CN111194042A - Heterogeneous network robust energy efficiency optimization method based on non-orthogonal multiple access - Google Patents

Heterogeneous network robust energy efficiency optimization method based on non-orthogonal multiple access Download PDF

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CN111194042A
CN111194042A CN202010116426.9A CN202010116426A CN111194042A CN 111194042 A CN111194042 A CN 111194042A CN 202010116426 A CN202010116426 A CN 202010116426A CN 111194042 A CN111194042 A CN 111194042A
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small cell
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energy efficiency
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cell user
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CN111194042B (en
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徐勇军
谢豪
李国权
陈前斌
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Aerospace Xintong Technology Co ltd
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention relates to a heterogeneous network robust energy efficiency optimization method based on non-orthogonal multiple access, and belongs to the technical field of resource allocation in a wireless network. And modeling the resource optimization problem into a mixed integer nonlinear fractional programming problem by considering the interference power constraint of the macro cell user, the resource block allocation constraint, the maximum transmitting power constraint of the small cell base station and the service quality constraint of the small cell user. And (3) considering an ellipsoid bounded channel uncertain model, converting the original problem into an equivalent convex optimization form by using a convex relaxation method, a Buckbach method and a continuous convex approximation method, and obtaining an analytic solution of the small cell user transmitting power by using a Lagrange duality method. Compared with the algorithm under perfect channel state information, the method has better energy efficiency and robustness.

Description

Heterogeneous network robust energy efficiency optimization method based on non-orthogonal multiple access
Technical Field
The invention belongs to the technical field of wireless network resource allocation, and relates to a heterogeneous network robust energy efficiency optimization method based on non-orthogonal multiple access.
Background
With the rapid increase of the number of intelligent terminal devices, the data volume of the network is larger and larger, and therefore, higher requirements are put forward on the aspects of throughput, spectrum efficiency and the like of the wireless network. The heterogeneous network receives wide attention because the throughput and the coverage rate of the system can be improved, and the problem of uneven distribution of cell edge user resources is solved. On the other hand, as spectrum resources become more scarce, non-orthogonal multiple access (NOMA) techniques have been proposed to solve the spectrum resource scarcity problem. NOMA differs from traditional orthogonal multiple access in that it allows multiple users to multiplex spectrum resources at different power levels on the same frequency band. Therefore, the related research based on the non-orthogonal multiple access heterogeneous network has important significance.
With diversification and intellectualization of communication terminal services, energy consumption of mobile equipment is more and more serious. In order to reduce carbon dioxide emission and realize green communication, on the premise of ensuring a certain data rate, how to reduce the power consumption of a circuit is a significant problem. Therefore, the research of maximizing energy efficiency based on heterogeneous NOMA networks is critical. At present, the research on the problem is mainly discussed under perfect channel state information, and due to the influence of estimation error, feedback delay and quantization error of an actual physical channel, the actual channel state information is difficult to obtain. Therefore, it is of great significance to study the resource allocation problem under imperfect state information.
Disclosure of Invention
In view of this, the present invention provides a heterogeneous network robust energy efficiency optimization method based on non-orthogonal multiple access, which considers macro cell user interference power constraint, small cell base station maximum transmission power constraint, resource block allocation constraint and small cell user minimum data rate constraint, and establishes a network model and a system model for a heterogeneous NOMA network with energy efficiency maximization as an optimization target. And (3) considering an ellipsoid bounded channel uncertainty model, converting the original non-convex problem into an equivalent convex optimization problem by using a convex relaxation method, a Buckbach method and a continuous convex approximation method, and obtaining the optimal transmitting power and resource block allocation factor of the small cell user by using a Lagrange dual method.
