CN114785469A

CN114785469A - Serving cell pilot frequency determination method, serving cell pilot frequency determination device, electronic equipment and storage medium

Info

Publication number: CN114785469A
Application number: CN202210375188.2A
Authority: CN
Inventors: 李立华; 周茅玲; 孙轩轩
Original assignee: Beijing University of Posts and Telecommunications
Current assignee: Beijing University of Posts and Telecommunications
Priority date: 2022-04-11
Filing date: 2022-04-11
Publication date: 2022-07-22
Anticipated expiration: 2042-04-11
Also published as: CN114785469B

Abstract

The invention provides a method, a device, electronic equipment and a storage medium for determining a serving cell pilot frequency, wherein the method comprises the following steps: determining one or more pilot allocation configurations corresponding to the dense region based on a first set of APs serving the dense region, and determining one or more pilot allocation configurations corresponding to the sparse region based on a second set of APs serving the sparse region; and determining target pilot frequency allocation configuration in one or more pilot frequency allocation configurations corresponding to the dense area and one or more pilot frequency allocation configurations corresponding to the sparse area so as to maximize the total downlink rate of the system corresponding to the target pilot frequency allocation configuration. The embodiment of the invention can respectively determine one or more pilot frequency distribution configurations aiming at the dense area and the sparse area by dividing the dense area and the sparse area, and can relieve the pilot frequency pollution of the dense area by determining the target pilot frequency distribution configuration, and the UE in different geographic positions in the service area can obtain good communication quality.

Description

Serving cell pilot frequency determination method, serving cell pilot frequency determination device, electronic equipment and storage medium

Technical Field

The present invention relates to the field of wireless communications technologies, and in particular, to a serving cell pilot determination method and apparatus, an electronic device, and a storage medium.

Background

In a Cell-free large-scale Multiple input Multiple output (CF mimo) system, which is a network of distributed antennas, the design goal is to provide nearly uniform high communication quality in a given geographic area.

In a related art, a CF mimo system mainly performs pilot allocation for User Equipment (UE) in a service area in a scenario where the UE is uniformly distributed in the service area. Since the UE is freely movable in the service area, the distribution of UEs within a region is not always uniformly distributed. In the area with dense UE, the pilot frequency multiplexing condition is serious, the generated pilot frequency pollution is more, and the data transmission quality is not favorable; in an area where the UEs are sparse, the multiplexing degree of the pilot frequencies among multiple UEs is relatively low, the pilot frequency pollution is relatively low, and the better data transmission quality can be obtained, which results in that the UEs in different geographical positions in the service area cannot all obtain good communication quality.

Disclosure of Invention

The invention provides a method and a device for determining a serving cell pilot frequency, electronic equipment and a storage medium, which are used for solving the defect that in the prior art, UE (user equipment) in different geographical positions in a serving area cannot obtain good communication quality all, and realizing that the UE in different geographical positions in the serving area can obtain good communication quality all.

In a first aspect, the present invention provides a method for determining a serving cell pilot, including:

determining one or more pilot allocation configurations corresponding to a dense region based on a first set of APs serving the dense region, and determining one or more pilot allocation configurations corresponding to a sparse region based on a second set of APs serving the sparse region;

determining a target pilot frequency allocation configuration in one or more pilot frequency allocation configurations corresponding to the dense region and one or more pilot frequency allocation configurations corresponding to the sparse region, so as to maximize a total system downlink rate corresponding to the target pilot frequency allocation configuration;

wherein the dense region and the sparse region are determined based on historical user equipment distribution data in a service region, the service region including the dense region and the sparse region, the first set of APs being disjoint from the second set of APs.

Optionally, according to a serving cell pilot determining method provided by the present invention, the determining, based on a first AP set in a dense service area, one or more pilot allocation configurations corresponding to the dense service area includes:

determining a large-scale fading coefficient between each AP in the first set of APs and the user equipment in the dense area if the number of the plurality of orthogonal pilot sequences is less than the number of the user equipment in the dense area;

and acquiring one or more pilot frequency allocation configurations corresponding to the dense area based on a large-scale fading coefficient between each AP in the first AP set and the user equipment in the dense area and a distance between each AP in the first AP set and the user equipment in the dense area.

Optionally, according to a serving cell pilot determining method provided by the present invention, the obtaining one or more pilot allocation configurations corresponding to the dense area based on a large-scale fading coefficient between each AP in the first AP set and the user equipment in the dense area and a distance between each AP in the first AP set and the user equipment in the dense area includes:

determining a channel similarity between each AP in the first set of APs and the user equipment in the dense area based on a large-scale fading coefficient between each AP in the first set of APs and the user equipment in the dense area;

determining a first joint matrix based on a channel similarity between each AP in the first set of APs and the user equipment in the dense area and a distance between each AP in the first set of APs and the user equipment in the dense area, wherein each row in the first joint matrix is used for representing an interference degree between the user equipment served by the same AP;

sorting elements corresponding to each row in the first joint matrix from small to large to obtain a second joint matrix corresponding to the first joint matrix;

based on the number of the plurality of orthogonal pilot frequency sequences, obtaining a group corresponding to each row of the second combined matrix;

and performing pilot frequency distribution on the group corresponding to each row of the second combined matrix to obtain one or more pilot frequency distribution configurations corresponding to the dense area.

Optionally, according to a serving cell pilot determining method provided by the present invention, the determining, based on a second AP set serving a sparse region, one or more pilot allocation configurations corresponding to the sparse region includes:

determining a service relationship between each AP of the second AP set and the user equipment in the sparse region based on a large-scale fading coefficient between each AP of the second AP set and the user equipment in the sparse region, wherein the service relationship is used for representing a condition that the AP provides service for the user equipment;

determining a third association matrix based on a service relationship between each AP of the second set of APs and the user equipment in the sparse region and a channel estimation between each AP of the second set of APs and the user equipment in the sparse region, wherein the third association matrix is used for characterizing the interference degree between all the user equipment in the sparse region;

acquiring a fourth combined matrix based on a target interference threshold and the third combined matrix, wherein the number of rows and columns of the fourth combined matrix is the same as that of the third combined matrix, a value of a second element is 1 when a first element is greater than or equal to the target interference threshold, a value of the second element is 0 when the first element is less than the target interference threshold, the first element is any one element in the third combined matrix, and the second element is an element in the fourth combined matrix, which is the same as the matrix row and column number of the first element;

determining a structure of a target graph based on the fourth combined matrix, and determining an information amount corresponding to each edge between all vertexes of the target graph based on the third combined matrix, wherein each vertex of the target graph and each user equipment in the sparse area have a unique corresponding relation, and the number of vertexes of the target graph is the same as that of the user equipment in the sparse area;

performing a coloring operation on each vertex of the target graph based on the number of the plurality of orthogonal pilot sequences, and determining one or more coloring configurations, wherein any one coloring configuration of the one or more coloring configurations comprises color information corresponding to all the vertices of the target graph;

determining one or more pilot allocation configurations corresponding to the sparse regions based on the one or more coloring configurations.

Optionally, according to a serving cell pilot determining method provided by the present invention, the coloring operation for the first time includes:

determining a vertex with the largest interference value sum as a starting vertex in all vertices of the target graph based on the third association matrix, wherein the interference value sum corresponding to any one target vertex of the target graph is the sum of elements corresponding to a target row in the third association matrix, and the target vertex corresponds to the target row;

and selecting a first color from a color list, and coloring the starting vertex, wherein the number of the colors of the color list is equal to the number of the plurality of orthogonal pilot sequences.

Optionally, according to a serving cell pilot determining method provided by the present invention, the nth coloring operation includes:

determining a third vertex in one or more second vertices adjacent to the first vertex based on the information amount of each edge connected with the first vertex, wherein the user equipment corresponding to the third vertex has the largest interference to the user equipment corresponding to the first vertex;

selecting a second color from the color list, and coloring the third vertex so that the color of the third vertex is different from the color corresponding to any vertex adjacent to the third vertex;

configuring an information amount corresponding to an edge between the first vertex and the second vertex to be 0;

wherein the first vertex is a vertex to be colored in the (N-1) th coloring operation, N is an integer, and N is greater than or equal to 2.

Optionally, according to a serving cell pilot determining method provided by the present invention, the determining, based on the one or more coloring configurations, one or more pilot allocation configurations corresponding to the sparse region includes:

screening the one or more coloring configurations based on a color use time threshold and a color use time corresponding to each coloring configuration to obtain one or more target coloring configurations, so that the color use time corresponding to each target coloring configuration is less than or equal to the color use time threshold;

determining one or more pilot allocation configurations corresponding to the sparse regions based on the one or more target coloring configurations;

wherein the color usage number threshold is determined based on a number of user equipments in the sparse region and a number of the plurality of orthogonal pilot sequences.

Optionally, according to a serving cell pilot determining method provided by the present invention, before determining, based on the first set of APs serving a dense region, one or more pilot allocation configurations corresponding to the dense region, and determining, based on the second set of APs serving a sparse region, one or more pilot allocation configurations corresponding to the sparse region, the method further includes:

determining the dense region and the sparse region based on historical user equipment distribution data in the service area;

determining the first set of APs based on a distance threshold and distances between all APs of the serving area and a center location of the dense area;

determining the APs of all the APs except the first AP set as the second AP set.

