CN113473422A - B5G-oriented wireless energy-carrying D2D network efficient resource allocation method - Google Patents

B5G-oriented wireless energy-carrying D2D network efficient resource allocation method Download PDF

Info

Publication number
CN113473422A
CN113473422A CN202110834530.6A CN202110834530A CN113473422A CN 113473422 A CN113473422 A CN 113473422A CN 202110834530 A CN202110834530 A CN 202110834530A CN 113473422 A CN113473422 A CN 113473422A
Authority
CN
China
Prior art keywords
user
pair
cellular
power
energy
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110834530.6A
Other languages
Chinese (zh)
Other versions
CN113473422B (en
Inventor
徐勇军
杨蒙
陈前斌
李国权
周继华
黄东
赵涛
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Lingchuang Xingtong Technology Co ltd
Original Assignee
Chongqing University of Post and Telecommunications
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Chongqing University of Post and Telecommunications filed Critical Chongqing University of Post and Telecommunications
Priority to CN202110834530.6A priority Critical patent/CN113473422B/en
Publication of CN113473422A publication Critical patent/CN113473422A/en
Application granted granted Critical
Publication of CN113473422B publication Critical patent/CN113473422B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention relates to a B5G-oriented wireless energy-carrying D2D network efficient resource allocation method, belongs to the technical field of wireless network resource allocation, and establishes a robust energy efficiency resource allocation model for jointly optimizing transmission power, sub-channels and power splitting factors by considering user minimum rate constraint, minimum collected energy constraint, power splitting and channel allocation factor constraint. Based on a Dinkelbach method and a Worst-case method, an original NP-hard problem is converted into a deterministic convex optimization problem, and an analytic solution is obtained by utilizing a matching theory and a Lagrangian dual theory. The invention has better robustness and higher energy efficiency.

