CN105409268B - network capacity and coverage optimization method and device - Google Patents

network capacity and coverage optimization method and device Download PDF

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CN105409268B
CN105409268B CN201380078520.8A CN201380078520A CN105409268B CN 105409268 B CN105409268 B CN 105409268B CN 201380078520 A CN201380078520 A CN 201380078520A CN 105409268 B CN105409268 B CN 105409268B
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user group
base station
user
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张洁涛
庄宏成
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Huawei Technologies Co Ltd
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
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    • H04W28/086—Load balancing or load distribution among access entities
    • H04W28/0861—Load balancing or load distribution among access entities between base stations

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Abstract

optimization method and device for network capacity and coverage, which takes network capacity and coverage as optimization targets and adopts combined optimization of load balancing and interference coordination to achieve optimization of network capacity and coverage, concretely, achieves load balancing effect by dynamic association of user groups and base station management, realizes inter-cell interference coordination by dynamic adjustment of subband resource usage of each user group, takes capacity and coverage as optimization utility functions and optimization targets as utility functions of the lowest utility user group in the maximum whole network, and executes combined optimization of capacity and coverage, load balancing and interference coordination by iterative optimization of load balancing and interference coordination.

Description

network capacity and coverage optimization method and device
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method and an apparatus for optimizing network capacity and coverage of networks.
Background
Self-Organizing networks (SON) realize real-time automatic maintenance of networks through Self-configuration, Self-optimization, Self-healing and other operations, thereby greatly reducing Network maintenance of manual intervention and greatly reducing operation and maintenance cost for operators. Radio Frequency (RF) capacity and coverage optimization, load balancing, and interference coordination are all important use cases for SON. The main practice of existing network optimization is based on single-case optimization, such as: RF capacity and coverage optimization, or load balancing and interference coordination, etc. are each optimized individually as separate use cases under specific network conditions. However, due to the complex interrelation among the three, the optimization of each case alone cannot achieve the optimization of the whole network, and the joint optimization among various cases cannot be achieved in the prior art.
Disclosure of Invention
The embodiment of the invention provides network capacity and coverage optimization methods and devices, which are used for realizing joint optimization among various use cases and optimizing the whole network.
, there are methods for optimizing network capacity and coverage, the method includes:
dividing the system sub-bands; setting corresponding capacity and coverage weight for each user group; according to the sub-frequency band and the user group relation, user group division is carried out, and according to the user association and resource allocation of the cell reported by each base station and user group information, the user group information is used for initializing optimization parameters (b, E, p); wherein, b is a base station-user association vector, E is a sub-frequency band sharing matrix, and p is a user group power vector;
when b is a given value, optimizing each sub-frequency band sharing matrix and a user group power vector, namely (E, p), so as to realize the interference coordination of the network;
according to said (E, p) optimized, i.e. (E)*，p*) Optimizing the base station-user association vector b for realizing load balance of the network;
judging whether the power of each user group is converged, and if so, obtaining optimized (b)*，E*，p*)；
According to obtaining E*Computing a user group-subband assignment matrix, i.e. F*(b) to be acquired*，F*，p*) The configuration is issued to the base station with the corresponding user group, and the base station performs resource allocation on the associated users, thereby realizing the combined optimization of load balancing and interference coordination.
In possible implementation manners, according to the aspect, the initialization setting (b, p) is specifically performed according to the actual association and power of each user group reported by the base station, and the initialization setting is performed by the method of randomly setting each element value by the E.
In a second possible implementation manner, according to the th aspect, the optimizing each subband sharing matrix and the user group power vector when b is a given value, that is, the optimizing (E, p), specifically includes:
maximizing the utility function of the user group with worst capacity and coverage utility function in the network when b is a given value, namely:
Figure GPA0000209097330000031
since the utility function is: u. ofc(p，E)：＝pc/[G(E)p+n]cForm (b), thus, defines:
Figure GPA0000209097330000032
then the optimal configuration (p)*，E*) Satisfy the requirement of
Figure GPA0000209097330000033
Thus can be pushed out Copt＝1/ρ(Λ(E*) Where ρ is Λ (E)*) Of petrong root;
after verification, the optimal configuration E corresponding to the optimal utility function of the whole network is obtained by minimizing the rho (Lambda (E))*I.e. by
Figure GPA0000209097330000034
According to
Figure GPA0000209097330000035
Obtaining p of optimal configuration*。
In a third possible implementation manner, according to the aspect , the optimizing the base station-user association vector b according to the optimized (E, p) specifically includes:
for user group c, the interference items corresponding to maximizing the capacity and coverage are as follows:
Figure GPA0000209097330000036
by the reaction ofcSelecting optimal c for each user group in the range of BS
Figure GPA0000209097330000041
So that Ic(p*，b，E*) And (4) minimizing.
In a fourth possible implementation manner, according to the th aspect, when it is determined that the power of each user group is not converged, the optimized base station-user association vector is used as an input to performWhen b is a given value, optimizing each sub-band sharing matrix and user group power vector, and executing the optimization according to the optimized (E, p) to optimize the base station-user association vector b; until the power of each user group is not converged, thereby obtaining optimized (b)*，E*，p*)。
In a fifth possible implementation manner, according to the aspect , the method obtains E*Computing a user group-subband assignment matrix, i.e. F*The method specifically comprises the following steps:
will E*Wherein the group i of user groups sharing the same sub-band is assigned to F*The ith sub-band in (1).
In a sixth possible implementation manner, according to the th aspect, the setting a corresponding capacity and a coverage weight for each user group specifically includes:
and setting the same or different capacity and coverage weight values for each user group.