In order to achieve the purpose, the invention provides the following technical scheme:
a heterogeneous network robust energy efficiency optimization method based on non-orthogonal multiple access comprises the following steps:
s1: initialChanging system parameters, and setting iteration times LmaxPerforming iterative initialization;
s2: calculating small cell user transmitting power
Figure BDA0002391632510000021
S3: calculating resource block allocation factors
Figure BDA0002391632510000022
S4: calculating the total energy efficiency t of all small cells;
s5: updating a Lagrange multiplier comprising a small cell base station maximum transmit power constraint multiplier lambdan(l) Macrocell user interference power constraint multiplier phim(l) Minimum data rate constraint multiplier for small cell users
Figure BDA0002391632510000023
And resource block allocation constraint multiplier
Figure BDA0002391632510000024
S6: judging whether the transmitting power of the small cell base station is less than or equal to a maximum transmitting power threshold value; if yes, go to S7; otherwise, go to S9;
s7: calculating the interference power of the small cell base station to the macro cell user, and judging whether the value is less than or equal to the interference power threshold of the macro cell user; if yes, go to S8; otherwise, go to S9;
s8: calculating the data rate of the small cell user, and judging whether the value is greater than or equal to the minimum data rate threshold of the small cell user; if yes, go to S9; otherwise, the next iteration is entered, returning to S2.
S9: judging whether the current iteration times are larger than the maximum iteration times or not; if yes, ending, and outputting the optimal transmitting power and the resource block allocation factor of the small cell user; otherwise, the next iteration is entered, returning to S2.
Further, in S1, the system parameters include a resource block number K, a macrocell user number M, and a time period for transmitting the system parameters,Small cell number N, small cell user number U, background noise delta2Small cell base station to small cell user channel gain
Figure BDA0002391632510000025
Channel gain from macrocell subscriber transmitter to microcell subscriber
Figure BDA0002391632510000026
Interference channel gain from small cell base station to macro cell user
Figure BDA0002391632510000027
Circuit power consumption P of all small cell networkscInterference power threshold of small cell base station to macro cell user
Figure BDA0002391632510000028
Minimum data rate threshold for small cell users
Figure BDA0002391632510000029
Maximum transmitting power of small cell base station
Figure BDA00023916325100000210
Energy efficiency t, upper bound epsilon of estimation error of interference channel gain from small cell base station to macro cell usermUpper bound of channel gain error from small cell base station to small cell user
Figure BDA00023916325100000211
Channel gain error upper bound from macrocell user transmitter to small cell user
Figure BDA00023916325100000216
Further, in S2, the small cell user transmission power is based on
Figure BDA00023916325100000212
Calculation of where [ x]+＝max{0,x}；
Figure BDA00023916325100000213
Is a coefficient in a continuous convex approximation method and is expressed as
Figure BDA00023916325100000214
Wherein
Figure BDA00023916325100000215
Representing the signal-to-interference-and-noise ratio of the previous iteration of the ith small cell user passing through resource block k in the nth small cell.
Further, in S3, the resource block allocation factor is determined according to
Figure BDA0002391632510000031
Calculation, indicating that the k-th resource block is always allocated
Figure BDA0002391632510000032
The ith small cell user in the largest nth small cell base station, wherein
Figure BDA0002391632510000033
Wherein the content of the first and second substances,
Figure BDA0002391632510000034
representing the data rate after the convex conversion.
Further, in S4, the total energy efficiency of all the small cells is based on
Figure BDA0002391632510000035
Is calculated, wherein
Figure BDA0002391632510000036
Representing the actual transmit power of the small cell user.
Further, in S5, the small cell base station maximum transmission power constraint multiplier λn(l) Macrocell user interference power constraint multiplier phim(l) Small cell user minimum rate constraint multiplier
Figure BDA0002391632510000037
And resource block allocation conventionBundle multiplier
Figure BDA0002391632510000038
The update expression of (a) is as follows:
Figure BDA0002391632510000039
Figure BDA00023916325100000310
Figure BDA00023916325100000311
Figure BDA00023916325100000312
where l represents the number of iterations, d1(l)、d2(l)、d3(l) And d4(l) Respectively represents lambdan(l)、φm(l)、
Figure BDA00023916325100000313
And
Figure BDA00023916325100000314
step size of (2).
