CN109450494B - CoMP-based heterogeneous network channel and power resource joint allocation method - Google Patents

CoMP-based heterogeneous network channel and power resource joint allocation method Download PDF

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CN109450494B
CN109450494B CN201811340269.9A CN201811340269A CN109450494B CN 109450494 B CN109450494 B CN 109450494B CN 201811340269 A CN201811340269 A CN 201811340269A CN 109450494 B CN109450494 B CN 109450494B
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channel
allocation
power
user
base station
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CN109450494A (en
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马冬
高云
盛洁
朱灿焰
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Suzhou University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/022—Site diversity; Macro-diversity
    • H04B7/024—Co-operative use of antennas of several sites, e.g. in co-ordinated multipoint or co-operative multiple-input multiple-output [MIMO] systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0058—Allocation criteria
    • H04L5/0071—Allocation based on fairness other than the proportional kind
    • 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
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • 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

Abstract

The application provides a CoMP-based heterogeneous network channel and power resource joint allocation method, wherein the method comprises the following steps: constructing a heterogeneous network, wherein each layer of base station in the heterogeneous network supports a CoMP joint transmission mode; establishing a mathematical model of heterogeneous network channel and power resource allocation based on constraint conditions: ensuring that the sum of the transmission power allocated to each channel by each base station does not exceed the maximum transmission power limit of the base station and ensuring that each channel is allocated to at most one user; determining a channel allocation vector based on the mathematical model
Figure DDA0001862361890000011
And power allocation vector of each base station
Figure DDA0001862361890000012
To maximize throughput for the worst user. The resource joint allocation method and the resource joint allocation device can give consideration to both system performance and user fairness.

Description

CoMP-based heterogeneous network channel and power resource joint allocation method
[ technical field ] A method for producing a semiconductor device
The application particularly relates to a CoMP-based heterogeneous network channel and power resource joint allocation method.
[ background of the invention ]
Data traffic for wireless communication systems has seen explosive growth over the last decade. It is expected that global mobile data traffic will increase by more than 100 times in 2010 to 2020. Therefore, the next generation 5G mobile communication network requires that the theoretical peak transmission rate can reach more than 10Gb, which is hundreds of times higher than the current 4G network transmission rate (20-100 Mbps). The traditional single-layer wireless network access technology is close to the theoretical limit and cannot meet the requirement. Therefore, heterogeneous networks with overlay coverage of multiple layers in the same region become an important choice for 5G networks, and network deployment will show a trend of densification.
By adding low-power nodes such as a small cell (small cell) base station, a relay or a distributed antenna and the like in a traditional macro cell, the heterogeneous network shortens the signal transmission distance between the base station and the terminal, enhances the received signal strength and further improves the system capacity. The network load is effectively shared by realizing multilayer coverage in the hot spot area, and better internet experience is brought to users. At the same time, however, to efficiently utilize spectrum resources, the macro cell and the small cell typically share the same spectrum resources. Therefore, the interlayer interference is a bottleneck problem in the heterogeneous network technology development.
Coordinated multipoint (CoMP) technology is a key technology in Release 11 and later releases of LTE-a evolution, and aims to suppress or eliminate inter-cell interference and improve the quality of service for cell-edge users. The base stations supporting the CoMP technology exchange information such as users and channel states through a backhaul link, and jointly schedule system resources, so that co-channel signals from other base stations do not cause serious interference to users, and even can become useful signals. It has been well studied and applied in conventional macro cell networks, proving its effectiveness in solving the cell interference problem. Therefore, introducing CoMP into a heterogeneous network to suppress inter-layer interference and improve system performance becomes a new research hotspot in the field of heterogeneous networks.
[ application contents ]
The embodiment of the application provides a CoMP-based heterogeneous network channel and power resource joint allocation method, so that the system throughput is improved, and the fairness among users is protected.
The method for joint distribution of the heterogeneous network channel and the power resource based on CoMP comprises the following steps:
constructing a heterogeneous network, wherein each layer of base station in the heterogeneous network supports a CoMP joint transmission mode;
establishing a mathematical model of channel and power resource allocation of the heterogeneous network with the aim of maximizing the throughput of the worst user, wherein the mathematical model is expressed as:
Figure BDA0001862361870000021
wherein the constraint conditions of the mathematical model comprise: ensuring that the sum of the transmission powers allocated to the channels by each base station does not exceed the maximum transmission power limit for that base station, the formula is: subject to
Figure BDA0001862361870000022
And, ensuring that each channel is allocated to at most one user, the formula is:
Figure BDA0001862361870000023
calculating channel allocation vector based on channel and power resource joint allocation algorithm
Figure BDA0001862361870000024
And power allocation vector of each base station
Figure BDA0001862361870000025
Further, the heterogeneous network is composed of a macro base station in the center of the network and B small base stations within the coverage range of the macro base station, wherein the types of the small base stations are one or a combination of micro, pico and femto base stations.