In a second aspect, the present invention further provides a serving cell pilot determining apparatus, including:

a first determining module, configured to determine one or more pilot allocation configurations corresponding to a dense region based on a first set of APs serving the dense region, and determine one or more pilot allocation configurations corresponding to a sparse region based on a second set of APs serving the sparse region;

a second determining module, configured to determine a target pilot allocation configuration in one or more pilot allocation configurations corresponding to the dense region and one or more pilot allocation configurations corresponding to the sparse region, so that a total downlink rate of a system corresponding to the target pilot allocation configuration is maximum;

In a third aspect, the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the processor implements the serving cell pilot determination method according to any of the above.

In a fourth aspect, the present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a serving cell pilot determination method as described in any of the above.

In a fifth aspect, the present invention also provides a computer program product comprising a computer program, which when executed by a processor, implements the serving cell pilot determination method as described in any of the above.

According to the serving cell pilot frequency determining method, the serving cell pilot frequency determining device, the serving cell electronic device and the serving cell storage medium, the serving area is divided into the dense area and the sparse area, one or more pilot frequency allocation configurations corresponding to the dense area can be determined based on the first AP set, one or more pilot frequency allocation configurations corresponding to the sparse area can be determined based on the second AP set, and then the target pilot frequency allocation configuration is determined, so that the total downlink rate of the system corresponding to the target pilot frequency allocation configuration is maximized, the one or more pilot frequency allocation configurations corresponding to the dense area and the one or more pilot frequency allocation configurations corresponding to the sparse area can be screened, the screened pilot frequency allocation configuration can relieve pilot frequency pollution of the dense area, UE (user equipment) in different geographic positions in the serving area can be achieved, and good communication quality can be obtained.

Drawings

In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is one of schematic diagrams of a CF mimo system provided in the related art;

FIG. 2 is a second schematic diagram of a CF mMIMO system provided in the related art;

fig. 3 is a flowchart illustrating a serving cell pilot determination method according to the present invention;

FIG. 4 is a schematic diagram of a CF mMIMO system provided by the present invention;

fig. 5 is a second flowchart of the serving cell pilot determination method provided by the present invention;

fig. 6 is a third schematic flowchart of a serving cell pilot determination method provided by the present invention;

FIG. 7 is one of the experimental simulation diagrams provided by the present invention;

FIG. 8 is a second simulation diagram of the experiment provided by the present invention;

FIG. 9 is a third schematic diagram of the experimental simulation provided by the present invention;

FIG. 10 is a fourth schematic diagram of the experimental simulation provided by the present invention;

FIG. 11 is a fifth schematic diagram of the experimental simulation provided by the present invention;

FIG. 12 is a sixth schematic view of the experimental simulation provided by the present invention;

fig. 13 is a schematic structural diagram of a serving cell pilot determination apparatus provided in the present invention;

fig. 14 is a schematic structural diagram of an electronic device provided by the present invention.

Detailed Description

To facilitate a clearer understanding of embodiments of the present invention, some relevant background information is first presented below.

Fig. 1 is a schematic diagram of a CF mimo system provided in the related art, and as shown in fig. 1, in the CF mimo system, M Access Points (APs) may all be equipped with multiple antennas, and serve K UEs with single antenna on the same time frequency band resource, and M is much greater than K. The link from UE to AP is called uplink, the transmission link from AP to UE is called downlink, and each AP is connected to a Central Processing Unit (CPU) of the CF mimo system through a backhaul link to perform information transmission. The system adopts a Time Division Duplex (TDD) working mode, and each coherent interval can be divided into 3 stages:

in the uplink training phase of the first phase, the UE sends the pilot sequence allocated by the UE to the AP through the uplink, and the AP performs Channel estimation by using the received pilot signal at the receiving end to obtain Channel State Information (CSI);

in the uplink data transmission stage of the second stage, the UE sends data to the AP, the AP firstly detects local signals and then sends the data to a CPU of the CF mMIMO system, and the CPU of the CF mMIMO system can intensively detect the UE data according to the received data and the estimated value of the statistical channel;

in the downlink data transmission stage of the third stage, the AP performs power control and precoding on data to be sent to the UE through the power coefficient allocated by the CPU of the CF mimo system and the locally estimated channel, and sends the data to the UE.

Compared to cellular networks, the advantages brought by CF mimo are mainly reflected in 3 aspects: (1) the signal-to-noise ratio is higher and more uniform, and the variation of the signal-to-noise ratio is smaller; (2) the anti-interference capability is stronger; (3) coherent transmission may increase the signal-to-noise ratio.

When a CF mimo system is widely distributed with a large number of APs, in a conventional full-connectivity (as shown in fig. 1) mode, if a current UE is far away from some APs, the APs may cause strong interference to UEs around the APs to serve the UE, which affects the overall performance of the system. In order to overcome this drawback, in the related art, a User-Centric (UC) CF mimo is proposed.

Fig. 2 is a second schematic diagram of a CF mimo system provided in the related art, and as shown in fig. 2, in the user-centric CF mimo system, each UE is served by only a part of APs, which requires less backhaul overhead compared to the conventional CF mimo system, and is superior to the conventional CF mimo system in terms of UE reachable rate and higher in energy efficiency than the conventional CF mimo system for most UEs in the network.

In practical scenarios, the distribution of UEs within a service area is not always uniform, for example, for a city area, there may be a small portion of area (such as tourist attractions) with more dense UEs during holidays or active days. Under the condition that the distribution of the UE in the service area is not uniform, the CF mMIMO system in the related art has the following two defects:

(1) when the number of the pilot frequencies is insufficient, the number of the UEs in the same area is large and the pilot frequencies are multiplexed, and when channel estimation is performed, the UE is affected by interference of numerous other UEs near the UE, so that pilot frequency pollution becomes more serious, and for the AP in the dense area, limited power resources need to be allocated to signals of multiple transmitted users, so that the receiving rate of the UE is also lost;

(2) if the AP still serves all the UEs, the higher requirement is made on the coverage capability of the AP, and the CPU of the CF mMIMO system is required to have more computing capability; and serving a UE at a long distance, the interference caused to the UE devices near the AP will be stronger, resulting in larger overhead and interference.

In order to overcome the above drawbacks, the present invention provides a method, an apparatus, an electronic device, and a storage medium for determining a pilot frequency of a serving cell, which can achieve that UEs located at different geographic positions in a serving area can obtain good communication quality by determining a pilot frequency allocation configuration for a dense area and a sparse area, respectively.

In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Fig. 3 is a schematic flowchart of a serving cell pilot determination method provided by the present invention, and as shown in fig. 3, an execution main body of the serving cell pilot determination method may be an electronic device or a module in the electronic device, for example, a CPU in a CF mimo system. The method comprises the following steps:

step 301, determining one or more pilot allocation configurations corresponding to a dense region based on a first AP set serving the dense region, and determining one or more pilot allocation configurations corresponding to a sparse region based on a second AP set serving the sparse region;

wherein the dense region and the sparse region are determined based on historical user equipment distribution data in a service region, the service region comprising the dense region and the sparse region, the first set of APs being disjoint from the second set of APs;

specifically, in the process of pilot allocation, pilot allocation configurations may be respectively determined for dense areas and sparse areas in a service area, one or more pilot allocation configurations may be determined for UEs in the dense areas based on a first set of APs serving the dense areas, and one or more pilot allocation configurations may be determined for UEs in the sparse areas based on a second set of APs serving the sparse areas.

Alternatively, the serving cell may be a service area providing a communication service for the user equipment.

Optionally, one or more pilot allocation configurations corresponding to the dense region and one or more pilot allocation configurations corresponding to the sparse region may be determined simultaneously in a parallel processing manner.

Optionally, after determining one or more pilot allocation configurations corresponding to the dense region, one or more pilot allocation configurations corresponding to the sparse region may be determined.

Optionally, after determining one or more pilot allocation configurations corresponding to the sparse regions, one or more pilot allocation configurations corresponding to the dense regions may be determined.

Optionally, one or more pilot allocation configurations may be determined for the UE in the dense region based on the first set of APs by one or more pilot allocation algorithms.

Optionally, one or more pilot allocation configurations may be determined for the UE in the sparse region based on the second set of APs by one or more pilot allocation algorithms.

Therefore, the pilot allocation configurations may be determined for the dense region and the sparse region, respectively, and one or more pilot allocation configurations corresponding to the dense region and one or more pilot allocation configurations corresponding to the sparse region may be used to determine the target pilot allocation configuration.

Step 302, determining a target pilot frequency allocation configuration in one or more pilot frequency allocation configurations corresponding to the dense region and one or more pilot frequency allocation configurations corresponding to the sparse region, so as to maximize a total system downlink rate corresponding to the target pilot frequency allocation configuration;

specifically, after one or more pilot frequency allocation configurations corresponding to the dense region and one or more pilot frequency allocation configurations corresponding to the sparse region are obtained, based on the optimization target that the total downlink rate of the system corresponding to the target pilot frequency allocation configuration is the maximum, screening is performed in the pilot frequency allocation configuration corresponding to the dense region and the pilot frequency allocation configuration corresponding to the sparse region, so as to screen out one pilot frequency allocation configuration corresponding to the dense region and one pilot frequency allocation configuration corresponding to the sparse region, and further determine the target pilot frequency allocation configuration.

It can be understood that, after determining the target pilot allocation configuration, the CPU in the CF mimo system may issue the target pilot allocation configuration to the UE in the service area through the AP in the service area.

Optionally, fig. 4 is a schematic diagram of a CF mimo system provided by the present invention, as shown in fig. 4, the CF mimo system may include a CPU, a plurality of APs, and a plurality of UEs, where the CPU may divide a dense area and a sparse area in a service area, may divide one or more dense areas in the service area, and may also divide one or more sparse areas in the service area, based on historical user equipment distribution data in the service area.

