A method for 5G wireless resource slicing and scheduling based on equivalent bandwidth

Abstract

The application relates to a 5G wireless resource slicing and scheduling method based on equivalent bandwidth, which comprises the following steps: determining a user channel matrix of a user according to the relationship between the user and the transceiving antenna of a base station, constructing a received signal vector to solve an equivalent channel matrix of the user, performing singular value decomposition on the equivalent channel matrix, obtaining a user rate function, determining a number limitation condition of space division users, performing Lagrange series expansion on the user rate function and retaining the first two terms to obtain a user demand bandwidth function, bringing the optimal power of the user and the number limitation condition of the space division users into the user demand bandwidth function to construct an equivalent spectrum bandwidth function representing the equivalent spectrum bandwidth required by the user, constructing an AC admission strategy and a resource allocation model, combining a Markov model, and allocating resources to each user by using the AC admission strategy and the resource allocation model, so that the user can be accurately allocated resources, and resource waste is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, in particular to a method for 5G wireless resource slicing and scheduling based on equivalent bandwidth. BACKGROUND

[0002] With the full deployment of 5G, large-scale 5G applications still face various challenges. Among them, 5G industry applications still face the problem of flexible adaptation of various different demand services on the 5G network. The 5G new network can cut different virtual networks for different demand services through network slicing technology, thereby flexibly adapting to the needs of different services. The existing research on 5G slicing mainly focuses on the 5G core network (i.e., wired network). In the 5G wireless access network, what a slice contains, how to quantitatively calculate the size, what the user admission strategy is, and how to allocate resources and other issues have not been well solved, which is not conducive to the system to accurately allocate resources to users, resulting in waste of resources. SUMMARY

[0003] Therefore, it is necessary to provide a method for 5G wireless resource slicing and scheduling based on equivalent bandwidth, which can enable the system to accurately allocate resources to users and reduce resource waste.

[0004] A method for 5G wireless resource slicing and scheduling based on equivalent bandwidth, the method comprising:

[0005] Step 1, determining a user channel matrix of a user according to the relationship between the user and the transceiving antenna of a base station, constructing a received signal vector after removing the interference between users by using the precoding matrix corresponding to the user according to the user channel matrix of the user, and solving the received signal vector to obtain an equivalent channel matrix of the user;

[0006] Step 2, singular value decomposition of the equivalent channel matrix to obtain a user rate function and determine a number limit condition of space division users;

[0007] Step 3, Lagrange series expansion of the user rate function and retaining the first two terms to obtain a user demand bandwidth function, bringing the optimal power of the user and the number limit condition of the space division users into the user demand bandwidth function, and mapping the resources occupied in the frequency domain, the spatial domain and the power domain into an equivalent frequency spectrum bandwidth of a MIMO-OFDMA system to construct an equivalent frequency spectrum bandwidth function representing the equivalent frequency spectrum bandwidth required by the user;

[0008] Step 4, constructing an AC admission strategy and a resource allocation model according to the equivalent frequency spectrum bandwidth function;

[0009] Step 5, in the MIMO-OFDMA system, the AC admission strategy and the resource allocation model are used to allocate resources to users under different service users and different quality of service requirements in combination with the Markov model.

[0010] In one embodiment, the user channel matrix of the user is determined according to the relationship between the user and the transceiving antenna of the base station, the received signal vector after interference between users is removed by using the precoding matrix corresponding to the user is constructed according to the user channel matrix of the user, and the received signal vector is solved to obtain the equivalent channel matrix of the user, including:

[0011] According to the relationship between the user and the transceiving antenna of the base station, the user channel matrix H of the user is extracted j , the received signal vector y after interference between users is removed by using the precoding matrix corresponding to the user is constructed according to the user channel matrix of the user j , the received signal vector y j is expressed as:

[0012]

[0013] Wherein, the first term is the signal required by user j at the receiving end itself, and the second term represents the interference signal of other users to user j, T k Indicates the precoding matrix, x j Indicates the signal sent to user j, x k Indicates the signal sent to other users k, q j Indicates noise, and S indicates the user set.

