CN103687026A - Control method for uplink resources in LTE network - Google Patents

Abstract

The invention provides a control method for uplink resources in an LTE (Long Term Evolution) network. The method comprises the following steps: determining the weight parameter of the transmission rate of each user according to a preset user permission and a transmission demand; grouping each user according to positions through a clustering algorithm to obtain a plurality of user groups; performing mathematic modeling according to the weight parameter of the transmission rate of each user and the user groups by using a single-target multi-constraint algorithm to obtain a mathematic model; solving the mathematic model by using a preset maximum flow algorithm, and performing uplink resource control on each user. The method provided by the invention is low in calculation complexity, and is more accurate to uplink resource control of the users.

Description

Ascending resource control method in LTE network

Technical field

The present invention relates to LTE network optimization technical field, particularly relate to ascending resource control method in a kind of LTE network.

Background technology

In 4G LTE network, user's the demand diversity that becomes, wireless environment presents complicated time-varying characteristics, and RRM seems particularly important efficiently.The management of LTE network radio resources refers in LTE network carries out reasonable distribution and effectively management to limited Radio Resource, improve to greatest extent the utilance of wireless frequency spectrum, ensure the normal operation of network, simultaneously, meet service quality (Quality of Service, the QoS) demand of different radio user terminal in network.Radio Resource comprises time, frequency and transmitting power etc.In transmitting uplink data, RRM mainly comprises: through-put power is controlled and subcarrier (frequency) distributes two aspects.

In LTE network, uplink power control all plays an important role always, is mainly reflected in following three aspects: 1) control physical uplink channel data and transmit required transmitting energy, save subscriber equipment (User Equipment, UE) energy, the service time of improving UE; 2) guarantee enough transmitting powers, the energy loss that path loss, shadow fading and the rapid fading of compensation transmission channel brings, the validity of assurance transfer of data; 3) in suitable region, reduce the through-put power of channel, to guarantee to reduce the interference to other links as far as possible.From whether applying feedback signal, Poewr control method can be divided into open Loop Power control and close-loop power control.In TD-LTE system, because uplink and downlink link is in same frequency range, the decline situation of up-downgoing channel is basically identical.Base station (eNodeB, eNB) can obtain by long-term observation the channel fading situation of link, the received power of direct estimation channel.If employing open Loop Power control, at the receiving terminal of uplink power control, can accept power by setting link target, to keep transfer of data in acceptable Packet Error Ratio.Answer in contrast, in LTE system, the major function of closed-loop control is the impact that compensation rapid fading causes up channel, guarantees that channel quality does not have too large variation.The measurement result of closed-loop control Main Basis eNB to uplink quality, compares meticulous compensation to channel loss.Yet, to control and compare with open loop, closed-loop control need to accurately be controlled transmitting power by feedback, and this can consume a large amount of signalings, increases the time overhead that Signalling exchange brings.

In LTE network, owing to using on a large scale in physical layer OFDM technology, make frequency band be subdivided into the subcarrier of many quadratures.For fear of interference, different users can be transmitted by the different subcarrier of choice for use.The distribution of subcarrier and scheduling become the important component part of RRM in LTE network.In the face of different time varying channel situations, system need to be transmitted the different subcarrier of user assignment of demand for difference, to satisfy the demands.In practice, at the PUCCH(Physical of LTE networked physics layer Uplink Control Channel, under the scheduling of control signal Physical Uplink Control Channel), through-put power is controlled with subcarrier and is distributed and can unify to be optimized control, under the prerequisite of meeting consumers' demand, elevator system performance to greatest extent.

At subcarrier, distribute with through-put power and control in combined optimization problem, generally need to meet two basic restrictive conditions: 1) subcarrier can only be used by a user.If a plurality of users access same subcarrier simultaneously, can produce serious phase mutual interference.2) unique user can access many subcarriers simultaneously.Under OFDM technical support, tackle different transmission demands, user can access the subcarrier of varying number, and available bandwidth is broadened like this.

Existingly for uplink sub-carrier, distribute and through-put power jointly controls technology, most actual demand and restrictive condition of not considering user and LTE system, and most of computation complexity is higher.

