CN109769257B - Heterogeneous network robust resource allocation and duration hybrid optimization method based on energy efficiency - Google Patents

Heterogeneous network robust resource allocation and duration hybrid optimization method based on energy efficiency Download PDF

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CN109769257B
CN109769257B CN201910039681.5A CN201910039681A CN109769257B CN 109769257 B CN109769257 B CN 109769257B CN 201910039681 A CN201910039681 A CN 201910039681A CN 109769257 B CN109769257 B CN 109769257B
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femtocell
subcarrier
user
sub
power
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CN109769257A (en
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吴翠先
杨洋
杨蒙
徐勇军
李雯静
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Chongqing Information Technology Designing Co ltd
Chongqing University of Post and Telecommunications
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Chongqing University of Post and Telecommunications
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Abstract

The invention requests to protect a heterogeneous network robust resource allocation and duration hybrid optimization method based on energy efficiency, and belongs to the technical field of resource allocation in a cognitive heterogeneous wireless network. By considering transmission power limitation, cross-layer interference constraint and transmission duration collision probability constraint, firstly, sub-carrier allocation is realized by using a suboptimal allocation scheme, then, a convex optimization problem is obtained by using a Bernstein approximation method, then, optimal transmission time is obtained by using Taylor series expansion, finally, optimal transmission power is obtained by using a Lagrangian function method, and an optimal resource allocation method based on iteration is provided. Simulation results show that the performance of macro users can be well protected while the optimal transmission time is obtained by the method, and the method has strong robustness.

Description

Heterogeneous network robust resource allocation and duration hybrid optimization method based on energy efficiency
Technical Field
The invention belongs to the technical field of resource allocation in a cognitive heterogeneous wireless network, and particularly relates to a robust resource allocation method based on user interrupt probability in the cognitive heterogeneous wireless network.
Background
With the rapid development of communication technology in recent years, various mobile devices and intelligent applications are in a large number, and while the number of the mobile devices and the intelligent applications is increasing, higher requirements on the aspects of the frequency spectrum utilization rate, coverage, capacity and the like of a network are also provided. Therefore, the cognitive heterogeneous network is developed, and by deploying the femtocell with cognitive capability on the premise of not increasing the number of the macrocells, the load of the macrocells can be effectively reduced, and the service quality and the spectrum utilization rate of users can be improved. However, deployment of cognitive femto cells on a large scale may result in significant increase in energy consumption, which becomes increasingly large with rapid development of communication technologies if effective energy efficiency control schemes are not adopted. Therefore, in order to meet the development requirement of green communication, the research on the energy efficiency problem of the cognitive heterogeneous network is very important.
Determining the optimal perception, transmission time and resource allocation strategy in each frame is very critical to the maximization of the energy efficiency of the cognitive heterogeneous network. At present, the research on the optimal transmission time of the cognitive radio network only considers the network scenes of a pair of primary users and secondary users, and the models cannot be suitable for the multi-user and multi-carrier scenes. On the other hand, the traditional heterogeneous network resource allocation algorithm only studies the resource allocation problem under perfect channel state information. However, in an actual communication scenario, perfect channel state information is difficult to obtain accurately due to transmission delay and channel estimation errors. Therefore, the method has more practical significance for researching the resource allocation and transmission duration hybrid optimization problem under the channel uncertainty.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. The heterogeneous network robust resource allocation and duration hybrid optimization method based on energy efficiency is provided, the performance of macro users can be well protected while the optimal transmission duration is obtained, and the method has strong robustness. The technical scheme of the invention is as follows:
a heterogeneous network robust resource allocation and duration hybrid optimization method based on energy efficiency comprises the following steps:
s1: initializing system parameters, wherein the system parameters comprise the number M of macro cellular users, the number F of femto cellular users, the number N of available subcarriers, channel gain and the total circuit power consumption value P of the femto cellular networkc totalMaximum transmitting power value P of femtocell useri maxMaximum transmission power value of subcarrier
