CN104918262A - Network optimization method and apparatus - Google Patents

Abstract

The embodiments of the invention provide a network optimization method and apparatus. The method comprises the following steps: according to obtained transmit-receive performance parameters of each user, grouping the users to form at least two user groups, and respectively obtaining connectivity of each user group; respectively obtaining a power sharing factor of each user and a power sharing factor of each user group; according to the power of each node, an antenna downtilt angle, the power sharing factor of each user and the power sharing factor of each user group, obtaining an optimization value of the connectivity of each user group and an optimization value of the connectivity of each user; according to the optimization value of the connectivity of each user group, obtaining optimization values of the power of each node and the antenna downtilt angle; repeating the above steps until the inertia frequency reaches a first preset threshold; and obtaining optimization values of the power of each node, the antenna downtilt angle and the connectivity of each user. According to the embodiments of the invention, the system capacity, the coverage performance and the system resource utilization rate are improved.

Description

Network optimized approach and device

Technical field

The embodiment of the present invention relates to the communication technology, particularly relates to a kind of network optimized approach and device.

Background technology

Self-organization of network technology (Self-Organized Network, be called for short SON) refer to that network carries out the operation such as self-configuring, self-optimizing, certainly healing automatically according to network condition, realize real-time automated network to safeguard, thus can manual intervention be reduced, reduce O&M cost.Capacity and coverage optimization (Coverage and Capacity Optimization is called for short COO), load balancing (Load Balancing is called for short LB) are two important use-cases of SON.

The optimization of cell-level that what existing capacity and coverage optimization technology realized is, to improve cell average spectral efficiency and edge spectrum efficiency for target, maximizes capacity and the covering of each community as far as possible; But for whole net, prior art cannot make full use of capacity and the covering of system resource and the system of maximization most, especially when offered load distribution is very uneven, heavy duty community is difficult to the demand meeting community user due to inadequate resource, thus cause the decline of QoS (Quality of Service is called for short QoS).Therefore, prior art can not make the capacity of system and covering performance reach optimization.

Existing load-balancing technique is the switching carrying out triggering part user according to the load difference in each community running of statistics, the certain customers of high capacity community are reconnected and receives contiguous low loaded cell, the load of each community is made to be able to equilibrium by the load transfer realizing minizone, ensure the reduction of inter-cell load difference, thus network capacity is had to the impact in front, but, the load of prior art Jin Yi community is measurement index, and the capacity of system and covering performance cannot be made to reach optimization.

Summary of the invention

The embodiment of the present invention provides a kind of network optimized approach and device, with resolution system capacity and covering performance and the low problem of resource utilization.

First aspect, the embodiment of the present invention provides a kind of network optimized approach, and wherein, described method comprises:

Transmitting-receiving performance parameter according to each user obtained is divided into groups to described each user, forms at least two user's groups, and obtains the connectivity of described each user's group respectively; Wherein, the connectivity of described user's group, is used to indicate the associated nodes of user's group;

Obtain the power sharing learning of described each user and the power sharing learning of described each user's group respectively;

According to the power sharing learning of the power of each node, Downtilt, described each user and the power sharing learning of described each user's group, obtain described each internuncial optimal value of user's group and internuncial optimal value of described each user; Wherein, the connectivity of described user, is used to indicate the associated nodes of user;

According to internuncial optimal value of described each user's group, obtain the described power of each node and the optimal value of Downtilt;

Repeat above-mentioned steps, until iterations reaches default first threshold;

Obtain internuncial optimal value of the power of each node in described network, Downtilt and each user.

According to first aspect, in the first possible implementation of first aspect, the power sharing learning of the described power according to described each node, Downtilt, described each user and the power sharing learning of described each user's group, obtain described each internuncial optimal value of user's group and internuncial optimal value of described each user, comprising:

According to the connectivity of the power of described each node, the power sharing learning of described each user's group and described each user's group, determine the power of described each user's group;

Travel through the candidate association node of described each user's group respectively, according to power and the power sharing learning of the Downtilt of described each node and described each user's group, obtain the interference correlative of described each user's group and the candidate association of corresponding candidate association node;

Described each user's group object associated nodes corresponding with the least interference correlative of candidate association in corresponding candidate association node is respectively associated;

According to maximum and the target capabilities of the least interference correlative of described each user's group, the power of described each user's group, upgrade the power of described each user's group;

Repeat above-mentioned steps, until the power of described each user's group upgrades amplitude be no more than default Second Threshold;

Obtain described each internuncial optimal value of user's group and the optimal value of power;

According to the relevance that internuncial optimal value of described each user group and user and user organize, determine internuncial optimal value of described each user.

According to first aspect, in the implementation that the second of first aspect is possible, described internuncial optimal value according to described each user's group, obtains the described power of each node and the optimal value of Downtilt, comprising:

Travel through candidate's Downtilt of described each node respectively, according to internuncial optimal value of described each user's group, obtain the interference correlative of described each node to the candidate association of candidate's Downtilt of correspondence;

The object Downtilt that described each node is corresponding with the least interference correlative of candidate association in corresponding candidate's Downtilt is respectively associated;

According to maximum and the performance limits of the least interference correlative of described each node, the power of described each node, upgrade the power of described each node;

Repeat above-mentioned steps, until the power of described each node upgrades amplitude be no more than the 3rd default threshold value;

Obtain the optimal value of Downtilt and the optimal value of power of described each node.

According to first aspect, in the third possible implementation of first aspect, described transmitting-receiving performance parameter is: the Reference Signal Received Power RSRP that user measures, and/or, the signal to noise ratio SINR of user.

