CN111246560A - Wireless energy-carrying communication time slot and power joint optimization method - Google Patents

Wireless energy-carrying communication time slot and power joint optimization method Download PDF

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CN111246560A
CN111246560A CN202010216085.2A CN202010216085A CN111246560A CN 111246560 A CN111246560 A CN 111246560A CN 202010216085 A CN202010216085 A CN 202010216085A CN 111246560 A CN111246560 A CN 111246560A
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energy
optimization
omega
power
time slot
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CN111246560B (en
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吕玉静
刘玉涛
郑博文
魏萌
秦茜
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CETC 54 Research Institute
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
    • H04W52/267—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the information rate
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J50/00—Circuit arrangements or systems for wireless supply or distribution of electric power
    • H02J50/20—Circuit arrangements or systems for wireless supply or distribution of electric power using microwaves or radio frequency waves
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J50/00—Circuit arrangements or systems for wireless supply or distribution of electric power
    • H02J50/80—Circuit arrangements or systems for wireless supply or distribution of electric power involving the exchange of data, concerning supply or distribution of electric power, between transmitting devices and receiving devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H04W52/36—TPC using constraints in the total amount of available transmission power with a discrete range or set of values, e.g. step size, ramping or offsets
    • H04W52/367—Power values between minimum and maximum limits, e.g. dynamic range
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Power Engineering (AREA)
  • Quality & Reliability (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a wireless energy-carrying communication time slot and power joint optimization method, and a traditional communication system has high transmission energy consumption due to the fact that radio frequency signal energy cannot be effectively utilized. In order to save energy of the communication system, the communication system is proposed to transmit information and energy simultaneously in a plurality of time slots so as to realize wireless energy carrying communication. Firstly, dividing transmission time into a plurality of time slots, and sending information or energy in different time slots by a transmitter according to requirements; then, the receiver receives information or collects energy in a corresponding time slot, and the collected energy is used for providing circuit energy consumption; and finally, by jointly optimizing the time slot and the power allocation, the transmission rate of the system is maximized on the basis of ensuring the energy requirement of the system. Simulation results show that: compared with an equal-time power distribution algorithm, the transmission efficiency of the algorithm is improved by about 40 bps; and the performance of the proposed algorithm is remarkably improved as the number of time slots is increased. Therefore, the algorithm effectively improves the transmission performance by collecting wireless energy.

Description

Wireless energy-carrying communication time slot and power joint optimization method
Technical Field
The invention relates to a wireless energy-carrying communication time slot and power joint optimization method.
Background
The existing wireless energy-carrying communication time slot and power joint optimization method mainly comprises the following steps:
(1) by optimizing the distribution coefficients of the time slots and the power streams, the information transmission performance can be maximized on the basis of ensuring sufficient acquired energy, and the consumption of energy transmission on resources is reduced.
(2) According to the wireless energy-carrying communication under multi-antenna transmission, part of antennas are used for energy transmission, and the rest of antennas are used for information transmission, so that the grading gain of the wireless energy-carrying communication is improved.
(3) The wireless energy-carrying communication under the multi-carrier and multi-user scenes effectively optimizes the performance of the wireless energy-carrying communication by reasonably distributing carrier waves and user resources for information transmission and energy transmission.
The prior method for jointly optimizing the time slot and the power of the linear energy-carrying communication has the following disadvantages
(1) Channel fading has some impact on wireless energy-carrying communications.
(2) Information and energy are transmitted in equal time without considering the influence of channel time variation on performance
The invention researches a multi-time slot wireless energy-carrying communication system, a transmitter can transmit information or energy in any time slot, and the transmission efficiency of the system under a fading channel is effectively improved through the joint optimal allocation of the time slot and the power.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method for joint optimal allocation of timeslots and powers, which avoids the above-mentioned disadvantages of the background art.