Figure BDA0001947109260000021
Cross-layer interference temperature value IthAnd an interruption probability threshold e; initializing subcarrier allocation state sets
Figure BDA0001947109260000022
Figure BDA0001947109260000023
Setting the number of iterations T representing the set of subcarrier allocation states defined hereinmaxPerforming iterative initialization;
s2: acquiring channel information; the sub-carrier allocation is carried out by utilizing a sub-optimal allocation scheme, and a sub-carrier allocation state set is updated;
s3: judging whether all the sub-carriers are allocated to the femtocell user, if so, entering S4; otherwise, returning to S2;
s4: obtaining the optimal transmission time length of the system, the optimal power of the femtocell user and the optimal energy efficiency of the femtocell network, and updating the transmitting power limiting factor lambda of the femtocell useri o(t)、Subcarrier transmit power control factor
Figure BDA0001947109260000024
And macro cell quality of service protection factor lambdaq(t)；
S5: judging whether the sum of the transmitting power of the femtocell user on all subcarriers is less than or equal to the maximum transmitting power; if yes, go to S6; otherwise, go to S7;
s6: obtaining a convex optimization problem by using a Bernstein approximation method, calculating the interference power of all femtocell users to a macro-cell macro base station, and judging whether the interference power is less than or equal to an interference power threshold value; if yes, go to S7; otherwise, go to S8;
s7: judging whether the transmitting power of the subcarrier is less than or equal to the maximum transmitting power of the subcarrier; if yes, go to S8; otherwise, taking the optimal transmitting power as the maximum transmitting power of the sub-carrier and entering the next iteration;
s8: judging whether the current iteration times are larger than the maximum iteration times, if so, ending the process to obtain the optimal transmission time of the system, the optimal transmitting power of the femtocell user and the optimal energy efficiency of the femtocell network; otherwise, the next iteration is entered, returning to S4.
Further, the step S2 allocates subcarriers by using a suboptimal allocation scheme, and updates the subcarrier allocation state set, which specifically includes:
obtaining channel information based on
Figure BDA0001947109260000025
The sub-carriers are distributed, i 'represents the user i' to which the corresponding sub-carrier is distributed, and the auxiliary variable
Figure BDA0001947109260000031
Wherein the content of the first and second substances,
Figure BDA0001947109260000032
is the data transmission time, p, during which no macrocell user is present on the sub-carriers within each framenRepresenting the allocated initial power, C, on each sub-carrieri,nIs from the firstThe equivalent channel gain on the nth sub-carrier for the i femtocell users to the femtocell cognitive access node,
Figure BDA0001947109260000033
representing the probability that the nth subcarrier is idle and that the femto access point makes a correct decision,
Figure BDA0001947109260000034
represents initial transmission time, tau represents cognitive heterogeneous network perception time, Pc totalIs the total circuit power consumption value, N, of the femtocell networktotalIs the total subcarrier number of the OFDM system; after each subcarrier allocation, according to
Figure BDA0001947109260000035
The set of sub-carrier allocation states is updated.
Further, the step S4 of obtaining the optimal transmission duration specifically includes:
local optimal transmission duration of the system is based on
Figure BDA0001947109260000036
Calculation, where α is the idle rate parameter of the macrocell user and w (t-1) is an auxiliary variable, denoted as
Figure BDA0001947109260000037
Global optimum transmission duration based on
Figure BDA0001947109260000038
Calculating, wherein [ x]+＝max{0,x}，
Figure BDA0001947109260000039
Representing the upper limit value of the transmission duration.
Further, in S4, the femtocell user' S optimal transmission power is based on
Figure BDA00019471092600000310
And (c) calculating, wherein,
Figure BDA00019471092600000311
the optimal data transmission time when no macro cell user exists on the sub-carrier in each frame; phi (t-1) is an auxiliary variable and can be expressed as
Figure BDA00019471092600000312
Wherein χ is a Buckbacher method non-negative conversion factor, ωi,nTo approximate the interference link gain after using the bernstein method.
Further, in step S4, the femtocell network is optimally energy efficient
Figure BDA00019471092600000313
Calculation of where ri,n(t) is the SINR of the ith femtocell user on the nth subcarrier, which can be expressed as
Figure BDA0001947109260000041
Wherein h isi,nIs the direct channel gain, p, on subcarrier n from the ith femtocell user to the femtocell access pointw,nIs the transmission power, h, of the w-th macrocell user on subcarrier nw,nIs the direct channel gain, σ, of the w-th macrocell user to femtocell access point on subcarrier n2Representing additive white gaussian noise;
Figure BDA0001947109260000042
representing the optimum transmission power for the femtocell user, T is the total duration of each frame, i.e., T ═ τ + TdWhere τ is the femtocell user perception time, T, within each framedThe time for the femtocell user data transmission is within each frame.