Summary of the invention

Based on this, the invention provides ascending resource control method in a kind of LTE network, the method computation complexity is low, to user's ascending resource, controls more accurate.

In LTE network, an ascending resource control method, comprises the steps:

According to default user right and transmission demand, determine the weight parameter of each user's transmission rate;

By clustering algorithm, each user is divided into groups according to position, obtain a plurality of user's groups, each user organizes and forms a subscriber unit

According to the weight parameter of each user's transmission rate and described user's group, utilize single goal multiple constraint algorithm to carry out mathematical modeling, obtain Mathematical Modeling;

Use default max-flow algorithm to solve described Mathematical Modeling, each user is carried out to ascending resource control.

Ascending resource control method in above-mentioned LTE network, the subcarrier of LTE network is distributed with through-put power combined optimization problem and is modeled as a single goal multiple constraint problem, authority by analysis user is combined the weight parameter of the transmission rate that determines each user with transmission demand, make resource distribute more realistic demand; Wherein, positional information is topmost to user's transmission channel gain effects, for this reason, by the position at analysis user place, user is divided into groups, and the group of then take is carried out allocation of radio resources as computing unit, can between computation complexity and algorithm performance optimum, do and compromise like this; After setting up Mathematical Modeling, in order further to obtain arithmetic speed faster, according to actual environmental parameter, Mathematical Modeling is changed over to a maximum flow problem, and use improved max-flow algorithm to solve it, obtain needed result of each user being carried out to ascending resource control.

Accompanying drawing explanation

Fig. 1 is ascending resource control method schematic flow sheet in one embodiment in LTE network of the present invention.

Fig. 2 is the model schematic diagram of single subdistrict in Fig. 1.

Fig. 3 is that Fig. 1 sub-carriers is distributed schematic diagram.

Fig. 4 is the schematic diagram of network flow in Fig. 1.

Embodiment

Below in conjunction with embodiment and accompanying drawing, the present invention is described in further detail, but embodiments of the present invention are not limited to this.

As shown in Figure 1, the present embodiment provides a kind of technical scheme of the LTE of being suitable for network uplink allocation of radio resources, mainly comprises that subcarrier distributes and through-put power is controlled two aspects.According to user's authority and user's real-time Transmission demand, user's transmission rate is set to suitable weight, according to user's positional information, by K-means clustering method, user is divided into groups, and using group and be optimized as computing unit, on the basis of analytical weight and grouping, use single goal multiple constraint algorithm to carry out mathematical modeling to RRM, finally Mathematical Modeling is changed over to a maximum flow problem, and use improved max-flow algorithm to solve this mixed integer optimization problem, concrete steps are as follows:

S11, according to default user right and transmission demand, determine the weight parameter of each user's transmission rate;

S12, by clustering algorithm, each user is divided into groups according to position, obtain a plurality of user's groups;

S13, according to the weight parameter of each user's transmission rate and described user's group, utilize single goal multiple constraint algorithm to carry out mathematical modeling, obtain Mathematical Modeling;

S14, use default max-flow algorithm to solve described Mathematical Modeling, each user is carried out to ascending resource control.

For step S11, suppose single subdistrict, the total number of users that need to carry out transfer of data in community is K, the target of system, in order to meet under the restriction prerequisite of user emission power, maximizes the weighted sum of all users' transmission rate.The illustraton of model of system as shown in Figure 2.

The weight coefficient of supposing user k is w _k, w _ksize directly had influence on the priority of resource distributional equity and user data transmission between user.If weight w _karrange greatlyr, this user can obtain more resource, and has higher priority in transmission; Otherwise if weight setting is less, this user can only obtain less resource and even can not get transmission channel.In the present embodiment, according to user's authority situation and the data volume that needs transmission, jointly determine user's weight.User right service type main and that user charges, user apply for has relation.If user charges are higher, or the data volume that need to transmit of user large (for example, system provides Video service, large scale network broadcast etc.), now, the weight of this user's transmission rate is larger; Otherwise when user's paying amount is not very large, or its data volume that need to transmit is less, does not occupy very wide channel, the weight setting of this user's transmission rate is less.