Further, the channel and power resource joint allocation algorithm includes:
step 301, setting the number of iteration times: t is 1, maximum number of iterations tmax(ii) a Setting initial power:
Figure BDA0001862361870000031
setting multipliers mu, upsilon and beta, step lengths alpha and lambda and an error threshold epsilon;
step 302, carrying out t iterations, and solving an optimal channel allocation vector rho (t) based on an allocation vector algorithm; solving an optimal power distribution vector p (t) based on a multi-base station power joint distribution algorithm; updating multipliers lambda (t), mu (t), alpha (t) and beta (t) respectively;
step 303, return to step 302, until | | | mu (t +1) -mu (t) | purple2< epsilon and | | | beta (t +1) -beta (t) | non-woven phosphor2< epsilon, or t ═ tmax；
And step 304, outputting a channel allocation vector rho and a power allocation vector p.
Further, the allocation vector algorithm includes:
step 401, a channel set Φ ═ {1, 2.., C };
setting initial channel allocation parameters
Figure BDA0001862361870000032
User cumulative rate
Figure BDA0001862361870000033
Calculating the transmission rate of user on each channel
Figure BDA0001862361870000034
Step 402, iterative calculation: finding the user u with the minimum transmission rate*I.e. by
Figure BDA0001862361870000035
Finding the channel c which can make the user u obtain the maximum transmission rate*I.e. by
Figure BDA0001862361870000036
Order channel allocation variable
Figure BDA0001862361870000037
Updating channel set phi ═ phi- { c*User rate
Figure BDA0001862361870000038
Step 403, return to step 402 until
Figure BDA0001862361870000039
Step 404, outputting the channel allocation vector ρ.
Further, the multi-base station power joint allocation algorithm includes:
step 501, setting the number of iteration times: k is 1; setting an initial power allocation vector p (0) as p (t-1) obtained by a channel and power resource joint allocation algorithm;
step 502, the kth iteration: b is more than or equal to 0 and less than or equal to B for all base stations B; based on
Figure BDA00018623618700000310
Calculating GcAnd
Figure BDA00018623618700000311
calculation of 1/. mu.b: for any c, find
Figure BDA00018623618700000312
And sorting in descending order; let j decrement from C to 1
If it is not
Figure BDA00018623618700000313
Stopping the operation;
Figure BDA00018623618700000314
based on formula
Figure BDA0001862361870000041
Wherein [ ·]+Max (0,) to obtain
Figure BDA0001862361870000042
Figure BDA0001862361870000043
Step 503, find the worst user rate
Figure BDA0001862361870000044
k is k + 1; return to step 502 until rminConverging;
step 504, output power distribution vector p.
In the technical scheme, a mathematical model is established by taking the maximum worst user throughput as a target, and then an iterative algorithm based on Lagrangian dual decomposition is designed. In each iteration, the original problem is decomposed into 4 subproblems, so that the calculation complexity of the algorithm is reduced, and a channel allocation algorithm and a power allocation algorithm based on iteration and a dichotomy are respectively designed. The method is superior to other existing methods in the aspects of user throughput, fairness and the like, and the iterative algorithm can realize convergence in a small number of iterations.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a comparison of average rates of users of the present application;
FIG. 2 is a user average rate normalized probability density estimation of the present application;
fig. 3 is a long-term comparison analysis of the worst rate of the user of the present application, where U is 30;
FIG. 4 is a diagram illustrating worst user rates in different user density scenarios according to the present application;
fig. 5 shows the worst user rate, U being 30, for different base station densities in the present application;
FIG. 6 is a comparison of fairness indices according to the present application;
FIG. 7 is a comparison of maximum scheduling time intervals of user channels according to the present application;
FIG. 8 illustrates the convergence of the multi-base-station power joint allocation algorithm of the present application;
FIG. 9 illustrates the convergence of the power resource joint allocation algorithm of the present application;
FIG. 10 is a comparison of fairness indices for channel allocation and simplicity algorithm according to the present application;
fig. 11 is a comparison of power distribution algorithm iteration and power distribution algorithm multiplier convergence based on dichotomy in the present application.
[ detailed description ] embodiments
For better understanding of the technical solutions of the present application, the following detailed descriptions of the embodiments of the present application are provided with reference to the accompanying drawings.
It should be understood that the embodiments described are only a few embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the examples of this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The heterogeneous network is composed of a macro base station (macro base station) in the center of the network and B small base stations within the coverage area of the macro base station. The small base stations can be micro, pico, femto base stations and the like. That is, the resource allocation scheme proposed by the present application is applicable to a multi-layer heterogeneous network. Let the serial number of the macro base station be 0, and the serial number of each small base station be 1 or more and B or less. Under the joint transmission mode, the macro base station and each small base station cooperate to provide data transmission service for U users.
Figure BDA0001862361870000051
Is the maximum transmission power of base station B (B ∈ {0, 1.., B }).
