A multi-user resource allocation method and system based on carrier aggregation for LTE-A system

Abstract

This invention discloses a multi-user resource allocation method and system for LTE-A systems based on carrier aggregation, belonging to the field of wireless communication. First, the specific carrier used by each user is determined during the carrier selection phase. Then, an integer linear programming problem is formulated for resource block allocation. By proving that the coefficient matrix of the constraints of this integer linear programming problem is a fully monomon matrix, the original optimization problem is relaxed into a linear programming problem that can be directly solved using existing convex optimization tools. Multi-user MIMO gain is applied to carrier aggregation, thereby further improving the system throughput, outperforming existing methods under different user distribution settings. This method fully exploits multi-user MIMO gain; considering the need for each resource block in a practical communication system to support multi-user transmission, it avoids the limitation of existing literature where each resource block can only serve a single user.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology and relates to a method and system for multi-user resource allocation in an LTE-A system based on carrier aggregation. Background Technology

[0002] Future communication systems place higher demands on data transmission rates and service quality. To address this challenge, it is necessary to improve spectrum efficiency using advanced technologies such as MIMO, and to make more rational use of increasingly scarce wireless resources. Radio Resource Management (RRM) aims to improve the throughput of the entire network system based on limited frequency bands and transmit power conditions, or to reduce system bandwidth and power while ensuring service quality and fairness. Therefore, it is necessary to flexibly adjust parameters such as transmit power, user allocation, beamforming, data transmission rate, handover criteria, modulation scheme, and error correction coding strategy under randomly changing channel conditions (caused by large-scale and small-scale fading), according to the service characteristics and QoS requirements of different users in the system, in order to maximize the full utilization of radio spectrum and wireless network infrastructure resources.

[0003] Spectrum aggregation (or carrier aggregation) was introduced by 3GPP in its new LTE-Advanced standard, a candidate radio interface technology for 5G systems. Since the introduction of LTE-Advanced, the concept of radio resource management in LTE networks supporting carrier aggregation has received considerable attention. Previous studies on radio resource management in LTE-Advanced have mostly treated carrier selection, resource block (RB) allocation, and modulation and coding scheme (MCS) allocation as completely independent problems, leading to a decline in network performance. Recent research (Rostami S, Arshad K, Rapajic P. Optimum radio resource management in carrier aggregation based LTE-advanced systems[J]. IEEE Transactions on Vehicular Technology, 2017, 67(1): 580-589.) considers solving the carrier selection and resource block allocation problems jointly, constructing an integer linear programming problem based on proportional fairness utility and proving that the problem can be simplified to a linear programming problem, thus achieving optimal solution with lower complexity. The algorithm is further extended after introducing user QoS constraints and power consumption constraints.

[0004] It is worth noting that the radio resource management schemes in the above-mentioned decoupled or combined LTE-Advanced systems all assume that each resource block can only serve one user. However, in actual communication systems, it is desirable to allocate each resource block to multiple users to obtain multi-user gain, thereby further improving system throughput. Summary of the Invention

[0005] The purpose of this invention is to solve the problem that existing wireless resource management schemes assume that each resource block can only serve one user and that each resource block cannot be allocated to multiple users to obtain multi-user gain, resulting in low system throughput. This invention provides a multi-user resource allocation method and system for LTE-A systems based on carrier aggregation.

[0006] To achieve the above objectives, the present invention employs the following technical solution:

[0007] The present invention proposes a multi-user resource allocation method for an LTE-A system based on carrier aggregation, comprising the following steps:

[0008] Obtain the user's single-carrier capability on the carrier, and select the carrier with the larger single-carrier capability as the carrier to be used for the user based on the single-carrier capability;

[0009] Group the users to be scheduled and iterate through all their groupings; based on the traversed user groups and the carriers used, formulate the resource block allocation optimization problem and constraints.

[0010] The solution to the resource block allocation optimization problem is obtained, and the resource allocation is realized by combining the multi-user MIMO gain.

[0011] Preferably, the single-carrier capability (CC) is obtained. m The methods for _cap are as follows:

[0012]

[0013] Among them, CC m _SpecEff represents the spectral efficiency of the user on carrier m, CC m _BW represents the bandwidth of carrier m.

[0014] Preferably, the resource block allocation optimization problem is formulated as follows:

[0015]

[0016] stCx≤q

[0017] 0≤x≤1

[0018] Among them, g TLet x be the transpose of the concatenated vector, x be the optimized vector formed by concatenating the binary variables a and b allocated to the resource block, C be the coefficient matrix, and q be the normalized constraint vector.