[0014] Definition Indicates the joint channel matrix of all users in the user set S except user j, and singular value decomposition is performed on the joint channel matrix to obtain the null space matrix of the joint channel matrix

[0015] Let Wherein, Indicates the precoding partial matrix of user j for eliminating interference between users, B j Indicates the part of the precoding matrix for maximizing the rate of user j, and the equivalent channel matrix of user j is obtained Wherein,

[0016] In one embodiment, the singular value decomposition is performed on the equivalent channel matrix to obtain the user rate function, and the number limit condition of the space division user is determined, including:

[0017] The equivalent channel matrix of user j singular value decomposition is performed, and the equivalent channel matrix is decomposed into a plurality of parallel sub-channels, wherein the number of the parallel sub-channels is determined by the rank of the equivalent channel matrix .

[0018] According to the sum of the bit rates of all the parallel sub-channels, a user rate function R j of the user j on one sub-carrier is obtained j , and the user rate function R

[0019]

[0020] wherein λ jl is the lth eigenvalue of the product of the equivalent channel matrix of the user j and the transpose of the equivalent channel matrix; P is the transmission power allocated to the user in the base station; N j is the rank of the equivalent matrix; σ 2 is the covariance of the additive white Gaussian noise vector, and M is the number of the transmission antennas of the base station;

[0021] Analysis is made on the null space matrix to determine the number limitation condition of the multiplexed users when the equivalent channel matrix is in a rich scattering environment, and the number limitation condition of the spatial division users is: wherein N k is the number of the receiving antennas of the user.

[0022] In one embodiment, the user rate function is subjected to Lagrange series expansion and the first two terms are reserved to obtain a user demand bandwidth function, the optimal power of the user and the number limitation condition of the spatial division users are brought into the user demand bandwidth function, the resources occupied in the frequency domain, the space domain and the power domain are uniformly mapped into the equivalent frequency spectrum bandwidth of the MIMO-OFDMA system to construct an equivalent frequency spectrum bandwidth function representing the equivalent frequency spectrum bandwidth required by the user, including:

[0023] The user rate function of the user j is subjected to Lagrange series expansion and the first two terms are reserved to obtain the user demand bandwidth function C j W of the user j, and the expression of the user demand bandwidth function C j W of the user j is:

[0024]

[0025] wherein C j is the number of the sub-carriers of the user j, W is the bandwidth of one OFDM sub-carrier, P0 is the optimal power, a j is the error rate requirement of the user;

[0026] Wherein, the best power P0 is obtained by taking partial derivative of the user demand bandwidth function and setting it to 0, and the expression of the best power P0 is:

[0027]

[0028] The expression of the best power P0 and the number of space users are substituted into the user demand bandwidth function, so that the resources occupied in the frequency domain, the space domain and the power domain are uniformly mapped to the equivalent frequency bandwidth of the MIMO-OFDMA system under different service users and different quality of service requirements in the MIMO-OFDMA system, to construct the equivalent frequency bandwidth function δ j The expression of the equivalent frequency bandwidth δ j is:

[0029]

[0030] Wherein, N0 is the assumed number of user receiving antennas, and g is the number of users in the user set S.

[0031] In one embodiment, the expression of the AC admission policy is:

[0032]

[0033] Wherein, is defined as the binary AC indicator of the user arriving in the e service of the i tenant, ARP e,i represents the priority of the current arriving user, ARP e′,i represents the priority of the arrived user, u e′,i is the number of users of the i tenant served, δ e′,i represents the equivalent frequency bandwidth of the i tenant served, δ e,i represents the equivalent frequency bandwidth of the current arriving user in the e service of the i tenant, C max,i is the available capacity of the i tenant, e ′ represents the service type of the served user.