The technical scheme adopted by the invention is as follows:
the wireless energy-carrying communication time slot and power joint optimization method comprises the following steps:
(1) the multi-time slot wireless energy-carrying communication system divides the transmission time into a plurality of time slots, and sets all the time slots to be omegaNThe transmitter sends information signal or energy signal to the receiver in each time slot according to the requirement, and the set of the information time slots is set to be omegaIThe energy time slot set is omegaE，ΩIAnd ΩESatisfy the requirement of
Figure BDA0002424467780000021
ΩI∪ΩE＝ΩN；
(2) Setting a constraint condition: the energy collected by the receiver is higher than the minimum energy requirement EminTotal emission energy of the transmitter is less than maximum emission energy Emax(ii) a And an optimization problem is established according to the constraint condition; the optimization problem is represented as:
Figure BDA0002424467780000022
s.t.EH≥Emin
ET≤Emax
pi≥0,i＝1,2,...,N
Figure BDA0002424467780000023
ΩI∪ΩE＝ΩN
in the formula, piFor the transmission power of the transmitter in the ith time slot, EHEnergy harvested for the receiver, ETIs the total transmitted energy of the transmitter, hiIs the channel gain of the i-slot,
Figure BDA0002424467780000024
is the channel noise power, R is the channel capacity, N is a positive integer;
(3) by usingSolving the optimization problem by a Lagrange multiplier method and calculating an information time slot set omegaISet of energy slots omegaEAnd transmit power { piThe optimal value of.
The method for solving the optimization problem by using the Lagrange multiplier method in the step (3) specifically comprises the following steps:
the Lagrangian function is expressed as
Figure BDA0002424467780000025
In the formula, mu1Not less than 0 and mu2Lagrange multiplier is greater than or equal to 0;
function L (omega)I,ΩE,{pi}) is expressed as
Figure BDA0002424467780000031
According to the dual principle, the function L is a convex function, which indicates that the optimization problem has duality, and therefore, the simplified optimization problem is expressed as:
Figure BDA0002424467780000032
s.t.μ1≥0；μ2≥0
wherein, mu1And mu2The optimal solution of (a) is solved by a sub-gradient method, which is expressed as:
Figure BDA0002424467780000033
Figure BDA0002424467780000034
in the formula: t is the number of iterations,
Figure BDA0002424467780000035
and
Figure BDA0002424467780000036
is the iteration step, τ represents time; optimal μ is obtained when the above equation converges1And mu2At this time, the constraint condition E is satisfiedH＝EminAnd ET＝Emax(ii) a To obtain the optimum mu1And mu2Then, the simplified optimization problem is solved to obtain omegaE、ΩIAnd { piThe optimal value of.
Wherein, solving the simplified optimization problem specifically comprises: the simplified optimization problem is decomposed into three sub-optimization problems of power optimization, time slot optimization and joint optimization, and the specific solving process of the three sub-optimization problems is as follows:
power optimization: fixed set of time slots omegaEAnd ΩIOptimizing the power { pi}；
{piThe optimum value of (b) is obtained by the following formula
Figure BDA0002424467780000041
Will function L (Ω)I,ΩE,{pi}) into the above formula { piThe optimal value formula of
Figure BDA0002424467780000042
When i ∈ ΩIWhen is, piNot less than 0, to obtain
Figure BDA0002424467780000043
When i ∈ ΩEWhen it is obtained
Figure BDA0002424467780000044
In the formula: p is a radical ofmaxFor sub-slot power maximum, symbol (x)+Represents the maximum between x and 0, η represents the energy harvesting efficiency;
wherein the mu is updated by a sub-gradient method1And mu2Up to mu1And mu2All converge, mu to converge1And mu2The optimal value formula after the derivation of the formula is substituted to obtain the optimized power { pi}；
Time slot optimization: given an optimized power piH, optimize omegaEAnd ΩI；
According to the formula L (omega)I,ΩE,{pi}) to obtain:
Figure BDA0002424467780000051
in the formula:
Figure BDA0002424467780000052
Figure BDA0002424467780000053
when { piWhen fixed, Ψ is a constant value, so optimization of the problem only needs to be done
Figure BDA0002424467780000054
Take a maximum value, i.e.