The invention has the beneficial effects that: compared with the algorithm under perfect channel state information, the scheme of the invention has better energy efficiency and robustness, and improves the robustness and throughput of the heterogeneous wireless network.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a diagram of a system model of the present invention;
FIG. 2 is a flow chart of the algorithm of the present invention;
FIG. 3 is a power convergence diagram of the algorithm of the present invention;
fig. 4 is a graph of uncertainty versus small cell user data rate for different resource allocation schemes.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
As shown in fig. 1, the present invention contemplates a multi-cellular, multi-user heterogeneous NOMA network. M macro cell users perform data transmission with a macro base station in an uplink transmission mode, a small cell base station transmits data to small cell users in a downlink transmission mode, a macro cell network and N small cell networks exist in the networks, and each small cell serves U small cell users, wherein the macro cell users and the small cell users are connected in series, and the macro cell network and the small cell network are connected in series, so that
Figure BDA0002391632510000041
And define the macro cell user set as
Figure BDA0002391632510000042
Assuming that each resource block is a unit bandwidth, there are K resource blocks, and the resource block set is defined as
Figure BDA0002391632510000043
Without loss of generality, assume a channel gain of
Figure BDA0002391632510000044
Our goal is to maximize the energy efficiency of all small cell users under QoS constraints of small cell users, resource block allocation constraints, maximum transmit power constraints of small cell base stations, and cross-layer interference constraints of macro cell users, so under perfect channel state information, this optimization problem can be represented by jointly optimizing transmit power and resource block allocation factor:
P1:
Figure BDA0002391632510000051
Figure BDA0002391632510000052
C2:
Figure BDA0002391632510000053
C3:
Figure BDA0002391632510000054
C4:
Figure BDA0002391632510000055
wherein the content of the first and second substances,
Figure BDA0002391632510000056
is the transmission rate of the ith small cell user in the nth small cell through the kth resource block,
Figure BDA0002391632510000057
represents the signal-to-interference-and-noise ratio of the ith small cell user in the nth small cell through the kth resource block,
Figure BDA0002391632510000058
indicating intra-cell interference from other small cell users,
Figure BDA0002391632510000059
representing cross-layer interference from the macrocell user transmitter,
Figure BDA00023916325100000510
representing the transmission power, delta, of a macrocell user2Representing additive zero-mean gaussian white noise,
Figure BDA00023916325100000511
is the corresponding small cell user minimum data rate threshold. PcAnd
Figure BDA00023916325100000512
respectively representing the circuit power consumption of the whole small cell network and the maximum transmission power threshold of the nth small cell base station.
Figure BDA00023916325100000513
Representing mth macrocell userAn interference power threshold.
Figure BDA00023916325100000514
Representing the channel gain from the small cell base station to the small cell user,
Figure BDA00023916325100000515
representing the channel gain of a macrocell user transmitter to a small cell user,
Figure BDA00023916325100000516
representing the interfering channel gain from the small cell base station to the macro cell user. C1 is resource block allocation factor constraint, ensuring that each resource block is allocated to only one small cell, C2 represents the maximum transmit power constraint of the nth small cell base station, C3 and C4 ensure the QoS of each user, the former represents the cross-layer interference constraint of all small cell base stations to the mth macro cell user, and the latter represents the minimum data rate constraint of the ith small cell user in the nth small cell.
Since the constraint C1, the optimization problem P1 is a mixed integer programming problem, the convex relaxation method is used to separate the discrete variables
Figure BDA00023916325100000517
The relaxation is in a range of [0,1 ]]A continuous real variable is introduced into a new variable
Figure BDA00023916325100000518
The optimization problem P1 may be restated as:
P2:
Figure BDA0002391632510000061
Figure BDA0002391632510000062
Figure BDA0002391632510000063
Figure BDA0002391632510000064
C4,
to overcome the effects of uncertainty, the uncertainty of the channel is taken into account in the optimization problem P2. According to the robust optimization theory, an ellipsoid bounded channel error is considered, and channel gain is modeled as follows:
Figure BDA0002391632510000065
Figure BDA0002391632510000066
Figure BDA0002391632510000067
wherein
Figure BDA0002391632510000068
And
Figure BDA0002391632510000069
respectively, representing corresponding sets of channel parameter uncertainties.
Figure BDA00023916325100000610
And
Figure BDA00023916325100000611
respectively representing the corresponding channel estimate and estimation error. Thus, based on the worst criteria principle, the macrocell user interference power constraint can be rewritten as:
Figure BDA00023916325100000612
in order to guarantee the basic QoS requirements of each small cell user, uncertain parameters in the minimum transmission rate constraints of the small cell users are also considered. Thus, based on the worst criteria principle, the small cell user minimum data rate constraint can be rewritten as:
Figure BDA00023916325100000613
wherein:
Figure BDA00023916325100000614
wherein
Figure BDA00023916325100000615
Indicating the determined interference power.
