CN110072275B - Transmission power distribution method applied to femtocell network - Google Patents

Transmission power distribution method applied to femtocell network Download PDF

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CN110072275B
CN110072275B CN201810066131.8A CN201810066131A CN110072275B CN 110072275 B CN110072275 B CN 110072275B CN 201810066131 A CN201810066131 A CN 201810066131A CN 110072275 B CN110072275 B CN 110072275B
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transmission power
water
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power allocation
fairness
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CN110072275A (en
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谭露
周振宇
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North China Electric Power University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/243—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account interferences
    • H04W52/244—Interferences in heterogeneous networks, e.g. among macro and femto or pico cells or other sector / system interference [OSI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power

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Abstract

The invention designs a water-injection transmission power allocation strategy in a femtocell network with proportional fairness and maximum-minimum fairness. A heuristic floating ceiling water-filling algorithm is proposed herein that aims to determine the transmission power allocation for each Femtocell User (FU) such that the allocation not only achieves optimal utility ratio fairness, but also maximum-minimum fairness, while satisfying interference temperature constraints at the Macrocell Base Station (MBS). All transmission power allocation issues considered by the present invention are realized in a centralized manner, due to the strict interference constraints. More specifically, we assume that one control point has global knowledge of all channel gains in the macro-femto heterogeneous network and then derive the optimal transmission power allocation by solving a convex optimization problem. After the optimal power distribution at any FU is obtained by using a water injection algorithm, the optimal power batch strategy and the maximum-minimum power distribution strategy at each FU are iteratively obtained by using a heuristic algorithm.