In a second aspect, there are methods for optimizing network capacity and coverage, the method including:
dividing the system sub-bands; setting corresponding capacity and coverage weight for each user group; carrying out user group repartitioning according to the sub-frequency band and the user group relation, and initializing optimization parameters (b, E, p, r and theta) according to the user association and resource allocation, antenna downward inclination angle and user group information of the cell reported by each base station; b is a base station-user association vector, E is a sub-frequency band sharing matrix, p is a user group power vector, r is a base station power vector, and theta is an antenna downward inclination angle vector;
under the current (r, theta) configuration, when b is a given value, optimizing each sub-frequency band sharing matrix and a user group power vector, namely (E, p), so as to realize the interference coordination of the network; and according to said (E, p), i.e. (E), optimized*，p*) Optimizing the base station-user association vector b for realizing load balance of the network; thereby obtaining optimized (b, E, p) in the current (r, θ) configuration;
optimizing a base station antenna downtilt angle theta at the current iteration by minimizing a base station interference correlation term at a given r and an optimized (b, E, p);
optimizing the power r of the base station through fixed point iteration;
judging whether the power of each base station is converged; if converged, obtaining optimized (b)*，E*，p*，r*，θ*)；
According to obtaining E*Computing a user group-subband assignment matrix, i.e. F*(b) to be acquired*，F*，p*，r*，θ*) The configuration is issued to the base station with the corresponding user group, and the base station performs resource allocation on the associated users, thereby realizing the combined optimization of radio frequency optimization, load balancing and interference coordination.
In a th possible implementation manner, according to the second aspect, the optimizing the current base station antenna downward inclination angle θ by minimizing the base station interference related term at a given r and an optimized (b, E, p) specifically includes:
for a base station, the interference term corresponding to maximizing the capacity and coverage isBy passing at thetabs∈ΘbsSelecting optimal antenna downtilt angles for each base station bs within range
Figure GPA0000209097330000052
So that Ibs(r, θ) is minimized.
In a second possible implementation manner, according to the second aspect, the base station power r is optimized through fixed point iteration; the method specifically comprises the following steps:
adopting a fixed point iterative optimization method, combining the power constraint of each base station, and enabling the maximum utility function of each base station to pass through
Figure GPA0000209097330000053
In which gamma is used as the basis for the fixed-point iterative optimization implementation ofbsIs the lowest signal to interference plus noise ratio.
In a third aspect, kinds of network capacity and coverage optimizing devices are provided, the devices comprise a transceiver and a processor;
the transceiver is used for receiving the cell user association and resource allocation reported by each base station and user group information used for initializing optimization parameters (b, E, p); wherein, b is a base station-user association vector, E is a sub-frequency band sharing matrix, and p is a user group power vector; to be obtained (b)*，F*，p*) Configuring and issuing the configuration to a base station with a corresponding user group, and performing resource allocation on the associated users by the base station, thereby realizing the combined optimization of load balancing and interference coordination;
the processor is configured to divide a system sub-band; setting corresponding capacity and coverage weight for each user group; carrying out user group repartitioning according to the sub-frequency band and the user group relation, and using the user association and resource allocation of the cell reported by each base station and user group information to initialize optimization parameters (b, E, p); when b is a given value, optimizing each sub-frequency band sharing matrix and a user group power vector, namely (E, p), so as to realize the interference coordination of the network; according to said (E, p) optimized, i.e. (E)*，p*) Optimizing the base station-user association vector b for realizing load balance of the network; judging whether the power of each user group is converged, and if so, obtaining optimized (b)*，E*，p*) (ii) a According to obtaining E*Computing a user group-subband assignment matrix, i.e. F*Obtained (b)*，F*，p*)。
In possible implementation manners, according to the third aspect, the initialization setting of (b, p) in the processor is specifically performed according to the actual association and power of each user group reported by the base station, and the initialization setting of E is performed by randomly setting each element value.
In a second possible implementation manner, according to the third aspect, the optimizing, in the processor, each subband sharing matrix and a user group power vector when b is a given value, that is, the optimizing (E, p), specifically includes:
maximizing the utility function of the user group with worst capacity and coverage utility function in the network when b is a given value, namely:
since the utility function is: u. ofc(p，E)：＝pc/[G(E)p+n]cForm (b), thus, defines:
Figure GPA0000209097330000071
then the optimal configuration (p)*，E*) Satisfy the requirement of
Figure GPA0000209097330000072
Thus can be pushed out Copt＝1/ρ(Λ(E*) Where ρ is Λ (E)*) Of petrong root;
after verification, the optimal configuration E corresponding to the optimal utility function of the whole network is obtained by minimizing the rho (Lambda (E))*I.e. by
Figure GPA0000209097330000073
According to
Figure GPA0000209097330000074
Obtaining p of optimal configuration*。
In a third possible implementation manner, according to the third aspect, the optimizing, by the processor, the base station-user association vector b according to the optimized (E, p) specifically includes:
for user group c, the interference items corresponding to maximizing the capacity and coverage are as follows:
Figure GPA0000209097330000075
by the addition of a compound in bcSelecting optimal c for each user group in the range of BS
Figure GPA0000209097330000076
So that Ic(p*，b，E*) And (4) minimizing.
In a fourth possible implementation manner, according to the third aspect, when it is determined that the power of each user group is not converged, the processor optimizes the subband sharing matrix and the user group power vector by using the optimized base station-user association vector as an input, and performs the optimization of the base station-user association vector b according to the optimized (E, p); until the power of each user group is not converged, thereby obtaining optimized (b)*，E*，p*)。
In a fifth possible implementation manner, according to the third aspect, the processor obtains E*Computing a user group-subband assignment matrix, i.e. F*The method specifically comprises the following steps:
will E*Wherein the group i of user groups sharing the same sub-band is assigned to F*The ith sub-band in (1).
In a sixth possible implementation manner, according to the third aspect, the setting, in the processor, a corresponding capacity and a corresponding coverage weight for each user group specifically includes:
and setting the same or different capacity and coverage weight values for each user group.