In the present embodiment, the weight parameter of the transmission rate of user k is determined by following formula:

$w_k=\mathrm{\α}\frac{M_k}{\underset{k\∈K}{\mathrm{\Σ}}{M}_k}+(1-\mathrm{\α})\frac{R_k}{\underset{k\∈K}{\mathrm{\Σ}}{R}_k}$

In formula, α represents weight factor, 0≤α≤1; M _kthe authority factor that represents k default user, represent the authority factor that all users are total; R _kthe transmission rate requirements that represents k default user, this can detect user by base station needs obtain for which type of service, and different services need to have different minimum transmission rate, just can satisfy the demands,

the transmission rate requirements that represents total user.

For step S12, analyze the propagation model of channel, can find the carrier frequency of subcarrier and the gain that two of residing positions of user factor has determined channel jointly.Compare with carrier frequency, the bandwidth of subcarrier is low on the order of magnitude, and this channel gain difference that makes in different sub carrier the difference due to carrier frequency cause is not very large.The variation main cause of channel gain is the position at user place, and close its channel gain of user in same position or position is basic identical.For this reason, the present embodiment can divide into groups according to position to each user by K-means clustering algorithm, by the positional information of analysis user, user is carried out to Clustering, and concrete steps can be as follows:

1) select at random I user as the initial center point of cluster, be made as O _i, corresponding class is made as C _i, i ∈ 1,2 ..., I}, I≤K;

2) calculate the similarity d (O between remaining user and each central point user _i, u), u ∈ 1,2 ..., I}, u ≠ O _i;

3) will be assigned in corresponding class with the user of each central point similarity minimum (being that Euclidean distance is the shortest), and with Euclidean distance, calculate the value of evaluation function

4) respectively at all kinds of C _iin select randomly a non-central some user q _i, calculate d (q _i, u), q _i∈ C _i, u ∈ 1,2 ..., I}, q _i≠ u;

5) calculate evaluation function E', if E'< is E, use q _ireplace O _i, and the most similar user is adjusted in corresponding new class.

6) if evaluation function E has reached minimum value or satisfactory value, finish, otherwise forward step 4) to.

After user is divided into groups, the channel yield value of the subcarrier that user in group is occupied can be thought the same.So the user's group of can usining is carried out resource distribution as minimum computing unit, the through-put power of the user in group is set the same, can significantly reduce amount of calculation like this.Weight for each group can be set as all set of user's weight in group, and equation expression is as follows:

$w_i=\underset{k\&Element;C_i}{\mathrm{\&Sigma;}}{w}_k.$

For step S13, the overall goal of mathematical modeling is under the restrictive condition of maximum transmission power that meets user, maximizes the weighted sum of user's transmission rate.Mathematical Modeling is expressed as:

$\underset{c_{i,n},p_{i,n}}{\max }\underset{i\&Element;I,n\&Element;N}{\mathrm{\&Sigma;}}{w}_i{c}_{i,n}\log (1+g_{i,n}{p}_{i,n})$

In formula, w _ithe weight that represents the transmission rate of each user's group, I represents total user grouping number; This weight coefficient is explained in detail in step S12.P _i,nrepresent that user organizes the transmitting power of i on subcarrier n, be also the transmitting power of the interior all users of group on subcarrier n simultaneously.C _i,n=1 represents that i user organizes unit distribution and obtain n bar subcarrier, otherwise is c _i,n=0.

n ₀what represent is noise power spectral density, B _nthe bandwidth that represents n bar subcarrier, h _i,nrepresent that i user's group gains in n bar sub-carrier channels.In urban district, under non-line-of-sight propagation environment, channel gain can be expressed as:

h _i,n＝46.3+33.9×lgf _i,n-13.82×lgh _b-a(h _m)+(44.9-6.55×lgh _b)×lgd _i+Cm

In formula, a (h _m)=(1.1 * lgf _i,n-0.7) * h _m-(1.56 * lgf _i,n-0.8), f _i,nwhat represent is that user organizes the carrier frequency of i on n bar subcarrier, h _mthe antenna height of the measurement travelling carriage representing, h _bthe antenna height that represents to measure base station, Cm is for distinguishing urban environment or rural environment, under urban environment, Cm=3dB; And under rural environment, Cm=0dB, d _ithe user who represents organizes i to the distance between base station.In step S12, the expression formula of channel gain is analyzed, because carrier frequency is compared with channel width and wanted high several orders of magnitude, so channel gain is mainly to be determined to the distance base station by user, by the determining positions at user place.