Second aspect, the embodiment of the present invention provides a kind of network optimization device, and wherein, described device comprises:

Grouped element, for dividing into groups to described each user according to the transmitting-receiving performance parameter of each user obtained, forms at least two user's groups, and obtains the connectivity of described each user's group respectively; Wherein, the connectivity of described user's group, is used to indicate the associated nodes of user's group;

First acquiring unit, the power sharing learning that power sharing learning and described each user for obtaining described each user are respectively organized;

Second acquisition unit, for the power sharing learning of the power according to each node, Downtilt, described each user and the power sharing learning of described each user's group, obtain described each internuncial optimal value of user's group and internuncial optimal value of described each user; Wherein, the connectivity of described user, is used to indicate the associated nodes of user;

3rd acquiring unit, for the internuncial optimal value according to described each user's group, obtains the described power of each node and the optimal value of Downtilt;

Iteration unit, for grouped element, described first acquiring unit, described second acquisition unit and described 3rd acquiring unit described in iteration control, until iterations reaches default first threshold;

Acquiring unit, for obtaining internuncial optimal value of the power of each node in described network, Downtilt and each user.

According to second aspect, in the first possible implementation of second aspect, described second acquisition unit, specifically for:

According to the connectivity of the power of described each node, the power sharing learning of described each user and described each user's group, determine the power of described each user's group;

Travel through the candidate association node of described each user's group respectively, according to power and the power sharing learning of the Downtilt of described each node and described each user's group, obtain the interference correlative of described each user's group and the candidate association of corresponding candidate association node;

Described each user's group object associated nodes corresponding with the least interference correlative of candidate association in corresponding candidate association node is respectively associated;

According to maximum and the target capabilities of the least interference correlative of described each user's group, the power of described each user's group, upgrade the power of described each user's group;

Repeat above-mentioned steps, until the power of described each user's group upgrades amplitude be no more than default Second Threshold;

Obtain described each internuncial optimal value of user's group and the optimal value of power;

According to the relevance that internuncial optimal value of described each user group and user and user organize, determine internuncial optimal value of described each user.

According to second aspect, in the implementation that the second of second aspect is possible, described 3rd acquiring unit, specifically for:

Travel through candidate's Downtilt of described each node respectively, according to internuncial optimal value of described each user's group, obtain the interference correlative of described each node to the candidate association of candidate's Downtilt of correspondence;

The object Downtilt that described each node is corresponding with the least interference correlative of candidate association in corresponding candidate's Downtilt is respectively associated;

According to maximum and the performance limits of the least interference correlative of described each node, the power of described each node, upgrade the power of described each node;

Repeat above-mentioned steps, until the power of described each node upgrades amplitude be no more than the 3rd default threshold value;

Obtain the optimal value of Downtilt and the optimal value of power of described each node.

According to second aspect, in the third possible implementation of second aspect, described transmitting-receiving performance parameter is: the Reference Signal Received Power RSRP that user measures, and/or, the signal to noise ratio SINR of user.

The network optimized approach that the embodiment of the present invention provides and device, by carrying out combined optimization to the capacity of network and covering and load balancing, to solve in prior art capacity and cover and load balancing single optimization and cause the problem that the capacity of system and covering performance and resource utilization are low separately, the capacity of system and covering performance is made to be able to optimization, and ensure that the load of minizone is relative equilibrium, improve resource utilization ratio.

Accompanying drawing explanation

In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.

The flow chart of the network optimized approach that Fig. 1 provides for the embodiment of the present invention;

The flow chart of the load balancing that Fig. 2 provides for the embodiment of the present invention;

The flow chart of the capacity that Fig. 3 provides for the embodiment of the present invention and coverage optimization;

The structural representation of the network optimization device that Fig. 4 provides for the embodiment of the present invention.

Embodiment

For making the object of the embodiment of the present invention, technical scheme and advantage clearly, below in conjunction with the accompanying drawing in the embodiment of the present invention, technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.

The technical scheme that the embodiment of the present invention provides can be applied to various cordless communication network, such as: global mobile communication (global system for mobile communication, referred to as GSM) system, code division multiple access (code division multiple access, referred to as CDMA) system, Wideband Code Division Multiple Access (WCDMA) (wideband code division multiple access, referred to as WCDMA) system, universal mobile communications (universal mobile telecommunication system, referred to as UMTS) system, GPRS (general packet radio service, referred to as GPRS) system, Long Term Evolution (long term evolution, referred to as LTE) system, advanced Long Term Evolution (long term evolution advanced, referred to as LTE-A) system, global interconnection inserting of microwave (worldwide interoperability for microwave access, referred to as WiMAX) system etc.Term " network " and " system " can be replaced mutually.

In embodiments of the present invention, node can be and subscriber equipment (user equipment, referred to as UE) or other communication site such as relay carry out the equipment that communicates, described node can be such as base station (basestation, referred to as BS), the communication overlay of specific physical region can be provided.Such as, base station can be specifically base transceiver station (Base Transceiver Station, referred to as BTS) in GSM or CDMA or base station controller (Base Station Controller, referred to as BSC); Also can be the Node B (Node B, referred to as NB) in UMTS or the radio network controller (Radio Network Controller, referred to as RNC) in UMTS; Also can be Home eNodeB; It can also be the evolved base station (Evolutional Node B, referred to as eNB or eNodeB) in LTE; Or also can be other access network equipments providing access service in cordless communication network, the present invention limit.In embodiments of the present invention, user can be distributed in whole wireless network, and each user can be static or mobile.

The flow chart of the network optimized approach that Fig. 1 provides for the embodiment of the present invention.As shown in Figure 1, the network optimized approach that the embodiment of the present invention provides, comprising:

101, according to current network configuration, obtain the connectivity that each user is current, and calculate the transmitting-receiving performance parameter of described each user and carry out user grouping, form at least two user's groups, determine the relevance that user and user organize, determine the connectivity of described each user's group; Wherein, the connectivity of described user, is used to indicate the associated nodes of user; The connectivity of described user's group, is used to indicate the associated nodes of user's group.

