CN116684883A - Spectrum optimization method and device - Google Patents
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- H—ELECTRICITY
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/14—Spectrum sharing arrangements between different networks
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Abstract
The disclosure provides a spectrum optimization method and device, relates to the field of data processing, and can give consideration to flexibility of use of a terminal user and safety of data access, and improve use experience of the user. The specific technical scheme is as follows: acquiring network operation data uploaded by a user and a plurality of cells; acquiring target spectrum efficiency of a plurality of cells according to network operation data; and performing spectrum matching distribution according to the bilateral matching principle and the target spectrum efficiency of the cells to obtain a target spectrum optimal strategy of each cell in the cells. According to the method and the system, the network operation data uploaded by the user and the cells are obtained, the target spectrum efficiency of the cells is obtained, spectrum matching distribution is carried out according to the bilateral matching principle and the target spectrum efficiency of the cells, and the target spectrum optimal strategy of each cell in the cells is obtained, so that the optimal solution set of the perception degree and the energy consumption efficiency of the system user can be obtained, the system spectrum efficiency is effectively improved, and the user experience is improved.
Description
Technical Field
The disclosure relates to the field of data processing, and in particular relates to a frequency spectrum optimization method and device.
Background
Currently, a traditional macro cell and a simultaneous common frequency full duplex (Co-time Co-frequency Full Duplexing, CCFD) cell overlapping networking mode exist in a network framework, and under the scene that the traditional macro cell and the CCFD cell are Co-networked, a large amount of Cross-link Interference (Cross-Link Interference, CLI) and Self-Interference (SI) exist outside transmission channels between a terminal and the cell. The macro cell and the CCFD cell station adopt different resource allocation modes, thereby increasing the complexity of network channel interference and reducing the utilization rate of the system spectrum.
In the above framework, how to effectively perform resource allocation and interference optimization is a technical key for improving the anti-interference capability of the whole network and the spectrum efficiency of the system. Because the CCFD cell has the characteristic of supporting time-frequency domain uplink and downlink duplexing, the cell resource elastic adjustment in the system can be realized in the CLI/SI interference constraint range, thereby realizing dynamic adjustment according to the service requirement of the coverage area and ensuring the optimal adjustment of the user perception and the system spectrum efficiency.
Therefore, how to balance the user perceptibility and the energy consumption efficiency, so as to realize the optimal adjustment of the user perceptibility and the system spectrum efficiency, and improve the user experience, becomes a problem to be solved.
Disclosure of Invention
The embodiment of the disclosure provides a spectrum optimization method and device, which can give consideration to the use flexibility of a terminal user and the safety of data access, and promote the use experience of the user. The technical scheme is as follows:
according to a first aspect of embodiments of the present disclosure, there is provided a spectrum optimization method, the method comprising: acquiring network operation data uploaded by a user and a plurality of cells; acquiring target spectrum efficiency of the cells according to the network operation data; and performing spectrum matching distribution according to a bilateral matching principle and the target spectrum efficiency of the cells to obtain a target spectrum optimal strategy of each cell in the cells.
Specifically, network operation data uploaded by the user and the cells can be obtained under the initial given resource configuration. For example, channel correlation measurement data, scheduling policy data, and user measurement data may be obtained in a serving cell, during a scheduling period uploaded by a serving user.
Based on the scheme, the target spectrum efficiency of the cells is obtained by obtaining the network operation data uploaded by the users and the cells, spectrum matching distribution is carried out according to the bilateral matching principle and the target spectrum efficiency of the cells, and the target spectrum optimal strategy of each cell in the cells is obtained, so that the optimal solution set of the perception degree and the energy consumption efficiency of the system user can be obtained, and the user experience is improved.
In some embodiments, the performing spectrum matching allocation with the target spectrum efficiency of the plurality of cells according to the bilateral matching principle, and obtaining the target spectrum optimization policy of each cell in the plurality of cells includes: dividing each cell in the plurality of cells into a disturbed cell set and a scrambling cell set according to the bilateral matching principle, and establishing two set utility functions according to channel relevance and a service model; sorting the interfered cell set and the scrambling cell set according to the two set utility functions; and performing spectrum matching distribution according to the ordered disturbed cell set, the disturbed cell set and the target spectrum efficiency of the cells to obtain a target spectrum optimal strategy of each cell in the cells.
Specifically, each cell in the multiple cells can be divided into a disturbed cell set and a scrambling cell set according to a bilateral matching principle, and two set utility functions are established according to channel relevance and a service model; then sorting the interfered cell set and the scrambling cell set according to the utility functions of the two sets; and finally, performing spectrum matching distribution according to the sorted disturbed cell set, the disturbed cell set and the target spectrum efficiency of the cells to obtain a target spectrum optimal strategy of each cell in the cells.
Based on the scheme, given two solutions of user association and resource allocation, dividing the cells into two types of disturbed and disturbed according to the two perception promotion cooperation relations, and defining two types of cell preference relations based on a bilateral matching principle. According to the preference relation, the interference exerting cell finishes the resource transfer to the interference exerting cell, so that the balance of the cell spectrum efficiency in the system is achieved, the optimal solution set of the perception degree and the energy consumption efficiency of the system user can be obtained, and the user experience is improved.
In some embodiments, the obtaining the target spectrum efficiency of the plurality of cells according to the network operation data includes: and obtaining target spectrum efficiency of the cells through user and cell association degree reshaping, network resource configuration retrieval and spectrum efficiency matching cooperation according to the network operation data.