According to the method for determining the pilot frequency of the service cell, provided by the invention, the service area is divided into the dense area and the sparse area, one or more pilot frequency distribution configurations corresponding to the dense area can be determined based on the first AP set, one or more pilot frequency distribution configurations corresponding to the sparse area can be determined based on the second AP set, and then the target pilot frequency distribution configuration is determined, so that the total downlink rate of the system corresponding to the target pilot frequency distribution configuration is maximum.

Optionally, the determining, based on the first set of APs serving the dense area, one or more pilot allocation configurations corresponding to the dense area includes:

Specifically, in the process of pilot allocation, pilot allocation configurations may be respectively determined for a dense region and a sparse region in a service region, and in the case that the number of multiple orthogonal pilot sequences is smaller than the number of user equipment in the dense region, one or more pilot allocation configurations corresponding to the dense region may be obtained based on a large-scale fading coefficient between each AP in the first AP set and the user equipment in the dense region and a distance between each AP in the first AP set and the user equipment in the dense region;

specifically, based on the second AP set serving the sparse region, one or more pilot allocation configurations may be determined for the UE in the sparse region, and then based on an optimization target that the total downlink rate of the system corresponding to the target pilot allocation configuration is the maximum, the pilot allocation configuration corresponding to the dense region and the pilot allocation configuration corresponding to the sparse region are screened out, one pilot allocation configuration corresponding to the dense region and one pilot allocation configuration corresponding to the sparse region are screened out, and then the target pilot allocation configuration may be determined.

Optionally, at a number (τ) of multiple orthogonal pilot sequences_p) Greater than or equal to the number of UEs in dense areas (| U)^dense|) the orthogonal pilot sequences may be allocated to the UEs in the dense area, and the UEs in the dense area may be configured to be served by the same AP set in a full-connected manner, so that a pilot allocation configuration corresponding to the dense area may be obtained. In this case, interference of the UE in the dense area at the time of data reception mainly comes from the influence of the UE in the sparse area, and there is no interference between the UEs in the dense area at all.

It can be understood that, the embodiment of the present invention provides a geographical and channel-based Pilot Assignment (LHPA) algorithm, which reduces Pilot multiplexing for close UEs near the same area based on the characteristics of the geographical Location and the channel, thereby reducing Pilot pollution in the area.

Optionally, at a number (τ) of multiple orthogonal pilot sequences_p) Less than the number of UEs in dense areas (| U)^dense|) under the condition, the main difference between the UEs in the dense area under the condition is the geographical position and the channel condition, and the pilot frequency distribution can be carried out by adopting an LHPA algorithm;

specifically, the process of pilot allocation by using the LHPA algorithm may include: determining a large-scale fading coefficient between each AP in the first AP set and user equipment in a dense area; further, based on a large-scale fading coefficient between each AP in the first AP set and the user equipment in the dense area and a distance between each AP in the first AP set and the user equipment in the dense area, one or more pilot allocation configurations corresponding to the dense area may be obtained.

It can be understood that, for the pilot allocation in the dense area, when the number of pilot resources provided by the system is greater than the number of UEs in the dense area, there is no pilot pollution between UEs in the dense area; even if the number of UEs in the dense area is too many and the UEs must be multiplexed, the effect of pilot multiplexing is reduced compared to the allocation algorithm without considering the scenario (without distinguishing the dense area from the sparse area). Moreover, pilot reuse can be reduced for nearby UEs in the same area based on the geographical location and the characteristics of the channel, thereby reducing pilot pollution in that area.

It can be understood that, for the pilot allocation in the sparse area, due to the influence of the geographic location, the pollution caused by the pilot multiplexing is also reduced compared with the allocation algorithm that does not consider this scenario (does not distinguish between the dense area and the sparse area).

Therefore, based on the large-scale fading coefficient between each AP in the first AP set and the user equipment in the dense area and the distance between each AP in the first AP set and the user equipment in the dense area, one or more pilot allocation configurations corresponding to the dense area may be determined, and by determining the target pilot allocation configuration, the one or more pilot allocation configurations corresponding to the dense area and the one or more pilot allocation configurations corresponding to the sparse area may be screened, so that the screened pilot allocation configurations may alleviate pilot pollution of the dense area, and UEs in different geographic positions in the service area may be realized, and good communication quality may be obtained.

Optionally, the obtaining one or more pilot allocation configurations corresponding to the dense area based on a large-scale fading coefficient between each AP in the first set of APs and the user equipment in the dense area and a distance between each AP in the first set of APs and the user equipment in the dense area includes:

sorting elements corresponding to each row in the first combined matrix from small to large to obtain a second combined matrix corresponding to the first combined matrix;

and performing pilot frequency distribution on the group corresponding to each row of the second combined matrix to acquire one or more pilot frequency distribution configurations corresponding to the dense area.

Specifically, in the process of pilot allocation, pilot allocation configurations may be respectively determined for a dense region and a sparse region in a service region, and when the number of multiple orthogonal pilot sequences is smaller than the number of user equipment in the dense region, based on a large-scale fading coefficient between each AP in the first AP set and the user equipment in the dense region, a channel similarity between each AP in the first AP set and the user equipment in the dense region may be determined, and then, in combination with a distance between each AP in the first AP set and the user equipment in the dense region, a first joint matrix may be determined;

specifically, after the first joint matrix is determined, the elements corresponding to each row in the first joint matrix may be sorted from small to large (ascending order sorting), a second joint matrix may be obtained, and then, based on the number of the multiple orthogonal pilot sequences, a group corresponding to each row of the second joint matrix may be obtained, and then, pilot allocation may be performed on the group corresponding to each row of the second joint matrix, and one or more pilot allocation configurations corresponding to the dense area may be obtained;

specifically, based on the second AP set serving the sparse area, one or more pilot allocation configurations may be determined for the UE in the sparse area, and then based on an optimization target that the total downlink rate of the system corresponding to the target pilot allocation configuration is the maximum, the pilot allocation configuration corresponding to the dense area and the pilot allocation configuration corresponding to the sparse area are screened out, so as to screen out one pilot allocation configuration corresponding to the dense area and one pilot allocation configuration corresponding to the sparse area, and further determine the target pilot allocation configuration.

Optionally, based on the large-scale fading coefficients between each AP in the first set of APs and the user equipment in the dense area, the large-scale fading matrix β of all UEs centered on the AP service may be determined_m：

β_m＝[β_m1,β_m2,...,β_mK]；

Wherein the content of the first and second substances,

k denotes the total number of UEs in the dense area, β_m1Represents the large-scale fading coefficient, β, between the mth AP in the dense area and the 1 st UE in the dense area_m2Represents the large-scale fading coefficient between the mth AP in the dense area and the 2 nd UE in the dense area, and so on, beta_mKRepresenting a large-scale fading coefficient between the mth AP in the dense area and the Kth UE in the dense area;

furthermore, the large-scale fading coefficient of the UE served by each AP can be calculated by the following formulaMean value beta_mCenter：

Wherein beta is_mkRepresenting a large-scale fading coefficient between the mth AP in the dense area and the kth UE in the dense area;

further, the channel similarity between the AP and the UEs served by it in the dense area may be calculated by the following channel similarity metric function:

furthermore, for the user equipment j served by the ith AP, the channel similarity F can be calculated by the following formula_ij：

Through the channel similarity metric function, a channel similarity matrix F formed by all UEs served by all APs can be obtained:

wherein, the first and the second end of the pipe are connected with each other,

m denotes the total number of all APs in the dense area, and K denotes the total number of UEs in the dense area.

It is to be understood that the above-mentioned channel similarity matrix F may be determined based on a large-scale fading coefficient between each AP in the first set of APs and the user equipment in the dense area, and the channel similarity matrix F includes a channel similarity between each AP in the first set of APs and the UE in the dense area.

Alternatively, the distance between each AP in the first set of APs and the user equipment in the dense area may be determined by the following formula:

wherein p is_jDenotes the coordinate position of the jth UE in the dense area, p_iDenotes the coordinate position of the ith AP in the dense area, P_ijRepresents the distance of the ith AP from the jth UE served by the ith AP;

further, based on the distances between the APs in the dense area and all the UEs in the dense area, a distance matrix P may be constructed:

It is to be understood that the distance matrix P comprises the distance between each AP in the first set of APs and the user equipment in the dense area.

Optionally, based on the channel similarity matrix F and the distance matrix P, a first joint matrix Q may be determined:

wherein the first joint matrix

The elements in Q may represent the joint values (joint channel similarity and distance) corresponding to the UEs in the dense region.

Optionally, sorting (ascending order processing) elements corresponding to each row in the first joint matrix Q from small to large may obtain a second joint matrix S corresponding to the first joint matrix:

matrix element S_ijRepresenting the joint value of the j-th ordered UE on the ith AP.

Optionally, after acquiring the second combining matrix S, the number τ of the plurality of orthogonal pilot sequences may be used_pGrouping a row corresponding to the ith AP in the second combined matrix S into N_gGroup (2):

wherein K represents the total number of UEs in the dense area;

further, grouping situation G of ith AP_iCan be expressed as:

optionally, in case of obtaining the grouping of the ith AP, G_iThereafter, orthogonal pilot assignment may be performed for each group of the ith AP, i.e., τ in each group_pEach UE selects the orthogonal pilot frequency sequence in turn to obtain the pilot frequency index set distributed by all the UEs of the ith AP

The pilot index set Ps_iCorresponding to a pilot frequency distribution configuration; further, by determining a set of pilot indexes corresponding to each AP in the dense area, one or more pilot allocation configurations corresponding to the dense area may be obtained.

