CN111314894B - NOMA (non-oriented access memory) and energy-carrying D2D fusion network-oriented robust resource allocation method - Google Patents

NOMA (non-oriented access memory) and energy-carrying D2D fusion network-oriented robust resource allocation method Download PDF

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CN111314894B
CN111314894B CN202010130686.1A CN202010130686A CN111314894B CN 111314894 B CN111314894 B CN 111314894B CN 202010130686 A CN202010130686 A CN 202010130686A CN 111314894 B CN111314894 B CN 111314894B
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CN111314894A (en
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徐勇军
刘子腱
李国权
陈前斌
刘期烈
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Aerospace Xintong Technology Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04J—MULTIPLEX COMMUNICATION
    • H04J11/00—Orthogonal multiplex systems, e.g. using WALSH codes
    • H04J11/0023—Interference mitigation or co-ordination
    • H04J11/0026—Interference mitigation or co-ordination of multi-user interference
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/53—Allocation or scheduling criteria for wireless resources based on regulatory allocation policies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
    • H04W72/542—Allocation or scheduling criteria for wireless resources based on quality criteria using measured or perceived quality
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
    • H04W72/543—Allocation or scheduling criteria for wireless resources based on quality criteria based on requested quality, e.g. QoS
    • 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 NOMA (non-orthogonal multiple access) and energy-carrying D2D fusion network-oriented robust resource allocation method, belonging to the technical field of communication. The method comprises the following steps: and constructing a NOMA-oriented and energy-carrying D2D fusion network. The cellular users adopt downlink NOMA transmission to realize that a plurality of users multiplex the same resource block; the D2D user is provided with an energy collecting circuit and adopts a substrate type spectrum access mode. And (4) establishing a resource allocation optimization problem with the total energy efficiency of the system as the maximum objective function by considering a random channel uncertainty model. And the power split ratio, the resource block allocation factor and the transmitting power are jointly optimized, so that the communication quality of a cellular user is ensured, and the energy efficiency of the system is maximized. Experimental results show that the method has high system energy efficiency and good robustness.

Description

NOMA (non-oriented access memory) and energy-carrying D2D fusion network-oriented robust resource allocation method
Technical Field
The invention belongs to the technical field of communication, and relates to a NOMA (non-oriented access MA) and energy-carrying D2D fusion network-oriented robust resource allocation method.
Background
With the rapid development of communication technology, available spectrum resources are less and less, the load of energy consumption is increased by the access of massive terminal equipment, and a base station also has a serious overload problem when meeting the rapidly-increasing network capacity requirement. Therefore, how to improve the spectrum utilization and energy efficiency and reduce the base station load becomes an important development direction of the 5G communication technology. In order to solve these problems, Non-Orthogonal Multiple Access (NOMA) and Wireless portable (SWIPT) technologies have come into use, on one hand, the Non-Orthogonal Multiple Access technology can allow Multiple users to share the same frequency and time resources through multiplexing of a Power domain, so that the frequency spectrum utilization rate and the number of the Access users are increased, and on the other hand, the Wireless portable technology can charge a mobile device by using energy carried by radio frequency signals, so that parallel transmission of data and energy is realized, the energy efficiency is increased, and the service life of a device battery is prolonged. In addition, the D2D (Device to Device) technology can realize direct data transmission between two peer user nodes, that is, point-to-point transmission, thereby saving spectrum resources and reducing the load of the base station.
Most of the existing research only considers the resource allocation problem of the NOMA-based D2D communication network under perfect channel state information. However, in an actual wireless communication network, it is difficult to acquire perfect channel state information due to system delay and quantization error. When the channel estimation error is large, it is difficult to ensure the service quality of the user, and further research is needed.
Disclosure of Invention
In view of this, the present invention provides a robust resource allocation method for a NOMA and energy-carrying D2D converged network, which ensures the service quality of cellular users and maximizes the energy efficiency of D2D users.