Description

B5G-oriented wireless energy-carrying D2D network efficient resource allocation method
Technical Field
The invention belongs to the technical field of wireless network resource allocation, and relates to a B5G-oriented efficient resource allocation method for a wireless energy-carrying D2D network.
Background
The Device-to-Device (D2D) technology allows direct communication between devices, has high capacity and energy efficiency, and is considered as one of the core technologies of 5G/B5G. With the increasing number of devices, how to effectively improve the energy efficiency of the system, prolong the service life of the devices and realize green communication is a key problem of the future D2D network.
The energy harvesting technology can effectively prolong the operating life of the energy-limited wireless network by harvesting energy from the environment. Since the radio frequency signal is widely applied to Wireless Information transmission, a Wireless Information and Power Transfer (SWIPT), i.e. a digital energy Transfer, has attracted much attention in recent years. The SWIPT technology allows wireless equipment to collect electromagnetic energy in the environment while receiving wireless information through a time switching or energy shunting mechanism, and can effectively improve data transmission efficiency and energy utilization rate. Therefore, how to realize the optimal time switching or energy splitting is the key point for realizing the energy efficiency maximization of the wireless network system based on SWIPT.
Disclosure of Invention
In view of this, the present invention aims to provide a B5G-oriented wireless energy-carrying D2D network efficient resource allocation method, which considers user minimum rate constraints, minimum collected energy constraints, power splitting and channel allocation factor constraints and establishes a robust energy-efficient resource allocation model for jointly optimizing transmission power, sub-channels and power splitting factors. Based on a Dinkelbach method and a Worst-case method, an original NP-hard problem is converted into a deterministic convex optimization problem, and an analytic solution is obtained by utilizing a matching theory and a Lagrangian dual theory.
In order to achieve the purpose, the invention provides the following technical scheme:
a B5G-oriented wireless energy-carrying D2D network efficient resource allocation method comprises the following steps:
s1: initializing system parameters and setting total iteration times LmaxPerforming iterative initialization;
s2: obtaining a resource block distribution factor;
s3: calculating a power splitting factor;
s4: calculating the power of the cellular user and the D2D user;
s5: calculating the total energy efficiency eta (l +1) of the network;
s6: updating a Lagrange multiplier corresponding to the constraint condition;
s7: calculating the data rate of each D2D user, and judging whether the value is less than or equal to the minimum rate threshold value of the corresponding D2D user; if yes, go to S8; otherwise, go to S12;
s8: calculating the data rate of each cellular user, and judging whether the value is less than or equal to the minimum data rate threshold value of the corresponding cellular user; if yes, go to S9; otherwise, go to S12;
s9: judging whether the energy received by each pair of D2D users is more than or equal to the minimum energy threshold of the D2D user link; if yes, go to S10; otherwise, go to S12;
s10: judging whether the transmitting power of each pair of D2D user transmitters is less than or equal to the maximum transmitting power of the D2D user; if yes, go to S11; otherwise, go to S12;
s11: judging whether the transmitting power of each cellular user is less than or equal to the maximum transmitting power of the cellular user; if yes, go to S12; otherwise, entering the next iteration and returning to S2;
s12: judging whether the current iteration times are larger than the maximum iteration times or not; if yes, ending, and outputting optimal transmitting power and resource block allocation factors of the D2D user and the cellular user; otherwise, the next iteration is entered, returning to S2.
Further, in step S1, the system parameters include the number of users M, D2D, the number of users N, and the background noise σ at the base station2Mth cellular user to base station channel gain gm,0N-th pair of D2D user transmitter to base station channel gain hn,0N-th pair of D2D user transmitter-to-receiver channel gains hn,nChannel gain g for m to n pairs of D2D user receiversm,nBackground noise at the nth pair D2D user receiverAcoustic power
Figure BDA0003173566130000021
Noise power introduced by the n-th pair D2D user receiver information decoding process
Figure BDA0003173566130000022
Energy collection efficiency factor
Figure BDA0003173566130000023
Circuit consumption P of all cellular users and any pair of D2D links in the systemcirAnd pcirMinimum rate threshold for nth pair of D2D users
Figure BDA0003173566130000024
Minimum rate threshold for mth cellular user
Figure BDA0003173566130000025
Maximum transmit power of nth pair D2D user transmitter
Figure BDA0003173566130000026
Maximum transmit power of mth cellular user
Figure BDA0003173566130000027
Minimum energy threshold for activating nth pair D2D user receiver energy harvesting circuits
Figure BDA0003173566130000028
Upper bound on channel uncertainty epsilon for the nth pair of D2D user transmitters to receiversnUpper bound on channel uncertainty v for cellular users m through nth pair D2D user receiversnAnd the nth pair D2D user transmitter to base station channel uncertainty upper bound ξ0。
Further, the obtaining of the resource block allocation factor in step S2 specifically includes the following steps:
s21: all D2D users send access requests to the first cellular user sub-channel in the preference list;
s22: all cellular users receive the D2D user pairs according to the preference list, and reject other requests;
s23: the received D2D was removed from the unmatched list, and the rejected D2D user pair removed the cellular user from the favorites list;
s24: searching for the exchange matching pair, if the exchange matching pair exists in the system, executing the exchange matching until the exchange matching pair does not exist in the system, and obtaining the resource block distribution factor xn,m。
Further, in step S3, the power splitting factor is:
Figure BDA0003173566130000031
wherein, l represents the number of iterations,
Figure BDA0003173566130000032
[x]+＝max(0,x),cnrepresenting the auxiliary variable SCA, eta is the non-negative auxiliary variable, mu, in the Dinkelbach methodn，ωn，νnAnd τnIs to constrain the corresponding lagrange multiplier.