Based on the scheme, the method defines three models of user relevance, resource searching and perception promotion, models according to the optimal solution set problem of the user perception and the system spectrum efficiency, and expresses the optimal solution set problem as a multi-constraint non-convex optimization problem. Substituting the channel correlation data, the (system) scheduling strategy data, the user measurement data and other related data into the self-adaptive training of the user and cell correlation model, and iteratively solving the optimization problem solution set to obtain the network related parameter adjustment range. In addition, the multi-constraint non-convex optimization problem is decomposed into three corresponding model non-convex optimization sub-problems (i.e., user and cell association remodeling, network resource configuration retrieval, and spectrum efficiency matching collaboration), thereby reducing problem iteration complexity and improving realizability.
In some embodiments, the method further comprises: substituting the channel correlation data, the scheduling policy data and the measurement data of the user into a user and cell correlation model.
Specifically, in the process of remodelling the association degree between the user and the cell, given the resource allocation and the scheduling cooperation relationship of the cell, relevant data such as channel correlation data, (system) scheduling policy data, user measurement data (including user signal-to-noise ratio data, mcs data, cross link interference data and self-interference cancellation data, for example) and the like are substituted into a model of the association degree between the user and the cell, and the link relationship between the user and the cell is remodelled according to the model and the network performance
Based on the scheme, the channel relation between the user and the cell is remolded by utilizing the system channel correlation data, the scheduling strategy data and the measurement data of the user, and the channel relation is adjusted according to the service requirement of the service user of the current cell. And in the range of channel quality and CLI/SI interference constraint, the users with the same service form are attributed to the same cell as much as possible.
In some embodiments, the obtaining the target spectrum efficiency of the plurality of cells according to the network operation data through user and cell association remodeling, network resource configuration retrieval, and spectrum efficiency matching cooperation includes: and obtaining target spectrum efficiency of the cells through the combination of the user and cell association degree reshaping, the network resource configuration retrieval and the spectrum efficiency matching according to the network operation data by utilizing a particle swarm algorithm.
For example, network resource retrieval may be performed based on network operational data, and the search factors (including global and local search factors) may be initialized given the cell resource configuration, user associated cells, and scheduling cooperation. According to the user association and scheduling cooperation relation of the current cell, a global initialization solution set and a local initialization solution set are defined, the resource allocation optimization problem is defined as all search positions, the fitness function is defined as the system spectrum efficiency, the number of users is positioned in the space dimension, and the spectrum efficiency optimal solution set is solved through a particle swarm optimization algorithm.
Based on the scheme, according to the remodeled channel relation and user distribution, the overall search is carried out based on a particle swarm optimization algorithm to solve a resource allocation sub-problem solution set by combining cell self-interference, inter-station and inter-user CLI constraint conditions, and the optimal solution set of the resource allocation is obtained through particle swarm optimization iterative computation.
According to a second aspect of embodiments of the present disclosure, there is provided a spectrum optimization apparatus comprising a memory and a processor. The memory has a stored program. When the program is executed in the processor, the processor is configured to: acquiring network operation data uploaded by a user and a plurality of cells; acquiring target spectrum efficiency of the cells according to the network operation data; and performing spectrum matching distribution according to a bilateral matching principle and the target spectrum efficiency of the cells to obtain a target spectrum optimal strategy of each cell in the cells.
Specifically, network operation data uploaded by the user and the cells can be obtained under the initial given resource configuration. For example, channel correlation measurement data, scheduling policy data, and user measurement data may be obtained in a serving cell, during a scheduling period uploaded by a serving user.
Based on the scheme, the target spectrum efficiency of the cells is obtained by obtaining the network operation data uploaded by the users and the cells, spectrum matching distribution is carried out according to the bilateral matching principle and the target spectrum efficiency of the cells, and the target spectrum optimal strategy of each cell in the cells is obtained, so that the optimal solution set of the perception degree and the energy consumption efficiency of the system user can be obtained, and the user experience is improved.
In some embodiments, the processor is specifically configured to divide each cell of the plurality of cells into a disturbed cell set and a disturbed cell set according to the bilateral matching principle, and establish two set utility functions according to a channel relevance and a service model; sorting the interfered cell set and the scrambling cell set according to the two set utility functions; and performing spectrum matching distribution according to the ordered disturbed cell set, the disturbed cell set and the target spectrum efficiency of the cells to obtain a target spectrum optimal strategy of each cell in the cells.
Specifically, each cell in the multiple cells can be divided into a disturbed cell set and a scrambling cell set according to a bilateral matching principle, and two set utility functions are established according to channel relevance and a service model; then sorting the interfered cell set and the scrambling cell set according to the utility functions of the two sets; and finally, performing spectrum matching distribution according to the sorted disturbed cell set, the disturbed cell set and the target spectrum efficiency of the cells to obtain a target spectrum optimal strategy of each cell in the cells.
Based on the scheme, given two solutions of user association and resource allocation, dividing the cells into two types of disturbed and disturbed according to the two perception promotion cooperation relations, and defining two types of cell preference relations based on a bilateral matching principle. According to the preference relation, the interference exerting cell finishes the resource transfer to the interference exerting cell, so that the balance of the cell spectrum efficiency in the system is achieved, the optimal solution set of the perception degree and the energy consumption efficiency of the system user can be obtained, and the user experience is improved.