[0034] In one embodiment, the resource allocation model is used to determine the number of physical resource blocks N ass,e,i allocated to the admitted users of the e service of the i tenant under a given state according to the equivalent frequency bandwidth required by the requesting user, the priority and the number of physical resource blocks in the cell.

[0035] The aforementioned 5G wireless resource slicing and scheduling method based on equivalent bandwidth determines the user's user channel matrix according to the relationship between the user and the base station's transceiver antennas. Based on the user's user channel matrix, a received signal vector is constructed after removing inter-user interference using the user's corresponding precoding matrix. The received signal vector is solved to obtain the user's equivalent channel matrix. Singular value decomposition is performed on the equivalent channel matrix to obtain the user rate function, and a spatial limitation on the number of users is determined. The user rate function is then expanded using a Lagrange series, and the first two terms are retained to obtain the user's required bandwidth function. Finally, the optimal power of the user and the spatial limitation are considered. The user quantity constraint is incorporated into the user demand bandwidth function, uniformly mapping the resources occupied in the frequency, spatial, and power domains to the equivalent spectrum bandwidth of the MIMO-OFDMA system. This constructs an equivalent spectrum bandwidth function that characterizes the equivalent spectrum bandwidth required by each user. Based on this equivalent spectrum bandwidth function, an AC admission strategy and resource allocation model are constructed. Under different service users and different service quality requirements in the MIMO-OFDMA system, combined with a Markov model, the AC admission strategy and resource allocation model are used to allocate resources to each user. This ensures accurate resource allocation for users and avoids resource waste. Attached Figure Description

[0036] To more clearly illustrate the embodiments of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and implementation methods:

[0037] Figure 1 This is a schematic diagram of a MIMO-OFDMA system in one embodiment;

[0038] Figure 2 This is a flowchart illustrating a method for 5G wireless resource slicing and scheduling based on equivalent bandwidth in one embodiment.

[0039] Figure 3 This is a schematic diagram of a multi-slice RAN scenario in one embodiment;

[0040] Figure 4 This is a flowchart of the wireless resource allocation process for a resource allocation model in one embodiment. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0042] This application provides a method for 5G wireless resource slicing and scheduling based on equivalent bandwidth, which can be applied to, for example... Figure 1The diagram illustrates a MIMO-OFDMA system established using the physical layer multiple access method employed in 5G communication systems. This MIMO-OFDMA system is a link within a cellular network where one base station provides services to multiple users. In this MIMO-OFDMA system, each base station has M ≥ 1 transmit antennas, and the number of users in the set of accessible users within the MIMO-OFDMA system is S, with each user configured with N ≥ 1 receive antenna. X = [x1, x2, ..., x...] S [T1, T2, ..., T] represents the transmitted signal vector matrix. S To transmit the precoding matrix corresponding to the signal, at the transmitting end, the base station sends a shared signal X containing coded symbols of S user data streams within the MIMO-OFDMA system. This signal passes through channel H. j The signal reaches user j; as the j-th user, the baseband signal it receives can be represented as y. j Since the resource slicing of 5G radio access networks is closely related to the method by which users occupy resources, i.e., the physical layer multiple access method, the MIMO-OFDMA system used in 5G can be viewed as performing multiple-input multiple-output processing on the signal data stream again on the basis of the OFDMA system. Through MIMO technology, one OFDM sub-channel can be equivalent to multiple independent parallel spatial channels. By analyzing the rate and spatial capacity limitations of the MIMO-OFDMA system, the resources of the MIMO-OFDMA system can be mapped to the frequency domain; this method can also be extended to multi-tenant slicing in general 5G systems. In radio access systems, resources are essentially only spectrum and power. In the 5G radio side, due to the introduction of multi-user MIMO, it is equivalent to spatially reusing the spectrum, thus increasing spatial resources. The size of these spatial resources depends on the rank of the joint user channel matrix, which is determined by the number of antennas.