Further, the cellular user has a power of step S4
Figure BDA0003173566130000033
Wherein the content of the first and second substances,
Figure BDA0003173566130000034
the power of the D2D user is:
Figure BDA0003173566130000035
wherein the content of the first and second substances,
Figure BDA0003173566130000036
λnand phimIs a correspondingly constrained lagrange multiplier.
Further, in step S5, the total energy efficiency of the network is:
Figure BDA0003173566130000037
wherein the content of the first and second substances,
Figure BDA0003173566130000038
Figure BDA0003173566130000039
Figure BDA00031735661300000310
Figure BDA0003173566130000041
Figure BDA0003173566130000042
further, in the step S6, the D2D user data rate auxiliary variable cnAnd dnCellular user data rate auxiliary variable amAnd bmSplitting factor constraint multiplier v in power splitter problemnD2D user data rate constraint multiplier taunEnergy harvesting constraint multiplier munAuxiliary variable qnConstraint multiplier omegan. D2D power constraint multiplier beta in power sub-problemnHoneycomb structureUser power constraint multiplier alphamAuxiliary variable zmConstraint multiplier phimCellular user rate constraint multiplier χmEnergy harvesting constraint multiplier thetanAuxiliary variable qnConstraint multiplier
Figure BDA00031735661300000416
And D2D user rate constraint multiplier lambdanThe update expression is as follows:
Figure BDA0003173566130000043
Figure BDA0003173566130000044
Figure BDA0003173566130000045
Figure BDA0003173566130000046
Figure BDA0003173566130000047
Figure BDA0003173566130000048
Figure BDA0003173566130000049
Figure BDA00031735661300000410
Figure BDA00031735661300000411
Figure BDA00031735661300000412
Figure BDA00031735661300000413
Figure BDA00031735661300000414
Figure BDA00031735661300000415
Figure BDA0003173566130000051
Figure BDA0003173566130000052
where l denotes the number of iterations, d1-d11Is the update step size for the corresponding lagrange multiplier.
The invention has the beneficial effects that: compared with the algorithm of random matching under perfect channel state information, the scheme of the invention has better energy efficiency and robustness, and improves the robustness and throughput of the D2D 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.
Drawings
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 graph of the convergence of the energy efficiency and power splitting factor of the system of the present invention;
FIG. 4 is a graph of the actual rate versus uncertainty of a D2D user under different resource allocation methods and different algorithms;
FIG. 5 is a diagram showing a relationship between total energy efficiency and collected energy threshold of a system under different resource allocation methods and 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 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 considers a D2D network uplink transmission scenario based on simultaneous data transmission, and m cellular users implement uplink transmission by using an ofdma method. The N D2D user pairs have the function of digital simultaneous transmission and multiplex the channel resources of cellular users by adopting a underlying spectrum sharing mode. Only one pair of D2D users may be allowed access at a time on each subchannel. Define cellular users and D2D user sets as
Figure BDA0003173566130000061
And
Figure BDA0003173566130000062
each subchannel is assumed to be subject to block fading. Our goal is to maximize the overall energy efficiency of the system under Qos constraints of cellular users, Qos constraints of D2D users, splitting factor constraints, maximum transmit power of cellular users and D2D users, and energy harvesting constraints, so this optimization problem can be represented by jointly optimizing cellular users, D2D user power, channel allocation factor, and power splitting factor under perfect channel state information:
Figure BDA0003173566130000063
wherein
Figure BDA0003173566130000064
Which represents the total rate of the system,
Figure BDA0003173566130000065
represents the mth beeRate of the cell user, PmRepresenting the transmission power, σ, of the mth cellular user2Representing the background noise power, x, at the base stationn,mIndicating that the nth D2D user occupies the channel of the mth cellular user, gm,0Representing the channel gain, p, from the m-th cellular user to the base stationnRepresents the transmit power, h, of the nth D2D usern,0Indicating the channel gain of the nth pair of D2D user transmitters to the base station,
Figure BDA0003173566130000066
representing the rate, p, of the nth pair of D2D user linksnRepresenting the power splitting factor, h, of the nth pair of D2D user receiversn,nRepresenting the channel gain of the nth pair D2D user transmitter to receiver,
Figure BDA0003173566130000067
representing the noise power introduced by the n-th pair D2D user receiver information decoding process,
Figure BDA0003173566130000068
representing the background noise power, g, at the nth pair of D2D user receiversm,nRepresenting the channel gains of cellular users m through nth pair D2D users,
Figure BDA0003173566130000071
representing the energy collected by the nth D2D user,
Figure BDA00031735661300000716
the efficiency of the collection of energy is expressed,
Figure BDA0003173566130000072
representing the total energy consumption of the system. PcirAnd pcirRepresenting the circuit consumption of all cellular users and any pair of D2D links within the system,
Figure BDA0003173566130000073
representing the minimum rate threshold for the nth pair of D2D users,
Figure BDA0003173566130000074
representing the minimum rate threshold for the mth cellular user,
Figure BDA0003173566130000075
represents the maximum transmit power of the nth pair of D2D user transmitters,
Figure BDA0003173566130000076
representing the maximum transmit power of the mth cellular user,
Figure BDA0003173566130000077
a minimum energy threshold that indicates activation of the nth pair D2D of user receiver energy harvesting circuits. C1Is a power splitting factor constraint; c2Is the minimum rate constraint for each pair of D2D user receivers; c3Is a minimum rate constraint for each cellular user; c4Is the maximum transmit power constraint for each pair of D2D users; c5Is a maximum transmit power constraint for each cellular user; c6Is the minimum energy harvesting constraint for each pair of D2D user receivers, the harvested energy being greater than the minimum activation energy of the energy harvesting circuit; c7Is a subchannel assignment constraint.