Described single goal multi-constraint condition:

Article one, subcarrier can only be organized occupied by a user.If a plurality of user's groups occupy same subcarrier simultaneously, can there is serious interference:

$\underset{i\&Element;I}{\mathrm{\&Sigma;}}{c}_{i,n}\&le;1,\&ForAll;n\&Element;N$

For guarantee fairness and organize in do not occur disturbing, unique user group occupies sub-carrier number need to be greater than the number of users in its group, can give each user's distributing radio resource:

$\underset{n\&Element;N}{\mathrm{\&Sigma;}}{c}_{i,n}\&GreaterEqual;i,\&ForAll;i\&Element;I$

All users' transmitting power should be positioned at acceptable scope, lower than its maximum transmission power:

$\underset{n\&Element;N}{\mathrm{\&Sigma;}}{p}_{i,n}\&le;P_i,\&ForAll;i\&Element;I$

All users' transmitting power should be more than or equal to 0, meets physical significance:

$p_{i,n}\&GreaterEqual;0,\&ForAll;i\&Element;I,\&ForAll;n\&Element;N$

C _i,n=1 represents that i user's component joined obtains n bar subcarrier, otherwise, c _i,n=0:

$c_{i,n}\&Element;\left\{\mathrm{0,1}\right\},\&ForAll;i\&Element;I,\&ForAll;n\&Element;N$

Specifically, exactly subcarrier is distributed to each user's group, meanwhile, set the through-put power of each user group, its object is to meet under user's the prerequisite of maximum transmission power, the weighting through-put power of maximum system and.Subcarrier distributes schematic diagram as shown in 3.

For step S14, the Mathematical Modeling in step S13, is to ask the maximum throughput meeting under certain constraints.For this reason, the combinatorial optimization problem under this allocation of radio resources model is converted into the maximum flow problem in graph theory.

User is organized to i, if it selects n sub-channels, its throughput w _ic _i,nlog (1+g _i,np _i,n) be designated as R _i,n, i.e. R _i,n=w _ic _i,nlog (1+g _i,np _i,n).Now, concerning same user i, w _iget definite value, c _i,n=1, from the analysis of step S12 the inside, there is relation the main position of organizing place with user of channel gain between user and base station, therefore regardless of which subcarrier of user's group selection, and g _i,ncan regard identical as, thereby, throughput R _i,ncan regard variable as is power p _i,nfunction.

The Mathematical Modeling of setting up in step S13 can be regarded maximum flow problem as, can utilize max-flow algorithm to solve this Mathematical Modeling.

Following Fig. 4, tectonic network stream, distributes to be converted to and in network flow, asks the max-flow from origin-to-destination solving ascending resource;

In Fig. 4, have I user to organize unit and N subcarrier, user and subcarrier are all the summits in network flow, increase virtual starting point S and virtual terminal T.Directed edge in network is constructed as follows: from starting point S to each user i, all have forward limit to be connected; From each user i to subcarrier n, all there is forward limit to be connected, represent that user i selects n subcarrier; From subcarrier n to terminal T, all there is forward limit to be connected.The maximum throughput allowing can be regarded the flow upper limit in network as.Due to throughput R _i,npower p _i,nfunction, this function is increasing function, the maximum power allowing for user can be calculated the throughput of this user's maximum.Thereby to user i the directed edge to subcarrier n, its maximum size is power p _i,nthroughput while getting maximum; Directed edge to starting point S to each user i, its maximum size also can adopt power p _i,nthroughput while getting maximum; Antithetical phrase carrier wave n is to the directed edge of terminal T, and its maximum size is max{R _{1, n}, R _{2, n}..., R _i,n.

In described Fig. 4, ask ascending resource assignment problem, be converted into and in network flow, asked the maximum flow problem from origin-to-destination.