[0019] Preferably, the coefficient matrix C = [C1 C2 C3 C4] T ;

[0020] Wherein, the coefficient matrix after constraint-normalization is C1=[O MP×MPGN L1], the transformation matrix of constraint one The coefficient matrix C2 after constraint 2 normalization is [O MPG×MPGN E MPG The coefficient matrix C3 after constraint trinormalization is [E] MPGN L3], the transformation matrix of constraint three The coefficient matrix C4 after constraint-four normalization is [E MPGN O MPGN×MPG ], O MP×MPGN O MPG×MPGN and O MPGN×MPG All are all-zero matrices. E is a real number identifier. MPG and E MPGN All are identity matrices, where M is the total number of carriers, P is the total number of resource blocks on each carrier, and G is the total number of user groups obtained through iteration. Let 1 be the transpose of a vector. N It is a vector, and its subscripts all represent the dimension of the matrix or vector. MP is the total number of carriers multiplied by the total number of resource blocks on each carrier, MPG is the total number of carriers multiplied by the total number of resource blocks on each carrier multiplied by the total number of user groups, and MPGN is the total number of carriers multiplied by the total number of resource blocks on each carrier multiplied by the total number of user groups multiplied by the number of users.

[0021] Preferably, the normalized constraint vector q is obtained as follows:

[0022] q = [q1 q2 q3 q4] T

[0023] Wherein, the constraint vector q1 = 1 after constraint one normalization MP The normalized constraint vector after constraint 2 is q2 = P2 × e, where e is the group carrier identifier vector, and the transformation matrix of constraint 2 is... Intermediate matrix in the transformation process of constraint 2 The constraint vector q3 = 0 after constraint trinormalization MPGN The constraint vector after constraint four normalization is q4 = P4 × c, where c is the user identifier vector within the group, and the transformation matrix of constraint four is...

[0024] Preferably, the method for obtaining the concatenated vector g is as follows:

[0025]

[0026] Wherein, instantaneous velocity vector This represents the instantaneous rate that user 1 in user group 1 can obtain on resource block 1 of carrier 1. This represents the instantaneous rate that user n in user group g can obtain on resource block p of carrier m. r represents the instantaneous rate that user N in user group G can obtain on resource block P of carrier M. T The transpose of the instantaneous velocity vector, 0 MPG Represents the zero vector. It is the transpose of the zero vector.

[0027] Preferably, the optimized vector x, obtained by concatenating the binary variables a and b for resource block allocation, is as follows:

[0028]

[0029] Among them, the group resource block allocation identifier vector User resource block allocation identifier vector Indicates whether resource block 1 on carrier 1 is allocated to user group 1. This indicates whether resource block p on carrier m is allocated to user group g. This indicates whether resource block P on carrier M is allocated to user group G, where M is the total number of carriers, P is the total number of resource blocks on each carrier, and G is the total number of user groups obtained through iteration. This indicates whether resource block 1 on carrier 1 has been allocated to user 1 in user group 1. This indicates whether resource block p on carrier m is allocated to user n in user group g. This indicates whether resource block P on carrier M is allocated to user N in user group G.

[0030] This invention proposes a multi-user resource allocation system for LTE-A systems based on carrier aggregation, comprising:

[0031] The carrier acquisition module is used to acquire the user's single-carrier capability on a carrier and select a carrier with a larger single-carrier capability as the carrier to be used based on the single-carrier capability.

[0032] The optimization problem establishment module is used to group users to be scheduled, traverse all grouping situations, and formulate resource block allocation optimization problems and constraints based on the traversed user groups and the carriers used.

[0033] An optimization problem-solving module is used to obtain the solution to the resource block allocation optimization problem and combine it with multi-user MIMO gain to realize resource allocation.

[0034] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of a multi-user resource allocation method for an LTE-A system based on carrier aggregation.

[0035] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a multi-user resource allocation method for an LTE-A system based on carrier aggregation.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] This invention proposes a multi-user resource allocation method for LTE-A systems based on carrier aggregation. First, the specific carrier used by each user is determined during the carrier selection phase based on spectral efficiency. Then, an integer linear programming problem is formulated for resource block allocation. By proving that the coefficient matrix of the constraints of this integer linear programming problem is a fully monomon matrix, the original optimization problem is relaxed into a linear programming problem that can be directly solved using existing convex optimization tools. Multi-user MIMO gain can be applied to carrier aggregation, thereby further improving the system throughput, outperforming existing methods under different user distribution settings. Therefore, this method fully exploits multi-user MIMO gain; considering the need for each resource block in a real communication system to support multi-user transmission, it avoids the limitation of existing literature where each resource block can only serve a single user. The formulated resource block allocation optimization problem can be further relaxed into a linear programming form while maintaining the same optimal solution, and can be easily solved using existing convex optimization tools such as CVX, with high solution efficiency and strong feasibility.

[0038] This invention proposes a multi-user resource allocation system for LTE-A systems based on carrier aggregation. Resource allocation is achieved by dividing the system into a carrier acquisition module, an optimization problem establishment module, and an optimization problem solving module. The modular approach ensures that each module is independent, facilitating unified management of all modules. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of the multi-user resource allocation method for the LTE-A system based on carrier aggregation according to the present invention.