The system is provided with C channels which are shared by the macro base station and the small base station. Defining channel allocation variables
Figure BDA0001862361870000052
Figure BDA0001862361870000053
Figure BDA0001862361870000054
Representing the channel c (c e 1,..., C)) to user U (U e { 1., U }), and vice versa
Figure BDA0001862361870000055
To avoid intra-cell co-channel interference, each channel is simultaneously allocated to at most one user. Is provided with
Figure BDA0001862361870000056
Transmission power allocated to base station b on channel c if
Figure BDA0001862361870000057
And is
Figure BDA0001862361870000058
Indicating that base station b will transmit data to user u on channel c. By the resource joint allocation scheme, the serving base station of the user u can be dynamically determined
Figure BDA0001862361870000061
Rather than pre-designating the serving base station for each user as in some prior art schemes and keeping it unchanged throughout the resource allocation process. The dynamic allocation scheme can allocate resources more flexibly and improve the spectrum efficiency of the system.
Order to
Figure BDA0001862361870000062
Indicating the radio link channel gain from base station b to user u on channel c. It is assumed that each base station can obtain accurate Channel State Information (CSI) of the user in time.
In CoMP joint transmission mode, user u receives signals from each cooperative base station, and the signal-to-noise ratio (SNR) obtained on channel c can be expressed as
Figure BDA0001862361870000063
Wherein N is0Is additive white gaussian noise power. On channel c, the maximum transmission rate available to user u is:
Figure BDA0001862361870000064
where Δ f is the channel bandwidth. Therefore, in one resource scheduling, the maximum throughput that can be obtained by user u can be expressed as:
Figure BDA0001862361870000065
the application determines the optimal channel allocation vector
Figure BDA0001862361870000066
And power allocation vector of each base station
Figure BDA0001862361870000067
The throughput of the worst user is maximized, so that the system throughput is improved and the fairness among the users is protected.
The preferred embodiment of the method for joint allocation of heterogeneous network channels and power resources based on CoMP comprises the following steps:
constructing a heterogeneous network, wherein each layer of base station in the heterogeneous network supports a CoMP joint transmission mode;
aiming at maximizing the throughput of the worst user, the following mathematical model is established:
Figure BDA0001862361870000071
Figure BDA0001862361870000072
Figure BDA0001862361870000073
Figure BDA0001862361870000074
Figure BDA0001862361870000075
wherein the constraint (4a) ensures that the sum of the transmission powers allocated to the channels by each base station does not exceed the maximum transmission power limit of the base station, and the constraint (4c) ensures that each channel is allocated to at most one user.
The method comprises the steps of firstly introducing a new variable into an objective function
Figure BDA00018623618700000714
To simplify the objective function while relaxing the 0-1 constraint of the channel allocation variable to the interval 0,1]Writing the equivalence of (4) as
Figure BDA0001862361870000076
Figure BDA0001862361870000077
Figure BDA0001862361870000078
Figure BDA0001862361870000079
Figure BDA00018623618700000710
Figure BDA00018623618700000711
Problem after rewriting(7) Although simplified to a single maximization problem, there are integer variables (channel allocation variables) at the same time
Figure BDA00018623618700000712
) And non-integer variables (power distribution variables)
Figure BDA00018623618700000713
) It is still a nonlinear mixed integer programming problem with very high computational complexity.
In order to find a fast convergence algorithm, the method firstly adopts a Lagrange dual decomposition method to relax constraint conditions, and reduces the complexity of the problem. For equation (7), its lagrangian equation is listed:
Figure BDA0001862361870000081
Figure BDA0001862361870000082
wherein μ ═ { μ ═ μ0,...,μB}、υ＝{υ1,...,υCAnd β ═ β1,...,βUAre lagrange multipliers corresponding to constraints (7a), (7c) and (7e), respectively. To this end, the Lagrangian dual function can be expressed as
Figure BDA0001862361870000083
Figure BDA0001862361870000084
Figure BDA0001862361870000085
By dual decomposition, it is possible to maximize at a given μ, upsilon and β
Figure BDA0001862361870000086
And solving the optimal resource allocation solution of the original problem. Through the lagrange dual method, the constraint condition of the problem (9) is reduced compared with that of the problem (7), but the solution of the problem (9) needs to determine the resource allocation vector and the lagrange multiplier at the same time, and still has higher computational complexity.
Therefore, the method further decomposes the original resource joint allocation problem into 4 subproblems: channel allocation, power allocation,
Figure BDA0001862361870000088
Selection of (1) and multiplier updating. And respectively solving the subproblems by adopting an iteration method until the algorithm is converged.
See algorithm 1 for detailed steps: and (3) a channel and power resource joint allocation algorithm.
Algorithm 1: channel and power resource joint allocation algorithm
Step 101, initialization:
setting the number of the iteration times: t is 1, maximum number of iterations tmax。
Setting initial power:
Figure BDA0001862361870000087
multipliers mu, upsilon and beta, step sizes alpha and lambda and an error threshold epsilon are set.
Step 102, the tth iteration:
the optimal channel allocation vector ρ (t) is found based on the channel allocation algorithm (algorithm 2).
Algorithm 3: multi-base station power joint allocation algorithm
And (3) solving an optimal power distribution vector p (t) based on a multi-base station power joint distribution algorithm (algorithm 3).