Particularly, described current network configuration, refers at current time, and the wireless parameter configuration of each node in network, as power division, Downtilt are arranged and other RRM parameters; Such as, can be the Reference Signal Received Power (Reference Signal Receiving Power, RSRP) that user measures, also can be the signal to noise ratio (SINR) of user to described transmitting-receiving performance parameter.

102, be the power that each user distributes according to the current associated nodes of each described user, obtain the power sharing learning of described each user and the power sharing learning of described each user group respectively.

Particularly, the power sharing learning of user, refers to that the power assigned by this user accounts for the proportion of the total allocation power of the associated nodes of this user; The power sharing learning of user's group, refers to that this user organizes the proportion that assigned power accounts for the total allocation power of the associated nodes of this user group.Under current network configuration is connected with user, node is that each user is assigned with corresponding power, the power sharing ratio of this user acquisition that what the power sharing learning of user was corresponding is; What the power sharing learning that user organizes was corresponding is that this user organizes the power sharing ratio obtained.

103, according to the power sharing learning of the power of described each node, Downtilt, described each user and the power sharing learning of described each user's group, described each internuncial optimal value of user's group and internuncial optimal value of described each user is obtained.

104, according to internuncial optimal value of described each user's group, the described power of each node and the optimal value of Downtilt is obtained.

105, the first threshold that iterations reaches default is judged: if iterations reaches described first threshold, then forward step 106 to; If iterations does not reach described first threshold, then iterations is added 1, and forward step 101 to.

106, network optimization result is obtained; Wherein, described network optimization result comprises: internuncial optimal value of described each user, and the described power of each node and the optimal value of Downtilt.

Particularly, the executive agent of the embodiment of the present invention can be network optimization device, and described network optimization device can be arranged on nodes or convergence device, also can be arranged on webmaster or separately and arrange.

The network optimized approach that the embodiment of the present invention provides, the first step, carry out dividing into groups to form different user's groups to each user, power sharing learning is organized according to the power of each node and Downtilt, user power sharing learning and user, the connectivity of all user's groups of optimization, reaches the load balancing of whole net; Second step, according to internuncial optimal value that each user organizes, the power of all nodes of optimization and Downtilt, the capacity and the covering performance that reach whole net maximize; By realizing the capacity of network and the combined optimization of covering and load balancing to the iteration of above two steps, to solve in prior art capacity and cover and load balancing single optimization and cause the problem that the capacity of system and covering performance and resource utilization are low separately, the capacity of system and covering performance is made to be able to optimization, and ensure that the load of minizone is relative equilibrium, improve resource utilization ratio.

The network optimized approach that the embodiment of the present invention provides, by carrying out combined optimization to the capacity of network and covering and load balancing, to solve in prior art capacity and cover and load balancing single optimization and cause the problem that the capacity of system and covering performance and resource utilization are low separately, the capacity of system and covering performance is made to be able to optimization, and ensure that the load of minizone is relative equilibrium, improve resource utilization ratio.

The flow chart of the load balancing that Fig. 2 provides for the embodiment of the present invention.The load balancing that the embodiment of the present invention provides is the further restriction for the method realizing whole net load balancing in above-described embodiment.As shown in Figure 2, the load-balancing method that the embodiment of the present invention provides, comprising:

201, according to power and Downtilt, the power sharing learning of each described user and the connectivity of each described user's group of each described node, the power of each described user's group is determined.

202, for each user's group, travel through the candidate association node of this user group, according to power, the power sharing learning of described each user and the Downtilt of described each node that this user organizes, obtain this user and organize the interference correlative with the candidate association of the candidate association node traversed, and candidate association node corresponding for minimum interference correlative is defined as the object associated nodes of this user group.

203, each described user's group is associated with its object associated nodes respectively.

204, according to maximum and the target capabilities of the least interference correlative of described each user's group, the power of described each user's group, the power of described each user's group is upgraded.

205, judge whether the renewal amplitude of the power of described each user's group is no more than default Second Threshold: if renewal amplitude is no more than described Second Threshold, then forward step 206 to; If renewal amplitude exceedes described Second Threshold, then forward step 201 to.

206, described each internuncial optimal value of user's group and the optimal value of power is obtained.

207, according to the relevance that internuncial optimal value of described each user group and user and user organize, internuncial optimal value of described each user is determined.

Particularly, the method that the embodiment of the present invention provides, according to power and the Downtilt of node each in network, the connectivity that in optimized network, all users organize and power, while the capacity and covering performance of the system of guarantee, realize the load balancing of whole net, thus improve network performance.Wherein, the performance of user's group at least can be represented by following three kinds of modes:

Mode 1, user organize the average behavior of interior all users, can reflect the volumetric properties that user organizes.

The poorest user performance in mode 2, user organize, can reflect the covering performance that user organizes.

Mode 3, user organize the interior average behavior of all users and the weighting of the poorest user performance, can reflect the trade off performance of the capacity that user organizes and covering.