It can be understood that the second joint matrix corresponding to the first joint matrix is obtained by sorting the elements corresponding to each row in the first joint matrix from small to large, so that the UE with large interference to other UEs can be located at the position in the front of the sequence as much as possible, and then in the process of pilot frequency allocation, the pilot frequency can be preferentially allocated to the UE in the front of the sequence (i.e., the pilot frequency selection priority can be supported), and then the pilot frequency pollution can be indirectly reduced.

Optionally, the service mode corresponding to the dense area may be a fully connected service mode, and specifically, each AP in the first set of APs may maintain full connection with all UEs in the dense area, and provide services for the UEs in the dense area in the fully connected mode.

Optionally, the service mode corresponding to the sparse region may be a service mode supporting AP selection, and specifically, for each AP in the second set of APs, all or part of UEs in the sparse region may be kept connected based on the service relationship, so as to provide services for the UEs that are kept connected.

It can be understood that the service area is divided into a dense area and a sparse area, and different service modes and pilot selection priorities can be adopted for the UEs in the dense area and the UEs in the sparse area to alleviate pilot pollution, so that the UEs in the service area can obtain consistent and good service.

Optionally, the determining, based on a second set of APs serving a sparse region, one or more pilot allocation configurations corresponding to the sparse region includes:

performing a coloring operation on each vertex of the target graph based on the number of a plurality of orthogonal pilot sequences, and determining one or more coloring configurations, wherein any one coloring configuration of the one or more coloring configurations comprises color information corresponding to all the vertices of the target graph;

Specifically, in the process of pilot allocation, pilot allocation configurations may be respectively determined for a dense region and a sparse region in a service region, and one or more pilot allocation configurations may be determined for UEs in the dense region based on a first AP set in the service dense region;

specifically, based on a large-scale fading coefficient between each AP in the second AP set and the user equipment in the sparse region, a service relationship between each AP in the second AP set and the user equipment in the sparse region may be determined, and then, in combination with channel estimation between each AP in the second AP set and the user equipment in the sparse region, a third association matrix may be determined, and then, based on a target interference threshold and the third association matrix, a fourth association matrix for constructing a target graph may be obtained;

specifically, after determining the structure of the target graph and the information amount corresponding to each edge in the target graph, traversing each vertex of the target graph, and performing coloring operation on each vertex of the target graph in the traversing process, so as to determine one or more coloring configurations, and further obtain one or more pilot frequency allocation configurations corresponding to the sparse region;

specifically, after one or more pilot frequency allocation configurations corresponding to the dense region and one or more pilot frequency allocation configurations corresponding to the sparse region are obtained, based on an optimization target that a system downlink total rate corresponding to the target pilot frequency allocation configuration is the maximum, screening is performed in the pilot frequency allocation configuration corresponding to the dense region and the pilot frequency allocation configuration corresponding to the sparse region, one pilot frequency allocation configuration corresponding to the dense region and one pilot frequency allocation configuration corresponding to the sparse region are screened out, and then the target pilot frequency allocation configuration can be determined.

It can be understood that, the embodiment of the present invention provides a Graph coloring-based GCAPS (joint Color based AP selection) algorithm, and specifically, for the pilot allocation in a sparse region, UEs in the sparse region are relatively dispersed in geographic locations, distances between UEs are relatively long, and meanwhile, the number of UEs in the sparse region is small and pilot resources are relatively small, and based on a service relationship (a service relationship between each AP in a second AP set and user equipment in the sparse region), a UE target Graph in the sparse region may be constructed to perform a coloring operation, so that it is avoided that there is a part of UEs with poor remote channel quality connected to an AP.

Optionally, for an AP (AP in the second AP set) serving the sparse area UE, the CPU end of the CF mimo system may form a matrix based on a large-scale fading coefficient between the sparse area UE and the AP as follows

Wherein M is^sparseTo serve the number of APs in a sparse area, | U^sparseL represents the number of UEs in the sparse area;

further, for each AP of the sparse area, the UE of the sparse area can be accumulated to obtain beta from high to low^sum∈R^Msparse×1The accumulated value corresponding to the ith AP can be calculated by the following formula

Accumulated value when the ith AP

Not less than the total accumulated value of the current AP

When the current AP finishes serving UE selection, accumulation is stopped, that is, the AP only serves UEs participating in accumulation in the downlink data transmission phase, where:

and then each AP is operated until all APs finish UE selection, so that the service relationship between all APs and all UEs can be determined, and the following service matrix S can be used_aRepresents:

wherein, the element a_ijIndicates whether the ith AP serves the jth UE, element a_ijA value of 1 represents a service, element a_ijA value of 0 represents no service.

Optionally, based on a service relationship between each AP of the second set of APs and the user equipment in the sparse region, a degree of similarity α 'between the ith UE in the sparse region and the jth UE in the sparse region served by the APs in the second set of APs may be determined'_ijSpecifically, 'may be determined by the following formula'_ij：

Wherein alpha is_iAnd alpha_jCan pass through the service matrix S_aDetermining;

based on channel estimates between each AP of the second set of APs and user equipment in the sparse region, a degree of channel similarity γ 'between an ith UE in the sparse region and a jth UE in the sparse region may be determined'_ijSpecifically, 'may be determined by the following formula'_ij：

Wherein, γ_iFor the variance of the channel estimation corresponding to the ith UE in the sparse region, the channel estimation corresponding to the ith UE can be determined through the channel estimation information reported by the AP; gamma ray_jThe channel estimation corresponding to the jth UE in the sparse region is determined by channel estimation information reported by the AP;

further, degree of similarity α 'based on service'_ijDegree of similarity with channel γ'_ijA joint service channel similarity value θ 'may be determined'_ijSpecifically, [ theta ] can be determined by the following formula'_ij：

θ′_ij＝α′_ij*γ′_ij；

Further, based on the joint serving channel similarity values between all UEs in the sparse region, the following third joint matrix θ' may be determined:

the dimension of theta' is K.K, and K is the number of the UE in the sparse area.

Alternatively, in order to eliminate interference caused by multiplexing pilots by the UEs with similar channels and serving similar parts, an average value of joint serving channel similarity values among all UEs in the sparse region may be used as the threshold λ_thresholdFiltering the third combined matrix to obtain a fourth combined matrix theta, wherein lambda_threshold＝sum(θ′)/(|U^sparse|·|U^sparse|-|U^sparse| the set of all UEs in the sparse region may be U)^sparseAll the UE number of the sparse area can pass through U^sparseDie length | U^sparseExpressed by | the fourth co-matrix θ may be expressed by a matrix:

wherein, in the fourth united matrix thetaElement of theta_ijCan be expressed as:

It can be understood that the method for determining a serving cell pilot provided in the embodiment of the present invention may be a Hybrid Service Resource Allocation (HSRA) method, and may ensure that UEs in different geographic locations in a Service area can all obtain good communication quality.

Therefore, one or more pilot frequency allocation configurations corresponding to the dense area are determined based on the first AP set, and by performing coloring operation on each vertex of the target map, one or more pilot frequency allocation configurations corresponding to the sparse area may be determined, and then by determining the target pilot frequency allocation configuration, the total downlink rate of the system corresponding to the target pilot frequency allocation configuration may be maximized, and the one or more pilot frequency allocation configurations corresponding to the dense area and the one or more pilot frequency allocation configurations corresponding to the sparse area may be screened, so that the screened pilot frequency allocation configuration may alleviate pilot frequency pollution of the dense area, and UEs in different geographic positions in the service area may all obtain good communication quality.

Optionally, the first coloring operation comprises:

Specifically, after determining the structure of the target graph and the information amount corresponding to each edge in the target graph, traversing each vertex of the target graph, and performing a coloring operation on each vertex of the target graph in the traversing process, in the coloring operation for the first time, based on the third association matrix, determining a vertex with the largest sum of interference values as an initial vertex in all vertices of the target graph, and further coloring the initial vertex;

specifically, after coloring the starting vertex, the remaining unpigmented vertices in the target graph may be colored, and after coloring all vertices in the target graph, one or more coloring configurations may be determined, so that one or more pilot allocation configurations corresponding to the sparse region may be obtained;

Alternatively, the first color may be one of a list of colors, and the "first" of the first color is not used to describe a particular order or precedence.

It can be understood that, by determining the vertex with the largest sum of interference values as the starting vertex, in the process of allocating the pilot, the pilot can be preferentially allocated to the UE corresponding to the vertex with the largest sum of interference values (that is, the pilot selection priority can be supported), and further, the pilot pollution can be indirectly reduced.

It can be understood that the service area is divided into a dense area and a sparse area, and different service modes and pilot selection priorities can be adopted for UEs in the dense area and UEs in the sparse area to mitigate pilot pollution, so that UEs in the service area can obtain consistent and good service.

Therefore, one or more pilot frequency allocation configurations corresponding to the dense region are determined based on the first AP set, and by performing coloring operation on each vertex of the target map, one or more pilot frequency allocation configurations corresponding to the sparse region can be determined, and then by determining the target pilot frequency allocation configuration, the total downlink rate of the system corresponding to the target pilot frequency allocation configuration is maximized, and one or more pilot frequency allocation configurations corresponding to the dense region and one or more pilot frequency allocation configurations corresponding to the sparse region can be screened, so that the screened pilot frequency allocation configuration can alleviate pilot frequency pollution of the dense region, and UEs in different geographic positions in the service region can all obtain good communication quality.