In some embodiments, the processor is specifically configured to: and obtaining target spectrum efficiency of the cells through user and cell association degree reshaping, network resource configuration retrieval and spectrum efficiency matching cooperation according to the network operation data.
Based on the scheme, the method defines three models of user relevance, resource searching and perception promotion, models according to the optimal solution set problem of the user perception and the system spectrum efficiency, and expresses the optimal solution set problem as a multi-constraint non-convex optimization problem. Substituting the channel correlation data, the (system) scheduling strategy data, the user measurement data and other related data into the self-adaptive training of the user and cell correlation model, and iteratively solving the optimization problem solution set to obtain the network related parameter adjustment range. In addition, the multi-constraint non-convex optimization problem is decomposed into three corresponding model non-convex optimization sub-problems (i.e., user and cell association remodeling, network resource configuration retrieval, and spectrum efficiency matching collaboration), thereby reducing problem iteration complexity and improving realizability.
In some embodiments, the processor is further configured to: substituting the channel correlation data, the scheduling policy data and the measurement data of the user into a user and cell correlation model.
Specifically, in the process of remodelling the association degree between the user and the cell, given the resource allocation and the scheduling cooperation relationship of the cell, relevant data such as channel correlation data, (system) scheduling policy data, user measurement data (including user signal-to-noise ratio data, mcs data, cross link interference data and self-interference cancellation data, for example) and the like are substituted into a model of the association degree between the user and the cell, and the link relationship between the user and the cell is remodelled according to the model and the network performance
Based on the scheme, the channel relation between the user and the cell is remolded by utilizing the system channel correlation data, the scheduling strategy data and the measurement data of the user, and the channel relation is adjusted according to the service requirement of the service user of the current cell. And in the range of channel quality and CLI/SI interference constraint, the users with the same service form are attributed to the same cell as much as possible.
In some embodiments, the processor is specifically configured to: and obtaining target spectrum efficiency of the cells through the combination of the user and cell association degree reshaping, the network resource configuration retrieval and the spectrum efficiency matching according to the network operation data by utilizing a particle swarm algorithm.
For example, network resource retrieval may be performed based on network operational data, and the search factors (including global and local search factors) may be initialized given the cell resource configuration, user associated cells, and scheduling cooperation. According to the user association and scheduling cooperation relation of the current cell, a global initialization solution set and a local initialization solution set are defined, the resource allocation optimization problem is defined as all search positions, the fitness function is defined as the system spectrum efficiency, the number of users is positioned in the space dimension, and the spectrum efficiency optimal solution set is solved through a particle swarm optimization algorithm.
Based on the scheme, according to the remodeled channel relation and user distribution, the overall search is carried out based on a particle swarm optimization algorithm to solve a resource allocation sub-problem solution set by combining cell self-interference, inter-station and inter-user CLI constraint conditions, and the optimal solution set of the resource allocation is obtained through particle swarm optimization iterative computation.
According to a third aspect of embodiments of the present disclosure, there is provided a spectrum optimisation apparatus comprising a processor and a memory having stored therein at least one computer instruction which is loaded and executed by the processor to carry out the steps performed in the spectrum optimisation method described in the first aspect and any embodiment of the first aspect.
According to a fourth aspect of an embodiment of the present disclosure, there is provided a computer program storage medium, characterized in that the computer program storage medium has program instructions, which when executed by a processor, cause the processor to perform the method of the first aspect.
According to a fifth aspect of an embodiment of the present disclosure, there is provided a chip system, characterized in that the chip system comprises at least one processor, and when the program instructions are executed in the at least one processor, the at least one processor is caused to perform the method according to the first aspect.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a schematic diagram of a channel relationship framework 100 under a conventional 5G macro station and CCFD station co-networking provided by the present disclosure;
fig. 2 is a schematic diagram of an energy consumption adjustment framework 200 under a multi-CCFD station co-networking provided by the present disclosure;
FIG. 3 is a flow chart of a spectrum optimization method 300 provided by an embodiment of the present disclosure;
fig. 4 is a block diagram of a spectrum optimization apparatus 400 according to an embodiment of the present disclosure;
fig. 5 is a block diagram of a spectrum optimization apparatus 500 provided in an embodiment of the present disclosure.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
Currently, a system network based on duplex technology can effectively perform resource allocation and interference management according to service changes, so as to realize multi-dimensional scalability adaptation of network coverage, service scheduling, system interference resistance and the like. Fig. 1 is a schematic diagram of a channel relationship framework 100 under Co-networking of a conventional 5G macro station and a CCFD station provided in the present disclosure, as shown in fig. 1, in a specific practical network framework, there is a conventional macro cell and a simultaneous Co-time Co-frequency Full Duplexing (CCFD) cell overlapping networking manner, and in a scenario where the conventional macro cell and the CCFD cell Co-networking, a large amount of Cross-link Interference (Cross-Link Interference, CLI) and Self-Interference (SI) exist outside transmission channels between a terminal and a cell. The macro cell and the CCFD cell station adopt different resource allocation modes (for example, the time domain adopts different frame structures, the frequency domain adopts different RB scheduling strategies), thereby increasing the complexity of network channel interference and reducing the utilization rate of the system spectrum.
In the above framework, how to effectively perform resource allocation and interference optimization is a technical key for improving the anti-interference capability of the whole network and the spectrum efficiency of the system. The traditional cell manually adjusts the cell frame structure by judging the network service type and the spectrum resource utilization rate so as to raise the user traffic, and the mode needs the whole network to cooperatively adjust, but cannot consider the real-time performance of the network service change, and partial user perception may be sacrificed. In addition, if an abnormal frame structure scene occurs, the same frequency interference can be caused, and the network performance is affected.