For mode 1: if consider, user organizes the average behavior of interior all users, then the descending average SINR of all users in user's group can be adopted as the performance index of network, organize c for user, user organizes the descending average behavior of all users in c , obtained by following formula (1):

$\begin{array}{c}{\stackrel{\‾}{u}}_c^{\left(\mathrm{DL}\right)}=\frac{1}{K_c}\underset{k\∈K^{\left(c\right)}}{\mathrm{\Σ}}{\mathrm{SINR}}_k\\ =\frac{1}{K_c}\·\underset{k\∈K^{\left(c\right)}}{\mathrm{\Σ}}\frac{q_v{v}_{\mathrm{kk}}}{I_k^{\left(\mathrm{DL}\right)}+{\mathrm{\σ}}_n^2}\\ \≥\frac{1}{K_c}\·\frac{{\mathrm{\Σ}}_{k\∈K^{\left(c\right)}}{q}_k{v}_{\mathrm{kk}}}{{\mathrm{\Σ}}_{k\∈K^{\left(c\right)}}{I}_k^{\left(\mathrm{DL}\right)}+K_c{\mathrm{\σ}}_n^2}\\ =\frac{1}{K_c}\·\frac{p_c\·{\mathrm{\Σ}}_{k\∈K^{\left(c\right)}}{\mathrm{\α}}_k{v}_{\mathrm{kk}}}{{\mathrm{\Σ}}_{k\∈K^{\left(c\right)}}{I}_k^{\left(\mathrm{DL}\right)}+K_c{\mathrm{\σ}}_n^2}\\ =\frac{1}{K_c}\·\frac{p_c\·{\mathrm{\Σ}}_{k\∈K^{\left(c\right)}}{g}_k}{{\mathrm{\Σ}}_{k\∈K^{\left(c\right)}}{\left[V_{\mathrm{\θ}}^T{A}_{\mathrm{\α}}p\right]}_k+K_c{\mathrm{\σ}}_n^2}\\ =\frac{1}{K_c}\·\frac{p_c\·{\mathrm{\Σ}}_{k\∈K^{\left(c\right)}}{g}_k}{{\left[A^T{V}_{\mathrm{\θ}}^T{A}_{\mathrm{\α}}p\right]}_c+K_c{\mathrm{\σ}}_n^2}\\ ={\underset{\‾}{u}}_c^{\left(\mathrm{DL}\right)}\end{array}---\left(1\right)$

Wherein: K _crepresent that user organizes the number of users in c; Κ ^(c)represent that user organizes the user index in c, c ∈ [1, C], C represents that user organizes number; K represents that user organizes the kth user (user k) in c, k ∈ [1, K], and K represents the number of users in network; SINR _krepresent the SINR of user k; the lower limit of the descending average SINR of all users in c is organized for user; q _krepresent that the associated nodes of user k is the power that user k distributes, v _kkrepresent the channel gain of associated nodes to this user k of user k, represent the interference of other node signals that user k is subject to when communicating with its associated nodes, represent average noise power, g _k=α _kv _kk;

wherein, represent coupling matrix, representing sends to the signal of user k1 to the receiving gain of user k2 when Downtilt is θ, V _θfor in self-interference and community in/the interference and coupling matrix that obtains after organizing interior distracter zero setting of user, V _θrepresent that the Downtilt organizing node belonging to c user is θ, and by the self-interference of each user and community/user organizes the user's interference volume after interior distracter zero setting;

j represents node-user-association matrix, J _bkthe connectivity of=1 expression user k, namely user k is assigned to node b, b ∈ [1, B], and B represents the interstitial content in network; U represents receiving gain matrix, U _bkrepresent the receiving gain of node b to user k;

J=BA ^t, B represents that node-user organizes incidence matrices, B _bc=1 represents that user organizes the connectivity of c, and namely user organizes c and is assigned to node b; A represents user-user group incidence matrices, A _kc=1 represents that user k and user organize the relevance of c, and namely user k is assigned to user and organizes c; A ^tα=1, α represents user power allocation vector α=[α ₁..., α _k] ^t, α _krepresent the power sharing learning of user k; A _αrepresent user-user group associated power matrix, A _α=diag{ α } A;

P represents that user organizes vector power, p=[p ₁..., p _c] ^t, p _crepresent that user organizes the power in c; P=diag{ β } B ^tr; β represents that user organizes power allocation vector, β=[β ₁..., β _c] ^t, β _crepresent that user organizes the power sharing learning of c, B β=1; R represents node power vector, r=[r ₁..., r _b] ^t, r _brepresent the power of node b.

Definition: interference matrix between user's group between user's group, the element of interference matrix G is interference volume g between user's group; with then formula (1) can be write as following formula (2):

${\underset{\‾}{u}}_c^{\left(\mathrm{DL}\right)}=\frac{g_c^{\left(\mathrm{\θ}\right)}}{K_c}\·\frac{p_c}{{\left[G^{T}p\right]}_c+n_c}---\left(2\right)$

According to the up-downgoing principle of duality, the lower limit that user organizes the up average SINR of all users in c is:

${\underset{\‾}{u}}_c^{\left(\mathrm{UL}\right)}=\frac{g_c^{\left(\mathrm{\θ}\right)}}{K_c}\·\frac{p_c}{[{\mathrm{Gp}]}_c+n_c}---\left(3\right)$

Definition: n=[n ₁..., n _c], and define an interference correlative D relevant with channel gain and interference volume ^-1gp+D ^-1n, then user organizes the performance of c minimise interference correlative corresponding during maximization is Ι _c(p):

$I_c\left(p\right)=\underset{b_c\∈B_c}{\min }{[D^{-1}\mathrm{Gp}+D^{-1}n]}_c---\left(4\right)$

Wherein, b _crepresent that user organizes a candidate association node of c, B _crepresent that user organizes the set of the candidate association node of c.

For each user's group, its associating with node can by single optimization, and uncorrelated with the relevance of other users group.