[0041] Figure 2 This is a system model diagram considered in this invention.

[0042] Figure 3 This is a comparison of the system throughput performance of the algorithm provided under three different user deployment scenarios and the comparison algorithm.

[0043] Figure 4 The algorithm performance provided in user edge distribution scenarios varies with N max The changes.

[0044] Figure 5 The algorithm performance provided in scenarios with random user distribution varies with N. max The changes.

[0045] Figure 6 The algorithm performance provided in a user-centric distributed scenario varies with N. max The changes.

[0046] Figure 7 This is a diagram of the multi-user resource allocation system of the LTE-A system based on carrier aggregation according to the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0048] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0049] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0050] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0051] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0052] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0053] The present invention will now be described in further detail with reference to the accompanying drawings:

[0054] Considering the vision of further utilizing multi-user gain to expand the optimization space in practical transmission systems, this invention breaks through the limitation in existing literature that each resource block can only serve a single user. It aims to provide a multi-antenna multi-user radio resource management method based on carrier aggregation in LTE-Advanced systems, which can significantly improve the system's transmission capability and further enhance the system throughput.

[0055] This invention proposes a multi-user resource allocation method for LTE-A systems based on carrier aggregation, such as... Figure 1 As shown, it includes the following steps:

[0056] S1. Obtain the user's single-carrier capability on the carrier, and select a carrier with a larger single-carrier capability for the user based on the single-carrier capability.

[0057] Acquiring single-carrier capability (CC) m The methods for _cap are as follows:

[0058]

[0059] Among them, CC m _SpecEff represents the spectral efficiency of the user on carrier m, CC m _BW represents the bandwidth of carrier m.

[0060] S2. Group the users to be scheduled and iterate through all their groupings; based on the traversed user groups and the carriers used, formulate the resource block allocation optimization problem and constraints.

[0061] The resource block allocation optimization problem is described as follows:

[0062]

[0063] stCx≤q

[0064] 0≤x≤1

[0065] Among them, g T Let x be the transpose of the concatenated vector, x be the optimized vector formed by concatenating the binary variables a and b allocated to the resource block, C be the coefficient matrix, and q be the normalized constraint vector.

[0066] The coefficient matrix C = [C1 C2 C3 C4] T ;

[0067] Wherein, the coefficient matrix after constraint-normalization is C1=[O MP×MPGN L1], the transformation matrix of constraint one The coefficient matrix C2 after constraint 2 normalization is [O MPG×MPGN E MPG The coefficient matrix C3 after constraint trinormalization is [E] MPGN L3], the transformation matrix of constraint three The coefficient matrix C4 after constraint-four normalization is [E MPGN O MPGN×MPG ], O MP×MPGN O MPG×MPGN and O MPGN×MPG All are all-zero matrices. E is a real number identifier. MPG and E MPGN All are identity matrices, where M is the total number of carriers, P is the total number of resource blocks on each carrier, and G is the total number of user groups obtained through iteration. Let 1 be the transpose of a vector. N It is a vector, and its subscripts all represent the dimension of the matrix or vector. MP is the total number of carriers multiplied by the total number of resource blocks on each carrier, MPG is the total number of carriers multiplied by the total number of resource blocks on each carrier multiplied by the total number of user groups, and MPGN is the total number of carriers multiplied by the total number of resource blocks on each carrier multiplied by the total number of user groups multiplied by the number of users.

[0068] The normalized constraint vector q is obtained as follows:

[0069] q = [q1 q2 q3 q4] T

[0070] Wherein, the constraint vector q1 = 1 after constraint one normalization MP The normalized constraint vector after constraint 2 is q2 = P2 × e, where e is the group carrier identifier vector, and the transformation matrix of constraint 2 is... Intermediate matrix in the transformation process of constraint 2 The constraint vector q3 = 0 after constraint trinormalization MPGN The constraint vector after constraint four normalization is q4 = P4 × c, where c is the user identifier vector within the group, and the transformation matrix of constraint four is...

[0071] The method for obtaining the concatenated vector g is as follows:

[0072]

[0073] Wherein, instantaneous velocity vector This represents the instantaneous rate that user 1 in user group 1 can obtain on resource block 1 of carrier 1. This represents the instantaneous rate that user n in user group g can obtain on resource block p of carrier m. r represents the instantaneous rate that user N in user group G can obtain on resource block P of carrier M. T The transpose of the instantaneous velocity vector, 0 MPG Represents the zero vector. It is the transpose of the zero vector.

[0074] The optimized vector x is as follows:

[0075]

[0076] Among them, the group resource block allocation identifier vector User resource block allocation identifier vector Indicates whether resource block 1 on carrier 1 is allocated to user group 1. This indicates whether resource block p on carrier m is allocated to user group g. This indicates whether resource block P on carrier M is allocated to user group G, where M is the total number of carriers, P is the total number of resource blocks on each carrier, and G is the total number of user groups obtained through iteration. This indicates whether resource block 1 on carrier 1 has been allocated to user 1 in user group 1. This indicates whether resource block p on carrier m is allocated to user n in user group g. This indicates whether resource block P on carrier M is allocated to user N in user group G.