The multipliers λ (t), μ (t), α (t), and β (t) are updated, respectively.
Step 103, returning to step 102 until | μ (t +1) - μ (t) | purple2< epsilon and | | | beta (t +1) -beta (t) | non-woven phosphor2< epsilon, or t ═ tmax。
And 104, outputting a channel allocation vector rho and a power allocation vector p.
Order to
Figure BDA0001862361870000091
Representing a locally optimal solution of the channel allocation vector with the known power allocation vector. According to the KKT condition of the optimal solution, the following relation can be obtained:
Figure BDA0001862361870000092
Figure BDA0001862361870000093
the vector is allocated to the channel, and only two values of 0 and 1 are taken. When in use
Figure BDA0001862361870000094
When there is
Figure BDA0001862361870000095
Namely, it is
Figure BDA0001862361870000096
When in use
Figure BDA0001862361870000097
When there is
Figure BDA0001862361870000098
Namely, it is
Figure BDA0001862361870000099
The two cases are combined to obtain an expression
Figure BDA00018623618700000910
Wherein the content of the first and second substances,
Figure BDA00018623618700000911
since the constraint (4c) of the channel allocation variable is relaxed in equation (8), the channel allocation scheme obtained by equation (13) cannot guarantee that each channel is allocated to only one user at most. Therefore, some documents based on the lagrangian algorithm use the H maximum value as the channel allocation method [ ], but this does not guarantee that a locally optimal solution is obtained. For this reason, the present application designs a channel allocation algorithm (see algorithm 2) based on the idea of max min.
As shown in step 102 of algorithm 1, in the tth iteration, algorithm 2: and the channel allocation algorithm calculates the local optimal solution of the current channel allocation based on the optimal power allocation vector p (t-1) obtained by the last iteration. The core idea is as follows: and allocating the best channel (the channel on which the user can obtain the maximum data transmission rate) to the user with the minimum accumulated rate until all channels are allocated.
And 2, algorithm: channel allocation algorithm
Step 201, initialization:
the channel set phi is {1, 2.
Setting initial channel allocation parameters
Figure BDA0001862361870000101
User cumulative rate
Figure BDA0001862361870000102
Calculating the transmission rate of user on each channel
Figure BDA0001862361870000103
Step 202, iteration:
finding the user u with the minimum transmission rate*I.e. by
Figure BDA0001862361870000104
Find out to useHuu (household)*Channel c for obtaining maximum transmission rate*I.e. by
Figure BDA0001862361870000105
Order channel allocation variable
Figure BDA0001862361870000106
Updating channel set phi ═ phi- "tonec *User rate
Figure BDA0001862361870000107
Step 203, return to step 202 until
Figure BDA0001862361870000108
And step 204, outputting a channel allocation vector rho.
Order to
Figure BDA0001862361870000109
For the local optimal solution of the power allocation vector under the condition that the channel allocation vector rho is known, according to the KKT condition of the optimal solution, the following relational expression can be obtained:
Figure BDA00018623618700001010
based on algorithm 2, the application knows that any channel c is allocated to user ucTherefore, (15) can be simplified to
Figure BDA0001862361870000111
When in use
Figure BDA0001862361870000112
When there is
Figure BDA0001862361870000113
Namely, it is
Figure BDA0001862361870000114
Bonding of
Figure BDA0001862361870000115
In a state of
Figure BDA0001862361870000116
And
Figure BDA0001862361870000117
the power allocation variable may be expressed as:
Figure BDA0001862361870000118
wherein [ ·]+＝max(0,·)。
Although the formula (19) has a similar form to the solution of the water injection method, it is not limited thereto
Figure BDA0001862361870000119
Is contained in
Figure BDA00018623618700001110
Is the power allocation variable of other cooperative base stations, needs and
Figure BDA00018623618700001111
are determined together. Therefore, equation (19) cannot directly find the power allocation solutions of all base stations. For this reason, this section proposes an iteration-based multi-base-station power joint allocation algorithm (algorithm 3). The algorithm is based on the optimal power distribution vector p (t-1) obtained in the previous iteration of the algorithm 1 and the Lagrangian multiplier beta (t-1), and the local optimal solution of the current power distribution vector p (t) is solved. The basic idea is as follows: in the kth iteration, other base stations are fixed
Figure BDA00018623618700001112
The power distribution value of (a) is calculated, and the water level line 1/mu of each base station b is calculated in turnbAnd the current power allocation vector of the base station is obtained according to the formula (19)
Figure BDA00018623618700001113
And updating the worst user rate and entering next iteration based on the current power distribution vector p (k) until the algorithm converges.
Algorithm 3: multi-base station power joint allocation algorithm
Step 301, initialization:
setting the number of the iteration times: k is 1.
The initial power allocation vector p (0) is set to p (t-1) obtained by algorithm 1.