Optionally, the nth time of the coloring operation includes:

Specifically, after determining the structure of the target graph and the information amount corresponding to each edge in the target graph, traversal may be performed on each vertex of the target graph, a coloring operation may be performed on each vertex of the target graph in the traversal process, in the nth coloring operation, a third vertex may be determined in one or more second vertices adjacent to the first vertex based on the information amount of each edge connected to the first vertex, and then a second color may be selected from the color list, and the third vertex is colored, so that the color of the third vertex is different from the color corresponding to any one vertex adjacent to the third vertex, and further, the information amount corresponding to the edge between the first vertex and the second vertex may be configured as 0;

specifically, after all vertices in the target graph are colored, one or more coloring configurations may be determined, and then one or more pilot frequency allocation configurations corresponding to the sparse regions may be obtained, and after one or more pilot frequency allocation configurations corresponding to the dense regions and one or more pilot frequency allocation configurations corresponding to the sparse regions are obtained, based on the optimization target that the total downlink rate of the system is the maximum corresponding to the target pilot frequency allocation configuration, screening is performed in the pilot frequency allocation configuration corresponding to the dense regions and the pilot frequency allocation configuration corresponding to the sparse regions, and one pilot frequency allocation configuration corresponding to the dense regions and one pilot frequency allocation configuration corresponding to the sparse regions are screened out, and then the target pilot frequency allocation configuration may be determined.

Alternatively, the second color may be one color in a color list, the "second" in the second color is not used to describe a specific order or sequence, and the second color may be the same color as the first color or may be a different color.

Alternatively, during the Nth shading operation, the probability P of transitioning from vertex i (the first vertex) to vertex j (the second vertex) may be determined by_ijThe formula determines the third vertex:

wherein, theta'_ijAnd θ'_isCan pass throughThe third combination matrix theta 'determines theta'_isRepresenting similarity values between other vertices (one or more second vertices) connected to vertex i, selecting P_ijThe vertex corresponding to the maximum value in (b) is used as the next traversal vertex (third vertex) of the vertex i.

Alternatively, during the nth coloring operation, the colors whose neighbors have been used can be collected at each vertex (which can be referred to as a dyeing vat): traversing all adjacent vertices, if an adjacent vertex has a color, placing the color in the dye bucket, selecting a second color for the current node that is not in the dye bucket, and assigning it to the current vertex. After the current shading operation is finished, the bucket may be emptied and moved to the next vertex. After the vertex (third vertex) is currently dyed, the information amount corresponding to the edge between the first vertex and the second vertex may be configured to be 0, which prevents the cyclic traversal of the trap graph traversal, and also represents that the UE of the two vertices has completed allocation, and does not need to repeat allocation.

It can be understood that, after each vertex is dyed, the structure of the target graph is updated once, until the information amount between all vertices in the target graph is 0, which represents that the dyeing of all vertices has been completed, and all dyeing configurations that satisfy that adjacent vertices are not dyed with the same color can be obtained based on the topological relation of the graph structure.

It can be understood that, by determining a third vertex in one or more second vertices adjacent to the first vertex, the user equipment corresponding to the third vertex has the largest interference with the user equipment corresponding to the first vertex, and then in the process of pilot allocation, a pilot may be preferentially allocated to the UE corresponding to the third vertex (that is, a pilot selection priority may be supported), so that pilot pollution may be indirectly reduced.

Optionally, the determining, based on the one or more coloring configurations, one or more pilot allocation configurations corresponding to the sparse regions includes:

Specifically, after determining the structure of the target graph and the information amount corresponding to each edge in the target graph, traversing each vertex of the target graph, and performing a coloring operation on each vertex of the target graph in the traversing process, thereby determining one or more coloring configurations;

specifically, after determining one or more coloring configurations, the one or more coloring configurations may be screened based on a threshold of color usage times and the color usage times corresponding to each coloring configuration, to obtain one or more target coloring configurations, and then one or more pilot allocation configurations corresponding to a sparse area may be determined;

Alternatively, in the case where the number Nc > 0 of coloring configurations, it may be based on a color usage number threshold [ | U [ ]^sparse|/τ_p]And screening the plurality of first coloring configurations to obtain one or more second coloring configurations so that the number of color usage times corresponding to each second coloring configuration is less than or equal to a color usage time threshold, wherein the set of all the UEs in the sparse region may be U^sparseThe number of all UEs in the sparse area can pass through U^sparseDie length of (U)^sparseI denotes that the number of orthogonal pilot sequences is tau_p。

Alternatively, in the case where the number Nc of coloring configurations is 0, the update target interference threshold (λ) may be adjusted by the following formula_threshold) Further, based on the updated target interference threshold, the joint filtering interference matrix (fourth joint matrix) may be obtained again, and the target graph may be constructed, and then each vertex of the target graph is subjected to a coloring operation, so that a coloring configuration may be determined:

where θ' represents a third joint matrix, the set of all UEs of the sparse region may be U^sparseAnd tt denotes the adjustment of a few times_thresholdThe number of all UEs in the sparse area can pass through U^sparseDie length | U^sparseI denotes, each time λ is adjusted_thresholdAfter which the value of tt is increased by 1.

Therefore, one or more pilot frequency allocation configurations corresponding to the dense region are determined based on the first AP set, each vertex of the target map is subjected to coloring operation, the obtained coloring configurations can be screened to obtain one or more target coloring configurations, one or more pilot frequency allocation configurations corresponding to the sparse region can be further determined, and then the target pilot frequency allocation configuration is determined to maximize the total downlink rate of the system corresponding to the target pilot frequency allocation configuration.

Optionally, before determining one or more pilot allocation configurations corresponding to the dense region based on the first set of APs serving the dense region and determining one or more pilot allocation configurations corresponding to the sparse region based on the second set of APs serving the sparse region, the method further includes:

determining that the APs except the first AP set in all the APs are the second AP set.

Specifically, based on historical user equipment distribution data in the service area, a dense area and a sparse area may be determined, and then a first AP set may be determined based on a distance threshold and distances between center positions of all APs of the service area and the dense area, and then an AP except the first AP set among all APs may be determined as a second AP set, so that the first AP set and the second AP set do not intersect;

specifically, in the process of pilot allocation, pilot allocation configurations may be respectively determined for a dense region and a sparse region in a service region, one or more pilot allocation configurations may be determined for a UE in the dense region based on a first AP set serving the dense region, and one or more pilot allocation configurations may be determined for a UE in the sparse region based on a second AP set serving the sparse region;

Alternatively, statistics may be performed on service conditions (historical UE distribution data) of all UEs in the service area, a UE service distribution map of the entire service area may be drawn based on the statistics data, and the dense area T may be determined by determining that the number of UEs exceeds a certain threshold^denseAnd a sparse region T^sparse. And then N can be acquired at the boundary of the dense region^denseCoordinate positions of the points:

the central coordinate C of the dense region can be determined by calculating with the following formula^center：

Alternatively, the process of determining an AP (first set of APs) serving a dense area UE may include: traversing all the APs in the service area, and calculating the distance of each AP from the center C of the dense area^centerDistance of the mth AP from the dense area center C^centerMay be a distance of

When the temperature is higher than the set temperature

Less than a given distance

(distance threshold), the current AP is selected as the UE in the dense service area, and then the APs except the first set of APs in all APs may be determined as the second set of APs.

Alternatively, in the process of determining an AP (first AP set) serving a dense area UE, the AP may be determined by the service matrix S_aRecording the service relationship between the AP and the UE, specifically determining the service relationship by the following expression:

wherein, the mth AP and UE (user equipment k of dense area)^denseUser equipment k of sparse area^sparse) If there is a service relationship (i.e. the mth AP provides service for the UE), S_aA in_mkIs set to 1, otherwise is 0.

Therefore, the first AP set and the second AP set may be determined based on the distance threshold and the distance between the AP of the service area and the center position of the dense area, and then one or more pilot allocation configurations corresponding to the dense area may be determined based on the first AP set, one or more pilot allocation configurations corresponding to the sparse area may be determined based on the second AP set, and then by taking the maximum total system downlink rate corresponding to the target pilot allocation configuration as an optimization target, the one or more pilot allocation configurations corresponding to the dense area and the one or more pilot allocation configurations corresponding to the sparse area may be screened, so that the screened pilot allocation configurations may alleviate pilot pollution of the dense area, and it may be achieved that UEs in different geographical positions in the service area may all obtain good communication quality.

Alternatively, fig. 5 is a second flowchart of the serving cell pilot determination method provided by the present invention, and as shown in fig. 5, the CF mimo system may include M APs and K UEs, where all APs may be connected to the CPU through a backhaul link, and each AP may be equipped with N antennas, and each UE may be a single antenna.

Alternatively, as shown in fig. 5, in the uplink training phase, the UE may transmit its own assigned pilot sequence to the AP via uplink, for example, the UE₁Pilots may be sent

UE_kPilots may be sent

UE_KPilots may be sent

Optionally, as shown in fig. 5, after the AP receives the pilot sequence sent by the UE, the AP may perform calculation to obtain channel estimation information and pilot pollution information, and further may send the channel estimation information and the pilot pollution information to the CPU.

Alternatively, as shown in fig. 5, the CPU may divide a dense region and a sparse region in a service region based on historical user equipment distribution data in the service region, and may further determine a first AP set of the dense service region and a second AP set of the sparse service region, and may further determine one or more pilot allocation configurations corresponding to the dense region based on the first AP set of the dense service region, and determine one or more pilot allocation configurations corresponding to the sparse service region based on the second AP set of the sparse service region, and may further determine a target pilot allocation configuration, and may further calculate a downlink reachable rate of the UE according to channel estimation information.