Description

Transmission power distribution method applied to femtocell network
Technical Field
The invention belongs to the field of wireless communication, and particularly relates to a heuristic floating-ceiling water injection algorithm, which determines transmission power distribution for each femtocell user FU, so that the power distribution can not only realize optimal utility ratio fairness, but also realize maximum-minimum fairness while meeting interference temperature constraint at a base station MBS of a macrocell, the former effectively improves joint utility and average SINR, and the latter protects the FU with poor channel, thereby increasing fairness index.
Background
As the 3G/4G traffic of mobile networks has grown substantially with the continuous development of personal mobile device functions and wireless technologies, the gap between the existing network capacity and the user traffic demand has grown, mobile network operators have attempted to cope with the rapidly growing demand for heterogeneous network content volume, and one of the most promising approaches to handle such data explosion is to offload traffic to femtocell networks. From the perspective of mobile network operators, femtocell networks are a cost-effective solution. Since the femtocell base station is small in size, it can be deployed in any business or residence by reusing the licensed spectrum of the macrocell.
In such macro-femto cell heterogeneous networks, spectrum allocation strategies can be divided into two categories: spectrum sharing and spectrum splitting. Under a spectrum sharing strategy, mobile network operators share the same spectrum between macro cells and femto cells. Thus, unless well managed, cross-layer interference, i.e., interference from one layer to another, may result in poor system performance. According to a spectrum splitting strategy, the spectrum is divided into multiple frequency bands, specifically allocated to each macro cell and femto cell, in which case cross-layer interference can be greatly mitigated.
In the present invention, we focus on spectrum sharing strategies, since spectrum sharing not only has higher spectrum utilization than spectrum splitting, but also does not require any centralized coordination between macro and femto cells in spectrum allocation. And in the present invention, the transmission power allocation for each femtocell is realized in a centralized manner by assuming that the centralized coordinator has global knowledge of the overall channel gain, rather than using the price as a signal for controlling interference received at the MBS.
Disclosure of Invention
The invention firstly provides a power distribution mechanism for realizing optimal utility ratio fairness and then provides a power distribution mechanism for realizing ratio fairness and maximum-minimum fairness. The specific implementation process is as follows:
we consider a macro-femto cell heterogeneous network in which a set of FBS' s
Figure GDA0002694779290000011
Are sparsely deployed within the coverage of the MBS. We focus on the spectrum sharing mode, where the same licensed spectrum is shared between macro cells and femto cells. Each femto cell may serve multiple FUs and different FUs may have different QoS preferences. An interference temperature constraint is imposed on each femtocell to control overall interference introduced to Macrocell Users (MUs). Furthermore, we focus on uplink transmission analysis. For simplicity we assume that FUs use time division multiple access, TDMA, to transmit data, whereas for a femto cell only one FU may try to transmit data in each time slot. At the same time, we can also perform similar analysis on multi-user scenarios. As shown in FIG. 1, the channel power gain from FU i to FBS femtocell base station, j is represented by hj,iIs shown and is from FU i channel power gain to MBS is given by giIt is given. FUi for transmitting power level piAnd (4) showing. Thus, the received signal to interference plus noise ratio SINR at fbsi can be expressed as:
Figure GDA0002694779290000021
here, the
Figure GDA0002694779290000022
Denotes all FU except FUi, p-iRepresenting a set of transmit power allocations, σ, of all FUs except FUi2Is the background noise level. Next, assume further that femtocells reusing the same spectrum should be far away from each other, i.e. a sparse deployment scenario. Thus, the co-layer interference is negligible, i.e. it is
Figure GDA0002694779290000023
The problem of solving the transmission power allocation can thus be simplified and a closed form power allocation solution can be obtained.
On the FU side, the SINR received at the FBS of FUi is represented by γiIs represented as follows:
Figure GDA0002694779290000024
the utility of FU i is modeled as a logarithmic function of the received SINR:
Figure GDA0002694779290000025
wherein λiIs a user correlation factor representing its utility function, i.e., utility gain.
A. Joint utility optimization
To optimize the joint utility of all FUs under interference constraints, we first model the power allocation as a problem as follows.
Problem 1 is described below:
Figure GDA0002694779290000031
Figure GDA0002694779290000032
Figure GDA0002694779290000033
where Q denotes the maximum interference level that MBS can tolerate, we can get the following propositions
Proposition 1: the total utility is maximized when cross-layer set interference on MBS reaches maximum interference level, i.e. sigmai∈Npigi＝Q。
Then question 1 can translate into the following question according to proposition 1.
Problem 2 is described below:
Figure GDA0002694779290000034
Figure GDA0002694779290000035
Figure GDA0002694779290000036
proposition 2: optimal solution to problem 2
Figure GDA0002694779290000037
Is unique
Figure GDA0002694779290000038