In a fourth aspect, there is provided kinds of network capacity and coverage optimizing apparatuses, the apparatuses including:
a transceiver and a processor;
the transceiver is used for receiving the user association and resource allocation, the antenna downward inclination angle and the user group information of the cell reported by each base station, and is used for initializing optimization parameters (b, E, p, r and theta) by the processor; b is a base station-user association vector, E is a sub-frequency band sharing matrix, p is a user group power vector, r is a base station power vector, and theta is an antenna downward inclination angle vector; to be obtained (b)*，F*，p*，r*，θ*) And configuring and transmitting the configuration to the base station with the corresponding user group.
The processor is configured to divide a system sub-band; setting corresponding capacity and coverage weight for each user group; according to the sub-frequency band and the user group relation, the user group weight is carried outDividing, and initializing optimization parameters (b, E, p, r, theta) according to the user association and resource allocation, antenna downward inclination angle and user group information of the cell reported by each base station; b is a base station-user association vector, E is a sub-frequency band sharing matrix, p is a user group power vector, r is a base station power vector, and theta is an antenna downward inclination angle vector; under the current (r, theta) configuration, when b is a given value, optimizing each sub-frequency band sharing matrix and a user group power vector, namely (E, p), so as to realize the interference coordination of the network; and according to said (E, p), i.e. (E), optimized*，p*) Optimizing the base station-user association vector b for realizing load balance of the network; thereby obtaining optimized (b, E, p) in the current (r, θ) configuration; optimizing the current base station antenna downtilt angle theta by minimizing the base station interference correlation term under the given r and the optimized (b, E, p); optimizing the power r of the base station through fixed point iteration; judging whether the power of each base station is converged; if converged, obtaining optimized (b)*，E*，p*，r*，θ*) (ii) a According to obtaining E*Computing a user group-subband assignment matrix, i.e. F*(b) to be acquired*，F*，p*，r*，θ*) The configuration is issued to the base station with the corresponding user group, and the base station performs resource allocation on the associated users, thereby realizing the combined optimization of radio frequency optimization, load balancing and interference coordination.
In possible implementation manners, according to the fourth aspect, the optimizing, by the processor, a current base station antenna downtilt angle θ by minimizing a base station interference correlation term at a given r and an optimized (b, E, p), specifically includes:
for a base station, the interference term corresponding to maximizing the capacity and coverage isBy the reaction ofcSelecting optimal c for each user group in the range of BS
Figure GPA0000209097330000092
So that Ic(p*，b，E*) And (4) minimizing, namely achieving the purpose of load balancing.
In a second possible implementation manner, according to the fourth aspect, the processor optimizes the base station power r through fixed point iteration; the method specifically comprises the following steps:
adopting a fixed point iterative optimization method, combining the power constraint of each base station, and enabling the maximum utility function of each base station to pass through
Figure GPA0000209097330000093
In which gamma is used as the basis for the fixed-point iterative optimization implementation ofbsIs the lowest signal to interference plus noise ratio.
Embodiments of the present invention provide methods and apparatuses for optimizing network capacity and coverage, and methods for optimizing network capacity and coverage provided by embodiments of the present invention target capacity and coverage, and optimize coverage and capacity while coordinating cell interference and relatively balancing load distribution through joint optimization of load balancing and interference coordination, thereby effectively improving network resource utilization efficiency.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of an optimization method for network capacities and coverage provided by method embodiment of the present invention;
fig. 2 is a simplified flow diagram of interference coordination operations in method embodiment of the present invention;
FIG. 3 is a simplified flowchart of the operation of load balancing for a network in method embodiment of the present invention;
fig. 4 is a simplified flow chart of methods for optimizing network capacity and coverage according to a second embodiment of the present invention;
fig. 5 is another simplified flowchart of methods for optimizing network capacity and coverage according to the second embodiment of the present invention;
fig. 6 is a schematic diagram of network capacity and coverage optimizing devices provided by a third embodiment of the method of the present invention;
fig. 7 is a schematic diagram of network capacity and coverage optimizing devices provided by the fourth embodiment of the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only partial embodiments of of the present invention, rather than all embodiments.
The technical scheme provided by the embodiment of the invention has the main idea that the optimization of the network capacity and the coverage is achieved by adopting the combined optimization of load balancing and interference coordination with the optimization target of the network capacity and the coverage. Specifically, the effect of load balancing is achieved through dynamic association of the user group and the base station; the inter-cell interference coordination is realized by dynamically adjusting the use of the sub-band resources of each user group; and taking the capacity and the coverage as optimization utility functions, and taking the optimization target as the utility function of the lowest utility user group in the maximized whole network. And performing combined optimization of capacity and coverage, load balancing and interference coordination through iterative optimization of load balancing and interference coordination.
In the solution provided in the embodiments of the present invention, the optimized system parameters are base station subband allocation, user group power allocation, and association of user groups to base stations.
For all active users in the network, each base station classifies a plurality of users associated with the base station in a close wireless environment (e.g., with close Signal to Interference and Noise ratios (SINRs) and physical locations) into groups, forming user groups, and the system performs operations on the users in units of user groups in subsequent operations.
Table 1 identification and definition
Figure GPA0000209097330000111
Figure GPA0000209097330000121
Example
Before describing the method, it should be understood that methods for optimizing network capacity and coverage are provided in the embodiments of the present invention:
in the network operation, for the user group c, the lower limit of the downlink average utility function is:
Figure GPA0000209097330000132
wherein,
Figure GPA0000209097330000133
Figure GPA0000209097330000134
and
according to the uplink and downlink dual, the lower limit of the uplink average utility function is as follows:
Figure GPA0000209097330000136
wherein,
Figure GPA0000209097330000137
(corresponding minimum)The interference term is)。
For the user with the worst utility in the user group c, the downlink utility function is:
Figure GPA0000209097330000139
wherein,
Figure GPA0000209097330000141
sc K-dimensional vectors, wherein the elements corresponding to the worst user in the user group c are 0, and S is [ S ]1|...|sC]T。
Similarly, its upstream utility function is:
wherein(the corresponding minimum interference term is
Figure GPA0000209097330000143
The defined uplink average utility function is correspondingly characterized as the capacity of the user group, and the defined uplink utility function of the user with the worst utility is correspondingly characterized as the coverage of the user group. Therefore, for joint optimization of coverage and capacity, it is equivalent to the need to jointly optimize the two utility functions, namely:
Figure GPA0000209097330000144
wherein, mu belongs to [0, 1] as the capacity and coverage weight of the user group, and determines the compromise relationship between the capacity and the coverage of the optimized user group, and the value is given by the operator according to the network requirement.