[0043] In one embodiment, such as Figure 2 As shown, a method for 5G radio resource slicing and scheduling based on equivalent bandwidth is provided, the method comprising:

[0044] Step 1: Determine the user's channel matrix based on the relationship between the user and the base station's transceiver antennas. Based on the user's channel matrix, construct the received signal vector after removing interference between users using the user's corresponding precoding matrix. Solve the received signal vector to obtain the user's equivalent channel matrix.

[0045] Step 2: Perform singular value decomposition on the equivalent channel matrix to obtain the user rate function and determine the number of spatial users.

[0046] Step 3: Perform a Lagrange series expansion on the user rate function and retain the first two terms to obtain the user demand bandwidth function. Substitute the user's optimal power and the number of spatially divided users into the user demand bandwidth function to uniformly map the resources occupied in the frequency domain, spatial domain, and power domain to the equivalent spectrum bandwidth of the MIMO-OFDMA system, so as to construct an equivalent spectrum bandwidth function that characterizes the equivalent spectrum bandwidth required by the user.

[0047] In a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiple Access (MIMO-OFDMA) system, a multi-antenna transmit optimal power-frequency domain mapping model is established based on the rate and bit error rate requirements of different services. This model yields the optimal power bandwidth in the power domain that meets the differentiated requirements of the services. Further considering the spatial-frequency domain mapping, the multiplexing capability of the MIMO system is utilized to determine the maximum number of spatially divided users allocated to a single subcarrier based on the number of transmit and receive antennas. This, combined with the optimal power bandwidth, yields the equivalent spectrum bandwidth for specific service communication, thereby achieving optimal adaptation of radio-side resources to meet the differentiated QoS (Quality of Service) requirements of different services.

[0048] Step 4: Construct the AC admission strategy and resource allocation model based on the equivalent spectrum bandwidth function.

[0049] The AC admission policy can be a slice-aware AC policy, which isolates user admission between different tenants by guaranteeing the proportion of available radio spectrum resources for each tenant, so that users from one tenant are not affected by other tenants when being admitted.

[0050] In one example, such as Figure 3 The diagram illustrates a multi-slice RAN scenario. This scenario consists of cells with a certain bandwidth, which is subdivided into Physical Resource Blocks (PRBs) of bandwidth B. It also includes N=2 tenants, each with two services (service types 1 and 2). Assuming that users generate sessions according to a Poisson arrival process and these sessions have exponential durations, the dynamic evolution of the number of admitted users for each service type and tenant is characterized by a continuous-time Markov chain (CTMC). Then, when a user generates a new session, they send an "admission request" (AC request) to the base station. The AC admission policy determines whether the system can accept the new request, specifically depending on the tenant's available capacity C. max,i, the equivalent spectrum bandwidth of the user and the ARP (Allocation and Retention Priority) corresponding to the priority of the user; as a result of the AC admission policy, the base station will reply to the request of the user with a "response" message (i.e. accept or reject). In addition, the result of the AC admission policy will also affect the transition between Markov states, specifically, the value of the sum of the equivalent spectrum bandwidth of the served users of the ith tenant (whose priority is higher than or equal to that of the new user) after accumulation and the equivalent bandwidth of the new user does not exceed the maximum available capacity C max,i , the user can be admitted, otherwise, the user is not admitted. Only when the user is admitted, the resource allocation policy will be executed, and the number of PRBs allocated to the admitted users of the e-th service of the ith tenant in a given state is calculated according to the equivalent spectrum resource of the requesting user, and the resource is provided to the requesting user.

[0051] Step 5, in a MIMO-OFDMA system, under different service users and different quality of service requirements, combining Markov model, AC admission policy and resource allocation model are used to allocate resources to each user.

[0052] Wherein, the Markov model can dynamically evolve the number of admitted users of each service and tenant.