In described step S13, have an important constraints, same subcarrier can only offer user's use, and this restrictive condition and maximum flow problem are distinguished a little, thereby when solving this model, the present embodiment will adopt improved max-flow algorithm.

Meanwhile, in step S14, throughput is regarded as to the flow of network, throughput that will selected every the limit of output after max-flow algorithm finishes, is subcarrier and the power that user distributes and step S13 requires, thereby need to converts throughput to power.

Therefore the present embodiment step S14 can be:

Tectonic network stream, asks the max-flow from origin-to-destination by solving in ascending resource distribution switching network stream;

Utilize Mathematical Modeling described in default max-flow Algorithm for Solving, wherein, described default max-flow algorithm is:

A) since a feasible flow (if the flow on every limit is 0);

B) search Ke Zeng road F.If Ke Zeng road F does not exist, finish, stream is max-flow; Otherwise go to step c);

C) Yan Kezeng road F increases flow Δ, and in the F of Bing Jiangkezeng road, the subcarrier node of process and the limit adjacent with subcarrier node thereof are removed; In remaining network diagram, search Ke Zeng road F, go to step b).

Utilize formula: R _i,n=w _ic _i,nlog (1+g _i,np _i,n), utilize the throughput calculation power p on every limit of max-flow algorithm output _i,n(only need to calculate the power that user i is assigned to subcarrier n);

Check each user's gross power.If meet

finish algorithm;

If the gross power of user i

if this user assignment k subcarrier, this user's power is redistributed, this user assignment is evenly set to P to the power of each subcarrier _i/ k, finishes algorithm.

In above-mentioned steps, why can uniform distribution power, be due to same user i, R _i,n=w _ic _i,nlog (1+g _i,np _i,n), and w _iget definite value, c _i,n=1, and no matter which subcarrier user selects, g _i,ncan regard as identical.Consider function y=log (1+cx ₁)+log (1+c (1-x ₁)), 0≤x wherein ₁≤ 1, the maximum of points of this function is x ₁=0.5.Thereby, if same user has selected two subcarriers, to the power averaging of each subcarrier, divide timing, can make this user's throughput reach maximum.The like, when same user has selected individual k subcarrier, the power averaging of each subcarrier is distributed.

Above-mentioned flow Δ can be explained as follows: establish stream and capacity that F and C are respectively network G, P is the road that meets following condition from starting point s to terminal t in network G: (1) is to every forward limit <i P, j>, F _ij< C _ij; (2) to every reverse edge <i in P, j>, F _ij> 0, Δ=min{C _ij-F _ij, F _ij.

Above-mentioned Ke Zeng road can be construed to: if the flow Δ > 0 on certain link, Here it is Yi Tiaokezeng road.

The present invention distributes the subcarrier of LTE network with through-put power combined optimization problem and is modeled as a single goal multiconstraint optimization problem.Authority by analysis user is combined the weight parameter of the transmission rate that determines each user with transmission demand; Make resource distribute more realistic demand.

Propagation model and channel gain by analysis user at diverse location, can find that the transmission range of channel gain generally and between carrier frequency and user and base station has relation.And with respect to carrier frequency, (general magnitude is 10 ⁹hz), (general magnitude is 10 to the bandwidth of subcarrier ⁵hz) impact final channel gain being brought is substantially negligible, and positional information is topmost to user's transmission channel gain effects.For this reason, can pass through the position at analysis user place, user is divided into groups, the user's group of then take is carried out allocation of radio resources as minimum calculation unit, can between computation complexity and algorithm performance optimum, do and compromise like this.

After setting up Mathematical Modeling, in order further to obtain arithmetic speed faster, we change over a maximum flow problem according to actual environmental parameter by Mathematical Modeling, and use improved max-flow algorithm to solve it, obtain needed optimum results.

The present invention is under the prerequisite of the channel gain correlative factor of analysis user, make full use of user's positional information, user is carried out to cluster, simultaneously, user's transmission rate weight and actual user behavior are connected, put forward a kind of ascending wireless resource distribution method of low complex degree.The present invention decides each user's transmission rate weight according to user charges and transmission demand, carrying out between allocation of radio resources simultaneously, according to user's positional information, by K-means clustering algorithm, user is divided into groups, up wireless network resource assignment problem is modeled as to the optimization problem of a single goal multiple constraint.Finally, utilize improved max-flow algorithm to solve this optimization problem, obtain optimization solution.The present invention has that computation complexity is low, the modeling large advantage of closing to reality two more, has important engineering using value.