Figure BDA0002424467780000055
Further obtain phii＞0,
Figure BDA0002424467780000056
I.e. only all phi need be selectediSub-carrier with more than 0 is reduced to omegaE，ΩI＝ΩN-ΩE；
Joint optimization: the joint optimization adopts an alternate iterative optimization algorithm, namely: initializing omegaEAnd ΩIOptimization of { pi}; fixed optimized piH, optimize omegaEAnd ΩI(ii) a Repeating the steps until convergence;
the specific process is as follows:
1) defining a set of timeslots as ΩN＝{1,2,3,...,N}，
Initialization
Figure BDA0002424467780000057
And
Figure BDA0002424467780000058
2) using an initial set of time slots, according to the formula
Figure BDA0002424467780000061
Optimizing Power { pi}；
3) Using { piCalculate phiiA value;
4) using formulas
Figure BDA0002424467780000062
Recalculate ΩEAnd ΩI；
5) Repeating the steps 2) -4) until the R value of the target function is converged to obtain omegaE、ΩIAnd { piThe optimal value of.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in:
the invention researches a wireless energy-carrying communication time slot and power joint optimization method. The transmitter can transmit information or energy in any time slot, and effectively improves the transmission efficiency of the system under a fading channel through the joint optimization allocation of the time slot and the power.
Drawings
FIG. 1 is a model of a wireless energy-carrying communication system of the present invention;
FIG. 2 is a schematic diagram of the time slot allocation of the multi-time-slot wireless energy-carrying communication of the present invention;
FIG. 3 is a graph of throughput as a function of maximum transmit energy for various methods of the present invention;
FIG. 4 is a graph of throughput as a function of transmit energy for different numbers of time slots in accordance with the present invention;
FIG. 5 is a graph of throughput as a function of minimum energy requirement for various methods of the present invention;
FIG. 6 is a graph of throughput for different slot numbers as a function of minimum energy requirement in accordance with the present invention;
FIG. 7 is a graph of throughput versus number of slots for the present invention;
FIG. 8 is information and energy power allocation under different methods of the present invention;
FIG. 9 is a power allocation of information and energy at different slot numbers according to the present invention;
fig. 10 is a throughput comparison of different wireless energy-carrying communication methods of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
A model of a multi-slot wireless energy-carrying communication system is shown in fig. 1. The wireless signal sent by the transmitter carries information due to information modulation and contains radio frequency energy by electromagnetic wave transmission. The receiver has an energy collection unit in addition to a conventional information decoding unit, and the receiver decodes information or collects energy in a specific time slot, and the collected energy can provide circuit energy consumption for the communication system. The energy collection unit is mainly composed of a rectification circuit and used for converting alternating current signals into direct current electric energy.
As shown in fig. 2, the multi-slot wireless energy-carrying communication system divides the transmission time into a plurality of time slots, the transmitter transmits information signals or energy signals in each time slot according to requirements, and the receiver decodes information or collects energy in the corresponding time slot. Assuming that the total number of slots is N, each slot is of length τ. Considering the time-varying channel, the transmitter has a transmission power p in the ith time sloti. The transmitter can select any time slot to transmit information or energy, and the information time slot set is assumed to be omegaIThe energy time slot set is omegaE，ΩIAnd ΩESatisfy the requirement of
Figure BDA0002424467780000072
ΩI∪ΩE＝ΩNIn the formula of middle omegaNIs a set of all time slots.
The specific treatment process comprises the following steps:
(1) multiple timesThe transmission time of the slot wireless energy-carrying communication system is divided into a plurality of time slots, and the set of all the time slots is set to be omegaNThe transmitter sends information signal or energy signal to the receiver in each time slot according to the requirement, and the set of the information time slots is set to be omegaIThe energy time slot set is omegaE，ΩIAnd ΩESatisfy the requirement of
Figure BDA0002424467780000071
ΩI∪ΩE＝ΩN；
(2) Setting a constraint condition: the energy collected by the receiver is higher than the minimum energy requirement EminTotal emission energy of the transmitter is less than maximum emission energy Emax(ii) a And an optimization problem is established according to the constraint condition; the optimization problem is represented as:
Figure BDA0002424467780000081
s.t.EH≥Emin
ET≤Emax
pi≥0,i＝1,2,...,N
Figure BDA0002424467780000082
ΩI∪ΩE＝ΩN
in the formula, piFor the transmission power of the transmitter in the ith time slot, EHEnergy harvested for the receiver, ETIs the total transmitted energy of the transmitter, hiIs the channel gain of the i-slot,
Figure BDA0002424467780000083
is the channel noise power, R is the channel capacity, N is a positive integer;
(3) solving the optimization problem by utilizing a Lagrange multiplier method and calculating an information time slot set omegaISet of energy slots omegaEAnd transmit power { piThe optimal value of.