Step 302, the kth iteration:
for all base stations B, B is more than or equal to 0 and less than or equal to B
Based on
Figure BDA0001862361870000121
Calculating GcAnd
Figure BDA0001862361870000122
calculation of 1/. mu.b：
For any c, find
Figure BDA0001862361870000123
And sorted in descending order.
Let j decrement from C to 1
If it is not
Figure BDA0001862361870000124
It is stopped.
Figure BDA0001862361870000125
Based on the formula (19), obtaining
Figure BDA0001862361870000126
Step 302, find the worst user rate
Figure BDA0001862361870000127
Return to step 302 until rminAnd (6) converging.
Step 304, outputting the power distribution vector p.
Algorithm 1: the channel and power resource joint allocation algorithm is operated by a resource manager of the base station. In the case of the LTE system, the base station resource manager executes the algorithm at the beginning of each TTI (1ms duration). The base station calls in the channel condition reported by the user in the last TTI
Figure BDA0001862361870000128
And setting initial values of parameters mu, upsilon, beta, alpha, lambda and epsilon required by the algorithm, and operating the algorithm 1 to obtain channel allocation vectors rho and p which are final output results. The allocation of radio resources (channel resources and power resources of the base station) is to determine these two parameters, and the base station knows which channel is allocated to which user in this TTI, that is, which user's data is transmitted with what power.
Algorithm 1: the channel and power resource joint allocation algorithm can realize fair resource allocation (the worst user rate target is maximized through maxmin), and the transmission rate obtained by each user is very close. This is evident in fig. 1, where the user rates are closest. The radio resource allocation algorithm has one angle that the operator wants the overall throughput to be large and another angle that the user wants fairness. But the channel and power resources are limited and the two angles have conflicts. But our algorithm aims to maximize worst case and to compromise fairness and system throughput. Fig. 1 illustrates user fairness, and fig. 2 illustrates that the user center rate is the highest and very concentrated, and illustrates that the overall user rate is relatively high and the overall system throughput is relatively good
In the algorithm 1, an algorithm 2 is called to obtain a channel allocation vector rho (t), an algorithm 3 is called to obtain an optimal power allocation vector p (t), and the two vectors are continuously updated in the algorithm 1 until the final rho and p are converged, namely output values. The resource allocation is very fast (in the order of ms) in the base station. The final calling of the method is the result of derivation, and a big problem decomposition method adopted by the derivation is adopted, so that the algorithm is simple, the method is good for a base station with limited computing capacity, and large memory and power are not needed, and fig. 8 to fig. 11 illustrate that the algorithm can be converged after being iterated for a plurality of times, namely, the algorithm 1 can output the final value only by calling the algorithms 2 and 3 for a plurality of times. If the optimization problem at max min is solved with MATLAB tools, one result cannot be reached in one day.
Extending the embodiment, algorithm 4 is another power allocation algorithm that replaces algorithm 3. In wireless network resource allocation, dichotomy is also often used to compute power allocation problems. The power distribution subproblem was solved by bisection, shown in algorithm 4. By analyzing the algorithm flow, it can be seen that the main difference between algorithm 3 and algorithm 4 is the update mode of multiplier μ. The algorithm 3 arranges possible values of mu in descending order, selects a proper water line, namely a proper value of mu, and calculates a power distribution solution according to the formula (19). And the algorithm 4 firstly obtains the mu value through the dichotomy, then updates the power distribution solution according to the formula (19), and finally updates the upper and lower limits of the mu according to the constraint condition for calculating the mu value in the next iteration. For the upper and lower limits of μ, different initial value settings largely affect the convergence speed of algorithm 4, and the upper limit of the maximum iteration number of algorithm 3 is the number C of subchannels, so algorithm 4 based on the bisection method has a larger uncertainty.
And algorithm 4: power distribution algorithm based on dichotomy
Step 401, initialization:
the number of iterations l is set to 1, and an error threshold value epsilon is set.
The initial power allocation vector p (0) is set to p (t-1) obtained by algorithm 4-1.
Step 402, the l-th iteration:
setting an upper limit mu of the multiplier mumaxAnd lower limit μmin。
For all base stations B, B is more than or equal to 0 and less than or equal to B
When in use
Figure BDA0001862361870000141
The following operations are executed:
Figure BDA0001862361870000142
based on equation (19), solve
Figure BDA0001862361870000143
If it is
Figure BDA0001862361870000144
Then update
Figure BDA0001862361870000145
Otherwise update
Figure BDA0001862361870000146
Step 403, finding the worst user rate
Figure BDA0001862361870000147
Return to step 402 until rminAnd (6) converging.
Step 404, outputting a power distribution vector p.
After solving the channel allocation subproblem and the power allocation subproblem, the lagrange dual function shown in (9) can be simplified, and only the sum of the objective function and the constraint condition is left
Figure BDA00018623618700001415
The relevant part, and it is re-expressed as:
Figure BDA0001862361870000148
Figure BDA0001862361870000149
order to
Figure BDA00018623618700001410
Is the optimal solution of the problem (20). The maximum value of the objective function is required and can be discussed in two cases. When in use
Figure BDA00018623618700001411
When considering
Figure BDA00018623618700001412
Is not negative, is to be
Figure BDA00018623618700001413
The value of (a) is the largest,
Figure BDA00018623618700001414
should be equal to 0; when in use
Figure BDA0001862361870000151
When the temperature of the water is higher than the set temperature,
Figure BDA0001862361870000152
should take the maximum value within its range, i.e.