For example, for the user group located at the center of the cell, the weight value can take a larger value, so that the capacity optimization weight value is higher than the coverage optimization weight value, and for the user group located at the edge of the cell, the weight value can take a smaller value, so that the coverage optimization weight value is higher than the capacity optimization weight value, namely, any user groups c have corresponding muc. For the whole network, the optimization goal of this scheme is to maximize the utility function of the user group with the lowest utility function, i.e.:
Figure GPA0000209097330000145
wherein,
Figure GPA0000209097330000151
is the target utility function for user group c, whose value depends on the rate requirements of each user in the user group.
Based on the above description of the idea of providing the method according to the embodiment of the present invention, the method provided by the present embodiment is described in detail below with reference to a specific operation method, as shown in fig. 1, the method includes:
step 101, a network coordinator (eCo ) presets the number of sub-bands of a system and the capacity and coverage weight of a user group according to network requirements, performs user group repartitioning according to the sub-bands and the user group relationship, and initializes (b, E, p) according to the user group conditions reported by each base station.
In step 101, the eCo divides the system sub-bands according to the design requirements of the network manager or the system, and sets the number of the system sub-bands as S; setting corresponding capacity and coverage weight mu for each user groupcThe eCo can set the same or different weight values for each user group; and initializing optimization parameters (b, E, p) according to the user association and resource allocation condition of the cell reported by each base station and user group information. Wherein, the setting of (b, p) is the actual association and power condition of each user group reported by the base station, E belongs to {0,1}C×Cthe matrix may be set in such a way that the values of the elements are randomly set.
According to the system bandwidth and the number of sub-bands, the eCo can calculate the bandwidth of each sub-band, if the bandwidth required by any user group services reported by the base station exceeds sub-bands, the user group is further divided into multiple user groups, and it is ensured that the service requirement of each user group can be met by the bandwidth of sub-bandsC′×C′。
And 102, under a given b, the eCo optimizes each sub-band sharing matrix and a user group power vector (E, p), and the main function of the step is to realize interference coordination of the network.
eCo optimizes each subband sharing matrix and user group power vector (E, p) at step 101 or step 104 (since b is an initialized value when step 102 is executed at time; during the latter iteration, eCo judges whether the user group powers after the operations of step 102 and step 103 converge in step 104, if not, b after step 103 optimization is taken as an input of step 102). in step 102, the main role is to perform network interference coordination, which aims at maximizing the utility function of the user group with the worst capacity and coverage utility function in the network at a given b, namely:
Figure GPA0000209097330000161
since the utility function can be written as: u. ofc(p，E)：＝pc/[G(E)p+n]cForm (b), thus, defines:
Figure GPA0000209097330000162
then the optimal configuration (p)*，E*) Satisfy the requirement ofThus can be pushed out Copt＝1/ρ(Λ(E*) Where ρ is Λ (E)*) Of (d) Dilonggen (Parron root). Through verification, the optimal configuration E corresponding to the optimal utility function of the whole network can be obtained by minimizing the rho (Lambda (E))*I.e. by
Figure GPA0000209097330000164
Thus, at a given b, the problem of maximizing the utility function for the user group in the network with the worst capacity and coverage utility function (i.e., the optimization problem) translates into a minimization of ρ (Λ (E)), the basic idea for solving the problem is to find the optimal power configuration at a given subband configuration matrix at step , and to find the optimal subband configuration matrix from the possible set at a given power configuration*，p*). The specific flow of interference coordination in step 102 is shown in fig. 2.
As shown in fig. 2, in S21, eCo initializes the number of iterations n: and (b, E, p) at the current iteration number is obtained. In S22, ρ (Λ (E)) is calculated for all elements E in the E matrixijThe partial derivatives of (1). At S23, the Pelongroot ρ (Λ (E) at the current iteration number is calculated(n))). In S24, according to the calculation result of S2, Nd directions with the smallest slope are found, and E' that minimizes ρ (Λ (E)) is calculated in the corresponding candidate set. In S25, ρ (Λ (E ')) and ρ (Λ (E') calculated in S23 are added(n)) Comparing to determine whether ρ (Λ (E ')) is less than ρ (Λ (E'))(n))). If yes, entering S26, and updating (E, p) under the current iteration times; if not, E*＝E(n)Jump to S28 to output the optimal (E)*，p*) Wherein p is*According to
Figure GPA0000209097330000171
And (4) obtaining. After the update at S26, the process proceeds to S27, the number of iterations is updated, and then the process returns to S23.
And 103, optimizing the base station-user association vector b by the eCo on the basis of the optimization (E, p) in the step 102, wherein the purpose of the step is to realize load balancing of the network.
Specifically, for user group c, the interference items corresponding to maximizing capacity and coverage are
Figure GPA0000209097330000172
In this step, bycSelecting optimal associated base station for each user group c in the range of the base station belonging to the community (BS)
Figure GPA0000209097330000173
So that Ic(p*，b，E*) And (4) minimizing, namely achieving the purpose of load balancing. The specific flow of implementing load balancing of the network in step 103 may be as shown in fig. 3.