[0053] Wherein, the resource allocation model is shown in the wireless resource allocation flowchart as shown in Figure 4 . For a given number of admitted users, the required resource quantity N req,e,i of each user request can be obtained. Figure 4 According to the process, the process is iterated from the user with a lower ARP value to the user with a higher ARP value, and as long as there is available spectrum resource, the resource is provided to the given ARP user, each user will get the corresponding required resource quantity N req,e,i , and before iterating to the next ARP, the available resource quantity will also be correspondingly reduced, if the ARP max , the available spectrum resource will remain unused. Conversely, when there is not enough resource to provide service resource for the given ARP user in the process of resource allocation (i.e. congestion state), the resource quantity N req,e,i allocated to each user of this ARP will be reduced in proportion.

[0054] The method for 5G wireless resource slicing and scheduling based on equivalent bandwidth, by determining the user channel matrix of the user according to the relationship between the user and the transceiving antenna of the base station, constructing the received signal vector after removing the interference between users by using the precoding matrix corresponding to the user according to the user channel matrix of the user, solving the received signal vector to obtain the equivalent channel matrix of the user, singular value decomposition of the equivalent channel matrix, obtaining the user rate function, determining the number limit condition of the space division user, carrying out Lagrange series expansion on the user rate function and retaining the first two terms to obtain the user demand bandwidth function, bringing the optimal power of the user and the number limit condition of the space division user into the user demand bandwidth function, mapping the resources occupied in the frequency domain, the space domain and the power domain into the equivalent frequency spectrum bandwidth of the MIMO-OFDMA system, to construct the equivalent frequency spectrum bandwidth function representing the equivalent frequency spectrum bandwidth required by the user, constructing the AC admission strategy and the resource allocation model according to the equivalent frequency spectrum bandwidth function, under different service users and different quality of service requirements in the MIMO-OFDMA system, combining the Markov model, using the AC admission strategy and the resource allocation model to allocate resources to each user, thereby accurately allocating resources to the user and avoiding waste of resources.

[0055] In one embodiment, the user channel matrix of the user is determined according to the relationship between the user and the transceiving antenna of the base station, and the received signal vector after removing the interference between users by using the precoding matrix corresponding to the user is constructed according to the user channel matrix of the user, and the received signal vector is solved to obtain the equivalent channel matrix of the user, including:

[0056] According to the relationship between the user and the transceiving antenna of the base station, the user channel matrix H of the user is extracted j , and the received signal vector y after removing the interference between users by using the precoding matrix corresponding to the user is constructed according to the user channel matrix of the user j , the received signal vector y j is expressed as:

[0057]

[0058] Wherein, the first term is the signal required by user j at the receiving end itself, and the second term represents the interference signal of other users to user j, T k represents the precoding matrix, x j represents the signal sent to user j, x k represents the signal sent to other users k, q j represents noise, and S represents the user set.

[0059] Definition denotes the joint channel matrix of all users in the user set S except user j, the joint channel matrix is singular value decomposed to obtain a null space matrix of the joint channel matrix

[0060] Let wherein, denotes a precoding partial matrix used by user j to cancel inter-user interference, B j denotes a part of the precoding matrix used to maximize the rate of user j, and an equivalent channel matrix of user j is obtained wherein,

[0061] wherein, the precoding matrix T k can make the second term 0, so as to remove the inter-user interference, and by solving the precoding matrix, an equivalent channel matrix of the user can be obtained, and a number limit condition of space division users in the system is given.

[0062] In one embodiment, the equivalent channel matrix is singular value decomposed to obtain a user rate function, and the number limit condition of space division users is determined, comprising:

[0063] The equivalent channel matrix of user j is singular value decomposed into a plurality of parallel sub-channels, wherein the number of parallel sub-channels is determined by the rank of the equivalent channel matrix According to the sum of bit rates of all parallel sub-channels, a user rate function R j of user j on one sub-carrier is obtained, and the user rate function R j is expressed as:

[0064]

[0065] wherein, λ jl is the lth eigenvalue of the product of the equivalent channel matrix of user j and the transpose of the equivalent channel matrix; P is the transmit power allocated to the user in the base station; N j is the rank of the equivalent matrix; σ 2 is the covariance of the additive white Gaussian noise vector, and M is the number of base station transmit antennas;

[0066] The null space matrix is analyzed to determine the number limit condition of users that can be multiplexed when the equivalent channel matrix is in a rich scattering environment as the number limit condition of space division users, wherein the number limit condition of space division users is: wherein, N k is the number of user receive antennas.