Wherein v is*Is a pullA grangian multiplier, satisfying:
Figure GDA0002694779290000039
the following was demonstrated: the objective functions shown in problem 2 are a strict concave and increasing function of p, and the constraints are affine. Thus, problem 2 is a convex optimization problem that can be solved by using the Karush-Kuhn-Tucker (KKT) optimization condition. The corresponding Lagrangian form of problem 2 is
Figure GDA00026947792900000310
Wherein u ═ { u ═iAnd v are lagrange multipliers.
KKT conditions were as follows:
Figure GDA0002694779290000041
eliminating u, one can get:
Figure GDA0002694779290000042
the smoothness and the complementary relaxation mean that:
Figure GDA0002694779290000043
and
Figure GDA0002694779290000044
where the second equation can be rewritten as:
Figure GDA0002694779290000045
transmission power allocation
Figure GDA0002694779290000046
Need to be feasible (i.e. to
Figure GDA0002694779290000047
) This can give:
Figure GDA0002694779290000048
we first assume that all FUs are homogenous, i.e. they have the same utility gain λ, so that the above equation can be converted to the following equation:
Figure GDA0002694779290000051
the formula is
Figure GDA0002694779290000052
In a piecewise linear increasing function of
Figure GDA0002694779290000053
There is a breakpoint, so the equation has a unique solution. We apply a water-filling algorithm to obtain the optimal solution, as shown in figure 2,
Figure GDA0002694779290000054
considered as the fill line for tank i. Then we increase the water level until the total water volume reaches Q, at which time the water depth of tank i is exactly that
Figure GDA0002694779290000055
The optimum value of (c).
Next, we consider all FUs to be heterogeneous, i.e. they may have different utility gains. As shown in fig. 3, the upper limit height of the can body i is
Figure GDA0002694779290000056
And QoSPreference lambdaifloating-ceiling proportional. Similarly, we increase the water level until the total water volume reaches Q, the water depth of tank i is
Figure GDA0002694779290000057
The optimum value of (c).
Figure 3 summarizes the steps involved in finding the optimal transmission power allocation strategy at each FBS. The computational complexity is:
Figure GDA0002694779290000058
it is noted that a larger perturbation Δ v, although reducing the computation time, results in a larger gap between the actual interference introduced and the maximum allowed interference level, i.e. Q.
B. Proportional fair
First we define if for any other power vector
Figure GDA0002694779290000059
The sum of the changes in the proportions is non-positive:
Figure GDA00026947792900000510
the power allocation vector
Figure GDA00026947792900000511
And the proportion is fair.
The best solution to problem 3 that can be achieved is proportional fairness, as defined above.
C. Max-min fairness
Max-min fairness is another common fairness criterion. Simply stated, a set of power allocations is maximally fair if it does not increase in utility without simultaneously decreasing the already small level of utility of another. This definition indicates that max-min fairness allocates the worst treated users, i.e. the users with the lowest utility, with the largest possible share without wasting any network resources. In particular, macro-femto cellsThe max-min fair power allocation in heterogeneous networks is defined as: power allocation vector
Figure GDA0002694779290000061
Is maximally and minimally fair, without losing feasibility or reducing usefulness when it is not possible to increase the utility of FUi
Figure GDA0002694779290000062
To another FUj
By definition above, our problem can be modeled as follows
Problem 3:
Figure GDA0002694779290000063
Figure GDA0002694779290000064
Figure GDA0002694779290000065
wherein Q represents the maximum interference level that MBS can tolerate; while
Figure GDA0002694779290000066
Representing a single SINR constraint that the user must achieve in order to successfully communicate. After building all the equations of the max-min fair power allocation problem, the next step is to solve it. Problem 3 is solved in two different ways, now introduced as follows:
1) best solution to the problem
MATLAB provides an optimization solver, fminmax, for the min-max constraint problem. To use the solver fminmax, we rewrite problem 3:
Figure GDA0002694779290000067
Figure GDA0002694779290000068
Figure GDA0002694779290000069
wherein
Figure GDA00026947792900000610
fminimax internally translates the max-min problem into an equivalent nonlinear programming problem by adding the form to the constraints given in problem 3 above
Figure GDA00026947792900000611
Then minimize γ onto p. fminimax uses a sequence quadratic programming SQP method to solve this problem.
2) We have developed a heuristic algorithm that solves instances of the problem faster than the optimal approach. We transform problem 3 by introducing a new decision variable z, which captures
Figure GDA0002694779290000071
Problem 4:
Figure GDA0002694779290000072
Figure GDA0002694779290000073
Figure GDA0002694779290000074
proposition 4: optimal solution to problem 4
Figure GDA0002694779290000075
Is unique:
Figure GDA0002694779290000076
wherein
Figure GDA0002694779290000077
At the same time z*Need to satisfy
Figure GDA0002694779290000078
Figure 4 summarizes the steps involved in finding a max-min fair transmission power allocation strategy at each FBS. The computational complexity is:
Figure GDA0002694779290000079
note that a larger perturbation az may reduce the computation time but may result in a larger gap between the actual interference introduced and the maximum allowed interference level, i.e. Q.
Drawings
FIG. 1 is a user's decision in a pricing scheme
FIG. 2 is an illustration of the floating ceiling waterflood WCWF algorithm