For mode 2: if consider user organize in the performance of the poorest user, then can adopt the performance index of descending SINR as network of the poorest user in user's group, organize c for user, user organizes the descending performance of the poorest user in c obtained by following formula (5):

Wherein: S represents the poorest user's oriental matrix of SINR in all user's groups, S=[s ₁| ... | s _c] ^t, s _ca K n dimensional vector n, s _crepresent that user organizes the poorest user of SINR in c and indicates vector, s _kc=1 represents that the SINR of the user k that user organizes in c organizes user the user that in c, SINR is the poorest, s _cin be except 1 except user organizes element corresponding to user the poorest in c, other elements are 0;

$C=\mathrm{diag}\{g_1^{\left(\mathrm{\θ}\right)},...,g_C^{\left(\mathrm{\θ}\right)}\};$

User organizes the performance of c the antithesis uplink interference correlative that minimizes corresponding during maximization is:

$I_c\left(p\right)=\underset{b_c\∈B_c}{\min }\underset{s_c}{\max }{[{\left({\mathrm{SC}}^{-1}{V}_{\mathrm{\θ}}^T{A}_{\mathrm{\α}}\right)}^{T}p+{\mathrm{\σ}}_n^2{\mathrm{SC}}^{-1}1]}_c---\left(6\right)$

For mode 3: if consider the average behavior of all users in user group and the weighting of the poorest user performance, then when user organizes the maximizing performance of c, corresponding minimise interference correlative can adopt following formula to characterize:

$I_c\left(p\right)=\{\mathrm{\μ}\·\underset{b_c\∈B_c}{\min }{[D^{-1}\mathrm{Gp}+n_c{D}^{-1}1]}_c+(1-\mathrm{\μ})\·\underset{b_c\∈B_c}{\min }\underset{s_c}{\max }{\left[{\mathrm{SC}}^{-1}(V_{\mathrm{\θ}}^T{A}_{\mathrm{\α}}p+{\mathrm{\σ}}_n^{2}1)\right]}_c\}---\left(7\right)$

Wherein, μ ∈ [0,1] is weighted value, can represent the average behavior of all users controlled in user's group and the poorest user performance compromise degree; Candidate association node corresponding for interference correlative minimum value is defined as the object associated nodes that user organizes c.

Visible, in conjunction with power and the Downtilt of node, organized the connectivity of c by optimization user, the optimal performance u that user organizes c can be calculated _c, wherein, u _ccan be the covering performance that user organizes the lower limit (volumetric properties corresponding to user's group) of the up average SINR of all users in c, the SINR(of the poorest user corresponds to user's group) and both comprehensive (corresponding to the capacity of user's group and the trade off performance of covering); Solve solution corresponding to above-mentioned formula (7) and correspond to the optimal performance u that acquisition user organizes c _c.

Said method adopts fixed point optimized algorithm, and in conjunction with the power of each node, solving of formula (7) can be passed through iteration optimization realize; Wherein, represent that user organizes the maximum of the power of c; for user organizes the target capabilities of c, depend on the rate requirement of user, n represents iterations.

For SON, need the performance of all users of the whole network optimized, for this reason, based on the fairness ensureing all users of the whole network, SON optimization aim is the performance maximizing user's group that in all user's groups, performance is the poorest, that is:

$\underset{p>0}{\max }\left(\underset{1\≤c\≤C}{\min }{u}_c\left(p\right)\right)s.t.\left|\right|p\left|\right|\≤P_{\max }---\left(8\right)$

Wherein, P _maxrepresent that user organizes the maximum of power in all user's groups.

The network optimum target capabilities that above-mentioned formula (8) reflects is equal to:

$\underset{p>0}{\min }\left|\right|p\left|\right|s.t.u_c\left(p\right)\≥u_c^{*}---\left(9\right)$

The load-balancing method that the embodiment of the present invention provides, adopt fixed point optimized algorithm, according to power and the Downtilt of node each in network, the connectivity that in optimized network, all users organize and power, while the capacity and covering performance of the system of guarantee, realize the load balancing of whole net, thus improve network performance.

The flow chart of the capacity that Fig. 3 provides for the embodiment of the present invention and coverage optimization.The capacity that the embodiment of the present invention provides and coverage optimization are the further restrictions of the method for the capacity and coverage optimization realizing network in above-described embodiment.As shown in Figure 3, the capacity that the embodiment of the present invention provides and coverage optimization method, comprising:

301, the power of each node described in initialization.

302, for each node, travel through candidate's Downtilt of this node, according to internuncial optimal value of described each user's group, obtain the interference correlative of this node based on the candidate's Downtilt traversed, and candidate's Downtilt corresponding for minimum interference correlative is defined as the object Downtilt of this node.

303, according to maximum and the performance limits of the least interference correlative of described each node, the power of described each node, the power of described each node is upgraded.

304, judge whether the renewal amplitude of the power of described each node is no more than the 3rd default threshold value: if renewal amplitude is no more than described 3rd threshold value, then forward step 305 to; If renewal amplitude exceedes described 3rd threshold value, then forward step 301 to.

305, the optimal value of Downtilt and the optimal value of power of described each node is obtained.

Particularly, the method that the embodiment of the present invention provides, according to internuncial optimal value that each user organizes, the Downtilt of all nodes and power in optimized network, while the capacity and covering performance of the system of guarantee, realize the load balancing of whole net, thus improve network performance.Wherein, the performance of node at least can be represented by following three kinds of modes:

The average behavior of all users in mode 1` node, can reflect the volumetric properties of node.

The poorest user performance in mode 2` node, can reflect the covering performance of node.

The average behavior of all users and the weighting of the poorest user performance in mode 3` node, can reflect the capacity of node and the trade off performance of covering.

For mode 1`: if consider the average behavior of all users in node, then can adopt the performance index of descending average SINR as network of all users in node, for node b, the descending average behavior of all users in node b obtained by following formula (10):

Wherein: Κ ^(b)represent the number of users in node b, C ^(b)represent the user's group index in node b, for the lower limit of the descending average SINR of users all in node b.