The method for solving the optimization problem by using the Lagrange multiplier method specifically comprises the following steps:
the Lagrangian function is expressed as
Figure BDA0002424467780000084
In the formula, mu1Not less than 0 and mu2Lagrange multiplier is greater than or equal to 0;
function L (omega)I,ΩE,{pi}) is expressed as
Figure BDA0002424467780000085
According to the dual principle, the function L is a convex function, which indicates that the optimization problem has duality, and therefore, the simplified optimization problem is expressed as:
Figure BDA0002424467780000086
s.t.μ1≥0；μ2≥0
wherein, mu1And mu2The optimal solution of (a) is solved by a sub-gradient method, which is expressed as:
Figure BDA0002424467780000091
Figure BDA0002424467780000092
in the formula: t is the number of iterations,
Figure BDA0002424467780000093
and
Figure BDA0002424467780000094
is the iteration step, τ represents time; optimal μ is obtained when the above equation converges1And mu2At this time, the constraint condition E is satisfiedH＝EminAnd ET＝Emax(ii) a To obtain the optimum mu1And mu2And then, solving the simplified optimization problem, and decomposing the simplified optimization problem into three sub-optimization problems of power optimization, time slot optimization and joint optimization, wherein the specific solving process of the three sub-optimization problems is as follows:
power optimization: fixed set of time slots omegaEAnd ΩIOptimizing the power { pi}；
{piThe optimum value of (b) is obtained by the following formula
Figure BDA0002424467780000095
Will function L (Ω)I,ΩE,{pi}) into the above formula { piThe optimal value formula of
Figure BDA0002424467780000096
When i ∈ ΩIWhen is, piNot less than 0, to obtain
Figure BDA0002424467780000097
When i ∈ ΩEWhen it is obtained
Figure BDA0002424467780000101
In the formula: p is a radical ofmaxFor sub-slot power maximum, symbol (x)+Represents the maximum between x and 0, η represents the energy harvesting efficiency;
wherein the mu is updated by a sub-gradient method1And mu2Up to mu1And mu2All converge, mu to converge1And mu2The optimal value formula after the derivation of the formula is substituted to obtain the optimized power { pi}；
Time slot optimization: given an optimized power piH, optimize omegaEAnd ΩI；
According to the formula L (omega)I,ΩE,{pi}) to obtain:
Figure BDA0002424467780000102
in the formula:
Figure BDA0002424467780000103
Figure BDA0002424467780000104
when { piWhen fixed, Ψ is a constant value, so optimization of the problem only needs to be done
Figure BDA0002424467780000111
Take a maximum value, i.e.
Figure BDA0002424467780000112
Further obtain phii＞0,
Figure BDA0002424467780000113
I.e. only all phi need be selectediSub-carrier with more than 0 is reduced to omegaE，ΩI＝ΩN-ΩE；
Joint optimization: the joint optimization adopts an alternate iterative optimization algorithm, namely: initializing omegaEAnd ΩIOptimization of { pi}; fixed optimized piH, optimize omegaEAnd ΩI(ii) a Repeating the steps until convergence;
the specific process is as follows:
1) defining a set of timeslots as ΩN＝{1,2,3,...,N}，
Initialization
Figure BDA0002424467780000114
And
Figure BDA0002424467780000115
2) using a primarySet of starting time slots according to the formula
Figure BDA0002424467780000116
Optimizing Power { pi}；
3) Using { piCalculate phiiA value;
4) using formulas
Figure BDA0002424467780000117
Recalculate ΩEAnd ΩI；
5) Repeating the steps 2) -4) until the R value of the target function is converged to obtain omegaE、ΩIAnd { piThe optimal value of.
Since the objective function R is a convex function, the value of R is non-decreasing for each iteration, i.e.:
Figure BDA0002424467780000118
therefore, when the R value converges, ΩI、ΩEAnd
Figure BDA0002424467780000121
all converge.
The optimization problem is decomposed into three sub-optimization problems of power optimization, time slot optimization and joint optimization, and simulation analysis is carried out on different methods.