Fig. 2 is a schematic diagram of an energy consumption adjustment framework 200 under a multi-CCFD station co-networking provided by the present disclosure, as shown in fig. 2, because the CCFD cell has a characteristic of supporting uplink and downlink duplexing in a time-frequency domain, a Flexible Frame structure (Flexible Frame) is fully and essentially configured for commercial deployment, which means that the constraint of traditional cell resource adjustment is broken, and cell resource elastic adjustment in a system can be implemented in the CLI/SI interference constraint range, so that dynamic adjustment according to service requirements of a coverage area is implemented, and optimal adjustment of user perceptibility and system spectrum efficiency is ensured. New challenges arise in adjusting spectrum resource allocation and scheduling in the system according to business characteristics, spectrum efficiency, user perception, and optimal yields need to be achieved in a multidimensional model.
Therefore, how to balance the user perceptibility and the energy consumption efficiency, to realize the optimal adjustment of the user perceptibility and the system spectrum efficiency, and to improve the user experience, becomes a problem to be solved.
In view of the above, the present disclosure provides a spectrum optimization method, which can improve the above-mentioned problems and enhance the user experience.
It should be appreciated that the embodiments of the present disclosure may be applied to 5G CCFD systems and similar wireless communication network systems, and may be deployed in scenarios where a conventional macrocell is co-networked with a CCFD cell, and a CCFD cell is co-networked.
An embodiment of the present disclosure provides a spectrum optimization method 300, as shown in fig. 3, the spectrum optimization method 300 includes the following steps:
s301, network operation data uploaded by a user and a plurality of cells are obtained.
Specifically, network operation data uploaded by the user and the cells can be obtained under the initial given resource configuration. For example, channel correlation measurement data, scheduling policy data, and user measurement data may be obtained in a serving cell, during a scheduling period uploaded by a serving user.
S302, obtaining target spectrum efficiency of a plurality of cells according to network operation data.
For example, to simplify the process of obtaining the target spectrum efficiency of the multiple cells, the target spectrum efficiency of the multiple cells may be obtained according to the network operation data through three steps of user-cell association remodeling, network resource configuration retrieval, and spectrum efficiency matching cooperation.
Further, the channel correlation data, the scheduling policy data, and the measurement data of the user may be substituted into the user-to-cell correlation model.
Specifically, in the process of remodeling the association degree between the user and the cell, given the resource configuration and the scheduling cooperation relationship between the cell, relevant data such as channel correlation data, (system) scheduling policy data, user measurement data (including user signal-to-noise ratio data, mcs data, cross link interference data and self-interference cancellation data, for example) and the like are substituted into a model of the association degree between the user and the cell, and the link relationship (in a user association degree module) between the user and the cell is reshaped according to the model and the network performance as follows:
The first step: the association factor (in the disclosed embodiment, the association factor is the lagrangian multiplier described below) is initialized given the cell resource configuration and scheduling coordination relationship.
And a second step of: and calculating the association degree of the users in each cell, and selecting the base station with the maximum user association function.
And a third step of: and updating the association factor according to the results before and after iteration.
Fourth step: the gradient convergence of the user association degree judges whether the iteration result before and after is smaller than a threshold value, if so, the user association cell is output; if not, returning to the second step.
And in the reconstruction process, link cross interference factors introduced by uplink and downlink asymmetric characteristics of the CCFD system are considered, iterative optimization is carried out on the cell with the maximum user association degree, so that gradient convergence between iterations of the user association degree function and difference between iterations are smaller than a threshold value, the iterative remodeling is completed, and the cell with the user association is output.
Optionally, according to the network operation data, a particle swarm algorithm is utilized to obtain target spectrum efficiencies of a plurality of cells through a user and cell association degree remodelling method, a network resource configuration retrieval method and a spectrum efficiency matching cooperation method.
For example, network resource retrieval may be performed based on network operational data, with the search factors being initialized given the relationship of cell resource configuration, user associated cells, and scheduling cooperation (including global and local search factors, i.e., c, described below 1 ，c 2 ). According to the user association and scheduling cooperation relation of the current cell, a global and local initialization solution set is defined, a resource allocation optimization problem is defined as all search positions, an adaptability function is defined as system spectrum efficiency, the number of users is positioned in space dimension, and a spectrum efficiency optimal solution set (namely, target spectrum efficiency of a plurality of cells) is solved through a particle swarm optimization algorithm, wherein the specific flow (in a network resource module) is as follows:
the first step: the search factor is initialized given the relationship of cell resource configuration, user association cell and scheduling cooperation.
And a second step of: initializing a global optimal solution set and a local optimal solution set, and defining a fitness function (target spectrum efficiency of a plurality of cells) and an initial position.
And a third step of: judging whether the iteration number is the maximum iteration number, and if so, outputting a global optimal solution set; if not, calculating the global optimal value and position in the iteration period of the first half stage of the solving process, and calculating the local optimal value and position of each particle in the iteration period of the second half stage of the solving process, comparing the fitness to find the maximum fitness value as the global optimal position.
Fourth step: and updating the searching speed and the weight factors, and returning to the third step.
The iterative solution process is divided into a first half-stage global search and a second half-stage local search for preventing the problems of local optimal solution set and low high dimensional nonlinear precision, and the two stages adopt a dynamic weight strategy to improve the retrieval capability.