To overcome the effects of uncertainty, the uncertainty of the channel is taken into account in P1. According to the robust optimization theory, an ellipsoid bounded channel error is considered, and channel gain is modeled as follows:
Figure BDA0003173566130000078
wherein the content of the first and second substances,
Figure BDA0003173566130000079
and
Figure BDA00031735661300000710
is the channel estimate, Δ hn,n,Δgm,nAnd Δ hn,0Is the corresponding estimation error, Rh，RgAnd
Figure BDA00031735661300000711
an uncertainty set is represented. According to a continuous convex approximation, the rate is approximated as
Figure BDA00031735661300000712
Figure BDA00031735661300000713
Wherein the content of the first and second substances,
Figure BDA00031735661300000714
Figure BDA00031735661300000715
according to the worst criterion and the continuous convex approximation method, the original optimization problem P1 can be converted into the following form:
Figure BDA0003173566130000081
wherein the content of the first and second substances,
Figure BDA0003173566130000082
zmand q isnIs the auxiliary variable which is the variable of the auxiliary variable,
Figure BDA0003173566130000083
ξ0，υnand
Figure BDA0003173566130000084
representing the upper bound of channel uncertainty.
Since P2 is a non-linear fractional programming problem. Therefore, it can be solved by the Buckbach method. Define the dickelbach function as:
Figure BDA0003173566130000085
wherein eta.gtoreq.0 represents energy efficiency. Thus, P2 can be restated as:
Figure BDA0003173566130000086
due to the binary variable xn,mP3 is still a non-convex optimization problem due to the coupling relationship with other variables to solve the problem, x is determined by using a matching algorithmn,mAnd then solving the remaining convex optimization problem.
The steps of the matching algorithm are as follows:
1. all D2D users sent access requests to the first cellular user sub-channel in the favorites list;
2. all cellular users receive the D2D user pairs according to the preference list, and reject other requests;
3. the received D2D was removed from the unmatched D2D user list, and the rejected D2D user removed the corresponding cellular user from the preference list;
4. repeat 1-3 until all D2D users in the system match a channel;
5. and searching a matching block pair in the system, and executing exchange matching.
The preferences of cellular users and D2D users are defined as utility functions, expressed as utility functions, respectively
Figure BDA0003173566130000091
Where k represents the cost of accessing the cellular user subchannel. The definition of a matching occlusion pair is: when two pairs of D2D users exchange their matched cellular user channels, the increased utility of both parties is a matched blocked pair.
Because of ρnAnd C1，
Figure BDA0003173566130000092
And C12In connection with this, based on the alternative optimization idea, the problem P3 is decomposed to contain only rhonSub-questions of other variables. Definition of
Figure BDA0003173566130000093
P3 can be re-described as
Figure BDA0003173566130000094
By using the lagrangian function, there are:
Figure BDA0003173566130000095
wherein, vn≥0，τn≥0，μn≥0，ωnAnd more than or equal to 0 is a Lagrangian multiplier corresponding to the constraint condition of the optimization problem P4.
By using the KKT condition, the optimal split factor is solved as:
Figure BDA0003173566130000096
wherein, [ x ]]+＝max(0,x)，
Figure BDA0003173566130000097
The lagrange multiplier can be updated using a gradient descent method
Figure BDA0003173566130000101
Figure BDA0003173566130000102
Figure BDA0003173566130000103
Figure BDA0003173566130000104
Wherein l represents the number of iterations, d1-d4Is the iteration step size. By selecting a proper step length, the convergence of the Lagrangian algorithm can be ensured.
Definition of same theory
Figure BDA0003173566130000105
At a determined
Figure BDA0003173566130000106
Under the conditions, P3 can translate into the following power distribution subproblem
Figure BDA0003173566130000107
Wherein the content of the first and second substances,
Figure BDA0003173566130000108
by KKT condition, the analytic solution of the optimal power can be obtained as
Figure BDA0003173566130000109
Figure BDA0003173566130000111
Wherein the content of the first and second substances,
Figure BDA0003173566130000112
Figure BDA0003173566130000113
the lagrange multiplier update law is:
Figure BDA0003173566130000114
Figure BDA0003173566130000115
Figure BDA0003173566130000116
Figure BDA0003173566130000117
Figure BDA0003173566130000118
Figure BDA0003173566130000119
Figure BDA00031735661300001110
wherein l represents the number of iterations, d5-d11Representing the step size. By selecting a proper step length, the convergence of the Lagrangian algorithm can be ensured. The system energy efficiency 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.
Simulation conditions are as follows: the simulation parameters are set to M-2, N-2,
Figure BDA00031735661300001111
σ2＝10-5W，
Figure BDA00031735661300001112
W，lmax＝104，T＝104，
Figure BDA00031735661300001113
Pcir＝0.04W，pcir＝0.02W，
Figure BDA00031735661300001114
Figure BDA00031735661300001115
εn∈[0,1]，υn∈[0,1]，ξ0∈[0,1]the channel fading model includes rayleigh fading, shadow fading, and path loss.
And (3) simulation results: in the present embodiment, fig. 3 shows a convergence diagram of the system energy efficiency and the power splitting factor in the present embodiment. Fig. 4 shows a graph of the actual rate versus uncertainty of D2D users under different resource allocation methods and different algorithms. FIG. 5 is a graph showing a relationship between total energy efficiency and collected energy threshold of the system under different resource allocation methods and different algorithms. Fig. 3 shows that the algorithm of the present invention can achieve convergence quickly, which means that the algorithm of the present invention can ensure the communication quality of cellular users well and has real-time performance. FIG. 4 shows the dependence on the channel uncertainty Δ hn,nThe actual rate of D2D users may decrease. 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. Fig. 5 shows that as the minimum collected energy threshold increases, the overall energy efficiency of the system decreases, and the energy efficiency of the algorithm of the present invention is always better than that of the non-robust algorithm and other algorithms. The experimental results of fig. 3, fig. 4 and fig. 5 show that the algorithm of the present invention guarantees the real-time performance, and simultaneously guarantees the service quality of the cellular users, and has good robustness and energy efficiency.
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 (7)