In order to achieve the purpose, the invention provides the following technical scheme:
a NOMA and energy-carrying D2D fusion network-oriented robust resource allocation method comprises the following steps:
in a NOMA-based and energy-carrying D2D converged network, the NOMA-based and energy-carrying D2D converged network comprises a macro base station, M cellular users and N pairs of D2D users, wherein the base station carries out data transmission with the cellular users through K resource blocks, the D2D users are provided with an energy collection circuit, and the cellular users adopt NOMA technology;
considering the downlink transmission scenario, multiple cellular users multiplex the same resource block, assuming that the channel gain is satisfied
Figure BDA0002395690770000021
The serial interference elimination is utilized, the interference is eliminated in sequence according to the increasing sequence of the channel gain, the co-channel interference is reduced, and a D2D user adopts substrate access to cause interference to users occupying the same resource block; the data rate for the ith cellular user is as follows:
Figure BDA0002395690770000022
wherein the content of the first and second substances,
Figure BDA0002395690770000023
transmitting power, M, allocated to cellular user i by base station through resource block kkIs the number of cellular users on resource block k,
Figure BDA0002395690770000024
for the channel gain on resource block k for base station to cell user i,
Figure BDA0002395690770000025
channel gain on resource block k for D2D user n to cellular user i;
Figure BDA0002395690770000026
allocating a factor to resource blocks of D2D users when
Figure BDA0002395690770000027
Indicating that D2D user n occupies resource block k, otherwise
Figure BDA0002395690770000028
For the transmit power, σ, of D2D user n on resource block k2Is the background noise power of the receiver;
to perform successive interference cancellation, the interference of user j with poor channel gain conditions is cancelled,
Figure BDA0002395690770000029
the signal to interference plus noise ratio of user i to decode user j signal is described as:
Figure BDA00023956907700000210
wherein the content of the first and second substances,
Figure BDA00023956907700000211
allocating the transmitting power of a cellular user j to a base station through a resource block k;
when the user j decodes the signal, the signal-to-interference-and-noise ratio is:
Figure BDA00023956907700000212
suppose that the k-th resource block is multiplexed by the D2D user n and the cellular user, and the signal-to-interference-and-noise ratio is:
Figure BDA00023956907700000213
wherein the content of the first and second substances,
Figure BDA00023956907700000214
is the proportionality coefficient of the information signal in the signal power,
Figure BDA00023956907700000215
the channel gain for D2D user n on resource block k,
Figure BDA00023956907700000216
for the channel gain on resource block k for base station to D2D user n,
Figure BDA00023956907700000217
channel gain on resource block k for D2D user n to cellular user i;
considering wireless energy carrying technology, the D2D receiver can collect energy from the surrounding radio frequency environment, and the energy collected by the nth D2D user is represented as:
Figure BDA0002395690770000031
wherein the content of the first and second substances,
Figure BDA0002395690770000032
is the proportionality coefficient of the energy signal in the signal power, theta is the energy collection efficiency coefficient;
the energy efficiency of each D2D user was:
Figure BDA0002395690770000033
optionally, in the NOMA-oriented and energy-carrying D2D-oriented fusion network, channel gain in an imperfect channel state is considered, and channel uncertainty is modeled as an additive model:
Figure BDA0002395690770000034
wherein the content of the first and second substances,
Figure BDA0002395690770000035
in order to achieve the gain of the rayleigh fading channel,
Figure BDA0002395690770000036
and
Figure BDA0002395690770000037
for the purpose of the channel gain estimation,
Figure BDA0002395690770000038
and
Figure BDA0002395690770000039
an estimation error that is a channel gain;
the channel gain is modeled as:
Figure BDA00023956907700000310
wherein L ═ d-vIs the path loss coefficient; d is the distance of the channel and v is the path loss exponent;
when the actual achievable data rate is less than the expected data rate, a communication disruption will be generated; the estimated data rate and the achievable maximum data rate for the D2D user are:
Figure BDA00023956907700000311
Figure BDA00023956907700000312
wherein the content of the first and second substances,
Figure BDA00023956907700000313
estimated signal-to-interference-and-noise ratios and the maximum achievable signal-to-interference-and-noise ratio are respectively;
the average interrupt rate sum for the D2D user is expressed as:
Figure BDA00023956907700000314
wherein Pr [ ] represents the probability that the D2D user estimates that the data rate is less than or equal to the maximum achievable data rate;
establishing an energy efficiency maximum optimization problem containing the interruption probability:
Figure BDA0002395690770000041
wherein the content of the first and second substances,
Figure BDA0002395690770000042
is the minimum data rate of cellular user i, tau is the outage probability threshold, PmaxIs the maximum transmit power of the base station,
Figure BDA0002395690770000043
the maximum transmit power for D2D user n.