[0077] S3. Obtain the solution to the resource block allocation optimization problem and combine it with the multi-user MIMO gain to achieve resource allocation.

[0078] The present invention adopts the following specific solution:

[0079] In this invention, an LTE-Advanced cell consisting of N users is considered. Each user's carrier aggregation (CA) capability (i.e., the maximum number of carriers it can support) is μ. In a given TTI, all users compete for M non-overlapping orthogonal carriers. Each carrier has P resource blocks. Each user to be scheduled can simultaneously use multiple resource blocks on multiple carriers, and each resource block can serve multiple users simultaneously. The base station is equipped with T transmit antennas, each user is equipped with R receive antennas, and each user can use a maximum of 2 data streams per resource block.

[0080] A multi-antenna, multi-user wireless resource management algorithm based on carrier aggregation decouples carrier selection and resource block allocation. In the carrier selection phase, firstly, the single-carrier capability of each user is calculated, i.e., the product of its spectral efficiency and bandwidth on each carrier. Secondly, based on the user's carrier aggregation capability, the top μ carriers with the largest single-carrier capabilities are selected as the user's carriers. In the resource block allocation phase, the N users are first grouped, and the total number of resources allocated is determined by permutations and combinations. The problem involves traversing a set of cases. A resource block allocation optimization problem in the form of integer linear programming is then formulated, with the optimization objective of maximizing system performance and rate. Each resource block serves only one set of users that maximizes the objective. Since the user sets are traversable, this constraint ensures that each resource block supports multiple users, thus utilizing the multi-user MIMO gain. It can be proven that the matrix of coefficients for all constraints in the formulated resource block allocation optimization problem is a fully monomodular matrix. Therefore, this integer linear programming problem can be further relaxed into a linear programming form while maintaining the same optimal solution. This linear programming form can be efficiently solved using existing convex optimization tools such as CVX, further improving the algorithm's implementability.

[0081] This invention provides a carrier aggregation-based radio resource management method for multi-user LTE-Advanced cells. Assuming there are N users in the cell, each user can aggregate a maximum of μ carriers. In a given Time Interval (TTI), all users compete for M non-overlapping orthogonal carriers. Each carrier has P resource blocks, and each user to be scheduled can simultaneously use multiple resource blocks on multiple carriers. Each resource block can serve multiple users simultaneously. The base station is equipped with T transmit antennas, each user is equipped with R receive antennas, and each user can use a maximum of 2 data streams per resource block. Please refer to the system model. Figure 2.

[0082] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention. The preferred embodiments include the following steps:

[0083] Step 1: In the carrier selection phase, first calculate the single-carrier capability for each user, that is, calculate the product of its spectral efficiency and bandwidth on each carrier. For example, the single-carrier capability of a user on carrier m is:

[0084]

[0085] Among them, CC m _SpecEff represents the spectral efficiency of the user on carrier m, CC m _BW represents the bandwidth of carrier m, calculated using Shannon's formula.

[0086] Step 2: Based on the specific single-carrier capabilities obtained in Step 1, select the top μ carriers with the largest single-carrier capabilities for each user as their carriers of use.

[0087] Step 3: In the resource block allocation phase, first, the N users to be scheduled are grouped, and all possible groupings are traversed. According to the principle of permutation and combination, there are a total of There are two types. For example, when N=2, that is, when there are only 2 users to be scheduled, there are a total of 2... N -1 = 2 2 -1 = 3 traversal cases, which are:

[0088] group1={user1},group2={user2},group3={user1,user2}

[0089] Step 4: Based on the traversal of user groups, a resource block allocation optimization problem in the form of integer linear programming is formulated with the optimization objective of maximizing system performance and rate. Each resource block serves only the set of users that best achieves the objective. Since the user groups are traversable, this constraint ensures that each resource block can support the service of multiple users, thus leveraging the multi-user MIMO gain.

[0090] Use a m,p,g ,b m,p,g,n ,c g,n ,e m,g These four 0-1 binary variables, with subscripts m = 1, ..., M representing the carrier index, p = 1, ..., P representing the resource block index on the carrier, g = 1, ..., G representing the traversal of the user group index, and n = 1, ..., N representing the user index among all users. Where a... m,p,gThis indicates whether resource block p on carrier m has been allocated to user group g; if so, this variable equals 1, otherwise it equals 0; b m,p,g,n This indicates whether resource block p on carrier m has been allocated to user n in user group g; if yes, this variable equals 1, otherwise it equals 0; c g,n This indicates whether user group g contains user n; if so, the variable equals 1, otherwise it equals 0; e m,g This variable indicates whether carrier m is assigned to user group g; if so, it equals 1, otherwise it equals 0. For a given TTI, the optimization problem can be formulated as:

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] C5:b m,p,g,n ,a m,p,g ∈{0,1}

[0097] Where, r m,p,g,n This represents the instantaneous rate that user n can obtain in user group g on resource block p of carrier m. It can be calculated using Shannon's formula. If the user is not included in this group, this variable equals 0. Since the specific carrier that each user can use has already been determined during the carrier selection phase, the variable e... m,g The variable c can be determined according to the following rule: The variable is equal to 1 only when all users in user group g can use carrier m; otherwise, the variable is equal to 0. Furthermore, since the specific users in each group are known during the user traversal and grouping in step three, the variable c... g,n It is also known that, therefore, the above optimization problem only has a m,p,g ,b m,p,g,n Two optimization variables. Constraint C1 states that each resource block serves only the group of users that achieves the optimal goal; Constraint C2 states that a carrier must be allocated to a user group before a resource block on that carrier can be allocated to that user group; Constraint C3 states that a resource block can only be allocated to users within that user group after it has been allocated to that user group; Constraint C4 states that a user can only be allocated a resource block if it is included in that user group; Constraint C5 states that a... m,p,g ,b m,p,g,nBoth optimization variables are binary variables (0 and 1), meaning they can only take the value 1 or 0. Observation shows that the above optimization problem is an integer linear programming problem. According to complexity theory, this problem is NP-hard. To further facilitate the solution, it is normalized.

[0098] Construct vector Represents the group resource block allocation identifier vector. This represents the user resource block allocation identifier vector. Represents the instantaneous velocity vector. Represents the group carrier identifier vector. Represents the user identifier vector within the group, where, Indicates whether resource block 1 on carrier 1 is allocated to user group 1. This indicates whether resource block p on carrier m is allocated to user group g. This indicates whether resource block P on carrier M is allocated to user group G, where M is the total number of carriers, P is the total number of resource blocks on each carrier, and G is the total number of user groups obtained through iteration, and so on. This indicates whether resource block 1 on carrier 1 has been allocated to user 1 in user group 1. This indicates whether resource block p on carrier m is allocated to user n in user group g. Indicates whether resource block P on carrier M is allocated to user N in user group G; Indicates whether carrier 1 is allocated to user group 1. Indicates whether carrier m is allocated to user group g. Indicates whether carrier M is allocated to user group G; This indicates whether user 1 is in user group 1. Indicates whether user n is in user group g. This indicates whether user N is in user group G. Concatenating vectors a and b and transposing them yields vector x, which represents the optimization vector. The identifier for a real number is used to indicate that each element in x is a real number, and the superscript indicates the dimension of x. Similarly, a concatenated vector is constructed. The coefficient matrix represents the optimization vector, 0 MPG Let represent the zero vector, and let the subscript indicate the dimension of the zero vector. The optimization objective of optimization problem P1 can be rewritten as:

[0099] Define O as an all-zero matrix, E as the identity matrix, and blkd as the block diagonal operation, i.e. This means that the first element B inside the parentheses is extended diagonally by the second element b times. The constraints of optimization problem P1 can be transformed into an equivalent canonical form:

[0100] C1x≤q1

[0101] Where, C1=[O MP×MPGN L1] is the coefficient matrix after constraint-normalization, O MP×MPGN This is a matrix consisting entirely of zeros. The subscripts indicate the dimensions of the matrix, and the same applies below. Let 1 be the transformation matrix of constraint one. G Let q1 represent a vector, and the subscript indicates the dimension of the vector; q1 = 1 MP This is the normalized constraint vector.

[0102] C2x≤q2

[0103] Where, C2=[O MPG×MPGN E MPG [E] represents the coefficient matrix after constraint-normalization. MPG Let be the identity matrix, and the subscripts indicate the dimension of the matrix, the same below; q2 = P2 × e is the constraint vector after constraint 2 normalization. Let be the transformation matrix of constraint two. This is the intermediate matrix in the transformation process of constraint 2.

[0104] C3x≤q3

[0105] Where, C3 = [E MPGN L3] is the coefficient matrix after constraint trinormalization. Here is the transformation matrix for constraint three; q3 = 0 MPGN This is the constraint vector after the constraint is normalized.

[0106] C4x≤q4

[0107] Where, C4 = [E MPGN O MPGN×MPG ] represents the coefficient matrix after constraint four normalization; q4 = P4 × c represents the constraint vector after constraint four normalization. Let be the transformation matrix of constraint four.

[0108] Therefore, the constraints can be further simplified as follows:

[0109] C = [C1 C2 C3 C4] T , q = [q1 q2 q3 q4] T

[0110] Where C is the normalized coefficient matrix and q is the normalized constraint vector.