And in the above flow, the frequency spectrum efficiency convergence threshold may be set according to the network scale and performance requirements, and the iteration number may be set according to the network depth.
And S303, performing spectrum matching distribution with the target spectrum efficiency of the cells according to a bilateral matching principle, and acquiring a target spectrum optimal strategy of each cell in the cells.
Specifically, each cell in the multiple cells can be divided into a disturbed cell set and a scrambling cell set according to a bilateral matching principle, and two set utility functions are established according to channel relevance and a service model; then sorting the interfered cell set and the scrambling cell set according to the utility functions of the two sets; and finally, performing spectrum matching distribution according to the sorted disturbed cell set, the disturbed cell set and the target spectrum efficiency of the cells to obtain a target spectrum optimal strategy of each cell in the cells.
For example, under the condition of given cell resource configuration, user association cell and network resource retrieval result relationship, each cell in the plurality of cells can be divided into a disturbed cell set and a disturbed cell set according to a bilateral matching principle, two set utility functions (preference degrees) are established according to channel association and a service model, then the disturbed cell set and the disturbed cell set can be ordered according to the two set utility functions, spectrum resources of the disturbed cell are transmitted by the disturbed cell according to the preference degrees in the ordered disturbed cell set and the disturbed cell set, and the disturbed cell is removed from the current set after transmission is completed until the two sets are empty sets and spectrum efficiency matching distribution is completed, so that a resource distribution adjustment strategy (i.e., a target optimal strategy) of each cell in the plurality of cells is obtained (the specific flow in the perception promotion module) is as follows:
the first step: according to the bilateral matching principle, each cell in a plurality of cells of the base station is divided into a disturbed cell set and a scrambling cell set, and two set utility functions are established according to the channel relevance and the service model.
And a second step of: and sequencing the interfered cell set and the scrambling cell set according to the two set utility functions.
And a third step of: and in the sorted interfered cell set and the scrambling cell set, the scrambling cell transmits the resource of the interfered cell according to the preference degree, and the resource transmission is completed.
Fourth step: judging whether the interfered cell set and the scrambling cell set are empty or not, if so, completing spectrum efficiency matching allocation, thereby obtaining a resource allocation adjustment strategy (namely, a target spectrum optimal strategy) of each cell in a plurality of cells; if not, returning to the second step.
Based on the scheme, the method defines three models of user relevance, resource searching and perception promotion, models according to the optimal solution set problem of the user perception and the system spectrum efficiency, and expresses the optimal solution set problem as a multi-constraint non-convex optimization problem. Substituting the channel correlation data, the (system) scheduling strategy data, the user measurement data and other related data into the self-adaptive training of the user and cell correlation model, and iteratively solving the optimization problem solution set to obtain the network related parameter adjustment range. In addition, the multi-constraint non-convex optimization problem is decomposed into three corresponding model non-convex optimization sub-problems (i.e., user and cell association remodeling, network resource configuration retrieval, and spectrum efficiency matching collaboration), thereby reducing problem iteration complexity and improving realizability.
And then, remolding the channel relation between the user and the cell by utilizing the system channel correlation data, the scheduling policy data and the measurement data of the user, and adjusting the channel relation according to the service requirement of the service user of the current cell. And in the range of channel quality and CLI/SI interference constraint, the users with the same service form are attributed to the same cell as much as possible.
And then, according to the remodeled channel relation and user distribution, combining with the cell self-interference, inter-station and inter-user CLI constraint conditions, carrying out global search to solve a resource allocation sub-problem solution set based on a particle swarm optimization algorithm, and obtaining a resource allocation optimal solution set through particle swarm optimization iterative computation.
And finally, giving two solutions of user association and resource allocation, dividing the cells into two types of disturbed and disturbed according to the two perception promotion cooperative relationship, and defining two types of cell preference degree relationship based on a bilateral matching principle. And according to the preference relation, the scrambling cell completes the resource transfer to the scrambling cell, thereby achieving the balance of the frequency spectrum efficiency of the cell in the system.
The method and the system provide definitions of three models based on user association, network resource allocation search and spectrum efficiency cooperation, channel correlation, scheduling strategies and user measurement are substituted into the model self-adaptive training, the channel relation between a user and a cell and the system cell resource allocation adjustment strategy are remodeled, the cell is divided into two types of interference and interference according to the spectrum efficiency cooperation relation between the user and the cell, energy consumption adjustment is carried out according to preference between the two types of interference so as to achieve equalization of two sets, and therefore the optimal solution set of the perception degree and the energy consumption efficiency of the system user can be obtained, and the user experience is improved.
Further, to more clearly embody the solution of the method 300, the disclosure herein fully enumerates an example, as follows:
first, assume that the current CCFD system has M ε {1,2,3, …, M+1} cells, where user j ε {1,2,3, …, N }, where M+1}<N，x jm Indicating the access relation between the user j and the cell m. There are several service subscribers in the current network and there are multiple traffic scenarios, e.g. traffic scenario a (low latency), traffic scenario B (large downlink traffic), scenario C (large uplink traffic), according to a non-coherent distributionTransaction).