Further, in step S4, the femtocell user transmit power limiting factor λi o(t) subcarrier transmit power control factor
Figure BDA0001947109260000043
And macro cell quality of service protection factor lambdaqThe update expression of (t) is as follows:
Figure BDA0001947109260000044
Figure BDA0001947109260000045
Figure BDA0001947109260000046
wherein, bo、bpAnd bqIs λi o(t)、
Figure BDA0001947109260000047
And λq(t) corresponding update step size; pi maxMaximum power, p, representing the transmission allowed for the ith femtocell usern maxRepresents the maximum power allowed to be transmitted on the nth subcarrier, IthAnd the cross-layer interference temperature threshold of the macro base station receiver is represented.
Further, the step S6 is specifically: by the formula
Figure BDA0001947109260000048
Judging whether the interference power of the femtocell user to the macro base station is less than or equal to an interference power threshold value; wherein, ω isi,nTo approximate the interference link gain after using the Bernstein method, is expressed as
Figure BDA0001947109260000049
Wherein the content of the first and second substances,
Figure BDA00019471092600000410
the representation defines equal, auxiliary variables
Figure BDA00019471092600000411
bi,nAnd ai,nRespectively, interference link gain Gi,nDistribution value upper and lower bounds; e is the interrupt probability threshold(ii) a Auxiliary variable
Figure BDA00019471092600000412
Wherein the auxiliary variable
Figure BDA00019471092600000413
Figure BDA00019471092600000414
And σnIs a bernstein approximation.
The invention has the following advantages and beneficial effects:
the invention considers spectrum sensing uncertainty and channel uncertainty, introduces the constraints of transmitting power, cross-layer interference limitation and transmission time collision probability, and establishes a network model and a mathematical model which accord with the reality for an uplink transmission link. The sub-carrier allocation is realized by using a suboptimal algorithm, then the optimal transmission time is obtained by using Taylor series expansion, finally the convex optimization problem is obtained by using a Bernstein approximation method, and an optimal resource allocation method based on iteration is provided.
The method is different from the traditional heterogeneous network energy efficiency-based resource allocation method, and the traditional resource allocation method is usually only used for optimizing the subcarrier allocation scheme and the power control problem. However, in cognitive heterogeneous networks, the determination of user transmission time in a femtocell network can greatly affect the energy efficiency of the system. In the current research on cognitive radio transmission time, most of the network scenes of single user and single user are considered, and a network model suitable for multiple carriers and multiple users does not exist.
Therefore, the method optimizes the resource allocation problem and the transmission duration by establishing a multi-user and multi-carrier cognitive heterogeneous network model and considering the imperfect channel state information, and provides a heterogeneous network robust resource allocation and duration hybrid optimization method based on energy efficiency. Compared with the prior art, the simulation method has better robustness because the imperfect channel state information is considered. On the other hand, compared with the existing heterogeneous network resource allocation scheme, the method of the patent adds the optimization variable of the introduced time dimension, and can further improve the energy efficiency of the system.
Drawings
FIG. 1 is a system model diagram of the preferred embodiment of the present invention
FIG. 2 is a frame structure diagram of the present invention
FIG. 3 is a flow chart of the present invention
FIG. 4 is a graph illustrating the energy efficiency and transmission time of femtocells for different macrocell users according to the present invention
FIG. 5 is a graph of the outage probability and uncertainty parameters for different resource allocation methods according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
the patent considers a double-layer uplink cognitive heterogeneous network model, M and F are used for respectively representing the number of active users in a macro cellular network and a cognitive femto cellular network, and a user set is respectively defined as
Figure BDA0001947109260000061
And
Figure BDA0001947109260000062
the femtocell user opportunistically uses the macrocell user's spectrum through the cognitively capable femtocell access point, and the macrocell user can use the subcarriers at any time. Suppose an OFDM system has NtotSub-carriers, a free sub-carrier set detected by the femtocell network is defined as
Figure BDA0001947109260000063
Defining the set of sub-carriers occupied by a macrocell user as
Figure BDA0001947109260000064
According to the above definition, the signal to interference plus noise ratio of the ith femtocell user on subcarrier n can be expressed as:
Figure BDA0001947109260000065
wherein p isi,nRepresenting the transmission power, h, of the ith femtocell user on subcarrier ni,nIs the direct channel gain, p, on subcarrier n from the ith femtocell user to the femtocell access pointw,nIs the transmission power, h, of the w-th macrocell user on subcarrier nw,nIs the direct channel gain, σ, of the w-th macrocell user to femtocell access point on subcarrier n2Representing additive white gaussian noise.