Figure BDA0001862361870000153
Combine the two cases and order
Figure BDA0001862361870000154
Can obtain
Figure BDA0001862361870000155
The optimal selection scheme is as follows:
Figure BDA0001862361870000156
to accomplish channel allocation and power allocation according to equations (13) and (19), the values of the lagrange multipliers μ, ν, and β need to be known.
The value of the multiplier is updated using a secondary gradient method. First, the dual problem of the problem (9) is expressed as
min D(μ,υ,β) (23)
subject to μ≥0,υ≥0,β≥0
Further, a secondary gradient expression of D (μ, upsilon, β) can be obtained:
Figure BDA0001862361870000157
Figure BDA0001862361870000158
Figure BDA0001862361870000159
since each channel is eventually allocated to a user and can only be allocated to one user at most in one resource scheduling, that is
Figure BDA00018623618700001510
This is always true for any channel C (C ∈ { 1.,. C }). Therefore, the temperature of the molten metal is controlled,
Figure BDA00018623618700001511
always true, only the multiplier updates for μ and β are needed. The updating method comprises the following steps:
μb(t+1)＝[μb(t)-λ(t)Δμb(t)]+ (25a)
βu(t+1)＝[βu(t)-α(t)Δβu(t)]+ (25b)
where λ (t) and α (t) are the update steps for the multipliers μ and β, respectively.
The performance of the proposed scheme and algorithm is verified in the LTE based heterogeneous network downlink. The number C of sub-channels is 50, the bandwidth Δ f of sub-channels is 180kHz,maximum transmission power of macro base station
Figure BDA0001862361870000161
The calculation of large scale fading follows the L-122.85 +34.88log10(d) Where d represents the base station to user distance in km. The small-scale fading model is normalized Rayleigh fading, and the noise power density is-174 dBm/Hz. Further, the mean square error of the shadow fading following the lognormal distribution is 10dB, and the penetration loss is 20 dB. 6 users and 12 traffic hot spot areas are uniformly distributed in the coverage area of the macro cell, and 2 users are uniformly distributed in each hot spot area.
In order to verify the channel and power resource joint allocation scheme (abbreviated as JSPA herein), the present application compares the proposed channel and power resource joint allocation scheme with another four typical heterogeneous network resource allocation schemes. The four reference schemes are: in a classical polling scheme (RR), each channel is sequentially distributed to each user according to a sequence number, and the transmission power of each base station is uniformly distributed on all the channels; channel and power joint resource allocation scheme aiming at maximizing system throughput; thirdly, based on the scheduling scheme (CDFS) of the cumulative distribution function, channel distribution is carried out according to the cumulative distribution function of the user rate, and the transmission power of the base station is distributed on all channels evenly; and fourthly, a proportional fair scheduling scheme (PFS) and a resource allocation scheme which gives consideration to the system throughput and the fairness among users.
The method and the device can be used for carrying out comparative analysis on the schemes under a macro-femto heterogeneous network scene. Maximum transmission power of femto base station
Figure BDA0001862361870000162
Fig. 1 shows the measured average transmission rate obtained for each user in 50 TTIs. The users are sorted in ascending order according to their respective average rates. The results show that the average rates of the users obtained by the JSP algorithm provided by the application are very similar, and a certain difference is present between the highest average rate and the lowest average rate obtained by RR and PFS, and the difference is about 6 Mbps. In the two schemes of TMS and CDFS, although the highest average rate value of users is much greater than 20Mbps, some users cannot obtain resources (the average rate is close to 0Mbps), and the rate difference between users is very obvious.
The normalized probability density estimation of the average user rate under different schemes is shown in fig. 2, and the results are analyzed from another angle. First, it can be seen that the mean rate distribution range is widest for both TMS and CDFS schemes (only a small fraction of 0-20Mbps is shown), followed by RR and PFS. The JSP scheme obtains the smallest average speed distribution range, which means that the average speed obtained by the user is the closest. On the other hand, the size of the average rate corresponding to the point with the highest probability density, that is, the average rate that most users can obtain, which is referred to as the concentrated rate, can also be observed from the graph. It can be seen that the average rate for most users in both TMS and CDFS is 0 Mbps. The collective rates of RR and PFS are around 11Mbps and 12Mbps, respectively, and are relatively high, but still lower than 13Mbps for the JSPA scheme. Fig. 1 and fig. 2 illustrate that the distribution range of the average speed of the users obtained by the JSPA scheme is most concentrated, that is, the difference of the average speed between the users is the smallest, and the value of the concentrated speed obtained by the JSPA scheme is the largest. The JSP scheme provided by the application can realize the balance between the user throughput and the fairness.