Because of the asymmetric uplink and downlink characteristics of the CCFD system, the disclosure defines a user signal-to-noise ratio (Signal to Interference plus Noise Ratio, SINR), and the specific formula is as follows:
wherein: gamma ray jm Signal-to-noise ratio for user j and cell m; p (P) jm The transmitting power of the cell m to the user j; h is a jm Channel gains for user j and cell m; p (P) jm m' Transmitting power to user j for other cells; h is a jm m' Interference channel gain for other cells; delta 2 Is additive white gaussian noise;cross-layer common-frequency cross-link interference for other cells, denoted Cli jm ；∑ j'∈m,j'≠j |h j'm | 2 x j'm P j'm For intra-cell m self-interference, denoted as Sic jm 。
Defining the actual transmission rate of the user j associated to the cell m as:
τ jm ＝Wlog 2 (1+γ jm )
defining the resource relation of the whole network system as follows:
Wherein:for the whole network resource->For cell m real resources, < >>P for cell resource adjustment factor m c Resources are reserved for the cell.
Considering maximum and minimum resource constraint, user perceived rate constraint, CLI/SI interference constraint and scheduling cooperation constraint of a CCFD system cell, an optimization problem with maximum spectrum efficiency (i.e., target spectrum efficiency) as a target is defined, specifically a formula (target problem P 1 )：
P 1 ：
Constraint (s.t.): c (C) 1 ：x jm ＝{0,1}
C 2 ：
C 3 ：
C 4 ：
C 5 ：
C 6 ：P min ≤P m ≤P max
Wherein: x= [ X ] jm ]；P＝[P jm ]；C 1 And C 2 Representing user cell association constraints; c (C) 3 Representing user perceived rate constraints; c (C) 4 And C 5 Representing system CLI/SI interference constraints; c (C) 6 Representing cell resource constraints.
From this, it can be seen that problem P 1 The non-convex characteristic is a non-linear optimization problem.
This problem optimization objective: on the premise of ensuring the perceptibility of network users, the spectrum efficiency of the system is effectively improved, and on the basis that the perceived gradient of the users is not reduced, the balanced and monotonous convergence of the spectrum resources of the system network is optimized.
Due to the problem P 1 The method has the characteristics of nonlinearity, network characteristic values have randomness, and the solving process is very complex. Therefore, the problem P 1 Split into three sub-problems: user and cell association remodeling, network resource configuration retrieval, spectrum efficiency matching collaboration (i.e., user and cell association remodeling, network resource configuration retrieval, and spectrum efficiency matching collaboration).
Under the condition of given cell resource allocation and scheduling cooperation relationship, the user and cell association degree problem is solved by substituting data such as channel correlation data, scheduling strategy data, measurement data of the user and the like into a user and cell association degree model, and the user and cell link relationship is remodeled according to the model and network performance, wherein the specific formula is as follows:
P 2 ：maxEE(X)
s.t.C 1 ，C 2 ，C 3 ，C 4 ，C 5
for continuous convex optimization problems, embodiments of the present disclosure solve using Lagrangian dual methods.
Introduction of Lagrangian multiplier gamma j And theta m Converting the inequality constraint condition into an equality constraint condition, wherein the converted equation is as follows:
defining an optimal user association function EE jm For calculating the association degree of user j and m channels of each cell, according to the SINR of the user in the scheduling period jm ，Mcs jm Cli jm ，Sic jm The optimal channel relation is calculated and solved, and the optimization problem after remodeling can be expressed as the following formula (namely, a user association degree function):
solving the optimal channel relation cell m according to the above formula * 。
And (3) solving a cell power distribution problem under the conditions of network resource configuration retrieval, given cell resource configuration, user associated cells and scheduling cooperation.
P 2 ：maxEE(P)
s.t.C 4 ，C5，C 6
Embodiments of the present disclosure use particle swarm optimization methods to optimize particles x id Defined as a set of feasible solutions P to the variable P in the optimization problem * The fitness function is defined as the system spectrum efficiency EE (P), the space dimension is positioned for the number of users, and the global optimal position gBest is defined id And the local optimum position pBest id . According to the global optimal solution and the local optimal solution, the particle speed and the position are continuously updated, and the specific formula is as follows:
v id (t+1)＝wv id (t)+c 1 rand[pBest id (t)-x id (t+1)]
x id (t+1)＝x id (t)+c 2 rand[gBest id (t)-x id (t)]
wherein: c 1 ，c 2 The method comprises the steps of determining a particle swarm learning factor according to the actual net scale and the optimization depth; the weight factor w is adjusted according to the retrieval speed.
The iterative solution process is divided into a first half stage global search and a second half stage local search for preventing the problems of a local optimal solution set and low high dimensional nonlinear precision, and the two stages adopt a dynamic weight strategy to improve the retrieval capability.
Spectrum efficiency matching cooperation, given cell resource allocation and user associated cell m * Cell resource allocation search result P * Under the relation, the whole network spectrum efficiency adjustment problem is expressed as the following formula:
P 2 ：maxEE(G)
s.t.C 3 ，C 4 ，C 5 ，C 6
according to the bilateral matching principle, a plurality of small objects are arrangedEach cell in the region is according to the disturbed cell set a- = { m - ∈M│G m -P m -P m c <0 and scrambling cell set A + ＝{m + ∈M│G m -P m -P m c > 0, two aggregate utility functions (preference) are built from the channel correlation and the traffic model. And sequencing the interfered cell set and the scrambling cell set according to the utility functions of the two sets, and completing the spectrum resource transmission of the interfered cell by the scrambling cell according to the preference degree, and removing the interfered cell from the current set after the transmission is completed until the two sets are empty sets and the spectrum matching distribution is completed, so as to obtain a resource distribution adjustment strategy (namely, a target spectrum optimal strategy) of each cell in the cells.