[0067] It should be understood that according to the number limit condition of space division users, it can be seen that the number of users simultaneously served by the system is subject to the number of base station transmit antennas M and the number of user receive antennas Nk with the constraint.

[0068] In one embodiment, the Lagrange series expansion is performed on the user rate function and the first two terms are reserved to obtain the user demand bandwidth function, the optimal power of the user and the number of spatial users are brought into the user demand bandwidth function, and the resources occupied in the frequency domain, the spatial domain and the power domain are uniformly mapped into the equivalent frequency spectrum bandwidth of the MIMO-OFDMA system to construct the equivalent frequency spectrum bandwidth function representing the equivalent frequency spectrum bandwidth required by the user, including:

[0069] The Lagrange series expansion is performed on the user rate function of the user j and the first two terms are reserved to obtain the user demand bandwidth function C j W of the user j. j The expression of the user demand bandwidth function C

[0070]

[0071] Wherein, C j is the number of subcarriers of the user j, W is the bandwidth of an OFDM subcarrier, P0 is the optimal power, a j is the error rate requirement of the user.

[0072] Wherein, the optimal power P0 is obtained by taking the partial derivative of the user demand bandwidth function and setting it to 0, and the expression of the optimal power P0 is:

[0073]

[0074] The expression of the optimal power P0 and the number of spatial users is substituted into the user demand bandwidth function, so that the resources occupied in the frequency domain, the spatial domain and the power domain are uniformly mapped into the equivalent frequency spectrum bandwidth of the MIMO-OFDMA system under different service users and different quality of service requirements in the MIMO-OFDMA system, to construct the equivalent frequency spectrum bandwidth function δ j representing the equivalent frequency spectrum bandwidth required by the user j. j The expression of the equivalent frequency spectrum bandwidth δ

[0075]

[0076] Wherein, N0 is the assumed number of user receiving antennas, and g is the number of users in the user set S.

[0077] It should be understood that the analysis considers the equivalent bandwidth of users after the subcarriers are spatially multiplexed by multiple users, and the maximum number of spatially multiplexed users for one subcarrier in the MIMO-OFDMA system is g; that is, through multiplexing in the MIMO-OFDMA system, one subcarrier is equivalent to being allocated to at most g users through multiplexing, and finally the mapping of the resources occupied by users of different services in the MIMO-OFDM system under different QoS requirements in different domains (i.e., the frequency domain, the spatial domain, and the power domain) is unified into an equivalent spectrum bandwidth function δ of the equivalent spectrum bandwidth required by the user j in the MIMO-OFDMA system. j The expression is:

[0078]

[0079] In one embodiment, the expression of the AC admission policy is:

[0080]

[0081] wherein, is defined as a binary AC indicator of the arrival of users in the e-th service of the i-th tenant, ARP e,i represents the priority of the currently arrived users, ARP e′,i represents the priority of the arrived users, u e′,i is the number of users of the i-th tenant that have been served, δ e′,i represents the equivalent spectrum bandwidth of the users of the i-th tenant that have been served, δ e,i represents the equivalent spectrum bandwidth of the currently arrived users in the e-th service of the i-th tenant, C max,i is the available capacity of the i-th tenant, e ′ represents the service type of the served users.

[0082] In one embodiment, the resource allocation model is used to determine the number of physical resource blocks N allocated to the admitted users of the e-th service of the i-th tenant in a given state according to the equivalent spectrum bandwidth required by the requesting users, the priority, and the number of physical resource blocks in the cell. ass,e,i .