Optionally, in the energy efficiency maximum optimization problem, the probabilistic constraint problem is converted into a non-probabilistic problem, and the data rate of the D2D user n is obtained as follows:
Figure BDA0002395690770000044
wherein the content of the first and second substances,
Figure BDA0002395690770000045
in order to be a coefficient of the path loss,
Figure BDA0002395690770000046
is Rayleigh fading channel gain, and
Figure BDA0002395690770000047
is a non-central chi-square distribution with a degree of freedom of 2,
Figure BDA0002395690770000048
as an inverse cumulative distribution function of the chi-squared distribution,
Figure BDA0002395690770000049
the average interrupt rate sum is expressed as:
Figure BDA00023956907700000410
the energy efficiency optimization problem is converted into:
Figure BDA0002395690770000051
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002395690770000052
the energy collected for the nth D2D receiver under consideration of channel parameter uncertainty.
Optionally, in the energy efficiency optimization problem, a fractional objective function is converted into a subtraction form by using a Dinkelbach method, an intermediate variable η is introduced, and the objective function is equivalent to:
Figure BDA0002395690770000053
due to the objective function with the transmitted power of the cellular user
Figure BDA0002395690770000054
Is monotonically decreased, in order to ensure the service quality of the cellular users and maximize the energy efficiency, the optimal transmission power of the cellular users satisfies:
Figure BDA0002395690770000055
simplifying constraint conditions to obtain the problem of maximum energy efficiency optimization:
Figure BDA0002395690770000056
wherein the content of the first and second substances,
Figure BDA0002395690770000057
and
Figure BDA0002395690770000058
is an intermediate variable;
optimization questionsQuestions contain integer variables
Figure BDA0002395690770000059
Will be provided with
Figure BDA00023956907700000510
Relaxation is in the interval [0,1]Above continuous variable, define
Figure BDA00023956907700000511
And performing variable substitution on the coupling variable to define
Figure BDA0002395690770000061
Obtaining:
Figure BDA0002395690770000062
the problem constraint is linear, the objective function is a concave function, and the problem constraint is a typical convex optimization problem.
Optionally, for the convex optimization problem, an analytic solution is obtained by using a lagrangian dual theory, and an optimized variable is iteratively updated by a gradient descent method to obtain an optimal resource allocation strategy;
(a) by establishing a Lagrangian function, and
Figure BDA0002395690770000063
and (3) calculating a partial derivative to obtain:
Figure BDA0002395690770000064
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002395690770000065
is a lagrange multiplier;
(b) will be provided with
Figure BDA0002395690770000066
Substituting Lagrangian function into
Figure BDA0002395690770000067
Updating:
Figure BDA0002395690770000068
wherein the content of the first and second substances,
Figure BDA0002395690770000069
χk，
Figure BDA00023956907700000610
psi is the Lagrange multiplier, epsilonxAn iteration step size not less than zero;
(c) solving for optimal resource block allocation factor
Figure BDA00023956907700000611
Namely, it is
Figure BDA00023956907700000612
Wherein the content of the first and second substances,
Figure BDA00023956907700000613
(d) to obtain
Figure BDA0002395690770000071
And
Figure BDA0002395690770000072
then, according to
Figure BDA0002395690770000073
And
Figure BDA0002395690770000074
to obtain
Figure BDA0002395690770000075
(e) When in useAfter the iteration obtains the convergence value, updating
Figure BDA0002395690770000076
Returning to the step (a) until the following conditions are met:
Figure BDA0002395690770000077
and T < T
At this time, an optimal allocation strategy of resource blocks, power and signal power split ratios is obtained.