Next, the variation of the worst rate of the user in the long-term scheduling process is observed, and the number of users is set to 30, and the result is shown in fig. 3. It can be seen that the worst average rate of TMS and CDFS is always 0Mbps throughout the resource allocation, and the worst average rate of RR and PFS fluctuates up and down, but generally the worst average rate of RR is higher than that of PFS. The worst average rate obtained by the JSPA scheme shows an increasing trend with TTI and is always higher than the other four schemes. The following conclusions can be drawn therefrom: in the long-term scheduling process, compared with other four reference schemes, the JSP scheme provided by the application can obtain the maximum worst average rate, and the target set by the scheme is realized.
In order to fully verify the performance of the JSPA scheme, this subsection sets up multiple simulation scenarios of different user densities and different base station densities to simulate various situations in reality. The worst user average rates obtained for the various scenarios were compared and the results are shown in fig. 4 and 5. Each point in the figure is the average of the rates obtained by each user over 50 TTIs. As can be seen from the figure, the worst average rate for TMS and CDFS is 0Mbps in any scenario. The worst average rates of JSPA, RR and PFS all show a tendency to decrease as the number of users increases, because as the number of users increases, the number of channels acquired by each user decreases. However, the worst average rate obtained by JSPA is always greater than the other four schemes, whether the number of users is less than the number of channels or in the opposite scenario. As can be seen from fig. 5, the worst average rates of the three schemes JSPA, RR and PFS become larger as the number of small base stations increases, and the worst rate of JSPA is always higher than the other schemes. In summary, the JSPA scheme maximizes the worst user average rate implementation in various scenarios compared to the other four schemes.
In order to intuitively measure the fairness of the scheme, the fairness index of the user rate in long-term scheduling is calculated:
Figure BDA0001862361870000181
wherein R isuThe average speed of the users u in the current TTI is represented, and the larger the value of the fairness index J is, the higher the fairness among the average speeds of the users is represented. Fig. 6 shows the fairness index for the user average rate as a function of TTI. In three fairness-considered resource allocation schemes, JSPA is always able to obtain a higher fairness index than RR and PFS. Furthermore, the fairness index of JSPA is incremented with TTI, converging to a maximum of 1 after about 4 TTIs. In other words, JSPA can achieve user rate fairness in a shorter scheduling time.
In addition, the maximum scheduling interval (MIT) of the worst user is also observed. The scheduling interval is the interval duration of the resource allocated to the user twice. And calculating the maximum scheduling interval of each user within 50 TTIs, and further taking out the scheduling interval value of the worst user, namely MIT. FIG. 7 compares MIT values for different schemes in three scenarios. Since the allocation principle of the RR scheme is to sequentially allocate channels to each user, its scheduling interval is minimal. The JSP scheme provided by the application can obtain a scheduling interval value which is very close to that of the RR scheme, and is superior to other three schemes. The above results demonstrate that the proposed JSPA scheme can enable users to obtain a more stable network connection service, and is suitable for applications with higher requirements on delay and delay jitter, such as video streaming, because the scheduling interval can reflect the time delay for users to obtain the service.
In the simulation process, the performance of the algorithm provided by the application is also verified. Fig. 8 and fig. 9 show the variation of the worst user rate with the number of iterations of the multi-base-station power joint allocation algorithm (algorithm 3) and the iterative channel and power resource joint allocation algorithm (algorithm 1) based on the lagrangian dual decomposition. And respectively observing under four heterogeneous network scenes. Simulation results show that both algorithm 3 and algorithm 1 can achieve convergence in a small number of iterations.
Fig. 10 compares the channel allocation algorithm 2 proposed in the present application with a simple algorithm derived based on lagrangian, and it can be seen that the proposed channel allocation algorithm can achieve a higher fairness index within a shorter resource allocation time, and better achieve fairness among users.