Thus, the optimization problem P2 can be restated as:
P3:
Figure BDA00023916325100000711
Figure BDA0002391632510000071
Figure BDA0002391632510000072
Figure BDA0002391632510000073
since the optimization problem P3 is a non-linear fractional programming problem. Therefore, it can be solved by the Buckbach method.
Define the dickelbach function as:
Figure BDA0002391632510000074
wherein t ≧ 0 represents energy efficiency. Thus, the optimization problem P3 can be restated as:
P4:
Figure BDA0002391632510000075
Figure BDA0002391632510000076
to solve this problem, the optimization problem P4 is transformed into a convex optimization problem using a continuous convex approximation method, and the lower bound iteration is used to obtain the optimal solution. The data rate can therefore be approximated as:
Figure BDA0002391632510000077
wherein
Figure BDA0002391632510000078
Figure BDA0002391632510000079
Wherein the content of the first and second substances,
Figure BDA00023916325100000710
representing the signal-to-interference-and-noise ratio of the previous iteration of the ith small cell user passing through resource block k in the nth small cell. Thus, the following equivalent convex optimization problem can be obtained:
P5:
Figure BDA0002391632510000081
Figure BDA0002391632510000082
Figure BDA0002391632510000083
by using the lagrangian function, there are:
Figure BDA0002391632510000084
wherein λ isn≥0，φm≥0，
Figure BDA0002391632510000085
Is the lagrangian multiplier corresponding to the constraint condition of the optimization problem P5.
Using the KKT condition, the optimal allocated power is solved as:
Figure BDA0002391632510000086
wherein, [ x ]]+Max {0, x }. To obtain the resource block allocation, the lagrangian function needs to be partially differentiated. Therefore, the k-th resource block is always allocated to
Figure BDA0002391632510000087
The ith small cell user in the largest nth small cell base station also has:
Figure BDA0002391632510000088
wherein
Figure BDA0002391632510000089
The lagrange multiplier may then be updated using a secondary gradient method:
Figure BDA00023916325100000810
Figure BDA00023916325100000811
Figure BDA00023916325100000812
Figure BDA00023916325100000813
wherein l represents the number of iterations, d1，d2，d3，d4Representing the step size. By selecting a proper step length, the convergence of the Lagrangian algorithm can be ensured. The small cell iterative energy-efficient resource allocation algorithm is shown in fig. 2.
The application effect of the present invention will be described in detail with reference to the simulation.
1) Simulation conditions
Suppose that there is one macro cellular network, two macro cellular users, and two small cellular networks, each of which contains two small cellular users. The radii of the macro-cells and each small cell are 500 meters and 20 meters, respectively, and the minimum distance between different small cells is 40 meters. The channel fading model includes rayleigh fading, shadow fading, and path loss, where the path loss index is 3. Other simulation parameters are given in table 1:
TABLE 1 simulation parameter Table
Figure BDA0002391632510000091
2) Simulation result
In the present embodiment, fig. 3 shows a power convergence diagram of the iterative algorithm of the present embodiment. Fig. 4 shows a graph of uncertainty versus small cell user rate under different resource allocation methods. Fig. 3 shows that the algorithm of the present invention can achieve convergence quickly, which means that the algorithm of the present invention can well guarantee the communication quality of the small cell users and has real-time performance. Fig. 4 shows that as the channel uncertainty ah increases, the data rate of the small cell user increases; as Δ g increases, the data rate decreases. On the other hand, when the channel uncertainty reaches a certain value, the data rate of the non-robust algorithm is lower than the minimum data rate, but the algorithm of the invention can be well controlled above the minimum data rate. The experimental results of fig. 3 and fig. 4 show that the algorithm of the present invention guarantees the real-time performance, and simultaneously guarantees the service quality of the small cell users, and has good robustness.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (6)

1. A heterogeneous network robust energy efficiency optimization method based on non-orthogonal multiple access is characterized in that: the method comprises the following steps:
s1: initializing system parameters and setting iteration times LmaxPerforming iterative initialization;
s2: calculating small cell user transmitting power
Figure FDA0002391632500000011
S3: calculating resource block allocation factors
Figure FDA0002391632500000012
S4: calculating the total energy efficiency t of all small cells;
s5: updating a Lagrange multiplier comprising a small cell base station maximum transmit power constraint multiplier lambdan(l) Macrocell user interference power constraint multiplier phim(l) Minimum data rate constraint multiplier for small cell users
Figure FDA0002391632500000013
And resource block allocation constraint multiplier
Figure FDA0002391632500000014
S6: judging whether the transmitting power of the small cell base station is less than or equal to a maximum transmitting power threshold value; if yes, go to S7; otherwise, go to S9;
s7: calculating the interference power of the small cell base station to the macro cell user, and judging whether the value is less than or equal to the interference power threshold of the macro cell user; if yes, go to S8; otherwise, go to S9;
s8: calculating the data rate of the small cell user, and judging whether the value is greater than or equal to the minimum data rate threshold of the small cell user; if yes, go to S9; otherwise, entering the next iteration and returning to S2;
s9: judging whether the current iteration times are larger than the maximum iteration times or not; if yes, ending, and outputting the optimal transmitting power and the resource block allocation factor of the small cell user; otherwise, the next iteration is entered, returning to S2.