The invention has the beneficial effects that: the invention establishes a NOMA-based wireless energy-carrying D2D network robust energy efficiency resource allocation model. The method is characterized in that the energy efficiency of a D2D user is maximized, the communication quality of a cellular user is guaranteed, meanwhile, the influence of channel uncertainty is considered, a robust resource allocation model based on the interrupt probability is established, the probability constraint problem is converted into a non-probability problem by using a Markov inequality and a relaxation method, the original NP-hard problem is converted into a deterministic convex optimization problem based on a Dinkelbach and variable replacement method, and the analytic solution is obtained by using a Lagrangian duality theory.
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 a better understanding of the objects, aspects and advantages of the present invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a system model of an embodiment of the present invention;
FIG. 2 is a flow chart of the solution of the present invention;
FIG. 3 is a graph of total energy efficiency of the system versus the number of D2D users under different algorithms;
fig. 4 is a graph of the equation for the probability of outage for D2D user versus channel estimation error under different algorithms.
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 the terms "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 intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, 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 described above will be understood by those skilled in the art according to the specific circumstances.
As shown in fig. 1 to fig. 2, the present invention provides a robust resource allocation method for a NOMA and energy-carrying D2D converged network, including:
step 1: a NOMA-based wireless energy-carrying D2D communication system is constructed, wherein a D2D user adopts substrate access, and each D2D receiver is provided with an energy collecting circuit which can collect energy from the surrounding radio frequency environment. The cellular users adopt non-orthogonal multiple access, a plurality of cellular users multiplex the same resource block, and utilize the serial interference elimination technology to eliminate the interference in sequence according to the ascending order of the channel gain. To improve the energy efficiency of D2D users, considering the quality of service of cellular users, the maximum transmit power of the base station and the uncertainty of channel parameters, the optimization problem is established as follows:
Figure BDA0002395690770000081
and the maximum energy efficiency of the system is obtained by jointly optimizing the transmission power of the cellular user, the transmission power of the D2D user, the resource block allocation factor and the signal power split ratio.
And 2, step: in the above optimization problem, C3The outage probability constraint for the D2D user, the outage probability threshold is τ, which will result when the maximum data rate that can actually be achieved is less than the expected data rate. For the convenience of solving, the probability constraint in the optimization problem needs to be converted into a non-probability constraint.
The estimated data rate and the achievable maximum data rate for the D2D user are:
Figure BDA0002395690770000091
Figure BDA0002395690770000092
wherein the content of the first and second substances,
Figure BDA0002395690770000093
respectively the estimated and the maximally attainable signal to interference and noise ratios,
Figure BDA0002395690770000094
are auxiliary variables, respectively expressed as:
Figure BDA0002395690770000095
Figure BDA0002395690770000096
Figure BDA0002395690770000097
Figure BDA0002395690770000098
wherein the content of the first and second substances,
Figure BDA0002395690770000099
is an estimate of the gain of the channel,
Figure BDA00023956907700000910
is the actual channel gain
For C3It can be converted into:
Figure BDA00023956907700000911
Figure BDA00023956907700000912
using the chi-square distribution and the markov inequality to obtain:
Figure BDA00023956907700000913
Figure BDA00023956907700000914
from the above derivation, the sum of the data rate of D2D user n and the average outage rate of the system can be expressed as:
Figure BDA00023956907700000915
Figure BDA00023956907700000916
and substituting the average interrupt rate into the maximum energy efficiency optimization problem to obtain a new optimization problem without probability constraint conditions:
Figure BDA0002395690770000101
and step 3: the optimization problem is a mixed integer type fractional programming problem, a series of conversion and simplification are considered to be carried out on the optimization problem, and a fractional objective function is converted into an equivalent subtractive form according to a Dinkelbach method, and the equation is expressed as follows:
Figure BDA0002395690770000102
constraint on decoding C1The method is simplified to obtain:
Figure BDA0002395690770000103
due to the fact that
Figure BDA0002395690770000104
When in use
Figure BDA0002395690770000105
When the constraint is satisfied, the constraint condition is written as:
Figure BDA0002395690770000106
wherein the content of the first and second substances,
Figure BDA0002395690770000107
suppose D2D user n multiplexes resource block k with cellular user i. Namely, it is
Figure BDA0002395690770000108
Will constrain C2Writing
Figure BDA0002395690770000109
Wherein the content of the first and second substances,
Figure BDA00023956907700001010
due to the following of the objective function
Figure BDA00023956907700001011
Monotonically decreases, and the cellular subscriber has an optimum transmit power in order to guarantee the quality of service and maximize the energy efficiency of the cellular subscriber
Figure BDA00023956907700001012
Must satisfy
Figure BDA00023956907700001013
For integer variables
Figure BDA0002395690770000111
Relax it to the interval [0,1 ]]Above continuous variable, define
Figure BDA0002395690770000112
And performing variable substitution on the coupling variable to define
Figure BDA0002395690770000113
To sum up, the optimization problem can be written as:
Figure BDA0002395690770000114
and 4, step 4: for the convex optimization problem, an analytic solution is obtained by utilizing a Lagrange dual theory, and an optimized variable is iteratively updated by a gradient descent method to obtain an optimal resource allocation strategy.