1. A B5G-oriented wireless energy-carrying D2D network efficient resource allocation method is characterized in that: the method comprises the following steps:
s1: initializing system parameters and setting total iteration times LmaxPerforming iterative initialization;
s2: obtaining a resource block distribution factor;
s3: calculating a power splitting factor;
s4: calculating the power of the cellular user and the D2D user;
s5: calculating the total energy efficiency eta (l +1) of the network;
s6: updating a Lagrange multiplier corresponding to the constraint condition;
s7: calculating the data rate of each D2D user, and judging whether the value is less than or equal to the minimum rate threshold value of the corresponding D2D user; if yes, go to S8; otherwise, go to S12;
s8: calculating the data rate of each cellular user, and judging whether the value is less than or equal to the minimum data rate threshold value of the corresponding cellular user; if yes, go to S9; otherwise, go to S12;
s9: judging whether the energy received by each pair of D2D users is more than or equal to the minimum energy threshold of the D2D user link; if yes, go to S10; otherwise, go to S12;
s10: judging whether the transmitting power of each pair of D2D user transmitters is less than or equal to the maximum transmitting power of the D2D user; if yes, go to S11; otherwise, go to S12;
s11: judging whether the transmitting power of each cellular user is less than or equal to the maximum transmitting power of the cellular user; if yes, go to S12; otherwise, entering the next iteration and returning to S2;
s12: judging whether the current iteration times are larger than the maximum iteration times or not; if yes, ending, and outputting optimal transmitting power and resource block allocation factors of the D2D user and the cellular user; otherwise, the next iteration is entered, returning to S2.
2. According toThe B5G-oriented wireless energy-carrying D2D network efficient resource allocation method of claim 1, wherein: in step S1, the system parameters include the number of users M, D2D, the number of users N, and the background noise σ at the base station2Mth cellular user to base station channel gain gm,0N-th pair of D2D user transmitter to base station channel gain hn,0N-th pair of D2D user transmitter-to-receiver channel gains hn,nChannel gain g for m to n pairs of D2D user receiversm,nBackground noise power at nth pair D2D user receiver
Figure FDA0003173566120000011
Noise power introduced by the n-th pair D2D user receiver information decoding process
Figure FDA0003173566120000012
Energy collection efficiency factor
Figure FDA0003173566120000013
Circuit consumption P of all cellular users and any pair of D2D links in the systemcirAnd pcirMinimum rate threshold for nth pair of D2D users
Figure FDA0003173566120000014
Minimum rate threshold for mth cellular user
Figure FDA0003173566120000015
Maximum transmit power of nth pair D2D user transmitter
Figure FDA0003173566120000016
Maximum transmit power of mth cellular user
Figure FDA0003173566120000017
Minimum energy threshold for activating nth pair D2D user receiver energy harvesting circuits
Figure FDA0003173566120000018
Upper bound on channel uncertainty epsilon for the nth pair of D2D user transmitters to receiversnUpper bound on channel uncertainty v for cellular users m through nth pair D2D user receiversnAnd the nth pair D2D user transmitter to base station channel uncertainty upper bound ξ0。
3. The B5G-oriented wireless energy-carrying D2D network efficient resource allocation method of claim 1, wherein: the obtaining of the resource block allocation factor in step S2 specifically includes the following steps:
s21: all D2D users send access requests to the first cellular user sub-channel in the preference list;
s22: all cellular users receive the D2D user pairs according to the preference list, and reject other requests;
s23: the received D2D was removed from the unmatched list, and the rejected D2D user pair removed the cellular user from the favorites list;
s24: searching for the exchange matching pair, if the exchange matching pair exists in the system, executing the exchange matching until the exchange matching pair does not exist in the system, and obtaining the resource block distribution factor xn,m。
4. The B5G-oriented wireless energy-carrying D2D network efficient resource allocation method of claim 1, wherein: in step S3, the power splitting factor is:
Figure FDA0003173566120000021
wherein, l represents the number of iterations,
Figure FDA0003173566120000022
[x]+＝max(0,x),cnrepresenting SCA auxiliary variable, eta is DinkelbacNon-negative auxiliary variable, mu, in the h methodn，ωn，νnAnd τnIs to constrain the corresponding lagrange multiplier.
5. The B5G-oriented wireless energy-carrying D2D network efficient resource allocation method of claim 1, wherein: the cellular user has a power of S4
Figure FDA0003173566120000023
Wherein the content of the first and second substances,
Figure FDA0003173566120000024
the power of the D2D user is:
Figure FDA0003173566120000025
wherein the content of the first and second substances,
Figure FDA0003173566120000031
λnand phimIs a correspondingly constrained lagrange multiplier.
6. The B5G-oriented wireless energy-carrying D2D network efficient resource allocation method of claim 1, wherein: in step S5, the total network energy efficiency is:
Figure FDA0003173566120000032
wherein the content of the first and second substances,
Figure FDA0003173566120000033
Figure FDA0003173566120000034
Figure FDA0003173566120000035
Figure FDA0003173566120000036
Figure FDA0003173566120000037
7. the B5G-oriented wireless energy-carrying D2D network efficient resource allocation method of claim 1, wherein: in the step S6, D2D user data rate auxiliary variable cnAnd dnCellular user data rate auxiliary variable amAnd bmSplitting factor constraint multiplier v in power splitter problemnD2D user data rate constraint multiplier taunEnergy harvesting constraint multiplier munAuxiliary variable qnConstraint multiplier omegan(ii) a D2D power constraint multiplier beta in power sub-problemnCellular user power constraint multiplier alphamAuxiliary variable zmConstraint multiplier phimCellular user rate constraint multiplier χmEnergy harvesting constraint multiplier thetanAuxiliary variable qnConstraint multiplier
Figure FDA0003173566120000038
And D2D user rate constraint multiplier lambdanThe update expression is as follows:
Figure FDA0003173566120000039
Figure FDA00031735661200000310
Figure FDA00031735661200000311
Figure FDA00031735661200000312
Figure FDA00031735661200000313
Figure FDA0003173566120000041
Figure FDA0003173566120000042
Figure FDA0003173566120000043
Figure FDA0003173566120000044
Figure FDA0003173566120000045
Figure FDA0003173566120000046
Figure FDA0003173566120000047
Figure FDA0003173566120000048
Figure FDA0003173566120000049
Figure FDA00031735661200000410
where l denotes the number of iterations, d1-d11Is the update step size for the corresponding lagrange multiplier.
CN202110834530.6A 2021-07-21 2021-07-21 B5G-oriented wireless energy-carrying D2D network efficient resource allocation method Active CN113473422B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110834530.6A CN113473422B (en) 2021-07-21 2021-07-21 B5G-oriented wireless energy-carrying D2D network efficient resource allocation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110834530.6A CN113473422B (en) 2021-07-21 2021-07-21 B5G-oriented wireless energy-carrying D2D network efficient resource allocation method