Alternatively, as shown in FIG. 5, the CPU may configure the target pilot allocation (as in FIG. 5)

And transmitting the power control coefficient and the AP selection scheme to the AP, wherein the AP selection scheme can comprise information of the first AP set and service relation between the second AP set and the UE in the sparse area.

Alternatively, as shown in fig. 5, in the downlink data transmission phase, the AP may perform power control and precoding on data to be sent to the UE through the power coefficient allocated by the CPU of the CF mimo system and the locally estimated channel, and may further allocate and configure based on the target pilot (as in fig. 5)

) Sending data to the UE, and the UE may receive downlink signals sent by the AP, for example, the UE₁Can receive a signal S₁，UE_kCan receive a signal S_k，UE_KCan receive a signal S_K。

It can be understood that, according to whether the location of the UE belongs to the dense region, the UE in the service region may be divided into the UE of the dense region and the UE of the sparse region, where the set of all UEs of the dense region may be U^denseThe set of all UEs of the sparse region may be U^sparseThe number of all UEs in the dense region can be U^denseDie length | U^denseI represents that the number of all UEs in the sparse area can pass through U^sparseDie length | U^sparse| represents, and then can be represented by a total number equation | U of the UE in the service area^dense|+|U^sparseAnd l is K, calculating the total number K of the service areas UE. The k-th UE of the dense region may be denoted as k^denseThe set of APs (first set of APs) serving UEs in a dense area may be denoted as M^denseThe k-th UE of the sparse region may be denoted as k^sparseThe set of APs (second set of APs) serving the UE of the sparse region may be denoted as M^sparseThe set of APs serving the kth UE of the sparse region may be represented as

Alternatively, as shown in FIG. 5, for the uplink training phase, at oneCoherence time interval tau_cThe duration for the uplink pilot training may be τ_p(τ_pIs a positive integer), satisfies τ_c＞τ_pAnd is present with τ_pA sequence of orthogonal pilots

(

Representing a real number set), the pilot sequence transmitted by the kth UE in the service area (including the dense area and the sparse area) may be represented as

Can indicate the power allocated to each pilot, each AP will receive the pilot sequences sent by all UEs, and the mth AP will receive the pilot sequence signals sent by all UEs

Specifically, the following "pilot sequence signal formula received by the AP" may be used to obtain:

wherein p is_kIndicating the transmission power allocated by the kth UE in the service area,

(

n in (N) denotes the number of antennas with which the AP is equipped) denotes additive noise that obeys a complex gaussian distribution,

indicating the channel between the mth AP and the kth UE in the service area.

Channel h between mth AP and kth UE in service area_mkCan be represented by the following formulaDetermining:

wherein, g_mkIs the small scale fading coefficient, g_mkCan be complex Gaussian random variables which are subjected to independent same distribution

β_mkMay be a large scale fading coefficient between the mth AP and the kth UE in the service area, which is related to path loss and shadow fading channels.

Large-scale fading coefficient beta between mth AP and kth UE in service area_mkCan be determined by the following formula of "large-scale fading coefficient formula":

wherein the content of the first and second substances,

representing shadow fading with standard deviation σ_sh，z_mkRepresents shading coefficients (shading coefficients), and

PL_mkrepresenting the path loss.

Path loss PL_mkCan be obtained by the following "formula of the triclinic model":

wherein d is_mkDenotes the distance between the mth AP and the kth UE in the service area, d₀And d₁Distance parameters of the tri-slope model.

L in the above three-slope model formula can be determined by the following formula:

wherein f represents carrier frequency and has the unit of MHZ; h is a total of_APAntenna height in meters (m) for the AP; h is a total of_uIn meters (m), is the antenna height of the UE.

In the above formula of pilot sequence signal received by AP

Is a pilot sequence transmitted by the kth UE in the service area,

the constraint of the following expression needs to be satisfied:

wherein the content of the first and second substances,

denotes a UE set multiplexing pilots with k UEs, k' being the UE set

Of (1).

And then can pass through

Multiplication by y_mObtaining y_mkTo estimate the channel between the mth AP and the kth UE in the service area, specifically calculate y_mkThe formula (c) is as follows:

above calculation of y_mkSecond term in the formula (1)

Because of the pollution caused by multiplexing pilot by different UEs when the pilot resource is limited, the kth UE pilot pollution in the service area can be obtained by the following "UE pilot pollution formula":

at the AP end, channel estimation may be performed by minimum mean square error estimation (MMSE), which may be specifically performed by "channel estimation" as follows

The formula of (1) obtains the channel estimation between the mth AP and the kth UE in the service area:

wherein the content of the first and second substances,

is the additive Gaussian noise variance of the downlink channel, and can be obtained by the following variance formula of channel estimation

Variance of (a):

therefore, for the uplink training phase, y is calculated by the above formula for the pilot sequence signal received by the AP_mkFormula of (2), UE pilot pollution formula and channel estimation

And calculating to obtain channel estimation information and pilot pollution information.

Optionally, as shown in fig. 5, for the downlink data transmission phase, after precoding and power control are performed on the transmission signal sent by the mth AP to the kth UE in the service area, the AP service vector needs to be multiplied because of selective service of the AP (that is, the AP serves all or part of the UEs in the area served by the AP).

For the AP serving the dense area UE (AP in the first set of APs), the transmission signal of the mth AP is represented as

The acquisition may be calculated by the following "dense area AP transmission signal formula":

wherein the content of the first and second substances,

is the total signal power of the transmitting end AP; eta_mkIs the power control coefficient between the mth AP and the kth UE; p is a radical of formula_mkRepresenting the transmission power of the mth AP to the kth UE;

whether the mth AP serving the dense area has a service relationship with the kth UE of the dense area: when the value is 1, the two have service relationship, and when the value is 0, the two have no service relationship. U shape^denseIs the set of all UEs in the dense area.

Transmitting a signal to a kth UE of the dense area for the mth AP;

is a precoding matrix between the mth AP and the kth UE according to the uplink channel estimation value

With TDD channel reciprocity, in case of a Maximum Ratio Transmission (MRT) precoding scheme being used for downlink,

the calculation can be obtained by the following formula:

for the APs serving the UE in the sparse area, different APs may serve different UEs, and different service vectors may be used for the corresponding APs, and the signal sent by the mth AP in the AP set (second AP set) serving the sparse area may be different

The acquisition may be calculated by the following "sparse area AP transmit signal formula":

wherein the content of the first and second substances,

is the total signal power of the transmitting end AP; eta_mkIs the power control coefficient between the mth AP and the kth UE; p is a radical of_mkRepresenting the transmission power of the mth AP to the kth UE;

for the mth AP to the kth UE,

representing the mth AP and rarity of the service sparse areaWhether the kth UE of the sparse area has service relation: when the value is 1, the two have a service relationship, and when the value is 0, the two have no service relationship. U shape^sparseIs the set of all UEs in the sparse region, M^sparseIs a set of APs serving sparse area UEs.

By using TDD channel reciprocity, under the condition of adopting MRT precoding mode in downlink,

the acquisition can be calculated by the following formula:

for a downlink signal receiving end of a dense area, a signal received by the kth UE

The signal receiving formula of the dense area UE can be obtained by calculation as follows:

further, can pass

Indicating the signal received by the k-th UE

The corresponding desired signal can be passed

Indicating the signal received by the k-th UE

The uncertainty of the corresponding precoding gain can be determined by

Indicating the signal received by the k-th UE

The corresponding multi-UE interference is transmitted,

and

the acquisition can be calculated by the following formula:

further, the unit bandwidth downlink reachable rate of the kth UE in the dense area can be obtained through the following "dense area UE downlink reachable rate formula":

for a downlink signal receiving end in a sparse area, a signal received by the kth UE

The acquisition may be calculated by the following "sparse area UE received signal formula":

further, can pass

Representing signals received by the kth UE

The corresponding desired signal can be transmitted

Indicating the signal received by the k-th UE

The uncertainty of the corresponding precoding gain can be determined by

Representing signals received by the kth UE

The corresponding multi-UE interference is transmitted,

and

the acquisition can be calculated by the following formula:

further, the downlink reachable rate per unit bandwidth of the kth UE in the sparse region can be obtained through the following "sparse region UE downlink reachable rate formula":

therefore, in the downlink data transmission stage, the unit bandwidth downlink reachable rate of any UE in the dense area can be obtained by calculating through the dense area AP sending signal formula, the dense area UE receiving signal formula and the dense area UE downlink reachable rate formula; through the sparse area AP signal sending formula, the sparse area UE signal receiving formula and the sparse area UE downlink reachable rate formula, the unit bandwidth downlink reachable rate of any UE in the sparse area can be obtained through calculation.

It can be understood that, under the condition that the UE distribution in the service area is not uniform, compared with the CF mimo system in the related art, the method for determining the serving cell pilot frequency provided by the embodiment of the present invention can achieve a higher downlink reachable rate of the UE.