(a) By establishing a Lagrangian function, and
Figure BDA0002395690770000115
and (3) calculating a partial derivative to obtain:
Figure BDA0002395690770000116
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002395690770000117
is a lagrange multiplier.
(b) Will be provided with
Figure BDA0002395690770000118
Substituting Lagrangian function into
Figure BDA0002395690770000119
Updating:
Figure BDA00023956907700001110
wherein the content of the first and second substances,
Figure BDA00023956907700001111
χk，
Figure BDA00023956907700001112
psi is the lagrange multiplier,εxis an iteration step size not less than zero.
(c) Solving for optimal resource block allocation factor
Figure BDA00023956907700001113
Namely, it is
Figure BDA00023956907700001114
Wherein the content of the first and second substances,
Figure BDA0002395690770000121
(d) to obtain
Figure BDA0002395690770000122
And
Figure BDA0002395690770000123
then according to
Figure BDA0002395690770000124
And
Figure BDA0002395690770000125
can find out
Figure BDA0002395690770000126
Figure BDA0002395690770000127
(e) Updating when the iteration has a converged value
Figure BDA0002395690770000128
Returning to the step (a) until the following conditions are met:
Figure BDA0002395690770000129
and T < T
At this time, an optimal allocation strategy of resource blocks, power and signal power split ratios is obtained.
In this embodiment, the proposed robust resource allocation method for the NOMA and energy-carrying D2D fusion network is compared with a non-wireless energy-carrying traditional energy efficiency algorithm, a non-wireless energy-carrying energy efficiency maximum robust algorithm, and a wireless energy-carrying energy efficiency algorithm.
As can be seen from fig. 3, as the number of D2D users increases, the system energy efficiency of the four algorithms increases, but the algorithm proposed by the present invention has the highest energy efficiency, whereas the traditional wireless energy-carrying energy efficiency algorithm has the energy efficiency similar to that of the energy efficiency maximization robust algorithm. The reason is that the traditional wireless energy-carrying energy efficiency algorithm compensates the energy consumption of the system by using the energy of the collected radio frequency signals, and the energy efficiency maximum robust algorithm has a higher average data rate by considering the interruption probability constraint. And the traditional energy efficiency algorithm which is not wireless energy-carrying has the lowest energy efficiency.
It can be seen from fig. 4 that under different algorithms, the interruption probability is related to the channel estimation error of the D2D communication link, and it can be seen from the figure that the variance of the channel estimation error of the D2D communication link
Figure BDA00023956907700001212
Very little time, the actual channel parameters are similar to the channel estimation values, and the system outage probability is zero. When in use
Figure BDA00023956907700001213
The probability of interruption of the system increases with it. And is provided with
Figure BDA00023956907700001210
And
Figure BDA00023956907700001211
the larger the interruption probability. Compared with the non-robust traditional energy efficiency algorithm, the interruption probability of the algorithm is lower and does not exceed the interruption probability threshold, which shows that the algorithm reduces the interruption probability of D2D communication by considering the uncertainty of the channel parameters and has good robust performance.