In simulation, the maximum number of time slots is set to be 20, the noise power is 1mW, the energy collection efficiency is 0.8, the time slot length is 1s, and the channel obeys Rayleigh distribution. The performance of the proposed method and the traditional equal-time power distribution method is analyzed in a simulation mode. The equal time power allocation method means that information and energy are allocated with the same number of transmission time slots, and the respective time slots are allocated with the same power.
FIG. 3 shows the variation of throughput rate with maximum transmit energy under different methods, and the results show that the proposed method has significant performance advantages over the equal-time power allocation method, when EmaxWhen 80mJ, the throughput rate of the proposed method is improved by about 40 bps; furthermore, when the transmission energy is increased, the throughput of the proposed methodThe rate is increased, which indicates that the proposed method can preferentially distribute power. Fig. 4 shows the variation of throughput with transmitted energy for different numbers of time slots. It can be seen that as the number of slots increases, the throughput also increases. Indicating that increasing the number of time slots may allocate more available time slots for wireless energy carrying communications.
Fig. 5 shows the variation of throughput rate with minimum energy requirement, and it can be seen that the higher the energy requirement, the lower the throughput rate, which indicates that the energy collection will occupy a certain transmission resource. Therefore, resources must be allocated reasonably, and a compromise between information and energy transmission is obtained, i.e., sufficient transmission energy is ensured and information transmission performance is improved as much as possible. Fig. 6 shows the variation of throughput with minimum energy for different numbers of slots. It can be seen that if the system energy demand is greater, i.e. when EminAbove 20mJ, the transmission rate R is less than 10 bps. The efficiency of energy collection should therefore be increased as much as possible in order to increase the transmission rate.
Figure 7 shows the variation of the system throughput rate with the number of slots. It can be seen that as the number of timeslots increases, throughput rate may be significantly improved, while throughput rate improvement of the equal-time power allocation method is not significant. This is because the number of time slots increases and the frequency selective fading increases, and the method herein has channel adaptivity and can effectively improve the throughput. FIG. 8 shows power allocation for different methods of information and energy, and it can be seen that the method herein can allocate more power for information as transmission energy increases; as energy demand increases, information power decreases and more power is used for energy transfer.
Fig. 9 is a power allocation of information and energy at different slot numbers. It can be seen that when the number of timeslots is increased from 20 to 40, the power allocated to energy is increased accordingly, because when the number of timeslots is increased, the method selects the timeslot with the best channel to transmit information, and therefore, less information power is required to obtain higher transmission performance, and the power for energy collection is increased accordingly.
Fig. 10 compares the throughput of different wireless energy-carrying communication methods, and it can be seen that the proposed method can obtain higher throughput. Wireless energy-carrying communication based on time shifting and power splitting allocates fixed time slots and power for information transmission and energy transmission, and cannot be changed adaptively according to changes of time and channel conditions. The multi-slot wireless energy-carrying communication proposed by the method can allocate optimal power according to the current channel condition in different time slots, so that the transmission performance can be improved.

Claims (3)

1. The method for jointly optimizing the time slot and the power of the wireless energy-carrying communication is characterized by comprising the following steps of:
(1) the multi-time slot wireless energy-carrying communication system divides the transmission time into a plurality of time slots, and sets all the time slots to be omegaNThe transmitter sends information signal or energy signal to the receiver in each time slot according to the requirement, and the set of the information time slots is set to be omegaIThe energy time slot set is omegaE，ΩIAnd ΩESatisfy the requirement of
Figure FDA0002424467770000011
ΩI∪ΩE＝ΩN；
(2) Setting a constraint condition: the energy collected by the receiver is higher than the minimum energy requirement EminTotal emission energy of the transmitter is less than maximum emission energy Emax(ii) a And an optimization problem is established according to the constraint condition; the optimization problem is represented as:
Figure FDA0002424467770000012
s.t.EH≥Emin
ET≤Emax
pi≥0,i＝1,2,...,N
Figure FDA0002424467770000013
ΩI∪ΩE＝ΩN
in the formula, piFor the transmission power of the transmitter in the ith time slot, EHEnergy harvested for the receiver, ETIs the total transmitted energy of the transmitter, hiIs the channel gain of the i-slot,
Figure FDA0002424467770000014
is the channel noise power, R is the channel capacity, N is a positive integer;
(3) solving the optimization problem by utilizing a Lagrange multiplier method and calculating an information time slot set omegaISet of energy slots omegaEAnd transmit power { piThe optimal value of.