At S31, eCo acquires current iteration m
Figure GPA0000209097330000174
At S32, the eCo initializes the interference related items of the current user group c
Figure GPA0000209097330000175
In S33-S36, the eCo polls the user group c for all possible base station associations b for that user groupcE to BS, finding the optimal association under the current iteration number m
Figure GPA0000209097330000176
So that corresponding IcAnd in S37, judging whether all user groups have been traversed or not, if not, returning to S32 to execute the optimal base station association optimization process for the next user groups, and if all the user groups have been traversed, entering S38 and outputting the optimal base station association of all the user groups under the current iteration number m
Step 104, eCo judges whether the convergence condition is satisfied,if not, using the optimized base station-user group association vector b of step 103 as the input of step 102, and iteratively executing steps 102 and 103 until the convergence condition is satisfied, thereby obtaining the optimal (b)*，E*，p*)。
In step 104, eCo judges whether the power of each user group after the operations of step 102 and step 103 is converged, if not, b after the optimization of step 103 is taken as the input of step 102, and then the optimization of step 102 and step 103 is iterated until the convergence condition is satisfied, that is, p calculated after the iteration is obtained*And p calculated after the last iterations*The difference is within a given range of preset thresholds close to 0, where the optimum (b) is obtained*，E*，p*)。
Step 105, eCo through E*Is calculated to obtain F*Then optimum (b) is determined*，F*，p*) Configuring and issuing the configuration to a base station with a corresponding user group, and carrying out resource allocation on the associated user by the base station.
Wherein, in step 105, eCo passes the obtained E*Is calculated to obtain F*Then optimum (b) is determined*，F*，p*) Configuring and issuing the configuration to a base station with a corresponding user group, and carrying out resource allocation on the associated user by the base station. Specifically, eCo will E*Wherein the group i of user groups sharing the same sub-band is assigned to F*The ith sub-band in (1). eCo according to optimized (b)*，F*，p*) And issuing the corresponding association vector, the subband partition matrix and the user group power vector to the corresponding base station, and performing corresponding resource allocation by the base station.
The network capacity and coverage optimization method provided by the embodiment of the invention aims at capacity and coverage, and optimizes coverage and capacity while cell interference coordination and load distribution are relatively balanced through combined optimization of load balancing and interference coordination, thereby effectively improving the use efficiency of network resources.
Example two
Embodiments of the present invention provide methods for optimizing network capacity and coverage, where in embodiment , optimization of network capacity and coverage is achieved by using joint optimization of load balancing and interference coordination as an optimization target, in this embodiment, -step Radio Frequency (RF) optimization is performed on the basis of embodiment , that is, optimization of base station power and antenna downtilt angle (r, θ)
Figure GPA0000209097330000191
I.e., maximizing the capacity and coverage utility function of the base station with the worst utility function under the constraint that each base station is power and downtilt angle limited in magnitude.
Wherein optimizing the capacity and coverage utility function of each cell is equivalent to minimizing the following base station interference related terms:
Figure GPA0000209097330000192
wherein psi ═ BATVθJT，T＝ABT，η∈[0，1]And determining the optimized compromise relationship between the base station capacity and the coverage for the capacity and the coverage weight of the base station, wherein the value is given by an operator according to the network requirement.
The idea of the embodiment of the present invention is to separate the optimization variables (r, θ) based on the optimization variables (b, E, p) in the above embodiment , and perform iterative optimization on θ and r, as shown in fig. 4, the method provided by this embodiment includes:
step 201, eCo presets the number of sub-bands of the system, and initializes (b, E, p, r, θ) from the user grouping reported by each cell.
In step 201, the eCo divides the system sub-bands according to the design requirements of the network manager or the system, and sets the number S of the system sub-bands; setting corresponding capacity and coverage weight mu for each user groupcThe eCo can set the same or different weight values for each user group; and according to the user association and resource allocation condition of the local cell reported by each base station and antenna downtiltCorners, and user group information to initialize optimization parameters (b, E, p, r, θ). Wherein, the setting of (b, p, r, theta) is the corresponding setting and allocation under the current condition of the base station, and E belongs to {0, 1}C×CThe matrix may be set in such a way that the values of the elements are randomly set.
According to the system bandwidth and the number of sub-bands, the eCo can calculate the bandwidth of each sub-band, if the bandwidth required by any user group services reported by the base station exceeds sub-bands, the user group is further divided into steps to ensure that the service requirement of each user group can be met by the bandwidth of sub-bandsC′×C′。
In step 202, the interference coordination operation in step 102 in the embodiment and the load balancing procedure operation in step 103 are performed to optimize (b, E, p) under the current (r, θ) configuration.
The eCo optimizes the antenna downtilt angle θ by minimizing the base station interference term, step 203.
In step 203, eCo is at a given r and optimized (b, E, p), i.e., (b)*，E*，p*) Next, the base station antenna downtilt angle θ at the current iteration is optimized by minimizing the base station interference correlation term. Specifically, for the bs, the interference term corresponding to maximizing the capacity and coverage is
Figure GPA0000209097330000201
In this step, by controlling the temperature at θbs∈ΘbsSelecting optimal antenna downtilt angles for each base station bs within rangeSo that Ibs(r, θ) is minimized.
In step 204, the eCo optimizes the base station power r through fixed point iteration.
In step 204, the eCo adopts a fixed-point iterative optimization method, and combines each base stationThe maximum utility function of each base station can be determined by
Figure GPA0000209097330000203
In which gamma isbsThe lowest Signal to Interference plus noise ratio (SINR) limit is usually-6.5 dB.
In step 205, the eCo determines whether the power convergence condition of each base station is satisfied. If not, returning to the second step, and then iteratively performing the optimization of the steps 202 to 204 until the convergence condition is satisfied, at which time the eCo obtains the optimal (b)*，E*，p*，r*，θ*)。
In step 205, eCo determines whether the power of each base station is converged after the operations of step 202 and step 204, if not, the power r of the base station is continuously updated, and then the optimization of steps 202 to 204 is performed iteratively until the convergence condition is satisfied, that is, r calculated after the iteration is performed for the time*And r calculated after iterations*The difference is within a given range of preset thresholds close to 0, where the optimum (b) is obtained*，E*，p*，r*，θ*)。
Step 206, eCo gets E*Is calculated to obtain F*Then optimum (b) is determined*，F*，p*，r*，θ*) Configuring and issuing the configuration to a base station with a corresponding user group, and carrying out resource allocation on the associated user by the base station.