Based on the scheme, three models of user relevance, resource searching and perception promotion are defined based on the scheme, and the model is modeled according to the optimal solution set problem of the user perception and the system spectrum efficiency, and is expressed as a multi-constraint non-convex optimization problem. Substituting the channel correlation data, the (system) scheduling strategy data, the user measurement data and other related data into the self-adaptive training of the user and cell correlation model, and iteratively solving the optimization problem solution set to obtain the network related parameter adjustment range. In addition, the multi-constraint non-convex optimization problem is decomposed into three corresponding model non-convex optimization sub-problems (i.e., user and cell association remodeling, network resource configuration retrieval, and spectrum efficiency matching collaboration), thereby reducing problem iteration complexity and improving realizability.
And then, remodelling the channel relation between the user and the cell based on Lagrange dual decomposition (or interior point method) by utilizing the system channel correlation data, the scheduling strategy data and the measurement data of the user, and adjusting the channel relation according to the service requirement of the current cell service user. And in the range of channel quality and CLI/SI interference constraint, the users with the same service form are attributed to the same cell as much as possible.
And then, according to the remodeled channel relation and user distribution, combining with the cell self-interference, inter-station and inter-user CLI constraint conditions, and carrying out global search to solve a resource allocation sub-problem solution set based on a particle swarm optimization algorithm. The particle position is defined as a feasible solution of the resource allocation problem, the dimension space is positioned as user distribution, the fitness function is positioned as a frequency spectrum efficiency problem, the channel quality and interference constraint are normalized and then defined as weights, and the optimal solution set of the resource allocation is obtained through particle swarm optimization iterative computation.
And finally, giving two solutions of user association and resource allocation, dividing the cells into two types of disturbed and disturbed according to the two perception promotion cooperative relationship, and defining two types of cell preference degree relationship based on a bilateral matching principle. And according to the preference relation, the scrambling cell completes the resource transfer to the scrambling cell, thereby achieving the balance of the frequency spectrum efficiency of the cell in the system.
According to the method, channel correlation, scheduling strategy and user measurement are substituted into model self-adaptive training, the non-convex optimization problem is solved through iterative loop, and resource equalization is carried out in a bilateral matching mode and the method acts on an actual network. By the method, the system spectrum efficiency can be effectively improved and the user experience can be improved on the premise of ensuring the perceptibility of network users.
Based on the spectrum optimization method described in the above embodiment corresponding to fig. 3, the following is an embodiment of the apparatus of the present disclosure, which may be used to execute the embodiment of the method of the present disclosure.
The disclosed embodiment provides a spectrum optimization device, as shown in fig. 4. The spectrum optimization apparatus 400 includes: a memory module 401 and a processing module 402.
The storage module 401 is used to store programs.
When the program is executed in the processing module 402, the processing module 402 is configured to perform the spectrum optimization method as described above.
The processing module 402 is configured to:
acquiring network operation data uploaded by a user and a plurality of cells;
acquiring target spectrum efficiency of the cells according to the network operation data;
and performing spectrum matching distribution according to a bilateral matching principle and the target spectrum efficiency of the cells to obtain a target spectrum optimal strategy of each cell in the cells.
Optionally, the processing module 402 is specifically configured to divide each cell of the plurality of cells into a disturbed cell set and a disturbed cell set according to the bilateral matching principle, and establish two set utility functions according to a channel relevance and a service model; sorting the interfered cell set and the scrambling cell set according to the two set utility functions; and performing spectrum matching distribution according to the ordered disturbed cell set, the disturbed cell set and the target spectrum efficiency of the cells to obtain a target spectrum optimal strategy of each cell in the cells.
Optionally, the processing module 402 is specifically configured to obtain, according to the network operation data, the target spectrum efficiencies of the plurality of cells through user-cell association remodeling, network resource configuration retrieval, and spectrum efficiency matching cooperation.
Optionally, the processing module 402 is further configured to substitute the channel correlation data, the scheduling policy data, and the measurement data of the user into a user-cell association model.
Optionally, the processing module 402 is specifically configured to obtain, according to the network operation data, target spectrum efficiencies of the plurality of cells through the cooperation of the user and cell association degree remodeling, the network resource configuration retrieval, and the spectrum efficiency matching by using a particle swarm algorithm.
According to the spectrum optimization equipment provided by the embodiment of the disclosure, the channel correlation, the scheduling policy and the user measurement are substituted into the model self-adaptive training based on the definition of the three models of user association, network resource configuration search and spectrum efficiency cooperation, the cell relationship between the user and the cell is remodeled, the system cell resource configuration adjustment policy is remodeled, the cell is divided into two types of interference and interference according to the spectrum efficiency cooperation relationship between the user and the cell, and the energy consumption is adjusted according to the preference degree between the two types, so that the balance of the two sets is achieved, and therefore the optimal solution set of the perception degree and the energy consumption efficiency of the system user can be obtained, and the experience of the user is improved.
Based on the spectrum optimization method described in the embodiment corresponding to fig. 3, the embodiment of the disclosure further provides a spectrum optimization device, as shown in fig. 5.
The spectrum optimization apparatus 500 comprises a memory 501 and a processor 502.
The memory 501 is used to store program instructions.
The processor 502 is configured to perform the spectrum optimization method described above, when the program is executed in the processor 502.