Fig. 11 compares the convergence of the multiplier μ for the two algorithms for the proposed iteration-based power distribution algorithm (algorithm 3) and the dichotomy-based power distribution algorithm (algorithm 4). It can be seen that algorithm 3 achieves convergence after about 8 iterations, whereas algorithm 4 is more complex. In one aspect, no matter the initial upper limit μ of μmaxIs, even in the best case, the algorithm 4 needs at least 12 iterations before it converges. On the other hand, if μmaxThe number of iterations required for the algorithm 4 to converge also appears to be very different, with a large uncertainty in convergence.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims (2)

1. A CoMP-based heterogeneous network channel and power resource joint allocation method is characterized by comprising the following steps:
constructing a heterogeneous network, wherein each layer of base station in the heterogeneous network supports a CoMP joint transmission mode;
establishing a mathematical model of channel and power resource allocation of the heterogeneous network with the aim of maximizing the throughput of the worst user, wherein the mathematical model is expressed as:
Figure FDA0003252255940000011
wherein the constraint conditions of the mathematical model comprise: ensuring that the sum of the transmission powers allocated to the channels by each base station does not exceed the maximum transmission power limit for that base station, the formula is: the constraint condition is
Figure FDA0003252255940000012
And, ensuring that each channel is allocated to at most one user, the formula is:
Figure FDA0003252255940000013
calculating channel allocation vector based on channel and power resource joint allocation algorithm
Figure FDA0003252255940000014
And power allocation vector of each base station
Figure FDA0003252255940000015
The channel and power resource joint allocation algorithm comprises the following steps:
step 301, setting the number of iteration times: t is 1, maximum number of iterations tmax(ii) a Setting initial power:
Figure FDA0003252255940000016
setting multipliers mu, upsilon and beta, step lengths alpha and lambda and an error threshold epsilon;
step 302, performing t iterations, calculating an optimal channel allocation vector rho (t) based on an allocation vector algorithm, and enabling
Figure FDA0003252255940000017
And
Figure FDA0003252255940000018
the power allocation variable is expressed as:
Figure FDA0003252255940000019
solving an optimal power distribution vector p (t) based on a multi-base station power joint distribution algorithm; updating multipliers lambda (t), mu (t), alpha (t) and beta (t) respectively;
step 303, return to step 302, until | | | mu (t +1) -mu (t) | purple2< epsilon and | | | beta (t +1) -beta (t) | non-woven phosphor2< epsilon, or t ═ tmax；
Step 304, outputting a channel allocation vector rho and a power allocation vector p;
the allocation vector algorithm comprises:
step 401, a channel set Φ ═ {1, 2.., C };
setting initial channel allocation parameters
Figure FDA0003252255940000021
User cumulative rate
Figure FDA0003252255940000022
Calculating the transmission rate of user on each channel
Figure FDA0003252255940000023
Step 402, iterative calculation: finding the user u with the minimum transmission rate*I.e. by
Figure FDA0003252255940000024
Find enable user u*Channel c for obtaining maximum transmission rate*I.e. by
Figure FDA0003252255940000025
Order channel allocation variable
Figure FDA0003252255940000026
Updating channel set phi ═ phi- { c*User rate
Figure FDA0003252255940000027
Step 403, return to step 402 until
Figure FDA0003252255940000028
Step 404, outputting a channel allocation vector rho;
the multi-base station power joint allocation algorithm comprises the following steps:
step 501, setting the number of iteration times: k is 1; setting an initial power allocation vector p (0) as p (t-1) obtained by a channel and power resource joint allocation algorithm;
step 502, the kth iteration: b is more than or equal to 0 and less than or equal to B for all base stations B; based on
Figure FDA0003252255940000029
Calculating GcAnd
Figure FDA00032522559400000210
calculation of 1/. mu.b: for any c, find
Figure FDA00032522559400000211
And sorting in descending order; let j decrement from C to 1 if
Figure FDA00032522559400000212
Stopping the operation;
Figure FDA00032522559400000213
based on formula
Figure FDA00032522559400000214
Wherein [ ·]+Max (0,) to obtain
Figure FDA00032522559400000215
Step 503, find the worst user rate
Figure FDA00032522559400000216
Return to step 502 until rminConverging; step 504, output power distribution vector p.
2. The method of claim 1, wherein the heterogeneous network consists of a macro base station in the center of the network and B small base stations within the coverage of the macro base station, wherein the small base stations are of one or a combination of micro, pico and femto base stations.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104935364A (en) * 2014-03-21 2015-09-23 日本电气株式会社 Method of carrying out distributed wave beam forming in heterogeneous network and apparatus thereof
US9414216B1 (en) * 2013-03-15 2016-08-09 Leidos, Inc. Method and system for multiple carrier resource allocation in LTE-advanced networks
CN106455077A (en) * 2016-09-13 2017-02-22 北京理工大学 Heterogeneous network multicast transmission proportionally fair sub-carrier allocation algorithm
CN107241799A (en) * 2016-03-28 2017-10-10 上海交通大学 The resource allocation of Energy Efficient and interference alignment integrated processes in heterogeneous network

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9042933B2 (en) * 2011-10-14 2015-05-26 Futurewei Technologies, Inc. Inter-cell interference coordination for wireless communication systems

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9414216B1 (en) * 2013-03-15 2016-08-09 Leidos, Inc. Method and system for multiple carrier resource allocation in LTE-advanced networks
CN104935364A (en) * 2014-03-21 2015-09-23 日本电气株式会社 Method of carrying out distributed wave beam forming in heterogeneous network and apparatus thereof
CN107241799A (en) * 2016-03-28 2017-10-10 上海交通大学 The resource allocation of Energy Efficient and interference alignment integrated processes in heterogeneous network
CN106455077A (en) * 2016-09-13 2017-02-22 北京理工大学 Heterogeneous network multicast transmission proportionally fair sub-carrier allocation algorithm

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
"Resource Allocation Scheme for HetNets with CoMP Joint Transmission";Yun Gao等;《2017 17th IEEE International Conference on Communication Technology》;20180517;第1-5页 *
"异构无线网络业务接入多目标优化控制算法";马冬等;《电子学报》;20160215;全文 *

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