In step 206, eCo passes the obtained E*Is calculated to obtain F*Then optimum (b) is determined*，F*，p*，r*，θ*) Configuring and issuing each base station, and performing corresponding configuration and resource allocation on the associated users by the base stations. Specifically, eCo will E*Wherein the group i of user groups sharing the same sub-band is assigned to F*The ith sub-band in (1). eCo according to optimized (b)*，F*，p*，r*，θ*) And issuing the corresponding association vector, the subband partition matrix, the user group power vector, the base station power vector and the base station antenna downtilt configuration to the corresponding base station, and performing corresponding configuration and resource allocation by the base station.
The network capacity and coverage optimization method provided by the embodiment of the invention aims at capacity and coverage, optimizes coverage and capacity while cell interference coordination and load distribution are relatively balanced through combined optimization of load balancing and interference coordination, and effectively improves the use efficiency of network resources, and further optimizes capacity and coverage through combining RF optimization to and further improves the use efficiency of network resources by .
For more convenient explanation of operations in the second embodiment, as shown in fig. 5, in S51, eCo completes initialization of parameters (b, E, p, r, θ) to be optimized, in S52, eCo initializes iteration number t: ═ 0 for base station bs to be currently calculated, in S53, eCo initializes interference related terms for base station bsI bs: and starting iterative calculation of the parameter to be optimized. In S4-S8, eCo is base station bs through polling all its possible antenna downtilts θbs∈ΘbsFind the interference term IbsTheta corresponding to minimumbs. Wherein (b, E, p) is obtained by the second embodiment optimization in S55, and is compared with the current θbs is also used for calculating IbsIn S59, the power of bs is updated by fixed point iteration (it should be noted that the application document has two identifiers, is bs, which identifies the bs-th base station, is bc, which indicates the base station associated with the c-th user group). in S510, the power of the base station obtained under the current iterative computation and the power of the base station obtained under the last iterative computations are judged to be convergent, if not convergent, the process goes to S511, which updates the iterative times and returns to the lower iterative computations, if convergent, the process goes to S512, which judges whether all bs. have been traversed in S512, if not all bs have been traversed, the process returns to S52, which iterates the next bs, and if all bs have been traversed, the process goes to S513, which outputs (b, E, p, r, θ) obtained after optimization.
EXAMPLE III
The embodiment of the invention provides network capacity and coverage optimization devices, which may be specifically eCo, but not limited to eCo as shown in fig. 6, and the device includes a transceiver 601 and a processor 602;
the transceiver 601 is configured to receive the cell user association and resource allocation reported by each base station, and user group information used to initialize the optimization parameters (b, E, p); wherein, b is a base station-user association vector, E is a sub-frequency band sharing matrix, and p is a user group power vector; to be obtained (b)*，F*，p*) Configuring and issuing the configuration to a base station with a corresponding user group, and performing resource allocation on the associated users by the base station, thereby realizing the combined optimization of load balancing and interference coordination;
the processor 602 is configured to divide a system subband; setting corresponding capacity and coverage weight for each user group; carrying out user group repartitioning according to the sub-frequency band and the user group relation, and using the user association and resource allocation of the cell reported by each base station and user group information to initialize optimization parameters (b, E, p); when b is a given value, optimizing each sub-frequency band sharing matrix and a user group power vector, namely (E, p), so as to realize the interference coordination of the network; according to said (E, p) optimized, i.e. (E)*，p*) Optimizing the base station-user association vector b for realizing load balance of the network; judging whether the power of each user group is converged, and if so, obtaining optimized (b)*，E*，p*) (ii) a According to obtaining E*Computing a user group-subband assignment matrix, i.e. F*Obtained (b)*，F*，p*)。
The network capacity and coverage optimizing device provided by the embodiment of the invention aims at capacity and coverage, and optimizes coverage and capacity while cell interference coordination and load distribution are relatively balanced through combined optimization of load balancing and interference coordination, thereby effectively improving the use efficiency of network resources.
Optionally, the initialization setting of (b, p) in the processor is specifically performed according to the actual association and power of each user group reported by the base station; and E, performing initialization setting by adopting a mode of randomly setting each element value.
Optionally, when b is a given value, optimizing each subband sharing matrix and a user group power vector, that is, (E, p), in the processor specifically includes:
maximizing the utility function of the user group with worst capacity and coverage utility function in the network when b is a given value, namely:
Figure GPA0000209097330000231
since the utility function is: u. ofc(p，E)：＝pc/[G(E)p+n]cForm (b), thus, defines:
Figure GPA0000209097330000232
then the optimal configuration (p)*，E*) Satisfy the requirement of
Figure GPA0000209097330000233
Thus can be pushed out Copt＝1/ρ(Λ(E*) Where ρ is Λ (E)*) Of petrong root;
after verification, the optimal configuration E corresponding to the optimal utility function of the whole network is obtained by minimizing the rho (Lambda (E))*I.e. by
Figure GPA0000209097330000234
Optionally, the optimizing, by the processor, the base station-user association vector b according to the optimized (E, p) specifically includes:
for user group c, the interference items corresponding to maximizing the capacity and coverage are as follows:
Figure GPA0000209097330000235
by the reaction ofcSelecting optimal c for each user group in the range of BS
Figure GPA0000209097330000241
So that Ic(p*，b，E*) And (4) minimizing.
Optionally, when the processor determines that the power of each user group is not converged, the optimized base station-user association vector is used as an input, when b is a given value, the processor optimizes each subband sharing matrix and the user group power vector, and executes the (E, p) according to the optimization to optimize the base station-user association vector b; until the power of each user group is not converged, thereby obtaining optimized (b)*，E*，p*)。
Optionally, the processor obtains E*Computing a user group-subband assignment matrix, i.e. F*The method specifically comprises the following steps:
will E*Wherein the group i of user groups sharing the same sub-band is assigned to F*The ith sub-band in (1).