2. The method for optimizing the robust energy efficiency of the heterogeneous network based on the non-orthogonal multiple access according to claim 1, wherein: in S1, the system parameters include the number of resource blocks K, the number of macro users M, the number of small cells N, the number of small cells U, and the background noise δ2Small cell base station to small cell user channel gain
Figure FDA0002391632500000015
Channel gain from macrocell subscriber transmitter to microcell subscriber
Figure FDA0002391632500000016
Interference channel gain from small cell base station to macro cell user
Figure FDA0002391632500000017
Circuit power consumption P of all small cell networkscInterference power threshold of small cell base station to macro cell user
Figure FDA0002391632500000018
Minimum data rate threshold for small cell users
Figure FDA0002391632500000019
Small cell base stationMaximum transmission power
Figure FDA00023916325000000110
Energy efficiency t, upper bound epsilon of estimation error of interference channel gain from small cell base station to macro cell usermUpper bound of channel gain error from small cell base station to small cell user
Figure FDA00023916325000000111
Channel gain error upper bound from macrocell user transmitter to small cell user
Figure FDA00023916325000000112
3. The method for optimizing the robust energy efficiency of the heterogeneous network based on the non-orthogonal multiple access according to claim 1, wherein: at S2, the small cell user transmission power is based on
Figure FDA0002391632500000021
Calculation of where [ x]+＝max{0,x}；
Figure FDA0002391632500000022
Is a coefficient in a continuous convex approximation method and is expressed as
Figure FDA0002391632500000023
Wherein
Figure FDA0002391632500000024
Representing the signal-to-interference-and-noise ratio of the previous iteration of the ith small cell user passing through resource block k in the nth small cell.
4. The method for optimizing the robust energy efficiency of the heterogeneous network based on the non-orthogonal multiple access according to claim 1, wherein: at S3, the resource block allocation factor is based on
Figure FDA0002391632500000025
Calculation, indicating that the k-th resource block is always allocated
Figure FDA0002391632500000026
The ith small cell user in the largest nth small cell base station, wherein
Figure FDA0002391632500000027
Wherein the content of the first and second substances,
Figure FDA0002391632500000028
representing the data rate after the convex conversion.
5. The method for optimizing the robust energy efficiency of the heterogeneous network based on the non-orthogonal multiple access according to claim 1, wherein: in S4, the total energy efficiency of all the small cells is based on
Figure FDA0002391632500000029
Is calculated, wherein
Figure FDA00023916325000000210
Representing the actual transmit power of the small cell user.
6. The method for optimizing the robust energy efficiency of the heterogeneous network based on the non-orthogonal multiple access according to claim 1, wherein: in S5, the constraint multiplier lambda of the maximum transmitting power of the small cell base stationn(l) Macrocell user interference power constraint multiplier phim(l) Small cell user minimum rate constraint multiplier
Figure FDA00023916325000000211
And resource block allocation constraint multiplier
Figure FDA00023916325000000212
The update expression of (a) is as follows:
Figure FDA00023916325000000213
Figure FDA00023916325000000214
Figure FDA00023916325000000215
Figure FDA00023916325000000216
where l represents the number of iterations, d1(l)、d2(l)、d3(l) And d4(l) Respectively represents lambdan(l)、φm(l)、
Figure FDA00023916325000000217
And
Figure FDA00023916325000000218
step size of (2).
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