FIG. 3 is an illustration of the floating ceiling waterflood WCWF algorithm
FIG. 4 is a flowchart for finding an optimal transmission power allocation strategy at each FBS
FIG. 5 is a flowchart of finding a max-min fair transmission power allocation strategy at each FBS
FIG. 6 is a graph of joint utility versus Q
FIG. 7 is a graph of average SINR versus Q
FIG. 8 is a graph of least utility versus Q
FIG. 9 is a graph of probability of decline versus Q
FIG. 10 is a graph of fairness index vs. Q
Detailed Description
Hair brushThe implementation of the present invention is divided into three steps, the first step is to build a model, and the second step is to implement an algorithm. Therein, we have established a macro-femto cell heterogeneous network, one group of which
Figure GDA0002694779290000083
Is sparsely deployed in the coverage of the MBS; the implementation process of the algorithm is shown in fig. 2, fig. 3 and fig. 4, which are similar to the heuristic algorithm based on water-filling in the disclosure to obtain the optimal transmission power allocation strategy and the max-min fair transmission power allocation strategy. Fig. 2 and 3 are flowcharts of a water filling algorithm, fig. 4 is a flowchart of finding an optimal transmission power allocation strategy at each FBS, and fig. 5 is a flowchart of finding a max-min fair transmission power allocation strategy at each FBS.
1) For the system model, we consider a macro-femto cell heterogeneous network, one set of which
Figure GDA0002694779290000081
Are sparsely deployed within the coverage of the MBS. Thus, the co-layer interference is negligible, i.e. it is
Figure GDA0002694779290000082
The problem of solving the transmission power allocation can thus be simplified and a closed form power allocation solution can be obtained.
2) To solve the above problem, we first use the water-filling algorithm to obtain the optimal transmit power level p at any FU iiAnd then a heuristic algorithm is utilized to respectively obtain an optimal transmission power distribution strategy and a maximum-minimum fair transmission power distribution strategy.
We have performed a number of simulations for the present invention. For comparison purposes, we introduce the following power and price based scheme. 1) Mean value: the transmit power allocated to the different FUs is the same. Before the overall interference of the MBS equals Q, the transmission power is utilized 2) based on price:
likewise, we consider a macro-femto cell heterogeneous network in which three FBSs are deployed within the coverage of an MBS. I amLet us assume that the channel gain from FU to FBS is as follows: h is1,1＝h2,2＝h3,3Without loss of generality, we assume that utility gains are randomly distributed in the interval (0,1), channel gains from FU to MBS (i.e., g)1，g2And g3) Also evenly distributed in the interval (0,1), each simulated scene was repeated 1000 times.
As shown in fig. 6 and 7, the combined utility and average SINR of FUs increases as the interference power constraint increases. The proposed proportional method is superior to other methods in improving the joint system utility and average SINR. The combined utility of the proposed proportional method is improved by 97.1%, 19.3% and 3.5% compared to the max-min, mean and price-based methods. The proposed proportional method average SINR increases 117.6%, 202.5% and 97.4%, respectively. This is not surprising since in the proposed scaling method, higher transmit power is allocated to FUs that cause less interference to the macro cell. As a result, the joint utility and average SINR may be improved.
As shown in fig. 8, the minimum utility of the proposed maxmin method is improved by 74.9%, 25.2% and 38.3% compared to the proportional method, the mean method and the price-based method, respectively. This is because the max-min fair allocation essentially gives the worst-handled users, i.e. "users with the lowest utility", as large a share as possible without wasting any network resources.
As shown in fig. 9, the proposed proportional method and price-based method reject data transmission to some FUs ", i.e. power allocation equal to 0". In the price-based approach, if the utility gets higher than the charged price, it voluntarily sets its transmission power to 0; whereas in the proposed scaling method, to optimize the joint utility, the transmission power is forced to be set to 0 for FUs with inefficient gain or poor channel conditions. As the interference power constraint increases, the packet loss probability decreases.
To further quantify fairness, we employ the concept of a fairness index. Suppose UiIs the utility of FU i, then we define the fairness index β as:
Figure GDA0002694779290000091
where n is the number of femtocells to which transmission power is allocated. The balance index has the property of 1 when all FUs have exactly the same effect, and it approaches when the cell number is severely unbalanced
Figure GDA0002694779290000092
As shown in fig. 10, the fairness index also increases as the interference power constraint increases. The minimum utility values of max-min values are improved by an average of 47.2%, 25.6% and 35.4% respectively, compared to proportional, average and price-based methods.
Although specific implementations of the invention are disclosed for illustrative purposes and the accompanying drawings, which are included to provide a further understanding of the invention and are incorporated by reference, those skilled in the art will appreciate that: various substitutions, changes and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the present invention should not be limited to the disclosure of the preferred embodiments and the drawings, but the scope of the invention is defined by the appended claims.