Publications (2)

Publication Number Publication Date
CN113473422A true CN113473422A (en) 2021-10-01
CN113473422B CN113473422B (en) 2022-06-21

Family

ID=77882077

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110834530.6A Active CN113473422B (en) 2021-07-21 2021-07-21 B5G-oriented wireless energy-carrying D2D network efficient resource allocation method

Country Status (1)

Country Link
CN (1) CN113473422B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115175147A (en) * 2022-07-28 2022-10-11 重庆邮电大学 Unmanned aerial vehicle-assisted D2D communication network robust energy efficiency optimization method
CN115413008A (en) * 2022-08-09 2022-11-29 南京邮电大学 Energy-carrying communication OFDM system energy efficiency optimization method based on ellipsoid method
WO2023069856A1 (en) * 2021-10-19 2023-04-27 Qualcomm Incorporated Energy harvesting based on power headroom

Citations (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103517347A (en) * 2013-10-21 2014-01-15 西安电子科技大学 QoS guarantee-counted D2D (Device-to-Device)-cellular communication power allocation scheme under wireless network
US20150305046A1 (en) * 2014-04-18 2015-10-22 Soongsil University Research Consortium Techno- Park D2d communications system and allocation method of resources and power using the same
CN105813209A (en) * 2016-03-08 2016-07-27 上海交通大学 Energy harvesting-based dynamic spectrum allocation method of D2D communication under cellular network
CN106792824A (en) * 2016-12-29 2017-05-31 重庆邮电大学 Cognitive heterogeneous wireless network robust resource allocation algorithm
CN107071914A (en) * 2017-04-19 2017-08-18 西安交通大学 Dynamic mode selection and energy distributing method in a kind of energy capture D2D networks
CN107172574A (en) * 2017-05-17 2017-09-15 东南大学 A kind of D2D user couple shares the power distribution method of frequency spectrum with phone user
WO2018045991A1 (en) * 2016-09-09 2018-03-15 Huawei Technologies Co., Ltd. Cellular scheduler apparatus, computer program, and method for selecting user equipment based on beam or delay state information
CN108495332A (en) * 2018-03-28 2018-09-04 山东大学 A kind of federated resource fair allocat method based on honeycomb D2D communication systems
CN108718445A (en) * 2018-05-10 2018-10-30 西安交通大学 A kind of D2D secure communication resource allocation methods of QoS drivings
CN109842931A (en) * 2019-03-13 2019-06-04 南京邮电大学 A kind of D2D cellular system resources distribution method based on NOMA
CN110167178A (en) * 2019-05-28 2019-08-23 山东大学 A kind of D2D federated resource fairness distribution method containing collection of energy
CN110213826A (en) * 2019-05-21 2019-09-06 重庆邮电大学 Take can communication network robust resource allocation methods for isomery under a kind of non-ideal communication channel
CN110417496A (en) * 2019-07-15 2019-11-05 重庆邮电大学 A kind of I'm well resource allocation methods of cognition NOMA network based on efficiency
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
CN110611902A (en) * 2019-09-19 2019-12-24 哈尔滨工程大学 D2D resource allocation method based on uplink and downlink spectrum joint multiplexing
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
CN111194042A (en) * 2020-02-25 2020-05-22 重庆邮电大学 Heterogeneous network robust energy efficiency optimization method based on non-orthogonal multiple access
CN111314894A (en) * 2020-02-28 2020-06-19 重庆邮电大学 NOMA (non-oriented access memory) and energy-carrying D2D fusion network-oriented robust resource allocation method
CN111465054A (en) * 2020-02-27 2020-07-28 南京邮电大学 D2D communication resource allocation method based on utility fairness
CN111988762A (en) * 2020-09-01 2020-11-24 重庆邮电大学 Energy efficiency maximum resource allocation method based on unmanned aerial vehicle D2D communication network
CN112702792A (en) * 2020-12-22 2021-04-23 杭州电子科技大学 Wireless energy-carrying network uplink and downlink resource joint allocation method based on GFDM
CN113055860A (en) * 2021-03-09 2021-06-29 国网河南省电力公司信息通信公司 D2D many-to-many resource allocation method in cellular network