The above embodiment has only expressed several execution mode of the present invention, and it describes comparatively concrete and detailed, but can not therefore be interpreted as the restriction to the scope of the claims of the present invention.It should be pointed out that for the person of ordinary skill of the art, without departing from the inventive concept of the premise, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection range of patent of the present invention should be as the criterion with claims.

Claims (10)

1. an ascending resource control method in LTE network, is characterized in that, comprises the steps:

According to default user right and transmission demand, determine the weight parameter of each user's transmission rate;

By clustering algorithm, each user is divided into groups according to position, obtain a plurality of user's groups;

According to the weight parameter of each user's transmission rate and described user's group, utilize single goal multiple constraint algorithm to carry out mathematical modeling, obtain Mathematical Modeling;

Use default max-flow algorithm to solve described Mathematical Modeling, each user is carried out to ascending resource control.

2. ascending resource control method in LTE network according to claim 1, is characterized in that, determines the weight parameter of each user's transmission rate by following formula:

$w_k=\mathrm{\&alpha;}\frac{M_k}{\underset{k\&Element;K}{\mathrm{\&Sigma;}}{M}_k}+(1-\mathrm{\&alpha;})\frac{R_k}{\underset{k\&Element;K}{\mathrm{\&Sigma;}}{R}_k};$

Wherein, w _kthe weight parameter that represents user k; α represents weight factor, 0≤α≤1; M _kthe authority factor that represents k default user,

the authority factor that represents all users; R _kthe transmission rate requirements that represents k default user,

the transmission rate requirements that represents all users.

3. ascending resource control method in LTE network according to claim 1, is characterized in that, by K-means clustering algorithm, each user is divided into groups according to position, and setting each user's group is minimum subscriber unit.

4. ascending resource control method in LTE network according to claim 3, is characterized in that, described step of each user being divided into groups according to position by K-means clustering algorithm is:

Randomly draw I user as the initial center point of cluster, be made as O _i, corresponding class is made as C _i, i ∈ 1,2 ..., I}, I≤K;

Calculate user remaining after extracting and the similarity d (O between each central point user _i, u), u ∈ 1,2 ..., I}, u ≠ O _i;

To be assigned in corresponding class with the minimum user of each central point similarity, and calculate the value of evaluation function $E=\underset{i=1}{\overset{k}{\mathrm{\&Sigma;}}}\underset{u\&Element;K}{\mathrm{\&Sigma;}}{|O_i-u|}^2$

Respectively at all kinds of C _iin select randomly a non-central some user q _i, calculate d (q _i, u), q _i∈ C _i, u ∈ 1,2 ..., I}, q _i≠ u;

Calculate evaluation function E', if E'< is E, use q _ireplace O _i, and the most similar user is adjusted in corresponding new class;

If evaluation function E has reached minimum value or satisfactory value, finish, otherwise repeat described respectively at all kinds of C _iin select randomly a non-central some user q _i, calculate d (q _i, step u).

5. ascending resource control method in LTE network according to claim 4, is characterized in that, the Euclidean distance by calculating each user and central point is as described similarity.

6. ascending resource control method in LTE network according to claim 4, is characterized in that, obtains the value of described evaluation function by compute euclidian distances.

7. ascending resource control method in LTE network according to claim 1, is characterized in that, the described single goal multiple constraint algorithm that utilizes carries out mathematical modeling, and the step that obtains Mathematical Modeling is:

Described Mathematical Modeling is: $\underset{c_{i,n},p_{i,n}}{\max }\underset{i\&Element;I,n\&Element;N}{\mathrm{\&Sigma;}}{w}_i{c}_{i,n}\log (1+g_{i,n}{p}_{i,n})$