There are four different situations where the femtocell access point determines whether a subcarrier is occupied by the macrocell network:
case 1: the mth subcarrier is not occupied by the macro cellular network, the femtocell access point judges that the subcarrier is not occupied, and the probability of the situation is assumed to be
Figure BDA0001947109260000066
Case 2: the mth sub-carrier is occupied by the macro-cellular network, but the femtocell access point judges that the sub-carrier is not occupied, and the probability of the situation is assumed to be
Figure BDA0001947109260000067
Case 3: the mth subcarrier is not occupied by the macro cellular network, but the femtocell access point judges that the subcarrier is occupied, and the probability of the situation is assumed to be
Figure BDA0001947109260000068
Case 4: the mth sub-carrier is occupied by the macro cellular network, the femtocell access point judges that the sub-carrier is not occupied, and the probability of the situation is assumed to be
Figure BDA0001947109260000071
According to the above analysis and the transmission characteristics of OFDM, the cross-layer interference power on the subcarrier n from the ith femtocell user to the macro base station can be expressed as
Figure BDA0001947109260000072
Wherein the content of the first and second substances,
Figure BDA0001947109260000073
representing out-of-band interference, G, from subcarrier n to subcarrier w caused by i femto-usersi,nRepresenting the channel gain of the ith femtocell user to the macro base station on subcarrier n.
The data frame structure is given in fig. 2, where a complete data frame comprises a sensing time of duration τ and a duration TdThe data transmission time of (1). Thus, the total frame length may be expressed as T ═ τ + Td. During the data transmission time, the macro cell user may re-occupy the sub-carriers, which will result in data collision, in which case the data transmission time is divided into two phases: a phase Td1A data transmission time indicating that no macro cell user is present; another phase Td2Is the time of transmission of the collision data that the macrocell user exists, and therefore, there is Td＝Td1+Td2. Suppose that the duration of the idle state and the occupied state of the macro cell user respectively follow the exponential distribution with the idle rate parameter alpha and the occupancy rate parameter beta, there are
Figure BDA0001947109260000074
Since the optimization goal of this patent is to maximize energy efficiency under cognitive heterogeneous networks, we will discuss the throughput and energy consumption of femtocells separately. The total throughput of the femtocell network is as follows:
Figure BDA0001947109260000075
wherein x isi,nE {0,1} is the subcarrier allocation factor, xi,n1 means that the nth subcarrier is allocated to the ith femtocell user in the cognitive femtocell network; otherwise, note xi,n＝0。
The total energy consumption of the cognitive femtocell network is:
Figure BDA0001947109260000076
wherein, Pc totalRepresenting the total circuit power consumption of the femtocell network.
Due to the dynamic characteristics of the wireless channel environment, it is difficult to obtain perfect channel state information in a wireless communication network, and thus there are some estimation errors of system parameters. Assuming that there is some uncertainty in the channel gain due to imperfect channel estimation error and noise uncertainty, the channel gain becomes
Figure BDA0001947109260000081
Wherein the content of the first and second substances,
Figure BDA0001947109260000082
representing the estimated channel gain from the femtocell user to the macro base station, these parameters being fully known to the femtocell network. Δ Gi,nIs the corresponding perturbation term (i.e. the estimation error) which is formed by the intervals
Figure BDA0001947109260000083
The method comprises the steps of defining, wherein,
Figure BDA0001947109260000084
representing the upper bound of the uncertainty field.
To obtain the subcarrier allocation scheme, data transmission time and power allocation scheme of the femtocell user, the following optimization problems can be solved to obtain:
Figure BDA0001947109260000085
wherein, C1Constraining the transmission power, P, of each femtocell useri maxMaximum power allowed to transmit for the ith femtocell user; c2The transmission power on each sub-carrier is limited,
Figure BDA0001947109260000086
maximum power allowed to be transmitted on subcarrier n; c3And C4Ensuring that each subcarrier can only be allocated to one femtocell user; c5Is interruption probability constraint of femtocell network to macro base station interference power to protect service quality of macrocell network under channel uncertainty, wherein ∈ represents interruption probability threshold, IthRepresenting a cross-layer interference temperature threshold of a macro base station receiver; c6And the proportion of collision time in the transmission phase is restricted, and theta represents the upper limit of the collision probability of the data transmission phase.