Patent Citations (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103517347A (en) * 2013-10-21 2014-01-15 西安电子科技大学 QoS guarantee-counted D2D (Device-to-Device)-cellular communication power allocation scheme under wireless network
US20150305046A1 (en) * 2014-04-18 2015-10-22 Soongsil University Research Consortium Techno- Park D2d communications system and allocation method of resources and power using the same
CN105813209A (en) * 2016-03-08 2016-07-27 上海交通大学 Energy harvesting-based dynamic spectrum allocation method of D2D communication under cellular network
WO2018045991A1 (en) * 2016-09-09 2018-03-15 Huawei Technologies Co., Ltd. Cellular scheduler apparatus, computer program, and method for selecting user equipment based on beam or delay state information
US20180077717A1 (en) * 2016-09-09 2018-03-15 Futurewei Technologies, Inc. Cellular scheduler apparatus, computer program, and method for selecting user equipment based on beam or delay state information
CN106792824A (en) * 2016-12-29 2017-05-31 重庆邮电大学 Cognitive heterogeneous wireless network robust resource allocation algorithm
CN107071914A (en) * 2017-04-19 2017-08-18 西安交通大学 Dynamic mode selection and energy distributing method in a kind of energy capture D2D networks
CN107172574A (en) * 2017-05-17 2017-09-15 东南大学 A kind of D2D user couple shares the power distribution method of frequency spectrum with phone user
CN108495332A (en) * 2018-03-28 2018-09-04 山东大学 A kind of federated resource fair allocat method based on honeycomb D2D communication systems
CN108718445A (en) * 2018-05-10 2018-10-30 西安交通大学 A kind of D2D secure communication resource allocation methods of QoS drivings
CN109842931A (en) * 2019-03-13 2019-06-04 南京邮电大学 A kind of D2D cellular system resources distribution method based on NOMA
CN110213826A (en) * 2019-05-21 2019-09-06 重庆邮电大学 Take can communication network robust resource allocation methods for isomery under a kind of non-ideal communication channel
CN110167178A (en) * 2019-05-28 2019-08-23 山东大学 A kind of D2D federated resource fairness distribution method containing collection of energy
CN110417496A (en) * 2019-07-15 2019-11-05 重庆邮电大学 A kind of I'm well resource allocation methods of cognition NOMA network based on efficiency
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
CN110611902A (en) * 2019-09-19 2019-12-24 哈尔滨工程大学 D2D resource allocation method based on uplink and downlink spectrum joint multiplexing
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
CN111194042A (en) * 2020-02-25 2020-05-22 重庆邮电大学 Heterogeneous network robust energy efficiency optimization method based on non-orthogonal multiple access
CN111465054A (en) * 2020-02-27 2020-07-28 南京邮电大学 D2D communication resource allocation method based on utility fairness
CN111314894A (en) * 2020-02-28 2020-06-19 重庆邮电大学 NOMA (non-oriented access memory) and energy-carrying D2D fusion network-oriented robust resource allocation method
CN111988762A (en) * 2020-09-01 2020-11-24 重庆邮电大学 Energy efficiency maximum resource allocation method based on unmanned aerial vehicle D2D communication network
CN112702792A (en) * 2020-12-22 2021-04-23 杭州电子科技大学 Wireless energy-carrying network uplink and downlink resource joint allocation method based on GFDM
CN113055860A (en) * 2021-03-09 2021-06-29 国网河南省电力公司信息通信公司 D2D many-to-many resource allocation method in cellular network