The processor 502 is configured to:
acquiring network operation data uploaded by a user and a plurality of cells;
acquiring target spectrum efficiency of the cells according to the network operation data;
and performing spectrum matching distribution according to a bilateral matching principle and the target spectrum efficiency of the cells to obtain a target spectrum optimal strategy of each cell in the cells.
Optionally, the processor 502 is specifically configured to divide each cell of the plurality of cells into a disturbed cell set and a disturbed cell set according to the bilateral matching principle, and establish two set utility functions according to a channel relevance and a service model; sorting the interfered cell set and the scrambling cell set according to the two set utility functions; and performing spectrum matching distribution according to the ordered disturbed cell set, the disturbed cell set and the target spectrum efficiency of the cells to obtain a target spectrum optimal strategy of each cell in the cells.
Optionally, the processor 502 is specifically configured to obtain, according to the network operation data, the target spectrum efficiencies of the plurality of cells through user-cell association remodeling, network resource configuration retrieval, and spectrum efficiency matching cooperation.
Optionally, the processor 502 is further configured to substitute the channel correlation data, the scheduling policy data, and the measurement data of the user into a user-cell association model.
Optionally, the processor 502 is specifically configured to obtain, according to the network operation data, target spectrum efficiencies of the plurality of cells through the cooperation of the user and cell association degree remodeling, the network resource configuration retrieval, and the spectrum efficiency matching by using a particle swarm algorithm.
Based on the spectrum optimization method described in the above embodiment corresponding to fig. 3, the present disclosure further provides a computer readable storage medium, for example, a non-transitory computer readable storage medium may be a Read Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like. The storage medium stores computer instructions for executing the spectrum optimization method applied to the terminal device or the server described in the embodiment corresponding to fig. 3, which is not described herein.
Based on the spectrum optimization method described in the embodiment corresponding to fig. 3, the embodiment of the disclosure further provides a chip system, where the chip system includes at least one processor, and when the program instructions are executed in the at least one processor, the at least one processor is caused to execute the spectrum optimization method applied to the terminal device or the server described in the embodiment corresponding to fig. 3, which is not described herein again.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A method of spectrum optimization, the method comprising:
acquiring network operation data uploaded by a user and a plurality of cells;
acquiring target spectrum efficiency of the cells according to the network operation data;
and performing spectrum matching distribution according to a bilateral matching principle and the target spectrum efficiency of the cells to obtain a target spectrum optimal strategy of each cell in the cells.
2. The method according to claim 1, wherein the performing spectrum matching allocation with the target spectrum efficiency of the plurality of cells according to the bilateral matching principle, and obtaining the target spectrum optimization policy of each of the plurality of cells, includes:
dividing each cell in the plurality of cells into a disturbed cell set and a scrambling cell set according to the bilateral matching principle, and establishing two set utility functions according to channel relevance and a service model;
sorting the interfered cell set and the scrambling cell set according to the two set utility functions;
and performing spectrum matching distribution according to the ordered disturbed cell set, the disturbed cell set and the target spectrum efficiency of the cells to obtain a target spectrum optimal strategy of each cell in the cells.
3. The method according to claim 1 or 2, wherein said obtaining target spectral efficiency of said plurality of cells from said network operation data comprises:
and obtaining target spectrum efficiency of the cells through user and cell association degree reshaping, network resource configuration retrieval and spectrum efficiency matching cooperation according to the network operation data.
4. A method according to claim 3, characterized in that the method further comprises:
substituting the channel correlation data, the scheduling policy data and the measurement data of the user into a user and cell correlation model.
5. The method of claim 3, wherein the obtaining the target spectral efficiency of the plurality of cells according to the network operation data through user-to-cell association remodeling, network resource configuration retrieval, and spectral efficiency matching cooperation comprises:
and obtaining target spectrum efficiency of the cells through the combination of the user and cell association degree reshaping, the network resource configuration retrieval and the spectrum efficiency matching according to the network operation data by utilizing a particle swarm algorithm.
6. A spectrum optimization device, comprising a memory and a processor;
The memory has a stored program;
when the program is executed in the processor, the processor is configured to:
acquiring network operation data uploaded by a user and a plurality of cells;
acquiring target spectrum efficiency of the cells according to the network operation data;
and performing spectrum matching distribution according to a bilateral matching principle and the target spectrum efficiency of the cells to obtain a target spectrum optimal strategy of each cell in the cells.
7. The apparatus of claim 6, wherein the processor is specifically configured to:
dividing each cell in the plurality of cells into a disturbed cell set and a scrambling cell set according to the bilateral matching principle, and establishing two set utility functions according to channel relevance and a service model;
sorting the interfered cell set and the scrambling cell set according to the two set utility functions;
and performing spectrum matching distribution according to the ordered disturbed cell set, the disturbed cell set and the target spectrum efficiency of the cells to obtain a target spectrum optimal strategy of each cell in the cells.
8. The apparatus according to claim 6 or 7, wherein the processor is specifically configured to:
And obtaining target spectrum efficiency of the cells through user and cell association degree reshaping, network resource configuration retrieval and spectrum efficiency matching cooperation according to the network operation data.
9. The apparatus of claim 8, wherein the processor is further configured to:
substituting the channel correlation data, the scheduling policy data and the measurement data of the user into a user and cell correlation model.
10. The apparatus of claim 8, wherein the processor is specifically configured to:
and obtaining target spectrum efficiency of the cells through the combination of the user and cell association degree reshaping, the network resource configuration retrieval and the spectrum efficiency matching according to the network operation data by utilizing a particle swarm algorithm.
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