After the optimization model is determined, the subcarrier allocation problem is discussed first. Let the initial power allocated to each subcarrier be
Figure BDA0001947109260000091
On the other hand, due to the variable TdSubject to condition C6C, thus the initial transmission time needs to be obtained before subcarrier allocation is performed, by using a taylor series expansion, C6Can be converted into
Figure BDA0001947109260000092
Therefore we assume that the initial transmission time is
Figure BDA0001947109260000093
We assume that the nth subcarrier is always allocated to the ith femtocell user with the highest energy efficiency under the same initial transmission power and transmission time. Thus, we can determine x by the following equationi,nThe value of (A) is as follows:
Figure BDA0001947109260000094
wherein the content of the first and second substances,
Figure BDA0001947109260000095
is the time of flight for which the femtocell user has not collided with the macrocell user after the Taylor series expansion is used, which can be expressed as
Figure BDA0001947109260000096
After the subcarrier allocation is completed, the integer constraint in the optimization problem OP1 is removed. On the other hand, in OP1, C5Instead of convex constraint form, by using Bernstein approximation, assume Gi,nIs distributed byi,n,bi,n]A limitation wherein ai,n,bi,nFrom the interval [ -1,1 [)]Constraint, definition
Figure BDA0001947109260000097
And
Figure BDA0001947109260000098
is provided with
Figure BDA0001947109260000099
Wherein the content of the first and second substances,
Figure BDA00019471092600000910
Figure BDA00019471092600000911
satisfy the requirement of
Figure BDA00019471092600000912
And sigman≥0，σnThe value of (d) depends on the given probability distribution cluster.
Brings the inequality (11) back to OP1, while determining the optimal subcarrier allocation scheme according to equation (10), with
Figure BDA0001947109260000101
Wherein the content of the first and second substances,
Figure BDA0001947109260000102
pi′,nrepresenting the actual power, r, allocated to the ith' femtoi′，n＝pi′，nCi′，nRepresenting the actual signal to interference plus noise ratio of the ith' th femtocell user on subcarrier n. Due to the fact that
Figure BDA0001947109260000103
For convenience, we describe hereinafter using the variable instead of i ═ i'.
To solve for OP2, we decompose it into terms on the variable TdAnd pi,nThe other variable is treated as a constant in the process of solving the corresponding subproblem. In respectively obtaining the optimum
Figure BDA0001947109260000104
And
Figure BDA0001947109260000105
and then, a bivariate problem coupling optimal solution can be obtained by using the hybrid optimization method provided by the patent. First, discussion is made regarding the optimal transmission time TdThe sub-problems of (1):
Figure BDA0001947109260000106
with respect to variable TdOP2(a) is a pseudo-concave problem, let
Figure BDA0001947109260000107
To obtain
Figure BDA0001947109260000108
In addition, due to C10Of the local optimal solution TdMust not exceed
Figure BDA0001947109260000109
Based on the above discussion, a globally optimal solution
Figure BDA00019471092600001010
Can be expressed as
Figure BDA0001947109260000111
Wherein, [ x ]]+＝max{0,x}。
The optimal power allocation problem is discussed next:
Figure BDA0001947109260000112
wherein the content of the first and second substances,
Figure BDA0001947109260000113
which represents the optimal overall frame length,
Figure BDA0001947109260000114
represents an optimal data transfer time at which the femto user does not collide with the macro user.
The objective function in OP2(b) is for pi,nSo that the optimum transmission power value can be found by using the Buckbach method, is defined
Figure BDA0001947109260000115
Where χ is a non-negative parameter.
By using the lagrange function method, there are:
Figure BDA0001947109260000116
wherein λ isi o，
Figure BDA0001947109260000117
And λqAre respectively constraint C7，C8And C11Lagrange multiplier.
By using the KKT condition, the optimal p corresponding to χi,nThe values are given by:
Figure BDA0001947109260000118
wherein the auxiliary variable
Figure BDA0001947109260000119
The lagrange multiplier can then be updated by employing a gradient method:
Figure BDA0001947109260000121
Figure BDA0001947109260000122
Figure BDA0001947109260000123
wherein, bo、bpAnd bqThe step length is T, the iteration times are represented by t, and the convergence of the dual algorithm can be ensured by selecting a proper step length.
