO-URLLC communication system model, calculate the throughput of the single-user MIMO system, and construct the original optimization problem based on the throughput of the single-user MIMO system and the power constraints of the signal vector transmitted by the base station; Step 3. By separating the independent variables, the original optimization problem is equivalently transformed into the first optimization problem by expressing the single-user throughput as a function of SINR; Step 4. Given the user throughput, transform the first optimization problem into a second optimization problem concerning power; Step 5. Divide the second optimization problem into several sub-problems, obtain the optimal solution for each sub-problem, and thus obtain the optimal solution for the second optimization problem; at the same time, prove the range of optimal user throughput, and determine whether the power constraint is satisfied under the given user throughput in step 4. If not, re-given the user throughput within the range of optimal user throughput, and return to step 4. If satisfied, obtain the final optimal solution.

2. The method for fair resource allocation among multiple users in a MIMO-URLLC scenario as described in claim 1, characterized in that, Multi-user MIMO-URLLC communication system models include: Transmitter: Equipped with a total of n t Base station with a single transmitting antenna; Receiver: A total of m multi-antenna users: (u1, u2, ..., u m ), The number of antennas corresponding to each user is (r1, r2, ..., r m And the total number of antennas is n. r n r ≤n t ; The MIMO-URLLC communication system model supports URLLC services.

3. The method for fair resource allocation among multiple users in a MIMO-URLLC scenario as described in claim 2, characterized in that, The original optimization problem is: Where max is the maximum value, p is the base station's transmit power, p is the power allocation vector, and tr(P) is the maximum value. 2 Q * Q) is the signal vector transmitted by the base station, R l (p) represents the throughput of the l-th multi-antenna user, and Q is the normalized precoding matrix. * P is the transpose of the normalized precoding matrix, and P is the power allocation matrix.

4. The method for fair resource allocation among multiple users in a MIMO-URLLC scenario as described in claim 3, characterized in that, The throughput R of the l-th multi-antenna user l (p) is: Where, γ k Let U be the SINR of the k-th antenna, and k be the user u. l The k-th antenna, V l The number of streams is r l The channel dispersion at time n is the block length of FBL coding during transmission, ∈ is the maximum decoding error probability, and r l This represents the number of antennas corresponding to the l-th multi-antenna user.

5. The method for fair resource allocation among multiple users in a MIMO-URLLC scenario as described in claim 4, characterized in that, Step 3 includes: tr(P) 2 Q * Q) is denoted as: By using ZF precoding and assuming that the throughput of each user is equal, the original optimization problem is equivalently transformed into the first optimization problem. Among them, p1, p2, p i , They are respectively the 1st, 2nd, i, and nth. r The power allocated to the antenna, a1, a2, a i , They are respectively the 1st, 2nd, i, and nth. r The power factor allocated to the root antenna.

6. The method for fair resource allocation among multiple users in a MIMO-URLLC scenario as described in claim 5, characterized in that, The first optimization problem is: Where, σ 2 This represents the normalized noise power.

7. The method for fair resource allocation among multiple users in a MIMO-URLLC scenario as described in claim 6, characterized in that, In step 4, for a given user throughput t, the first optimization problem is transformed into a second optimization problem concerning power: The optimal solution to the second optimization problem is pt * Verify whether it satisfies the power constraint; if not, decrease the value of t and reconstruct the second optimization problem; if it satisfies, then p * =pt * , where p * The optimal solution to the power allocation matrix is ​​t and pt. * .

8. The method for fair resource allocation among multiple users in a MIMO-URLLC scenario as described in claim 7, characterized in that, Step 5, which involves breaking down the second optimization problem, includes: Let the power allocation for user u1 be... User u l The power allocation is The power weight corresponding to user u1 is User u l The power weight is For a given t, construct m subproblems, where the l-th subproblem is: Where, r l For user u l The number of receiving antennas; For user u l The 1st, 2nd, ..., r l The power of the antenna; These respectively reflect user u l The 1st, 2nd, ..., r l The power weighting coefficient of the channel state at the root antenna.

9. The method for fair resource allocation among multiple users in a MIMO-URLLC scenario as described in claim 7, characterized in that, The range of values ​​for the optimal user throughput discussed in step 5 includes: The power is evenly distributed, that is, each transmit power is allocated p. eq for: At this point, the power pequ of each user after precoding l for: To determine the user's throughput, construct m subproblems with varying value ranges, where the l-th subproblem with varying value range is: Let Req be the optimal solution to the subproblem that takes m values ​​in each interval. l (p l ),(1≤l≤m) The optimal solution's range is: [t] min ,t max ],in t max =max{Req1,Req2,…,Req m }, t min =min{Req1,Req2,…,Req m }, Req1, Req2, ..., Req m Find the optimal solution to the subproblem for the 1st, 2nd, ..., mth value intervals respectively; t max Using t0 as the initial value for the search, a binary search method is used to search for t within the interval. When the interval length is less than a set threshold, the minimum value of the interval is taken as the minimum value for t. * Approximate to .

10. A multi-user fair resource allocation system in a MIMO-URLLC scenario, characterized in that, include: The communication system model building module is used to build multi-user MIMO-URLLC communication system models. The primal optimization problem construction module is used to calculate the throughput of a single-user MIMO system based on the multi-user MIMO-URLLC communication system model, and to construct the primal optimization problem based on the throughput of the single-user MIMO system and the power constraints of the signal vector transmitted by the base station. The first optimization problem transformation module is used to convert the original optimization problem into the first optimization problem by separating the independent variables through expressing the single-user throughput as a function of SINR. The second optimization problem transformation module is used to transform the first optimization problem into a second optimization problem concerning power, given the user throughput. The optimal solution acquisition module is used to break down the second optimization problem into several sub-problems, obtain the optimal solution for each sub-problem, and thus obtain the optimal solution for the second optimization problem; at the same time, it verifies the range of optimal user throughput and determines whether the power constraint is satisfied under the given user throughput condition. If it is not satisfied, the user throughput is re-given within the range of optimal user throughput and the module is returned to the previous module. If it is satisfied, the final optimal solution is obtained; the multi-user fair resource allocation system in the MIMO-URLLC scenario is used to execute the steps in the multi-user fair resource allocation method in the MIMO-URLLC scenario as described in any one of claims 1-9.

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