${\underset{\‾}{u}}_b^{\left(\mathrm{DL}\right)}=\frac{1}{K^{\left(b\right)}}\·\frac{r_b{g}^{\left(b\right)}}{{\left[{\mathrm{\ψ}}^{T}r\right]}_b+K^{\left(b\right)}{\mathrm{\σ}}_n^2}---\left(11\right)$

According to the up-downgoing principle of duality, in node b, the lower limit of the up average SINR of all users is:

${\underset{\‾}{u}}_b^{\left(\mathrm{UL}\right)}=\frac{g^{\left(b\right)}}{K^{\left(b\right)}}\·\frac{r_b}{{\left[\mathrm{\ψr}\right]}_b+K^{\left(b\right)}{\mathrm{\σ}}_n^2}---\left(12\right)$

Definition: $\mathrm{\ζ}={[K^{\left(1\right)}{\mathrm{\σ}}_n^2,...,K^{\left(B\right)}{\mathrm{\σ}}_n^2]}^T,\mathrm{\φ}=\mathrm{diag}\{g_1^{\left(1\right)}/K^{\left(1\right)},...,g_1^{\left(B\right)}/K^{\left(B\right)}\},$ The then performance of node b minimise interference correlative corresponding during maximization is:

$I_b\left(r\right)=\underset{{\mathrm{\θ}}_b\∈{\mathrm{\Θ}}_b}{\min }{[{\mathrm{\φ}}^{-1}\mathrm{\ψp}+{\mathrm{\φ}}^{-1}\mathrm{\ζ}]}_b---\left(13\right)$

Wherein, θ _brepresent candidate's Downtilt of node b, Θ _brepresent the set of candidate's Downtilt of node b, θ represents the Downtilt vector of nodes, θ=[θ ₁..., θ _b],

For mode 2: if consider the performance of the poorest user in node, then can adopt the performance index of descending SINR as network of the poorest user in node, for node b, the descending performance of the poorest user in node b obtained by following formula (14):

Wherein: T=diag{ α } Adiag{ β } B ^t, q=Tr.

According to the up-downgoing principle of duality, the ascending performance of the poorest user in node b for:

The performance of node b minimise interference correlative corresponding during maximization is:

$I_b\left(r\right)=\underset{{\mathrm{\θ}}_b\∈{\mathrm{\Θ}}_b}{\min }\underset{c\∈C^{\left(b\right)}}{\max }\underset{k\∈K^{\left(c\right)}}{\max }\frac{{\left[T^T{V}_{\mathrm{\θ}}r\right]}_k+{\mathrm{\σ}}_n^2}{{\mathrm{\β}}_c{\mathrm{\α}}_k{v}_{\mathrm{kk}}}---\left(16\right)$

For mode 3: if consider the average behavior of all users in node and the weighting of the poorest user performance, namely when the capacity of each community of combined optimization and covering performance, then during the maximizing performance of node b, corresponding minimise interference correlative can adopt following formula to characterize:

$I_b\left(r\right)=\mathrm{\μ}\·\underset{{\mathrm{\θ}}_b\∈{\mathrm{\Θ}}_b}{\min }\frac{g^{\left(b\right)}}{K^{\left(b\right)}}\·\frac{r_b}{{\left[\mathrm{\ψr}\right]}_b+K^{\left(b\right)}{\mathrm{\σ}}_n^2}+(1-\mathrm{\μ})\·\underset{{\mathrm{\θ}}_b\∈{\mathrm{\Θ}}_b}{\min }\underset{c\∈C^{\left(b\right)}}{\max }\underset{k\∈K^{\left(c\right)}}{\max }\frac{{\left[T^T{V}_{\mathrm{\θ}}r\right]}_k+{\mathrm{\σ}}_n^2}{{\mathrm{\β}}_c{\mathrm{\α}}_k{v}_{\mathrm{kk}}}---\left(17\right)$

Candidate's Downtilt corresponding for interference correlative minimum value is defined as the object Downtilt of node b.

Visible, in conjunction with internuncial optimal value of each user's group, by Downtilt and the power of optimization node b, the optimal performance u of node b can be calculated _b, wherein, u _bcan be the lower limit (volumetric properties corresponding to node) of the up average SINR of all users in node b, the SINR(of the poorest user corresponds to the covering performance of node) and both comprehensive (corresponding to the capacity of node and the trade off performance of covering); Solve solution corresponding to above-mentioned formula (17) and correspond to the optimal performance u obtaining node b _b.

Said method adopts fixed point optimization method, and in conjunction with the power of node b, solving of formula (17) can be passed through iteration optimization realize; Wherein, represent the maximum of the power of node b, γ _bfor minimum SINR thresholding, usually get-6.5dB, n represents iterations.

For SON, need the performance of all users of the whole network optimized, for this reason, based on the fairness ensureing all users of the whole network, SON optimization aim is the performance maximizing the node that performance is the poorest in all nodes, that is:

$\underset{r>0}{\max }\left(\underset{1\≤b\≤B}{\min }{u}_b\left(r\right)\right)s.t.\left|\right|r\left|\right|\≤r_{\max }---\left(18\right)$

Wherein, r _maxrepresent the maximum at all node interior joint power.

The capacity that the embodiment of the present invention provides and coverage optimization method, adopt fixed point optimized algorithm, according to internuncial optimal value of described each user's group, the Downtilt of all nodes and power in optimized network, while the capacity and covering performance of the system of guarantee, realize the load balancing of whole net, thus improve network performance.

In the network optimized approach that the embodiment of the present invention provides, the input dependence of load balance optimization process is in the power of the node of the output of capacity and coverage optimization process and Downtilt, meanwhile, the connectivity organized in the user of the output of load balance optimization process of the input dependence of capacity and coverage optimization process; The load balance optimization process and capacity and coverage optimization process that the embodiment of the present invention provides is performed, until iterations reaches process ends after default first threshold by loop iteration successively; Output network optimum results: internuncial optimal value of each described user, and the described power of each node and the optimal value of Downtilt.