The application effect of the present invention will be described in detail with reference to the simulation.
1) Simulation conditions
Considering two layers of uplink cognitive heterogeneous networks, the cell radius of the femtocell network and the cell radius of the macrocell network are respectively 30 meters and 500 meters. Assuming that the cognitive heterogeneous network consists of a macro cellular network and a femto cellular network, the number of macro cellular users and the number of femto cellular users are respectively 4 and 2. Suppose Ntotal32, where the set of idle subcarriers is
Figure BDA0001947109260000124
The set of subcarriers occupied by the macro cellular network is
Figure BDA0001947109260000125
False alarm
Figure BDA0001947109260000126
Missing inspection
Figure BDA0001947109260000127
Probability of sub-carrier occupation by MU
Figure BDA0001947109260000128
Respectively obey to [0.05,0.1 ]]，[0.01,0.05]And [0,1 ]]Are uniformly distributed. In the Bernstein approximation, we take
Figure BDA0001947109260000129
While limiting Gi,nThe value range of (A) is in the interval of [0.001,0.002]And (4) the following steps. Other simulation parameters are given in table 1.
TABLE 1 simulation parameter Table
Figure BDA00019471092600001210
Figure BDA0001947109260000131
2) Simulation result
In this embodiment, fig. 4 is a graph showing a relationship between femtocell energy efficiency and transmission time obtained by the method of this embodiment under different numbers of macrocell users. FIG. 5 is a graph showing the relationship between the outage probability and the uncertainty parameter under different resource allocation methods. In fig. 5, the robust resource allocation method in this embodiment is compared with a subcarrier averaging algorithm and a non-robust algorithm, and the interruption probability performance of the method under the condition of perfect spectrum sensing is considered at the same time, so that the proposed robust resource allocation method has the minimum interruption probability. The experimental results of fig. 4 and 5 show that the present invention can effectively control the outage probability while determining the optimal transmission time, protect the service quality of the femtocell user and the macrocell user, and have good robustness.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.

Claims (1)

1. A heterogeneous network robust resource allocation and duration hybrid optimization method based on energy efficiency is characterized by comprising the following steps:
s1: initializing system parameters, wherein the system parameters comprise the number M of macro cellular users, the number F of femto cellular users, the number N of available subcarriers, channel gain and the total circuit power consumption value P of the femto cellular networkc totalMaximum transmitting power value P of femtocell useri maxMaximum transmission power value of subcarrier
Figure FDA0003145326640000012
Cross-layer interference temperature value IthAnd an interruption probability threshold e; initializing subcarrier allocation state sets
Figure FDA0003145326640000013
Figure FDA0003145326640000014
Setting the number of iterations T representing the set of subcarrier allocation states defined hereinmaxPerforming iterative initialization;
s2: acquiring channel information; the sub-carrier allocation is carried out by utilizing a sub-optimal allocation scheme, and a sub-carrier allocation state set is updated;
s3: judging whether all the sub-carriers are allocated to the femtocell user, if so, entering S4; otherwise, returning to S2;
s4: obtaining the optimal transmission time length of the system, the optimal power of the femtocell user and the optimal energy efficiency of the femtocell network, and updating the transmitting power limiting factor of the femtocell user
Figure FDA0003145326640000015
Subcarrier transmit power control factor
Figure FDA0003145326640000016
And macro cell quality of service protection factor lambdaq(t)；
S5: judging whether the sum of the transmitting power of the femtocell user on all subcarriers is less than or equal to the maximum transmitting power; if yes, go to S6; otherwise, go to S7;
s6: obtaining a convex optimization problem by using a Bernstein approximation method, calculating the interference power of all femtocell users to a macro-cell macro base station, and judging whether the interference power is less than or equal to an interference power threshold value; if yes, go to S7; otherwise, go to S8;
s7: judging whether the transmitting power of the subcarrier is less than or equal to the maximum transmitting power of the subcarrier; if yes, go to S8; otherwise, taking the optimal transmitting power as the maximum transmitting power of the sub-carrier and entering the next iteration;