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 (3)

1. A NOMA and energy-carrying D2D fusion network-oriented robust resource allocation method is characterized by comprising the following steps: the method comprises the following steps:
in a NOMA-based and energy-carrying D2D converged network, the NOMA-based and energy-carrying D2D converged network comprises a macro base station, M cellular users and N pairs of D2D users, wherein the base station carries out data transmission with the cellular users through K resource blocks, the D2D users are provided with an energy collection circuit, and the cellular users adopt NOMA technology;
considering the downlink transmission scenario, multiple cellular users multiplex the same resource block, assuming that the channel gain is satisfied
Figure FDA0003529506840000011
The serial interference elimination is utilized, the interference is eliminated in sequence according to the increasing sequence of the channel gain, the co-channel interference is reduced, and a D2D user adopts substrate access to cause interference to users occupying the same resource block; the data rate for the ith cellular user is:
Figure FDA0003529506840000012
wherein the content of the first and second substances,
Figure FDA0003529506840000013
transmitting power, M, allocated to cellular user i by base station through resource block kkIs the number of cellular users on resource block k,
Figure FDA0003529506840000014
for the channel gain on resource block k for base station to cell user i,
Figure FDA0003529506840000015
channel gain on resource block k for D2D user n to cellular user i;
Figure FDA0003529506840000016
allocating a factor to resource blocks of D2D users when
Figure FDA0003529506840000017
Indicating that D2D user n occupies resource block k, otherwise
Figure FDA0003529506840000018
Figure FDA0003529506840000019
For the transmit power, σ, of D2D user n on resource block k2Is the background noise power of the receiver;
to perform successive interference cancellation, the interference of user j for a certain channel gain condition is cancelled,
Figure FDA00035295068400000110
the signal to interference plus noise ratio for user i to decode user j signals is described as:
Figure FDA00035295068400000111
wherein the content of the first and second substances,
Figure FDA00035295068400000112
allocating the transmitting power of a cellular user j to a base station through a resource block k;
when the user j decodes the signal, the signal-to-interference-and-noise ratio is:
Figure FDA00035295068400000113
suppose that the k-th resource block is multiplexed by the D2D user n and the cellular user, and the signal-to-interference-and-noise ratio is:
Figure FDA00035295068400000114
wherein the content of the first and second substances,
Figure FDA0003529506840000021
is the proportionality coefficient of the information signal in the signal power,
Figure FDA0003529506840000022
the channel gain for D2D user n on resource block k,
Figure FDA0003529506840000023
for the channel gain on resource block k for base station to D2D user n,
Figure FDA0003529506840000024
channel gain on resource block k for D2D user n to cellular user i;
considering wireless energy carrying technology, the D2D receiver can collect energy from the surrounding radio frequency environment, and the energy collected by the nth D2D user is represented as:
Figure FDA0003529506840000025
wherein the content of the first and second substances,
Figure FDA0003529506840000026
is the proportionality coefficient of the energy signal in the signal power, theta is the energy collection efficiency coefficient;
the energy efficiency of each D2D user was:
Figure FDA0003529506840000027
in the NOMA-oriented and energy-carrying D2D fusion network, channel gains under an imperfect channel state are considered, and channel uncertainty is modeled as an additive model:
Figure FDA0003529506840000028
wherein the content of the first and second substances,
Figure FDA0003529506840000029
in order to achieve the gain of the rayleigh fading channel,
Figure FDA00035295068400000210
and
Figure FDA00035295068400000211
for the purpose of the channel gain estimation,
Figure FDA00035295068400000212
and
Figure FDA00035295068400000213
an estimation error that is a channel gain;
the channel gain is modeled as:
Figure FDA00035295068400000214
wherein L ═ d-νIs the path loss coefficient; d is the distance of the channel, and v is the path loss index;
when the actual achievable data rate is less than the expected data rate, a communication disruption will be generated; the estimated data rate and the achievable maximum data rate for the D2D user are:
Figure FDA00035295068400000215
Figure FDA00035295068400000216
wherein the content of the first and second substances,
Figure FDA00035295068400000217
estimated signal-to-interference-and-noise ratios and the maximum achievable signal-to-interference-and-noise ratio are respectively;
the average interrupt rate sum for the D2D user is expressed as:
Figure FDA0003529506840000031
wherein Pr [ ] represents the probability that the D2D user estimates that the data rate is less than or equal to the maximum achievable data rate;
establishing an energy efficiency maximum optimization problem containing the interruption probability:
Figure FDA0003529506840000032
Figure FDA0003529506840000033
Figure FDA0003529506840000034
Figure FDA0003529506840000035
Figure FDA0003529506840000036
Figure FDA0003529506840000037
Figure FDA0003529506840000038
Figure FDA0003529506840000039
wherein the content of the first and second substances,
Figure FDA00035295068400000310
is the minimum data rate of cellular user i, tau is the outage probability threshold, PmaxIs the maximum transmit power of the base station,
Figure FDA00035295068400000311
maximum transmit power for D2D user n;
in the energy efficiency maximum optimization problem, a probability constraint problem is converted into a non-probability problem, and the data rate of the D2D user n is obtained as follows:
Figure FDA00035295068400000312
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA00035295068400000313
in order to be a coefficient of the path loss,
Figure FDA00035295068400000314
is Rayleigh fading channel gain, and
Figure FDA00035295068400000315
is a non-central chi-square distribution with a degree of freedom of 2,
Figure FDA00035295068400000316
as an inverse cumulative distribution function of the chi-squared distribution,
Figure FDA00035295068400000317
the average interrupt rate sum is expressed as:
Figure FDA00035295068400000318
the energy efficiency optimization problem is converted into:
Figure FDA0003529506840000041
Figure FDA0003529506840000042
Figure FDA0003529506840000043
Figure FDA0003529506840000044
Figure FDA0003529506840000045
Figure FDA0003529506840000046
Figure FDA0003529506840000047
wherein the content of the first and second substances,
Figure FDA0003529506840000048
the energy collected for the nth D2D receiver under consideration of channel parameter uncertainty.
2. The NOMA and energy-carrying D2D converged network-oriented robust resource allocation method according to claim 1, wherein: in the energy efficiency optimization problem, a Dinkelbach method is used for converting a fractional target function into a subtraction form, an intermediate variable eta is introduced, and the target function is equivalent to:
Figure FDA0003529506840000049
due to the objective function with the transmitted power of the cellular user
Figure FDA00035295068400000410
To ensure the service quality of the cellular users and maximize the energy efficiency, the optimal transmitting power of the cellular users meets the following requirements:
Figure FDA00035295068400000411
simplifying the constraint conditions to obtain the problem of maximum energy efficiency optimization:
Figure FDA00035295068400000412
Figure FDA00035295068400000413
Figure FDA00035295068400000414
Figure FDA00035295068400000415
Figure FDA00035295068400000416
Figure FDA00035295068400000417
wherein the content of the first and second substances,
Figure FDA00035295068400000418
and
Figure FDA00035295068400000419
is an intermediate variable;
the optimization problem includes integer variables
Figure FDA0003529506840000051
Will be provided with
Figure FDA0003529506840000052
Relaxation is in the interval [0,1]Above continuous variable, define
Figure FDA0003529506840000053
And performing variable substitution on the coupling variable to define
Figure FDA0003529506840000054
Obtaining:
Figure FDA0003529506840000055
Figure FDA0003529506840000056
Figure FDA0003529506840000057
Figure FDA0003529506840000058
Figure FDA0003529506840000059
Figure FDA00035295068400000510
the problem constraint is linear, the objective function is a concave function, and the problem constraint is a typical convex optimization problem.
3. The NOMA and energy-carrying D2D converged network-oriented robust resource allocation method according to claim 2, wherein: solving an analytic solution for the convex optimization problem by using a Lagrange dual theory, and iteratively updating an optimization variable by a gradient descent method to obtain an optimal resource allocation strategy;
(a) by establishing a Lagrangian function, and
Figure FDA00035295068400000511
and (3) calculating a partial derivative to obtain:
Figure FDA00035295068400000512
wherein the content of the first and second substances,
Figure FDA00035295068400000513
is a lagrange multiplier;
(b) will be provided with
Figure FDA00035295068400000514
Substituting Lagrangian function into
Figure FDA00035295068400000515
Updating:
Figure FDA00035295068400000516
wherein the content of the first and second substances,
Figure FDA00035295068400000517
χk，
Figure FDA00035295068400000518
psi is the Lagrange multiplier, epsilonxAn iteration step size not less than zero;
(c) solving for optimal resource block allocation factors
Figure FDA00035295068400000519
Namely, it is
Figure FDA00035295068400000520
Wherein the content of the first and second substances,
Figure FDA0003529506840000061
(d) to obtain
Figure FDA0003529506840000062
And
Figure FDA0003529506840000063
then according to
Figure FDA0003529506840000064
And
Figure FDA0003529506840000065
to obtain
Figure FDA0003529506840000066
(e) Updating when the iteration has a converged value
Figure FDA0003529506840000067
Returning to the step (a) until the following conditions are met:
Figure FDA0003529506840000068
and T < T
At this time, an optimal allocation strategy of resource blocks, power and signal power split ratios is obtained.