Wherein, w _ithe weight that represents the transmission rate of each user's group, I represents total subscriber unit number, p _i,nrepresent that user organizes the transmitting power of i on subcarrier n, c _i,n=1 represents that i subscriber units for assignment obtains n bar subcarrier, otherwise is c _i,n=0;

n ₀represent noise power spectral density, B _nthe bandwidth that represents n bar subcarrier;

H _i,nrepresent that i user's group is in n bar sub-carrier channels gain, wherein, in the urban district of setting under non-line-of-sight propagation environment,

h _i,n＝46.3+33.9×lgf _i,n-13.82×lgh _b-a(h _m)+(44.9-6.55×lgh _b)×lgd _i+Cm

Wherein, a (h _m)=(1.1 * lgf _i,n-0.7) * h _m-(1.56 * lgf _i,n-0.8), f _i,nrepresent that user organizes the carrier frequency of i on n bar subcarrier, h _mrepresent to measure the antenna height of travelling carriage, h _brepresent to measure the antenna height of base station; Under the urban environment of setting, Cm=3dB, under the rural environment of setting, Cm=0dB; d _ithe user who represents organizes i to the distance between base station;

Described single goal multiple constraint algorithm comprises following constraints:

A subcarrier can only be occupied by a subscriber unit, that is:

Sub-carrier number in a subscriber unit should be greater than the number of users in group, that is:

All users' transmitting power is lower than its maximum transmission power, that is:

All users' transmitting power should be more than or equal to 0, that is:

C _i,n=1 represents that i subscriber units for assignment obtains n bar subcarrier, otherwise, c _i,n=0, that is: $c_{i,n}\&Element;\left\{\mathrm{0,1}\right\},\&ForAll;i\&Element;I,\&ForAll;n\&Element;N.$

8. ascending resource control method in LTE network according to claim 7, is characterized in that, uses default max-flow algorithm to solve described Mathematical Modeling, and the step that obtains optimization solution is:

Tectonic network stream, distributes to be converted to and in network flow, asks the max-flow from origin-to-destination solving ascending resource:

Described network flow comprises I subscriber unit, a N subcarrier, virtual starting point S and virtual terminal T, and user's group and subcarrier are all the summits in network flow; Directed edge in network is constructed as follows: from starting point S to each user i, all have forward limit to be connected; From each user i to subcarrier n, all there is forward limit to be connected, represent that user i selects n subcarrier; From subcarrier n to terminal T, all there is forward limit to be connected; Directed edge to user i to subcarrier n, its maximum size is power p _i,nthroughput while getting maximum; Directed edge to starting point S to each user i, its maximum size also can adopt power p _i,nthroughput while getting maximum; Antithetical phrase carrier wave n is to the directed edge of terminal T, and its maximum size is max{R _{1, n}, R _{2, n}..., R _i,n;

Utilize Mathematical Modeling described in default max-flow Algorithm for Solving, wherein, described default max-flow algorithm is:

Since a feasible flow;

Search Ke Zeng road F, if Ke Zeng road F does not exist, present feasible stream is max-flow;

Otherwise Yan Kezeng road F increases flow Δ, in the F of Bing Jiangkezeng road, the subcarrier node of process and the limit adjacent with subcarrier node thereof are removed; The step of searching Ke Zeng road F described in carrying out in remaining network diagram;

Utilize formula R _i,n=w _ic _i,nlog (1+g _i,np _i,n) and every limit of described max-flow algorithm output on throughput calculation power p _i,n;

Check each user's gross power, if meet

end solves;

If the gross power of user i

if described user assignment k subcarrier, described user's power is redistributed, described user assignment is evenly set to P to the power of each subcarrier _i/ k.

9. ascending resource control method in LTE network according to claim 8, is characterized in that, described flow Δ is by formula Δ=min{C _ij-F _ij, F _ijsolve and obtain; Wherein, F and C are respectively stream and the capacity of network G, and P is the road that meets following condition from starting point s to terminal t in network G: to every forward limit <i P, j>, F _ij< C _ij; And to every reverse edge <i in P, j>, F _ij> 0, Δ=min{C _ij-F _ij, F _ij.

10. ascending resource control method in LTE network according to claim 9, is characterized in that, if the flow Δ > 0 on certain link, described link is Yi Tiaokezeng road.

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