The network optimized approach that the embodiment of the present invention provides, by the combined optimization of capacity and coverage optimization and load balancing, make cell load distribute while relative equilibrium, capacity and covering are optimized, and effectively promote Internet usage efficiency; Realize capacity and coverage optimization, and utilizing the up-downgoing principle of duality in the process of load balance optimization and use fixed point optimized algorithm, achieve the optimization based on model, and Fast Convergent.

The structural representation of the network optimization device that Fig. 4 provides for the embodiment of the present invention.As shown in Figure 4, the network optimization device 400 that the embodiment of the present invention provides, comprising:

Grouped element 401, for dividing into groups to described each user according to the transmitting-receiving performance parameter of each user obtained, forms at least two user's groups, and obtains the connectivity of described each user's group respectively; Wherein, the connectivity of described user's group, is used to indicate the associated nodes of user's group;

First acquiring unit 402, the power sharing learning that power sharing learning and described each user for obtaining described each user are respectively organized;

Second acquisition unit 403, for the power sharing learning of the power according to each node, Downtilt, described each user and the power sharing learning of described each user's group, obtain described each internuncial optimal value of user's group and internuncial optimal value of described each user; Wherein, the connectivity of described user, is used to indicate the associated nodes of user;

3rd acquiring unit 404, for the internuncial optimal value according to described each user's group, obtains the described power of each node and the optimal value of Downtilt;

Iteration unit 405, for grouped element described in iteration control 401, described first acquiring unit 402, described second acquisition unit 403 and described 3rd acquiring unit 404, until iterations reaches default first threshold;

Acquiring unit 406, for obtaining internuncial optimal value of the power of each node in described network, Downtilt and each user.

The network optimization device 400 that the embodiment of the present invention provides, may be used for the technical scheme performing network optimized approach embodiment shown in Fig. 1, it realizes principle and technique effect is similar, repeats no more herein.

Further, described second acquisition unit 403, specifically for:

According to the connectivity of the power of described each node, the power sharing learning of described each user and described each user's group, determine the power of described each user's group;

Travel through the candidate association node of described each user's group respectively, according to power and the power sharing learning of the Downtilt of described each node and described each user's group, obtain the interference correlative of described each user's group and the candidate association of corresponding candidate association node;

Described each user's group object associated nodes corresponding with the least interference correlative of candidate association in corresponding candidate association node is respectively associated;

According to maximum and the target capabilities of the least interference correlative of described each user's group, the power of described each user's group, upgrade the power of described each user's group;

Repeat above-mentioned steps, until the power of described each user's group upgrades amplitude be no more than default Second Threshold;

Obtain described each internuncial optimal value of user's group and the optimal value of power;

According to the relevance that internuncial optimal value of described each user group and user and user organize, determine internuncial optimal value of described each user.

Or described 3rd acquiring unit 404 further, specifically for:

Travel through candidate's Downtilt of described each node respectively, according to internuncial optimal value of described each user's group, obtain the interference correlative of described each node to the candidate association of candidate's Downtilt of correspondence;

The object Downtilt that described each node is corresponding with the least interference correlative of candidate association in corresponding candidate's Downtilt is respectively associated;

According to maximum and the performance limits of the least interference correlative of described each node, the power of described each node, upgrade the power of described each node;

Repeat above-mentioned steps, until the power of described each node upgrades amplitude be no more than the 3rd default threshold value;

Obtain the optimal value of Downtilt and the optimal value of power of described each node.

Or further, described transmitting-receiving performance parameter is: the Reference Signal Received Power RSRP that user measures, and/or, the signal to noise ratio SINR of user.

In several embodiment provided by the present invention, should be understood that, disclosed apparatus and method, can realize by another way.Such as, device embodiment described above is only schematic, such as, the division of described unit, be only a kind of logic function to divide, actual can have other dividing mode when realizing, such as multiple unit or assembly can in conjunction with or another system can be integrated into, or some features can be ignored, or do not perform.Another point, shown or discussed coupling each other or direct-coupling or communication connection can be by some interfaces, and the indirect coupling of device or unit or communication connection can be electrical, machinery or other form.

The described unit illustrated as separating component or can may not be and physically separates, and the parts as unit display can be or may not be physical location, namely can be positioned at a place, or also can be distributed in multiple network element.Some or all of unit wherein can be selected according to the actual needs to realize the object of the present embodiment scheme.

In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, also can be that the independent physics of unit exists, also can two or more unit in a unit integrated.Above-mentioned integrated unit both can adopt the form of hardware to realize, and the form that hardware also can be adopted to add SFU software functional unit realizes.

The above-mentioned integrated unit realized with the form of SFU software functional unit, can be stored in a computer read/write memory medium.Above-mentioned SFU software functional unit is stored in a storage medium, comprising some instructions in order to make a computer equipment (can be personal computer, server, or the network equipment etc.) or processor (processor) perform the part steps of method described in each embodiment of the present invention.And aforesaid storage medium comprises: USB flash disk, portable hard drive, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disc or CD etc. various can be program code stored medium.

Those skilled in the art can be well understood to, for convenience and simplicity of description, only be illustrated with the division of above-mentioned each functional module, in practical application, can distribute as required and by above-mentioned functions and be completed by different functional modules, internal structure by device is divided into different functional modules, to complete all or part of function described above.The specific works process of the device of foregoing description, with reference to the corresponding process in preceding method embodiment, can not repeat them here.

Last it is noted that above each embodiment is only in order to illustrate technical scheme of the present invention, be not intended to limit; Although with reference to foregoing embodiments to invention has been detailed description, those of ordinary skill in the art is to be understood that: it still can be modified to the technical scheme described in foregoing embodiments, or carries out equivalent replacement to wherein some or all of technical characteristic; And these amendments or replacement, do not make the essence of appropriate technical solution depart from the scope of various embodiments of the present invention technical scheme.