Optionally, the setting, in the processor, a corresponding capacity and a coverage weight for each user group specifically includes:
and setting the same or different capacity and coverage weight values for each user group.
Example four
The embodiment of the present invention provides kinds of network capacity and coverage optimization devices, which may specifically be a network coordinator eCo, but is not limited to eCo, as shown in fig. 7, the device includes a transceiver 701 and a processor 702;
the transceiver 701 is configured to receive user association and resource allocation, an antenna downtilt angle, and user group information of the local cell reported by each base station, and is configured to initialize optimization parameters (b, E, p, r, θ) by the processor; b is a base station-user association vector, E is a sub-frequency band sharing matrix, p is a user group power vector, r is base station power, and theta is an antenna downward inclination angle; to be obtained (b)*，F*，p*，r*，θ*) And configuring and transmitting the configuration to the base station with the corresponding user group.
The processor 702 is configured to divide a system subband; setting up facies for user groupsThe corresponding capacity and the coverage weight; carrying out user group repartitioning according to the sub-frequency band and the user group relation, and initializing optimization parameters (b, E, p, r and theta) according to the user association and resource allocation, antenna downward inclination angle and user group information of the cell reported by each base station; b is a base station-user association vector, E is a sub-frequency band sharing matrix, p is a user group power vector, r is base station power, and theta is an antenna downward inclination angle; under the current (r, theta) configuration, when b is a given value, optimizing each sub-frequency band sharing matrix and a user group power vector, namely (E, p), so as to realize the interference coordination of the network; and according to said (E, p), i.e. (E), optimized*，p*) Optimizing the base station-user association vector b for realizing load balance of the network; thereby obtaining optimized (b, E, p) in the current (r, θ) configuration; optimizing the current base station antenna downtilt angle theta by minimizing the base station interference correlation term under the given r and the optimized (b, E, p); optimizing the power r of the base station through fixed point iteration; judging whether the power of each base station is converged; if converged, obtaining optimized (b)*，E*，p*，r*，θ*) (ii) a According to obtaining E*Computing a user group-subband assignment matrix, i.e. F*(b) to be acquired*，F*，p*，r*，θ*) The configuration is issued to the base station with the corresponding user group, and the base station performs resource allocation on the associated users, thereby realizing the combined optimization of radio frequency optimization, load balancing and interference coordination.
The network capacity and coverage optimizing device provided by the embodiment of the invention aims at capacity and coverage, optimizes coverage and capacity while cell interference coordination and load distribution are relatively balanced through combined optimization of load balancing and interference coordination, and effectively improves the use efficiency of network resources, and further optimizes the capacity and coverage through combining RF optimization to and further improves the use efficiency of network resources through .
Optionally, the optimizing, in the processor, the current base station antenna downtilt angle θ by minimizing the base station interference correlation term under the given r and the optimized (b, E, p), specifically includes:
for a base station, the interference term corresponding to maximizing the capacity and coverage is
Figure GPA0000209097330000251
By the addition of a compound in bc∈BcSelecting optimal within range for each user group c
Figure GPA0000209097330000252
So that Ic(p*，b，E*) And (4) minimizing, namely achieving the purpose of load balancing.
Optionally, the processor optimizes the base station power r through fixed point iteration; the method specifically comprises the following steps:
adopting a fixed point iterative optimization method, combining the power constraint of each base station, and enabling the maximum utility function of each base station to pass throughIn which gamma is used as the basis for the fixed-point iterative optimization implementation ofbsIs the lowest signal to interference plus noise ratio.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer readable storage medium, which may include ROM, RAM, magnetic or optical disk, etc.
The network capacity and coverage optimizing methods and apparatuses provided by the embodiments of the present invention are described in detail above, and the principles and embodiments of the present invention are explained herein by applying specific examples, and the above description of the embodiments is only used to help understanding the method and the core idea of the present invention, meanwhile, for persons in the art, there are changes in the specific embodiments and application scope according to the idea of the present invention, and in conclusion, the content of the present description should not be construed as limiting the present invention.

Claims (12)

1, method for optimizing network capacity and coverage, said method comprising:
dividing the system sub-bands; setting corresponding capacity and coverage weight for each user group; according to the sub-frequency band and the user group relation, user group division is carried out, and according to the user association and resource allocation of the cell reported by each base station and user group information, the user group information is used for initializing optimization parameters (b, E, p); wherein, b is a base station-user association vector, E is a sub-frequency band sharing matrix, and p is a user group power vector;
when b is a given value, optimizing each sub-frequency band sharing matrix and a user group power vector, namely (E, p), so as to realize the interference coordination of the network;
according to said (E, p) optimized, i.e. (E)*,p*) Optimizing the base station-user association vector b for realizing load balance of the network;
judging whether the power of each user group is converged, and if so, obtaining optimized (b)*,E*,p*)；
According to obtaining E*Computing a user group-subband assignment matrix, i.e. F*(b) to be acquired*,F*,p*) The configuration is issued to the base station with the corresponding user group, and the base station performs resource allocation on the associated users, thereby realizing the combined optimization of load balancing and interference coordination.
2. The method according to claim 1, wherein the (b, p) initialization setting is specifically performed according to actual association and power of each user group reported by the base station; and E, performing initialization setting by adopting a mode of randomly setting each element value.
3. The method of claim 1,
when the power of each user group is judged not to be converged, the optimized base station-user association vector is used as input, when the b is executed as a given value, each sub-frequency band sharing matrix and the user group power vector are optimized, and the base station-user association vector b is optimized according to the optimized (E, p); until each useUntil the power of the user group is not converged, thereby obtaining optimized (b)*,E*,p*)。
4. The method of claim 1, wherein the obtaining E is based on*Computing a user group-subband assignment matrix, i.e. F*The method specifically comprises the following steps:
will E*Wherein the group i of user groups sharing the same sub-band is assigned to F*The ith sub-band in (1).