[0111] Accordingly, the optimization problem P1 can be expressed in its normalized form:

[0112]

[0113]

[0114] in, `<integer identifier>`. Given a monomorphic matrix, it is known that it remains a monomorphic matrix after undergoing the following four transformations, i.e., these four transformations preserve the monomorphism property:

[0115] Rule 1: Multiply the row or column by -1;

[0116] Rule 2: Select the pivot element to perform row and column permutations;

[0117] Rule 3: Delete duplicate rows or columns, or copy rows or columns;

[0118] Rule 4: Delete or add rows or columns that are all 0 or have only one non-zero element, 1.

[0119] Furthermore, it can be proven that the coefficients of the constraints in the integer linear programming problem P2 are a unitary matrix. The proof is as follows:

[0120] To facilitate the transformation, the coefficient matrix C is first split into:

[0121]

[0122] Step 1: Using Rule 4, which removes rows or columns containing only one non-zero element (1), we obtain the coefficient matrix after the transformation in Step 1.

[0123] C (1) =[L1 L3] T

[0124] Step 2: Using Rule 3, which removes duplicate rows or columns from L3, we can obtain the coefficient matrix after the transformation in the second step:

[0125] C (2) =[L1 -E MPG ] T

[0126] Step 3: Use Rule 1, i.e., -E MPG Multiplying by -1 yields the coefficient matrix after the third transformation:

[0127] C (3) =[L1 E MPG ] T

[0128] Step 4: Use Rule 2, i.e., select E. MPG By using all 1 elements as pivots and performing row and column permutations, we can obtain the coefficient matrix after the fourth transformation:

[0129] C (4)=[0 E MPG ] T

[0130] Step 5: Using Rule 4, which removes rows or columns containing only 0s, we can obtain the coefficient matrix after the transformation in step 5:

[0131] C (5) =E MPG

[0132] Easy to know E MPG Since C is a monomon matrix, the coefficient matrix C is also a monomon matrix. The proof is complete.

[0133] Existing literature has proven that when the coefficient matrix of the constraints of an integer linear programming problem is a monomon matrix, the integer linear programming problem can be relaxed into a corresponding linear programming problem while maintaining the same optimal solution. Therefore, the above optimization problem P2 can be further transformed into:

[0134]

[0135] stCx≤q

[0136] 0≤x≤1

[0137] The optimization problem P3 can be solved efficiently using existing convex optimization tools such as CVX, making the algorithm simpler and more feasible.

[0138] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0139] The advantages of this invention can be further illustrated by the following simulation results:

[0140] In this invention, the LTE-Advanced cell used as an example consists of N=6 users, each user's carrier aggregation (CA) capability is limited to 2, and users are deployed in three ways: 1) random distribution, 2) distribution in the cell center, and 3) distribution at the cell edge. In a given TTI, all users compete for M=3 non-overlapping orthogonal carriers, each carrier has P=10 resource blocks, and each user to be scheduled can simultaneously use multiple resource blocks on multiple carriers. Each resource block can serve multiple users simultaneously. The base station is equipped with T=30 transmit antennas, each user is equipped with R=3 receive antennas, and each user can use a maximum of 2 data streams per resource block.

[0141] When users are deployed in a randomly distributed manner, they are scattered within the base station's coverage area. To achieve near, medium, and far coverage, the distance from user n to the base station is:

[0142]

[0143] Among them, M con M represents the coverage area of ​​the base station. min Let N be the minimum range of the base station, N be the number of users, and rand be a random number between 0 and 1. The angles from the users to the base station follow a uniform distribution in the range [0, 2π]. Path loss is generated based on a simple path loss model.

[0144]

[0145] Where d0 is the far-field reference distance of the antenna, which is typically 10-100 meters for outdoor use, and γ is the path loss exponent. n Let P be the distance from user n to the base station. r For received power, P t For the transmit power, K is typically taken as the free-space path gain of the omnidirectional antenna at d0, which is:

[0146]

[0147] Where λ is the center frequency of the frequency point, and the shadow fading follows a log-normal distribution. Small-scale coefficients are generated using Rayleigh independent and identically distributed coefficients. Path losses at different frequencies are calculated based on the path loss formula. The shadow fading at the first frequency point is obtained according to the log-normal distribution, and then the shadow fading at the remaining frequencies is calculated based on the correlation of shadow fading at the frequency points.

[0148]

[0149] Among them, f i Let σ represent the shadow fading at the i-th frequency point, and σ be the shadow fading correlation coefficient. The specific parameter settings are shown in the table below:

[0150] Parameter name default value Remark Base station coverage 200m Minimum range of base stations 300m Antenna far-field reference distance 20m Outdoors, it is generally 10-100m Path loss index 6.5 The urban macro-level range is 3.7-6.5. Shadow fading distribution variance 3.65 Shadow fading correlation coefficient 0.9 maximum transmission power of base station 30dBm noise power -121.4473dbm

[0151] The algorithm was independently simulated more than 1,000 times using the Monte Carlo simulation method. The comparison scheme was the ORAA algorithm proposed in the literature (Rostami S, Arshad K, Rapajic P. Optimum radio resource management in carrier aggregation based LTE-advanced systems[J]. IEEE Transactions on Vehicular Technology, 2017, 67(1):580-589.), which has the limitation that each resource block can only serve a single user.