2. The method for jointly optimizing the wireless energy-carrying communication timeslot and power according to claim 1, wherein the step (3) of solving the optimization problem by using a lagrangian multiplier method specifically comprises:
the Lagrangian function is expressed as
Figure FDA0002424467770000015
In the formula, mu1Not less than 0 and mu2Lagrange multiplier is greater than or equal to 0;
function L (omega)I,ΩE,{pi}) is expressed as
Figure FDA0002424467770000021
According to the dual principle, the function L is a convex function, which indicates that the optimization problem has duality, and therefore, the simplified optimization problem is expressed as:
Figure FDA0002424467770000022
s.t.μ1≥0；μ2≥0
wherein, mu1And mu2The optimal solution of (a) is solved by a sub-gradient method, which is expressed as:
Figure FDA0002424467770000023
Figure FDA0002424467770000024
in the formula: t is the number of iterations,
Figure FDA0002424467770000025
and
Figure FDA0002424467770000026
is the iteration step, τ represents time; optimal μ is obtained when the above equation converges1And mu2At this time, the constraint condition E is satisfiedH＝EminAnd ET＝Emax(ii) a To obtain the optimum mu1And mu2Then, the simplified optimization problem is solved to obtain omegaE、ΩIAnd { piThe optimal value of.
3. The method of claim 2, wherein the solving of the simplified optimization problem comprises: the simplified optimization problem is decomposed into three sub-optimization problems of power optimization, time slot optimization and joint optimization, and the specific solving process of the three sub-optimization problems is as follows:
power optimization: fixed set of time slots omegaEAnd ΩIOptimizing the power { pi}；
{piThe optimum value of (b) is obtained by the following formula
Figure FDA0002424467770000031
Will function L (Ω)I,ΩE,{pi}) into the above formula { piThe optimal value formula of
Figure FDA0002424467770000032
When i ∈ ΩIWhen is, piNot less than 0, to obtain
Figure FDA0002424467770000033
When i ∈ ΩEWhen it is obtained
Figure FDA0002424467770000034
In the formula: p is a radical ofmaxFor sub-slot power maximum, symbol (x)+Represents the maximum between x and 0, η represents the energy harvesting efficiency;
wherein the mu is updated by a sub-gradient method1And mu2Up to mu1And mu2All converge, mu to converge1And mu2The optimal value formula after the derivation of the formula is substituted to obtain the optimized power { pi}；
Time slot optimization: given an optimized power piH, optimize omegaEAnd ΩI；
According to the formula L (omega)I,ΩE,{pi}) to obtain:
Figure FDA0002424467770000041
in the formula:
Figure FDA0002424467770000042
Figure FDA0002424467770000043
when { piWhen fixed, Ψ is a constant value, so optimization of the problem only needs to be done
Figure FDA0002424467770000044
Take a maximum value, i.e.
Figure FDA0002424467770000045
Further obtain phii＞0,
Figure FDA0002424467770000046
I.e. only all phi need be selectediSub-carrier with more than 0 is reduced to omegaE，ΩI＝ΩN-ΩE；
Joint optimization: the joint optimization adopts an alternate iterative optimization algorithm, namely: initializing omegaEAnd ΩIOptimization of { pi}; fixed optimized piH, optimize omegaEAnd ΩI(ii) a Repeating the steps until convergence;
the specific process is as follows:
1) defining a set of timeslots as ΩN＝{1,2,3,...,N}，
Initialization
Figure FDA0002424467770000047
And
Figure FDA0002424467770000048
2) using an initial set of time slots, according to the formula
Figure FDA0002424467770000051
Optimizing Power { pi}；
3) Using { piCalculate phiiA value;
4) using formulas
Figure FDA0002424467770000052
Recalculate ΩEAnd ΩI；
5) Repeating the steps 2) -4) until the R value of the target function is converged to obtain omegaE、ΩIAnd { piThe optimal value of.
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