5. The method according to claim 1, wherein setting the corresponding capacity and coverage weight for each user group specifically comprises:
and setting the same or different capacity and coverage weight values for each user group.
6, A method for optimizing network capacity and coverage, the method comprising:
dividing the system sub-bands; setting corresponding capacity and coverage weight for each user group; carrying out user group repartitioning according to the sub-frequency band and the user group relation, and initializing optimization parameters (b, E, p, r and theta) according to the user association and resource allocation, antenna downward inclination angle and user group information of the cell reported by each base station; b is a base station-user association vector, E is a sub-frequency band sharing matrix, p is a user group power vector, r is a base station power vector, and theta is an antenna downward inclination angle vector;
under the current (r, theta) configuration, when b is a given value, optimizing each sub-frequency band sharing matrix and a user group power vector, namely (E, p), so as to realize the interference coordination of the network; and according to said (E, p), i.e. (E), optimized*,p*) Optimizing the base station-user association vector b for realizing load balance of the network; thereby obtaining optimized (b, E, p) in the current (r, θ) configuration;
optimizing a base station antenna downtilt angle theta at the current iteration by minimizing a base station interference correlation term at a given r and an optimized (b, E, p);
optimizing the power r of the base station through fixed point iteration;
judging whether the power of each base station is converged; if converged, obtaining optimized (b)*,E*,p*,r*,θ*)；
According to obtaining E*Computing a user group-subband assignment matrix, i.e. F*To be acquiredThe configuration is issued to the base station with the corresponding user group, and the base station performs resource allocation on the associated users, thereby realizing the combined optimization of radio frequency optimization, load balancing and interference coordination.
7, kinds of network capacity and coverage optimizing device, which is characterized in that the device comprises a transceiver and a processor;
the transceiver is used for receiving the cell user association and resource allocation reported by each base station and user group information used for initializing optimization parameters (b, E, p); wherein, b is a base station-user association vector, E is a sub-frequency band sharing matrix, and p is a user group power vector; to be obtained (b)*,F*,p*) Configuring and issuing the configuration to a base station with a corresponding user group, and performing resource allocation on the associated users by the base station, thereby realizing the combined optimization of load balancing and interference coordination;
the processor is configured to divide a system sub-band; setting corresponding capacity and coverage weight for each user group; carrying out user group repartitioning according to the sub-frequency band and the user group relation, and using the user association and resource allocation of the cell reported by each base station and user group information to initialize optimization parameters (b, E, p); when b is a given value, optimizing each sub-frequency band sharing matrix and a user group power vector, namely (E, p), so as to realize the interference coordination of the network; according to said (E, p) optimized, i.e. (E)*,p*) Optimizing the base station-user association vector b for realizing load balance of the network; judging whether the power of each user group is converged, and if so, obtaining optimized (b)*,E*,p*) (ii) a According to obtaining E*Computing a user group-subband assignment matrix, i.e. F*。
8. The apparatus according to claim 7, wherein the (b, p) initialization setting in the processor is specifically performed according to actual association and power of each user group reported by the base station; and E, performing initialization setting by adopting a mode of randomly setting each element value.
9. The apparatus of claim 7, wherein the processor is configured to perform the optimization of the subband sharing matrix and the user group power vector when b is a given value, and perform the optimization of the base station-user association vector b according to the optimized (E, p), using the optimized base station-user association vector as an input when it is determined that the user group power is not converged; until the power of each user group is not converged, thereby obtaining optimized (b)*,E*,p*)。
10. The apparatus of claim 7, wherein the processor is configured to obtain E based on*Computing a user group-subband assignment matrix, i.e. F*The method specifically comprises the following steps:
will E*Wherein the group i of user groups sharing the same sub-band is assigned to F*The ith sub-band in (1).
11. The apparatus according to claim 7, wherein the setting, in the processor, of the corresponding capacity and coverage weight for each user group specifically includes:
and setting the same or different capacity and coverage weight values for each user group.
12, kinds of network capacity and coverage optimizing device, characterized in that, the device includes:
a transceiver and a processor;
the transceiver is used for receiving the reports from all the base stationsUser association and resource allocation, antenna downtilt angle, and user group information of the cell are used for initializing optimization parameters (b, E, p, r, theta) by the processor; b is a base station-user association vector, E is a sub-frequency band sharing matrix, p is a user group power vector, r is a base station power vector, and theta is an antenna downward inclination angle vector; to be obtained (b)*,F*,p*,r*,θ*) Configuring and issuing the configuration to a base station with a corresponding user group;
the processor is configured to divide a system sub-band; setting corresponding capacity and coverage weight for each user group; carrying out user group repartitioning according to the sub-frequency band and the user group relation, and initializing optimization parameters (b, E, p, r and theta) according to the user association and resource allocation, antenna downward inclination angle and user group information of the cell reported by each base station; b is a base station-user association vector, E is a sub-frequency band sharing matrix, p is a user group power vector, r is a base station power vector, and theta is an antenna downward inclination angle vector; under the current (r, theta) configuration, when b is a given value, optimizing each sub-frequency band sharing matrix and a user group power vector, namely (E, p), so as to realize the interference coordination of the network; and according to said (E, p), i.e. (E), optimized*,p*) Optimizing the base station-user association vector b for realizing load balance of the network; thereby obtaining optimized (b, E, p) in the current (r, θ) configuration; optimizing the current base station antenna downtilt angle theta by minimizing the base station interference correlation term under the given r and the optimized (b, E, p); optimizing the power r of the base station through fixed point iteration; judging whether the power of each base station is converged; if converged, obtaining optimized (b)*,E*,p*,r*,θ*) (ii) a According to obtaining E*Computing a user group-subband assignment matrix, i.e. F*(b) to be acquired*,F*,p*,r*,θ*) The configuration is issued to the base station with the corresponding user group, and the base station performs resource allocation on the associated users, thereby realizing the combined optimization of radio frequency optimization, load balancing and interference coordination.
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