[0152] Please see Figure 3 The system throughput performance of the proposed algorithm and its comparison algorithms are presented under three user deployment scenarios: (a) performance comparison for edge user distribution, (b) performance comparison for random user distribution, and (c) performance comparison for center user distribution. The star-shaped marker represents the multi-antenna multi-user radio resource management algorithm based on carrier aggregation, while the diamond-shaped marker represents the ORAA algorithm used for comparison. The results show that the algorithm provided in this invention, due to its utilization of multi-user MIMO gain, still achieves better throughput performance than the comparison schemes, even with some performance loss caused by the decoupling of carrier selection and RB allocation. Furthermore, the gain is more significant when the user distribution is close to the base station center or when the number of users is large.

[0153] Please see Figure 4 , 5 6. To further reduce algorithm complexity, when traversing the user grouping in step three, consider setting the maximum number of users N that a group can contain. max This is to eliminate user groups whose multi-user gains may be saturated. Figure 4 In the diagram, (a) represents the edge distribution of users, and (b) represents the algorithm performance as a function of N for that user distribution. max The changes. Figure 5 In the diagram, (a) represents the case of random user distribution, and (b) shows the algorithm performance as the user distribution changes with N. max The changes. Figure 6 (a) shows the user center distribution, and (b) shows the algorithm performance as a function of N for this user distribution. max The changes. Figure 4 , 5 6. N is given under different user distributions. maxThe system throughput performance of the algorithms provided (e.g., 2-6) and the comparison algorithms are presented. The star-shaped marker represents the carrier aggregation-based multi-antenna multi-user radio resource management algorithm, and the diamond-shaped marker represents the ORAA algorithm used for comparison. Results show that the algorithm provided in this invention achieves high performance gain when the maximum number of users within a group is set to half the total number of users, and this value can be further reduced when the user distribution is more peripheral. Therefore, it is recommended that the algorithm provided in this invention be used in practice. This eliminates the need to traverse all possibilities when grouping users, reducing the computational scale of the optimization problem, lowering algorithm complexity, and further improving computational efficiency.

[0154] In summary, this invention discloses a multi-user resource allocation method for LTE-A systems based on carrier aggregation, wherein carrier selection and resource block allocation are decoupled, and each resource block supports serving multiple users. First, the specific carrier used by each user is determined during the carrier selection phase based on spectral efficiency. Then, an integer linear programming problem is formulated for resource block allocation. By proving that the coefficient matrix of the constraints of this integer linear programming problem is a fully monomorphic matrix, the original optimization problem is relaxed into a linear programming problem that can be directly solved using existing convex optimization tools. With the help of the algorithmic technology of this invention, multi-user MIMO gain can be applied to carrier aggregation, thereby further improving the system throughput, outperforming existing algorithms under different user distribution settings.

[0155] A multi-user resource allocation system for LTE-A systems based on carrier aggregation, such as Figure 7 As shown, it includes a carrier acquisition module, an optimization problem establishment module, and an optimization problem solving module;

[0156] The carrier acquisition module is used to acquire the user's single-carrier capability on a carrier, and select a carrier with a larger single-carrier capability as the carrier to be used for the user based on the single-carrier capability.

[0157] The optimization problem establishment module is used to group the users to be scheduled, traverse all their grouping situations, and formulate resource block allocation optimization problems and constraints based on the traversed user groups and the carriers used.

[0158] The optimization problem solving module is used to obtain the solution to the resource block allocation optimization problem and combine it with the multi-user MIMO gain to realize resource allocation.

[0159] An embodiment of the present invention provides a terminal device comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the various method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above.

[0160] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention.

[0161] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0162] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0163] The memory can be used to store the computer program and / or module. The processor implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0164] If the modules / units integrated into the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0165] The multi-user resource allocation method for LTE-A systems based on carrier aggregation proposed in this invention has the following advantages:

[0166] 1) This invention takes into account the need for each resource block in a practical communication system to support the transmission of multiple users, thus avoiding the limitation in existing literature that each resource block can only serve a single user.

[0167] 2) The resource block allocation optimization problem in the form of integer linear programming can be further relaxed into linear programming form while maintaining the same optimal solution.

[0168] 3) The linear programming form of the resource block allocation optimization problem can be easily solved using existing convex optimization tools such as CVX, with high solution efficiency and strong feasibility.

[0169] In summary, the multi-antenna multi-user radio resource management algorithm based on carrier aggregation proposed in this invention is applicable to practical LTE-Advanced communication systems. It decouples multi-user carrier selection and resource block allocation and formulates an integer linear programming problem for resource block allocation that can be directly solved using existing convex optimization tools. It fully exploits the multi-user MIMO gain and further improves the system throughput. It outperforms existing algorithms under different user distribution settings.