s8: judging whether the current iteration times is greater than the maximum iteration times, if so, ending, otherwise, entering the next iteration, and returning to S4;
in step S2, the sub-carrier allocation is performed by using the sub-optimal allocation scheme, and the updating of the sub-carrier allocation status set specifically includes:
acquisition channelInformation according to
Figure FDA0003145326640000021
The sub-carriers are distributed, i 'represents the user i' to which the corresponding sub-carrier is distributed, and the auxiliary variable
Figure FDA0003145326640000022
Wherein the content of the first and second substances,
Figure FDA0003145326640000023
is the data transmission time, p, during which no macrocell user is present on the sub-carriers within each framenRepresenting the allocated initial power, C, on each sub-carrieri,nIs the equivalent channel gain on the nth sub-carrier from the ith femtocell user to the femtocell cognitive access node,
Figure FDA0003145326640000024
representing the probability that the nth subcarrier is idle and that the femto access point makes a correct decision,
Figure FDA0003145326640000025
represents initial transmission time, tau represents cognitive heterogeneous network perception time, Pc totalIs the total circuit power consumption value, N, of the femtocell networktotalIs the total subcarrier number of the OFDM system; after each subcarrier allocation, according to
Figure DEST_PATH_BDA0001947109260000035
Updating the subcarrier allocation state set;
the step S4 of obtaining the optimal transmission duration specifically includes:
local optimal transmission duration of the system is based on
Figure FDA0003145326640000028
Calculation where α is the idle rate parameter of the macrocell user and w (t-1) is the auxiliary variable, tableShown as
Figure FDA0003145326640000029
Global optimum transmission duration based on
Figure FDA00031453266400000210
Calculating, wherein [ x]+＝max{0,x}，
Figure FDA00031453266400000211
Representing the upper limit value of the transmission time;
at S4, the femtocell user' S optimal transmission power is based on
Figure DEST_PATH_BDA00019471092600000310
And (c) calculating, wherein,
Figure FDA00031453266400000213
the optimal data transmission time when no macro cell user exists on the sub-carrier in each frame; phi (t-1) is an auxiliary variable and can be expressed as
Figure FDA00031453266400000214
Wherein χ is a Buckbacher method non-negative conversion factor, ωi,nTo approximate the interference link gain after using the bernstein method;
in step S4, the femtocell network optimal energy efficiency is determined
Figure FDA00031453266400000215
Calculation of where ri,n(t) is the SINR of the ith femtocell user on the nth subcarrier, which can be expressed as
Figure FDA0003145326640000031
Wherein h isi,nIs the direct channel gain, p, on subcarrier n from the ith femtocell user to the femtocell access pointw,nIs the w-th macrocell user in the sub-carrierTransmission power on wave n, hw,nIs the direct channel gain, σ, of the w-th macrocell user to femtocell access point on subcarrier n2Representing additive white gaussian noise;
Figure FDA0003145326640000032
representing the optimum transmission power for the femtocell user, T is the total duration of each frame, i.e., T ═ τ + TdWhere τ is the femtocell user perception time, T, within each framedA femtocell user data transmission time for each frame;
in step S4, the femtocell user transmit power limiting factor
Figure FDA0003145326640000033
Subcarrier transmit power control factor
Figure FDA0003145326640000034
And macro cell quality of service protection factor lambdaqThe update expression of (t) is as follows:
Figure FDA0003145326640000035
Figure FDA0003145326640000036
Figure FDA0003145326640000037
wherein, bo、bpAnd bqIs composed of
Figure FDA0003145326640000038
And λq(t) corresponding update step size; pi maxMaximum power representing the ith femtocell user allowed to transmit,
Figure FDA0003145326640000039
Represents the maximum power allowed to be transmitted on the nth subcarrier, IthRepresenting a cross-layer interference temperature threshold of a macro base station receiver;
the step S6 specifically includes: by the formula
Figure FDA00031453266400000310
Judging whether the interference power of the femtocell user to the macro base station is less than or equal to an interference power threshold value; wherein, ω isi,nTo approximate the interference link gain after using the Bernstein method, is expressed as
Figure FDA00031453266400000311
Wherein the auxiliary variable
Figure FDA00031453266400000313
bi,nAnd ai,nRespectively, interference link gain Gi,nDistribution value upper and lower bounds; e is an interruption probability threshold; auxiliary variable
Figure FDA00031453266400000314
Wherein the auxiliary variable
Figure FDA00031453266400000315
Figure FDA00031453266400000316
And σnIs a bernstein approximation.
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