Specifically, in the CF mimo system in the related art, at the uplink pilot sequence transmission stage, all APs receive the pilot sequences sent by all UEs, and when the AP performs channel estimation on the UE that undertakes pilot multiplexing, the accuracy of the channel estimation result will be affected, and if the UE that undertakes pilot multiplexing is still served by the same AP, at the downlink data reception stage, according to the "dense area UE downlink reachable rate formula" and the "sparse area UE downlink reachable rate formula", it can be known that: the target signal on the molecule in the formula can cause that the target signal of a receiving end (UE) is received inaccurately after the signal after MRT precoding processing passes through a real channel due to inaccurate channel estimation; and the interference part on the denominator also causes that the signal after MRT precoding can not offset the influence of the real channel due to inaccurate channel estimation, thereby increasing the interference. I.e. the actual target signal portion decreases and the interference portion of the other UEs increases and the achievable rate of the UE decreases.

In the method for determining the serving cell pilot frequency provided by the embodiment of the present invention, isolation of a part of UEs in a pilot frequency multiplexing process can be achieved through AP selection, and the method can be known according to the "dense area UE downlink reachable rate formula" and the "sparse area UE downlink reachable rate formula": the interference part corresponding to the denominator in the formula can be reduced, and the target signal corresponding to the numerator in the formula is close to the real signal of the transmitting end, so that higher downlink reachable rate of the UE can be realized.

Optionally, fig. 6 is a third schematic flow chart of the serving cell pilot determining method provided by the present invention, as shown in fig. 6, the serving cell pilot determining method may include steps 601 to 606:

601, distributing random pilot frequency;

specifically, in the case of pilot allocation for the first time, the CPU of the CF mimo system may allocate pilots to the UEs in the service area based on a random pilot allocation manner.

Step 602, dividing a user equipment set in a sparse area and a user equipment set in a dense area;

specifically, the CPU of the CF mimo system may determine a dense area and a sparse area based on historical UE distribution data in the service area, and further may divide all UEs in the dense area into a user equipment set in the dense area and divide all user equipment in the sparse area into a user equipment set in the sparse area based on geographical location information of the UEs in the current service area in a period of allocating a pilot for the UEs.

Step 603, determining an AP set of a dense service area and determining an AP set of a sparse service area;

specifically, based on the distance threshold and the distances between all APs of the service area and the center position of the dense area, a set of APs (a first set of APs) serving the dense area may be determined; further, it may be determined that the APs other than the first set of APs are a set of APs (second set of APs) serving the sparse area.

Step 604, determining one or more pilot allocation configurations corresponding to the dense region;

specifically, at the number of multiple orthogonal pilot sequences (τ)_p) Greater than or equal to the number of UEs in dense areas (| U)^dense|) the orthogonal pilot sequences may be allocated to the UEs in the dense area, and the UEs in the dense area may be configured to be served by the same AP set in a full-connected manner, so that a pilot allocation configuration corresponding to the dense area may be obtained. In this case, interference of the UEs in the dense area at the time of data reception mainly comes from the influence of the UEs in the sparse area, while there is no interference at all between the UEs in the dense area.

Specifically, at the number of multiple orthogonal pilot sequences (τ)_p) Less than the number of UEs in dense areas (| U)^dense|) the pilot assignment may be performed by adopting LHPA algorithm, specifically, a large-scale fading coefficient between each AP in the first AP set and the user equipment in the dense area may be determined; further, based on a large-scale fading coefficient between each AP in the first AP set and the user equipment in the dense area and a distance between each AP in the first AP set and the user equipment in the dense area, one or more pilot allocation configurations corresponding to the dense area may be obtained.

Step 605, determining one or more pilot frequency allocation configurations corresponding to the sparse regions;

specifically, based on a large-scale fading coefficient between each AP of the second set of APs and the user equipment in the sparse region, a service relationship between each AP of the second set of APs and the user equipment in the sparse region may be determined, and the service relationship may be represented by a service matrix;

a joint interference matrix (third joint matrix) can be determined based on the service relationship between each AP of the second set of APs and the user equipment in the sparse region and the channel estimation between each AP of the second set of APs and the user equipment in the sparse region;

and further based on a target interference threshold (lambda)_threshold) And a third combined matrix, obtaining a combined filtering interference matrix (a fourth combined matrix), wherein the number of rows and columns of the fourth combined matrix is the same as that of the third combined matrix, the value of the second element is 1 under the condition that the first element is greater than or equal to the target interference threshold, the value of the second element is 0 under the condition that the first element is less than the target interference threshold, the first element is any one element in the third combined matrix, and the second element is the same element as the matrix row and column number of the first element in the fourth combined matrix;

further, based on the fourth combined matrix, a user association graph (target graph) can be determined, and based on the third combined matrix, the information amount corresponding to each edge between all vertexes of the target graph is determined, each vertex of the target graph and each user equipment in the sparse region have a unique corresponding relation, and the number of vertexes of the target graph is the same as that of the user equipment in the sparse region;

further, performing a coloring operation on each vertex of the target graph based on the number of the plurality of orthogonal pilot sequences, one or more coloring configurations may be determined (the number of the one or more coloring configurations may be denoted as Nc);

further, based on the one or more coloring configurations, one or more pilot allocation configurations corresponding to the sparse regions may be determined.

Alternatively, in the case where Nc > 0, it may be based on a color usage number threshold [ | U [ ]^sparse|/τ_p]And screening the plurality of first coloring configurations to obtain one or more second coloring configurations so that the color use times corresponding to each second coloring configuration are less than or equal to a color use time threshold, wherein the set of all UE in the sparse area can be U^sparseAll the UE number of the sparse area can pass through U^sparseDie length of (U)^sparseI indicates that the number of orthogonal pilot sequences is τ_p。

Alternatively, in the case where Nc is 0, the update target interference threshold (λ) may be adjusted by the following formula_threshold) Further, based on the updated target interference threshold, the joint filtering interference matrix (fourth joint matrix) may be obtained again, and the target graph may be constructed, and then each vertex of the target graph is subjected to a coloring operation, so that a coloring configuration may be determined:

where θ' represents a third association matrix, the set of all UEs of the sparse region may be U^sparseAnd tt denotes the adjustment of a_thresholdThe number of all UEs in the sparse area can pass through U^sparseDie length of (U)^sparseI denotes, each time λ is adjusted_thresholdAfter which the value of tt is increased by 1.

Step 606, determine the target pilot allocation configuration.

Specifically, the CPU of the CF mimo system may determine a target pilot allocation configuration in one or more pilot allocation configurations corresponding to the dense area and one or more pilot allocation configurations corresponding to the sparse area, so as to maximize a total downlink rate of the system corresponding to the target pilot allocation configuration.

Optionally, fig. 7 is one of the schematic diagrams of the experimental simulation provided by the present invention, fig. 8 is a second schematic diagram of the experimental simulation provided by the present invention, and fig. 9 is a third schematic diagram of the experimental simulation provided by the present invention, as shown in fig. 7-fig. 9, it is verified that the UE rate is seriously affected due to the non-uniform distribution of the UE through MATLAB simulation software. An experimental link simulates two scenes of square distribution and uneven distribution, the downlink rate of a system is simulated under the condition that the position distribution of UE is only changed for various traditional pilot frequency distribution schemes, the total rate of downlink users in the system is shown in figure 7, the minimum downlink user rate is shown in figure 8, the mean square error of the downlink user rate is shown in figure 9, and the mean square error can represent the balance among the reachable downlink rates of different users. Simulation results show that: the performance of the pilot frequency allocation algorithms is consistent with the degradation of the overall rate and the equalization of the communication quality of the users.

As shown in fig. 7-9, the curve indicated by the BalanceRPA mark in the figure represents a simulation curve corresponding to a Random Pilot Allocation (RPA) algorithm in the related art under the condition of uniform distribution of the UE, the curve indicated by the BalanceLBGPA mark in the figure represents a simulation curve corresponding to a Location-Based Greedy Pilot allocation (LBGPA) algorithm in the related art under the condition of uniform distribution of the UE, the curve indicated by the BalanceMI mark in the figure represents a simulation curve corresponding to a Maximum Increment (MI) algorithm in the related art under the condition of uniform distribution of the UE, the curve indicated by the unranancerpa mark in the figure represents a simulation curve corresponding to an LBGPA algorithm under the condition of non-uniform distribution of the UE, the simulation curve indicated by the unranancerpa mark in the figure represents a simulation curve corresponding to an un gpmi algorithm under the condition of non-uniform distribution of the UE, and the simulation curve indicated by the lbga mark in the condition of non-uniform distribution of the UE represents a simulation curve corresponding to an lbga mark in the graph, and (4) a simulation curve corresponding to the MI algorithm under the condition of uneven distribution of the UE.

The horizontal axis of the graph in fig. 7 represents the total rate of downlink users in units of (Mbits/s/Hz), and the vertical axis of the graph in fig. 7 represents the cumulative distribution rate. The horizontal axis of the graph in fig. 8 represents the downlink minimum user rate in units of (Mbits/s/Hz), and the vertical axis of the graph in fig. 8 represents the cumulative distribution rate. The horizontal axis of the graph in fig. 9 represents the downstream user rate mean square error, and the vertical axis of the graph in fig. 9 represents the cumulative distribution rate.

Optionally, fig. 10 is a fourth of the experiment simulation diagrams provided by the present invention, fig. 11 is a fifth of the experiment simulation diagrams provided by the present invention, and fig. 12 is a sixth of the experiment simulation diagrams provided by the present invention, as shown in fig. 10-fig. 12, the MATLAB simulation software is used to verify the improvement of the system speed in the case of UE uneven distribution. Fig. 10 shows the total rate of downlink users in the system, fig. 11 shows the downlink minimum user rate, and fig. 12 shows the mean square error of the downlink user rate, which may indicate the balance between the achievable downlink rates of different users. Simulation results show that: the embodiment of the invention has performance advantages in the scene of uneven user distribution, and the total system rate, the lower limit of the minimum rate and the user service balance are excellent.