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
YONGJUN XU; ZIJIAN LIU; ZHAOHUI YANG; CHONGWEN HUANG: ""Energy-Efficient Resource Allocation with Imperfect CSI in NOMA-based D2D Networks with SWIPT"", 《2021 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC)》 *
ZHENYU ZHOU; CAIXIA GAO; CHEN XU; TAO CHEN; DI ZHANG; SHAHID MUM: ""Energy-Efficient Stable Matching for Resource Allocation in Energy Harvesting-Based Device-to-Device Communications"", 《IEEE ACCESS》 *
徐勇军; 刘子腱; 李国权; 陈前斌; 林金朝: ""基于NOMA的无线携能D2D通信鲁棒能效优化算法"", 《电子与信息学报》 *
徐勇军; 彭瑶; 余晓磊; 陈前斌: ""面向5G协作通信***的资源分配技术综述"", 《重庆邮电大学学报(自然科学版)》 *
徐勇军; 李国权; 徐鹏; 陈前斌: ""异构无线网络资源分配算法研究综述"", 《重庆邮电大学学报(自然科学版)》 *
徐勇军; 谢豪; 陈前斌; 林金朝; 刘期烈: ""基于不完美CSI的异构NOMA网络能效优化算法"", 《通信学报》 *
柴蓉; 王令; 陈明龙; 陈前斌: ""基于时延优化的蜂窝D2D通信联合用户关联及内容部署算法"", 《电子与信息学报》 *
马忠贵; 宋佳倩: ""5G超密集网络的能量效率研究综述"", 《工程科学学报》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023069856A1 (en) * 2021-10-19 2023-04-27 Qualcomm Incorporated Energy harvesting based on power headroom
US11917556B2 (en) 2021-10-19 2024-02-27 Qualcomm Incorporated Energy harvesting based on power headroom
CN115175147A (en) * 2022-07-28 2022-10-11 重庆邮电大学 Unmanned aerial vehicle-assisted D2D communication network robust energy efficiency optimization 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
CN115413008A (en) * 2022-08-09 2022-11-29 南京邮电大学 Energy-carrying communication OFDM system energy efficiency optimization method based on ellipsoid method

Also Published As

Publication number Publication date
CN113473422B (en) 2022-06-21

Similar Documents

Publication Publication Date Title
CN113473422B (en) B5G-oriented wireless energy-carrying D2D network efficient resource allocation method
Yang et al. Optimal resource allocation in full-duplex ambient backscatter communication networks for wireless-powered IoT
CN109413724B (en) MEC-based task unloading and resource allocation scheme
Liu et al. Uplink resource allocation for NOMA-based hybrid spectrum access in 6G-enabled cognitive Internet of Things
Alqerm et al. Sophisticated online learning scheme for green resource allocation in 5G heterogeneous cloud radio access networks
CN110417496B (en) Cognitive NOMA network stubborn resource allocation method based on energy efficiency
CN102316597B (en) Resource scheduling method and device for multiple input multiple output (MIMO) system
KR102280903B1 (en) Joint Resource Allocation Apparatus and method for Multi-User OFDMA SWIPT Systems
CN108718445B (en) QoS-driven D2D secure communication resource allocation method
CN111314894B (en) NOMA (non-oriented access memory) and energy-carrying D2D fusion network-oriented robust resource allocation method
AlQerm et al. Enhanced machine learning scheme for energy efficient resource allocation in 5G heterogeneous cloud radio access networks
CN110784882B (en) Energy acquisition D2D communication resource allocation method based on reinforcement learning
CN103249157B (en) The resource allocation methods based on cross-layer scheduling mechanism under imperfect CSI condition
CN107613556B (en) Full-duplex D2D interference management method based on power control
CN101895991A (en) Cognitive radio system based on relay cooperative transmission and resource allocation method thereof
CN110769514A (en) Heterogeneous cellular network D2D communication resource allocation method and system
Panda Joint user patterning and power control optimization of MIMO–NOMA systems
CN110139282B (en) Energy acquisition D2D communication resource allocation method based on neural network
CN114219354A (en) Resource allocation optimization method and system based on federal learning
CN115866787A (en) Network resource allocation method integrating terminal direct transmission communication and multi-access edge calculation
Esmat et al. Joint channel selection and optimal power allocation for multi‐cell D2D communications underlaying cellular networks
CN102316596B (en) Control station and method for scheduling resource block
CN110061826B (en) Resource allocation method for maximizing energy efficiency of multi-carrier distributed antenna system
Elnourani et al. Underlay device-to-device communications on multiple channels
CN106851726A (en) A kind of cross-layer resource allocation method based on minimum speed limit constraint

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
TA01 Transfer of patent application right

Effective date of registration: 20220602

Address after: 518000 708, Huiyi building, No. 9, Zhongxin Road, Taoyuan community, Dalang street, Longhua District, Shenzhen, Guangdong

Applicant after: Shenzhen Hongyue Information Technology Co.,Ltd.

Address before: 400065 Chongqing Nan'an District huangjuezhen pass Chongwen Road No. 2

Applicant before: CHONGQING University OF POSTS AND TELECOMMUNICATIONS

Effective date of registration: 20220602

Address after: 518000 floor 25, block B, building 10, Shenzhen Bay science and technology ecological park, No. 10, Gaoxin South ninth Road, high tech Zone community, Yuehai street, Nanshan District, Shenzhen, Guangdong

Applicant after: Shenzhen lingchuang Xingtong Technology Co.,Ltd.

Address before: 518000 708, Huiyi building, No. 9, Zhongxin Road, Taoyuan community, Dalang street, Longhua District, Shenzhen, Guangdong

Applicant before: Shenzhen Hongyue Information Technology Co.,Ltd.

TA01 Transfer of patent application right
GR01 Patent grant
GR01 Patent grant