Claims (8)

1. a network optimized approach, is characterized in that, comprising:

Transmitting-receiving performance parameter according to each user obtained is divided into groups to described each user, forms at least two user's groups, and obtains the connectivity of described each user's group respectively; Wherein, the connectivity of described user's group, is used to indicate the associated nodes of user's group;

Obtain the power sharing learning of described each user and the power sharing learning of described each user's group respectively;

According to the power sharing learning of the power of each node, Downtilt, described each user and the power sharing learning of described each user's group, obtain described each internuncial optimal value of user's group and internuncial optimal value of described each user; Wherein, the connectivity of described user, is used to indicate the associated nodes of user;

According to internuncial optimal value of described each user's group, obtain the described power of each node and the optimal value of Downtilt;

Repeat above-mentioned steps, until iterations reaches default first threshold;

Obtain internuncial optimal value of the power of each node in described network, Downtilt and each user.

2. method according to claim 1, it is characterized in that, the power sharing learning of the described power according to described each node, Downtilt, described each user and the power sharing learning of described each user's group, obtain described each internuncial optimal value of user's group and internuncial optimal value of described each user, comprising:

According to the connectivity of the power of described each node, the power sharing learning of described each user and described each user's group, determine the power of described each user's group;

Travel through the candidate association node of described each user's group respectively, according to power and the power sharing learning of the Downtilt of described each node and described each user's group, obtain the interference correlative of described each user's group and the candidate association of corresponding candidate association node;

Described each user's group object associated nodes corresponding with the least interference correlative of candidate association in corresponding candidate association node is respectively associated;

According to maximum and the target capabilities of the least interference correlative of described each user's group, the power of described each user's group, upgrade the power of described each user's group;

Repeat above-mentioned steps, until the power of described each user's group upgrades amplitude be no more than default Second Threshold;

Obtain described each internuncial optimal value of user's group and the optimal value of power;

According to the relevance that internuncial optimal value of described each user group and user and user organize, determine internuncial optimal value of described each user.

3. method according to claim 1, is characterized in that, described internuncial optimal value according to described each user's group, obtains the described power of each node and the optimal value of Downtilt, comprising:

Travel through candidate's Downtilt of described each node respectively, according to internuncial optimal value of described each user's group, obtain the interference correlative of described each node to the candidate association of candidate's Downtilt of correspondence;

The object Downtilt that described each node is corresponding with the least interference correlative of candidate association in corresponding candidate's Downtilt is respectively associated;

According to maximum and the performance limits of the least interference correlative of described each node, the power of described each node, upgrade the power of described each node;

Repeat above-mentioned steps, until the power of described each node upgrades amplitude be no more than the 3rd default threshold value;

Obtain the optimal value of Downtilt and the optimal value of power of described each node.

4. method according to claim 1, is characterized in that, described transmitting-receiving performance parameter is: the Reference Signal Received Power RSRP that user measures, and/or, the signal to noise ratio SINR of user.

5. a network optimization device, is characterized in that, comprising:

Grouped element, for dividing into groups to described each user according to the transmitting-receiving performance parameter of each user obtained, forms at least two user's groups, and obtains the connectivity of described each user's group respectively; Wherein, the connectivity of described user's group, is used to indicate the associated nodes of user's group;

First acquiring unit, the power sharing learning that power sharing learning and described each user for obtaining described each user are respectively organized;

Second acquisition unit, for the power sharing learning of the power according to each node, Downtilt, described each user and the power sharing learning of described each user's group, obtain described each internuncial optimal value of user's group and internuncial optimal value of described each user; Wherein, the connectivity of described user, is used to indicate the associated nodes of user;

3rd acquiring unit, for the internuncial optimal value according to described each user's group, obtains the described power of each node and the optimal value of Downtilt;

Iteration unit, for grouped element, described first acquiring unit, described second acquisition unit and described 3rd acquiring unit described in iteration control, until iterations reaches default first threshold;

Acquiring unit, for obtaining internuncial optimal value of the power of each node in described network, Downtilt and each user.

6. device according to claim 5, is characterized in that, described second acquisition unit, specifically for:

According to the connectivity of the power of described each node, the power sharing learning of described each user and described each user's group, determine the power of described each user's group;

Travel through the candidate association node of described each user's group respectively, according to power and the power sharing learning of the Downtilt of described each node and described each user's group, obtain the interference correlative of described each user's group and the candidate association of corresponding candidate association node;

Described each user's group object associated nodes corresponding with the least interference correlative of candidate association in corresponding candidate association node is respectively associated;

According to maximum and the target capabilities of the least interference correlative of described each user's group, the power of described each user's group, upgrade the power of described each user's group;

Repeat above-mentioned steps, until the power of described each user's group upgrades amplitude be no more than default Second Threshold;

Obtain described each internuncial optimal value of user's group and the optimal value of power;

According to the relevance that internuncial optimal value of described each user group and user and user organize, determine internuncial optimal value of described each user.

7. device according to claim 5, is characterized in that, described 3rd acquiring unit, specifically for:

Travel through candidate's Downtilt of described each node respectively, according to internuncial optimal value of described each user's group, obtain the interference correlative of described each node to the candidate association of candidate's Downtilt of correspondence;

The object Downtilt that described each node is corresponding with the least interference correlative of candidate association in corresponding candidate's Downtilt is respectively associated;

According to maximum and the performance limits of the least interference correlative of described each node, the power of described each node, upgrade the power of described each node;

Repeat above-mentioned steps, until the power of described each node upgrades amplitude be no more than the 3rd default threshold value;

Obtain the optimal value of Downtilt and the optimal value of power of described each node.

8. device according to claim 5, is characterized in that, described transmitting-receiving performance parameter is: the Reference Signal Received Power RSRP that user measures, and/or, the signal to noise ratio SINR of user.