[0083] The method for 5G wireless resource slicing and scheduling based on equivalent bandwidth introduces multi-user MIMO to multiplex the spectrum in space; resource slicing divides different resources to different service types according to the QoS requirements of different services, and establishes a model of service occupying spectrum, the occupied spectrum bandwidth considers the requirements of QoS, the addition of spatial resources and the mapping of optimal power, and maps the resources occupied by different service users in different domains (frequency domain, spatial domain and power domain) under different QoS requirements to the frequency domain, thereby deducing the equivalent spectrum bandwidth occupied by the connection of different specific communication service users, and answering the quantitative calculation of the equivalent bandwidth occupied by the user for such service type, and the RAN resource slicing in the multi-tenant multi-user scenario is specified, and the wireless resource allocation strategy in the 5G multi-tenant multi-service scenario is given combined with the Markov model, and a cell model in the multi-tenant scenario is established, which includes defining services according to guaranteed bit rate (GBR) and allocation and retention priority (ARP) indicators, which are part of the 5G quality of service profile of new radio (NR), so that the QoS parameters play an important role in the definition of AC admission strategy and the allocation function of resources in the model.

[0084] Further, the proposed AC admission strategy can realize the isolation between different slices, so that the overload in one slice does not affect the acceptance of users in another slice, while maintaining the maximum capacity allowed by each slice, and the priority strategy between services can also be embodied by providing lower blocking probability to services with lower ARP (higher priority).

[0085] It should be understood that, although Figure 2 The steps in the flowchart of the method are shown in sequence according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, Figure 2 At least part of the steps in the method can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or sub-steps or stages of other steps.

[0086] The technical features of the above embodiments can be combined in any way. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present disclosure.

[0087] The above-described embodiments are merely illustrative of several embodiments of the present application, which are described in more detail and in a specific and detailed manner, but should not be construed as limiting the scope of the patent. It should be noted that for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, and these are all within the scope of the present application. Therefore, the scope of protection of the patent of the present application should be subject to the appended claims.

Claims

1. A method for 5G wireless resource slicing and scheduling based on equivalent bandwidth, characterized in that, The method includes: Step 1: Determine the user's channel matrix based on the relationship between the user and the base station's transceiver antennas. Based on the user's channel matrix, construct the received signal vector after removing interference between users using the user's corresponding precoding matrix. Solve the received signal vector to obtain the user's equivalent channel matrix. Step 2: Perform singular value decomposition on the equivalent channel matrix to obtain the user rate function and determine the limitation on the number of spatial users; Step 3: Perform a Lagrange series expansion on the user rate function and retain the first two terms to obtain the user demand bandwidth function. Substitute the user's optimal power and the number of spatial users into the user demand bandwidth function to uniformly map the resources occupied in the frequency domain, spatial domain and power domain to the equivalent spectrum bandwidth of the MIMO-OFDMA system, so as to construct an equivalent spectrum bandwidth function that characterizes the equivalent spectrum bandwidth required by the user. Step 4: Construct the AC admission strategy and resource allocation model based on the equivalent spectrum bandwidth function; Step 5: Under different service users and different service quality requirements in the MIMO-OFDMA system, resource allocation is performed for each user using the AC admission strategy and the resource allocation model in conjunction with the Markov model.