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CN113453358B (en) * 2021-06-11 2022-09-23 南京信息工程大学滨江学院 Joint resource allocation method of wireless energy-carrying D2D network
CN113473422B (en) * 2021-07-21 2022-06-21 深圳市领创星通科技有限公司 B5G-oriented wireless energy-carrying D2D network efficient resource allocation method
CN113613198B (en) * 2021-07-26 2023-06-20 河南浩宇空间数据科技有限责任公司 Unmanned aerial vehicle-assisted wireless energy-carrying D2D network resource allocation method
CN113596785B (en) * 2021-07-26 2023-08-08 山东顺和电子商务产业园有限责任公司 D2D-NOMA communication system resource allocation method based on deep Q network
CN114363938B (en) * 2021-12-21 2024-01-26 深圳千通科技有限公司 Cellular network flow unloading method
CN115175147B (en) * 2022-07-28 2024-03-22 重庆邮电大学 Unmanned aerial vehicle assisted D2D communication network robust energy efficiency optimization method
CN115442812B (en) * 2022-11-08 2023-04-07 湖北工业大学 Deep reinforcement learning-based Internet of things spectrum allocation optimization method and system

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108650689A (en) * 2018-04-03 2018-10-12 华南理工大学 Wireless portable communications system efficiency optimization method based on NOMA downlinks
CN109861866A (en) * 2019-02-22 2019-06-07 华南理工大学 Take the resource allocation methods minimized in energy multicarrier NOMA system based on transmission power
CN110602730A (en) * 2019-09-19 2019-12-20 重庆邮电大学 Resource allocation method of NOMA (non-orthogonal multiple access) heterogeneous network based on wireless energy carrying
CN110602722A (en) * 2019-09-17 2019-12-20 北京邮电大学 Design method for joint content pushing and transmission based on NOMA
CN110708711A (en) * 2019-10-10 2020-01-17 重庆邮电大学 Heterogeneous energy-carrying communication network resource allocation method based on non-orthogonal multiple access

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10547426B2 (en) * 2016-03-14 2020-01-28 Samsung Electronics Co., Ltd. Transmission of sounding reference signals in communication systems with carrier aggregation
US10701641B2 (en) * 2017-10-13 2020-06-30 Apple Inc. Interference mitigation in ultra-dense wireless networks

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108650689A (en) * 2018-04-03 2018-10-12 华南理工大学 Wireless portable communications system efficiency optimization method based on NOMA downlinks
CN109861866A (en) * 2019-02-22 2019-06-07 华南理工大学 Take the resource allocation methods minimized in energy multicarrier NOMA system based on transmission power
CN110602722A (en) * 2019-09-17 2019-12-20 北京邮电大学 Design method for joint content pushing and transmission based on NOMA
CN110602730A (en) * 2019-09-19 2019-12-20 重庆邮电大学 Resource allocation method of NOMA (non-orthogonal multiple access) heterogeneous network based on wireless energy carrying
CN110708711A (en) * 2019-10-10 2020-01-17 重庆邮电大学 Heterogeneous energy-carrying communication network resource allocation method based on non-orthogonal multiple access

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
《Resource Allocation for D2D Communications Underlaying》;Yijin Pan, Cunhua Pan;《IEEE WIRELESS COMMUNICATIONS LETTERS,》;20180201;全文 *
基于物联网系统的NOMA与SWIFT结合的研究;邱欢等;《电子技术应用》;20180906(第09期);全文 *
非正交多址接入下行协作网络的中断性能分析;邓超等;《中国科学技术大学学报》;20190715(第07期);全文 *

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