Claims (1)

1. A transmission power allocation method applied in a femtocell network, characterized in that: the method comprises the following steps:
s1: obtaining an optimal transmission power distribution strategy at each femto cell base station FBS by using a heuristic water filling algorithm;
s2: obtaining a maximum-minimum fair transmission power distribution strategy at each femto cell base station FBS by utilizing a heuristic algorithm;
the S1 includes the following substeps:
s11: to optimize the joint utility of all FUs under interference constraints, and after simplification and transformation, the power allocation is modeled as a problem
Figure FDA0002694779280000011
Figure FDA0002694779280000012
Figure FDA0002694779280000013
Wherein λ isiRepresenting the utility gain, P, of a femtocell useriA mark representing the transmission power;
solving the optimal solution of the problem by using the Karush-Kuhn-Tucker optimal condition and a water injection algorithm
Figure FDA0002694779280000014
Is unique
Figure FDA0002694779280000015
Wherein v is*Is a Lagrange multiplier, and satisfies the following conditions:
Figure FDA0002694779280000016
the formula is
Figure FDA0002694779280000017
In a piecewise linear increasing function of
Figure FDA0002694779280000018
There is a breakpoint, so the equation has a unique solution;
wherein the channel power gain from FUi to FBS femtocell base station j is represented by hj,iDenoted and channel power gain from FUi to MBS is by giGiven, the transmit power level of FUi is given by piIt is shown that,
s12: a water-filling algorithm is applied to obtain an optimal solution,
Figure FDA0002694779280000019
the water level of the water is increased until the total water amount reaches Q, and the water depth of the water tank i is gipi *The optimum value of (d);
s13: all FUs are considered heterogeneous, i.e. there may be different utility gains; upper limit height of can body i
Figure FDA0002694779280000021
With QoS preference λiThe upper float limit is proportional and, likewise, the water level is increased until the total water volume reaches Q and the water depth of tank i is gipi *The optimum value of (d);
s14: from FU to FU
Figure FDA00026947792800000214
Parameter λ of input FUii，gi，hi,iAnd σ2And Q, initializing shadow price v, and investigating initial possibility
Figure FDA0002694779280000022
If not, a small and feasible disturbance Δ v is considered, for each piAssignment of value
Figure FDA0002694779280000023
Output of
Figure FDA0002694779280000024
The optimal transmission power distribution at each FU can be obtained;
the S2 includes the following substeps:
s21: according to the max-min fairness definition, then max-min fairness can be modeled as the following problem
Figure FDA0002694779280000025
Figure FDA0002694779280000026
Figure FDA0002694779280000027
Wherein, Q represents the maximum interference level that the MBS can tolerate;
Figure FDA0002694779280000028
represents a single SINR constraint that the user must obtain in order to successfully communicate;
s22: to solve the problem of step S21, transformation is performed by introducing a new decision variable z, and capturing
Figure FDA0002694779280000029
The following problems may be encountered,
Figure FDA00026947792800000210
Figure FDA00026947792800000211
Figure FDA00026947792800000212
optimal solution
Figure FDA00026947792800000213
Is unique:
Figure FDA0002694779280000031
therein, sigmai∈Npigi-Q > 0, with z*Need to satisfy
Figure FDA0002694779280000032
S23: from FU to FU at each FBS according to a Max-Min fair transmission power allocation strategy
Figure FDA0002694779280000036
Parameter λ of input FUii，gi，hi,iAnd σ2And Q;
s24: initialize z to 0, consider the initial probability
Figure FDA0002694779280000033
If not, a small and feasible disturbance Δ z is taken into account, giving each piAssignment of value
Figure FDA0002694779280000034
Output of
Figure FDA0002694779280000035
A transmit power allocation satisfying max-min fairness at each FU is obtained.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105490794A (en) * 2015-12-15 2016-04-13 重庆邮电大学 Packet-based resource distribution method for orthogonal frequency division multiple access (OFDMA) femtocell double-layer network
CN106231610A (en) * 2016-09-30 2016-12-14 重庆邮电大学 Resource allocation methods based on sub-clustering in Femtocell double-layer network
CN106604401A (en) * 2017-03-13 2017-04-26 重庆邮电大学 Resource allocation method in heterogeneous network

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105490794A (en) * 2015-12-15 2016-04-13 重庆邮电大学 Packet-based resource distribution method for orthogonal frequency division multiple access (OFDMA) femtocell double-layer network
CN106231610A (en) * 2016-09-30 2016-12-14 重庆邮电大学 Resource allocation methods based on sub-clustering in Femtocell double-layer network
CN106604401A (en) * 2017-03-13 2017-04-26 重庆邮电大学 Resource allocation method in heterogeneous network

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
OFDM***Femtocell网络下行联合资源分配;李鑫滨等;《信号处理》;20160531;第31卷(第5期);第2-4页 *

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