2. The method according to claim 1, characterized in that, The process of determining the user's user channel matrix based on the relationship between the user and the base station's transceiver antennas, constructing a received signal vector after removing inter-user interference using the user's corresponding precoding matrix, and solving the received signal vector to obtain the user's equivalent channel matrix includes: Based on the relationship between the user and the base station's transmit and receive antennas, the user channel matrix H is extracted. j Based on the user's channel matrix, a received signal vector y is constructed after removing inter-user interference using the user's corresponding precoding matrix. j Received signal vector y j Represented as: The first term represents the signal required by user j at the receiving end, while the second term represents the interference signal from other users to user j. k Let x represent the precoding matrix of other user k. j This represents the signal sent to user j, x k q represents the signal sent to other user k. j Let S represent the noise for user j, and S represent the user set. definition This represents the joint channel matrix of all users in the user set S excluding user j. Singular value decomposition is performed on the joint channel matrix to obtain its null space matrix. make in, B represents the precoding portion matrix used by user j to eliminate inter-user interference. j This represents the portion of the precoding matrix used to maximize the rate of user j, yielding the equivalent channel matrix for user j. in, 3. The method according to claim 2, characterized in that, The step of performing singular value decomposition on the equivalent channel matrix to obtain the user rate function and determining the limitation on the number of spatially divided users includes: The equivalent channel matrix for user j Singular value decomposition is performed, resulting in multiple parallel sub-channels, the number of which is determined by the equivalent channel matrix. The order is determined by; The user rate function R for user j on a subcarrier is obtained by summing the bit rates of all parallel sub-channels. j User rate function R j Represented as: Where, λ jl Let N be the l-th eigenvalue of the equivalent channel matrix and the transpose of the equivalent channel matrix for user j; P is the transmit power allocated to the user in the base station; N j σ is the rank of the equivalent matrix; 2 Let M be the covariance of the additive white Gaussian noise vector, and M be the number of base station transmit antennas; The null space matrix is ​​analyzed to determine the constraint on the number of reusable users when the equivalent channel matrix is ​​in a scattering-rich environment. This constraint serves as the constraint on the number of spatially divided users. Where, N k The number of antennas for receiving users.

4. The method according to claim 3, characterized in that, The user rate function is expanded using a Lagrange series, and the first two terms are retained to obtain the user demand bandwidth function. The optimal power of the user and the constraint on the number of spatially divided users are then substituted into the user demand bandwidth function. This maps the resources occupied in the frequency, spatial, and power domains to the equivalent spectral bandwidth of the MIMO-OFDMA system, thus constructing an equivalent spectral bandwidth function that characterizes the equivalent spectral bandwidth required by the user. This includes: Expanding the user rate function of user j into a Lagrange series and retaining the first two terms, we obtain the user demand bandwidth function C of user j. j W, user j's user demand bandwidth function C j The expression for W is: Among them, C j Let W be the number of subcarriers for user j, W be the bandwidth of one OFDM subcarrier, P0 be the optimal power, and a be the number of subcarriers for user j. j For the user's bit error rate requirements; The optimal power P0 is obtained by taking the partial derivative of the user demand bandwidth function and setting it to 0. The expression for the optimal power P0 is as follows: Substituting the expression for the optimal power P0 and the constraint on the number of spatial users into the user demand bandwidth function, the resources occupied in the frequency domain, spatial domain, and power domain are uniformly mapped to the equivalent spectrum bandwidth of the MIMO-OFDMA system under different service users and different service quality requirements. This allows the construction of an equivalent spectrum bandwidth function δ that characterizes the equivalent spectrum bandwidth required by user j. j Equivalent spectral bandwidth δ j The expression is: Where N0 is the assumed number of user receiving antennas, and g is the number of users in the user set S.

5. The method according to claim 4, characterized in that, The expression for the AC admission policy is: in, Defined as the binary AC indicator of the service reached by the user in the ith tenant's ith service, ARP e,i Indicates the priority of the current user, ARP e′,i This indicates that the user's priority has been reached, u e′,i δ represents the number of users already served by the i-th tenant. e′,i δ represents the equivalent spectrum bandwidth of the users already served by the i-th tenant. e,i C represents the equivalent spectrum bandwidth currently reaching the user in the e-th service of the i-th tenant. max,i e represents the available capacity for the i-th tenant. ′ This represents the type of service that has been served to users.

6. The method according to claim 5, characterized in that, The resource allocation model is used to determine, in a given state, the number N of physical resource blocks allocated to the e-th service of the i-th tenant, based on the equivalent spectrum bandwidth required by the requesting user, the priority, and the number of physical resource blocks in the cell. ass,e,i .

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