[0170] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for multi-user resource allocation in a carrier aggregation based LTE-A system, characterized in that, Includes the following steps: Obtaining single carrier capability of a user on a carrier, selecting a single carrier front carrier for the user to use as the carrier according to the single carrier capability. Group the users to be scheduled and iterate through all their groupings; based on the traversed user groups and the carriers used, formulate the resource block allocation optimization problem and constraints. Obtain the solution to the resource block allocation optimization problem, and combine it with multi-user MIMO gain to achieve resource allocation; The problem statement for resource block allocation optimization is as follows: coefficient matrix splicing vectors The method to obtain it is as follows: Optimize vector as follows: Normalized constraint vector The method to obtain it is as follows: in, The maximum number of carriers that can be aggregated for each user. For the transpose of the concatenated vector, Binary variables allocated to resource blocks and The concatenated optimized vector, The coefficient matrix, The normalized constraint vector; To constrain a normalized coefficient matrix, The coefficient matrix after constraint-2 normalization is... To constrain the coefficient matrix after triple normalization, To constrain the coefficient matrix after quadrature normalization; This is the transpose of the instantaneous velocity vector. It is the transpose of the zero vector; Assign the transpose of the identifier vector to the group resource block. Assign the transpose of the identifier vector to the user resource block; For real number identifiers, It is the total number of carriers multiplied by the total number of resource blocks on each carrier. It is the total number of carriers multiplied by the total number of resource blocks on each carrier multiplied by the total number of user groups. To constrain a normalized constraint vector, Let be the constraint vector after constraint 2 normalization. To constrain the constraint vector after tri-normalization, The constraint vector is the constraint vector after constraint four normalization; the instantaneous velocity vector. , This represents the instantaneous rate that user 1 in user group 1 can obtain on resource block 1 of carrier 1. User group users in On carrier resource blocks The instantaneous rate that can be obtained is User group users in On carrier resource blocks The instantaneous rate that can be obtained is This represents the zero vector.

2. The multi-user resource allocation method for an LTE-A system based on carrier aggregation according to claim 1, characterized in that, Acquiring single-carrier capability The method is as follows: in, Indicates that the user is on the carrier Spectral efficiency on Indicates carrier The bandwidth.

3. The multi-user resource allocation method for an LTE-A system based on carrier aggregation according to claim 1, characterized in that, Constraint-normalized coefficient matrix Transformation matrix of constraint one The coefficient matrix after constraint 2 normalization The coefficient matrix after constraint trinormalization Transformation matrix of constraint three The coefficient matrix after constraint-four normalization , , and All are all-zero matrices. and All are identity matrices. The total number of carriers, The total number of resource blocks on each carrier. The total number of user groups obtained through iteration. It is the transpose of a vector. Let be a vector, and its subscripts represent the dimensions of the matrix or vector. It is the total number of carriers multiplied by the total number of resource blocks on each carrier.

4. The multi-user resource allocation method for an LTE-A system based on carrier aggregation according to claim 1, characterized in that, Normalized constraint vector The constraint vector after constraint 2 normalization , For the group carrier identifier vector, the transformation matrix of constraint two The intermediate matrix in the transformation process of constraint two The constraint vector after constraint trinormalization The constraint vector after constraint 4 normalization , Given the user identifier vector within the group, the transformation matrix of constraint four. .

5. The multi-user resource allocation method for an LTE-A system based on carrier aggregation according to claim 1, characterized in that, Group resource block allocation identifier vector User resource block allocation identifier vector , Indicates whether resource block 1 on carrier 1 is allocated to user group 1. Indicates carrier resource blocks on Assign to user group , Indicates carrier resource blocks on Assign to user group , The total number of carriers, The total number of resource blocks on each carrier. This represents the total number of user groups obtained through iteration. This indicates whether resource block 1 on carrier 1 has been allocated to user 1 in user group 1. Indicates carrier resource blocks on Assign to user group users in , Indicates carrier resource blocks on Assign to user group users in .

6. A multi-user resource allocation system for an LTE-A system based on carrier aggregation, characterized in that, The method described by any one of claims 1 to 5 includes: The carrier acquisition module is used to acquire the user's single-carrier capability on a carrier and select a carrier with a larger single-carrier capability as the carrier to be used for the user based on the single-carrier capability. The optimization problem establishment module is used to group users to be scheduled, traverse all grouping situations, and formulate resource block allocation optimization problems and constraints based on the traversed user groups and the carriers used. An optimization problem-solving module is used to obtain the solution to the resource block allocation optimization problem and combine it with multi-user MIMO gain to realize resource allocation.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes a computer program, it implements the steps of the multi-user resource allocation method for an LTE-A system based on carrier aggregation as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-user resource allocation method for LTE-A system based on carrier aggregation as described in any one of claims 1 to 5.

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