As shown in fig. 10-12, the curves indicated by the LBGPA label in the figures represent simulation curves corresponding to the LBGPA algorithm in the case of non-uniform distribution of the UE, the curves indicated by the MI label in the figures represent simulation curves corresponding to the MI algorithm in the case of non-uniform distribution of the UE, and the curves indicated by the HSRA label in the figures represent simulation curves corresponding to the HSRA algorithm provided in the embodiment of the present application in the case of non-uniform distribution of the UE.

The horizontal axis of the graph in fig. 10 represents the downlink user total rate in units of (Mbits/s/Hz), and the vertical axis of the graph in fig. 10 represents the cumulative distribution rate. The horizontal axis of the graph in fig. 11 represents the downlink minimum user rate in units of (Mbits/s/Hz), and the vertical axis of the graph in fig. 11 represents the cumulative distribution rate. The horizontal axis of the graph in fig. 12 represents the downstream user rate mean square error, and the vertical axis of the graph in fig. 12 represents the cumulative distribution rate.

According to the method for determining the pilot frequency of the service cell, the service area is divided into the dense area and the sparse area, one or more pilot frequency distribution configurations corresponding to the dense area can be determined based on the first AP set, one or more pilot frequency distribution configurations corresponding to the sparse area can be determined based on the second AP set, and then the target pilot frequency distribution configuration is determined, so that the total downlink rate of a system corresponding to the target pilot frequency distribution configuration is maximum, one or more pilot frequency distribution configurations corresponding to the dense area and one or more pilot frequency distribution configurations corresponding to the sparse area can be screened, the screened pilot frequency distribution configurations can relieve the pilot frequency pollution of the dense area, UE (user equipment) in different geographic positions in the service area can obtain good communication quality.

The serving cell pilot determining apparatus provided by the present invention is described below, and the serving cell pilot determining apparatus described below and the serving cell pilot determining method described above may be referred to in correspondence with each other.

Fig. 13 is a schematic structural diagram of a serving cell pilot determining apparatus provided in the present invention, and as shown in fig. 13, the apparatus includes: a first determination module 1301 and a second determination module 1302, wherein:

a first determining module 1301, configured to determine one or more pilot allocation configurations corresponding to a dense region based on a first set of APs serving the dense region, and determine one or more pilot allocation configurations corresponding to a sparse region based on a second set of APs serving the sparse region;

a second determining module 1302, configured to determine a target pilot allocation configuration in one or more pilot allocation configurations corresponding to the dense region and one or more pilot allocation configurations corresponding to the sparse region, so that a total downlink rate of a system corresponding to the target pilot allocation configuration is maximum; wherein the dense region and the sparse region are determined based on historical user equipment distribution data in a service region, the service region including the dense region and the sparse region, the first set of APs being disjoint from the second set of APs.

The device for determining the pilot frequency of the service cell provided by the invention can determine one or more pilot frequency allocation configurations corresponding to the dense area based on the first AP set by dividing the service area into the dense area and the sparse area, determine one or more pilot frequency allocation configurations corresponding to the sparse area based on the second AP set, and determine the target pilot frequency allocation configuration so as to maximize the total downlink rate of the system corresponding to the target pilot frequency allocation configuration.

Optionally, the first determining module is specifically configured to:

determining a first joint matrix based on channel similarity between each AP in the first set of APs and the user equipment in the dense area and a distance between each AP in the first set of APs and the user equipment in the dense area, wherein each row in the first joint matrix is used for representing interference degree between the user equipment served by the same AP;

Optionally, the first determining module is specifically configured to:

determining a service relationship between each AP of the second AP set and the user equipment in the sparse area based on a large-scale fading coefficient between each AP of the second AP set and the user equipment in the sparse area, wherein the service relationship is used for representing a condition that the AP provides service for the user equipment;

determining a third association matrix based on a service relationship between each AP of the second set of APs and the user equipment in the sparse region and a channel estimation between each AP of the second set of APs and the user equipment in the sparse region, wherein the third association matrix is used for characterizing an interference degree between all the user equipment in the sparse region;

acquiring a fourth combined matrix based on a target interference threshold and the third combined matrix, wherein the number of rows and columns of the fourth combined matrix is the same as that of the third combined matrix, a value of a second element is 1 when a first element is greater than or equal to the target interference threshold, and a value of the second element is 0 when the first element is less than the target interference threshold, the first element is any one element in the third combined matrix, and the second element is an element in the fourth combined matrix, which is the same as the matrix row and column number of the first element;

Optionally, the first determining module is specifically configured to:

determining a vertex with the largest interference value sum as an initial vertex in all vertices of the target graph based on the third association matrix, where the interference value sum corresponding to any one target vertex of the target graph is the sum of elements corresponding to a target row in the third association matrix, and the target vertex corresponds to the target row;

Optionally, the first determining module is specifically configured to:

determining one or more pilot allocation configurations corresponding to the sparse regions based on the one or more target coloring configurations; wherein the threshold number of color usage times is determined based on a number of user equipments in the sparse region and a number of the plurality of orthogonal pilot sequences.

Optionally, the apparatus further includes a third determining module, before determining one or more pilot allocation configurations corresponding to the dense region based on the first set of APs serving the dense region and determining one or more pilot allocation configurations corresponding to the sparse region based on the second set of APs serving the sparse region, configured to:

Fig. 14 is a schematic structural diagram of an electronic device provided in the present invention, and as shown in fig. 14, the electronic device may include: a processor (processor)1410, a communication Interface (Communications Interface)1420, a memory (memory)1430 and a communication bus 1440, wherein the processor 1410, the communication Interface 1420 and the memory 1430 communicate with each other via the communication bus 1440. Processor 1410 may invoke logic instructions in memory 1430 to perform a method of serving cell pilot determination, the method comprising:

determining a target pilot frequency allocation configuration in one or more pilot frequency allocation configurations corresponding to the dense region and one or more pilot frequency allocation configurations corresponding to the sparse region, so as to maximize a total downlink rate of a system corresponding to the target pilot frequency allocation configuration; wherein the dense region and the sparse region are determined based on historical user equipment distribution data in a service region, the service region including the dense region and the sparse region, the first set of APs being disjoint from the second set of APs.

In addition, the logic instructions in the memory 1430 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, and various media capable of storing program codes.

In another aspect, the present invention also provides a computer program product, the computer program product includes a computer program, the computer program can be stored on a non-transitory computer readable storage medium, when the computer program is executed by a processor, the computer can execute the serving cell pilot determination method provided by the above methods, the method includes:

In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program, which when executed by a processor, implements a serving cell pilot determination method provided by the above methods, the method including:

determining a target pilot frequency allocation configuration in one or more pilot frequency allocation configurations corresponding to the dense region and one or more pilot frequency allocation configurations corresponding to the sparse region, so as to maximize a total system downlink rate corresponding to the target pilot frequency allocation configuration; wherein the dense region and the sparse region are determined based on historical user equipment distribution data in a service region, the service region including the dense region and the sparse region, the first set of APs being disjoint from the second set of APs.

The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. A method for determining a serving cell pilot, comprising:

2. The method of claim 1, wherein the determining one or more pilot allocation configurations corresponding to the dense region based on the first set of APs serving the dense region comprises:

3. The method of claim 2, wherein the obtaining one or more pilot allocation configurations corresponding to the dense area based on a large-scale fading coefficient between each AP in the first set of APs and the user equipment in the dense area and a distance between each AP in the first set of APs and the user equipment in the dense area comprises:

4. The method as claimed in any of claims 1-3, wherein the determining one or more pilot allocation configurations corresponding to the sparse region based on the second set of APs serving the sparse region comprises:

determining a structure of a target graph based on the fourth union matrix, and determining an information amount corresponding to each edge between all vertexes of the target graph based on the third union matrix, wherein each vertex of the target graph and each user equipment in the sparse area have a unique correspondence, and the number of vertexes of the target graph is the same as the number of user equipment in the sparse area;

5. The method of claim 4, wherein the coloring operation for the first time comprises:

6. The method of claim 5, wherein the N-th coloring operation comprises:

7. The method as claimed in claim 4, wherein the determining one or more pilot allocation configurations corresponding to the sparse regions based on the one or more coloring configurations comprises:

screening the one or more coloring configurations based on a threshold of the number of color usage times and the number of color usage times corresponding to each coloring configuration to obtain one or more target coloring configurations, so that the number of color usage times corresponding to each target coloring configuration is smaller than or equal to the threshold of the number of color usage times;

8. The serving cell pilot determination method of any one of claims 1-3 or 5-7, wherein before determining the one or more pilot allocation configurations corresponding to the dense region based on the first set of APs serving the dense region and determining the one or more pilot allocation configurations corresponding to the sparse region based on the second set of APs serving the sparse region, the method further comprises:

9. A serving cell pilot determination apparatus, comprising:

a second determining module, configured to determine a target pilot allocation configuration in one or more pilot allocation configurations corresponding to the dense area and one or more pilot allocation configurations corresponding to the sparse area, so that a total system downlink rate corresponding to the target pilot allocation configuration is maximum;

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the serving cell pilot determination method according to any one of claims 1 to 8 when executing the program.

11. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the serving cell pilot determination method according to any of claims 1 to 8.

12. A computer program product comprising a computer program, wherein the computer program when executed by a processor implements the serving cell pilot determination method according to any of claims 1 to 8.