CN107171701B - Power distribution method of MassiveMIMO system based on hybrid energy acquisition - Google Patents

Power distribution method of MassiveMIMO system based on hybrid energy acquisition Download PDF

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CN107171701B
CN107171701B CN201710255619.0A CN201710255619A CN107171701B CN 107171701 B CN107171701 B CN 107171701B CN 201710255619 A CN201710255619 A CN 201710255619A CN 107171701 B CN107171701 B CN 107171701B
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CN107171701A (en
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张阳
张丹
庞立华
栾英姿
韩芮雨
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Guangzhou Its Communication Equipment Co ltd
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Xidian University
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    • H—ELECTRICITY
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    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B17/30—Monitoring; Testing of propagation channels
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
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    • H04B7/0426—Power distribution
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    • 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
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Abstract

The invention belongs to the technical field of mobile communication, and discloses a power distribution method of a Massive MIMO system based on hybrid energy acquisition. The invention establishes a hybrid energy acquisition model based on the uncertainty of energy acquisition, and finally allocates optimal power to each user by iteratively updating system information according to the time-varying characteristics of the channel state and the energy state and the coordination influence among users, so that the system can obtain the highest throughput and simultaneously maximally save the utilization of non-renewable resources, thereby achieving the purposes of green and energy saving.

Description

Power distribution method of MassiveMIMO system based on hybrid energy acquisition
Technical Field
The invention belongs to the technical field of mobile communication, and particularly relates to a power distribution method of a Massive MIMO system based on hybrid energy acquisition.
Background
With the development of energy collection technology, network energy is gradually abundant, not only can the traditional power grid be adopted for supplying power, but also environmental energy (wind energy, solar energy, vibration energy and the like) can be used for supplying power to a system, renewable resources are fully utilized, and self-supply energy supply of the future network is realized on the basis of energy conservation and emission reduction. A base station powered by renewable energy sources alone is therefore difficult to maintain a stable energy supply, and is not sufficient to guarantee minimum quality of service (QoS) requirements. The base station needs to adopt a hybrid energy supply mode of a complementary mode to meet continuous energy requirements and ensure long-term stability of the performance of the communication system. Massive MIMO is a key technology for the development of a fifth-generation mobile communication technology, green communication is a future development trend for realizing the Massive MIMO system, a hybrid energy acquisition technology can keep the continuous supply of energy and stable service quality, and when the renewable energy is sufficient, the renewable energy provides basic service, so that the energy consumption of a base station can be effectively reduced, and the network performance can be improved; when renewable energy is insufficient, the grid provides service, ensuring a continuous and long-lasting energy supply.
In summary, the problems of the prior art are as follows: most researches are only suitable for systems with single energy sources, few researches are carried out on the scene of coexistence of renewable energy sources and a power grid, and at present, the analysis of simpler wireless communication systems such as a relay channel and a multiple access channel which are composed of a single link and three nodes is mostly considered, and the researches are not suitable for Massive MIMO systems. While the Massive MIMO is a core technology of 5G, which is a trend of future communication development, how to apply hybrid energy acquisition to a Massive MIMO system is a problem in the prior art.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a power distribution method of a Massive MIMO system based on hybrid energy acquisition.
The invention is realized in this way, a Massive MIMO system is based on the power distribution method that the mixed energy gathers, characterized by that, the said Massive MIMO system is based on the uncertainty that the power distribution method that the mixed energy gathers establishes the mixed energy and gathers the model about energy acquisition, and according to channel state and energy state time varying characteristic and coordination influence among users, through updating the system information iteratively, distribute the optimum power for each user finally, while obtaining the highest throughput, the utilization of the maximum saving non-renewable resource;
the energy collection model adopts Poisson distribution to analyze the energy collection process and the channel fading variation process, and the energy arrival time and the channel fading variation time are countable and are respectively used
Figure BDA0001273301650000021
And
Figure BDA0001273301650000022
represents a sequence of energy arrival times and a sequence of channel variation times, and
Figure BDA0001273301650000023
the interval of the time sequence according to the properties of the Poisson distribution
Figure BDA0001273301650000024
And
Figure BDA0001273301650000025
respectively obey mean value of 1/lambdaeAnd 1/lambdafThe distribution of indices;
the optimal power allocation is expressed as:
Figure BDA0001273301650000026
wherein
Figure BDA0001273301650000027
And
Figure BDA0001273301650000028
respectively the transmission power of the signals provided by the energy harvesting and the grid,
Figure BDA0001273301650000029
and
Figure BDA00012733016500000210
consuming power for the circuits provided by the energy harvesting and the grid respectively,
Figure BDA00012733016500000211
battery capacity as energy harvester, Ein(i) For the energy to be collected at time i,
Figure BDA00012733016500000212
for maximum power supplied by the grid, PmaxMaximum value of signal transmission power, N represents the number of channel changes, Q-1 represents the number of energy arrivals, η is power amplifier efficiency, LiIs the i ═ { 1., Q + N } time interval, and [ x ═ x]+＝max(0,x)，
Figure BDA0001273301650000031
Denotes that 0. ltoreq. x. ltoreq.pc，pcα, τ, γ, μ are lagrange multipliers for the total circuit power consumption.
Further, the power distribution method of the Massive MIMO system based on hybrid energy acquisition comprises the following steps:
step one, in a Massive MIMO downlink system, a base station stores collected energy in a rechargeable battery, and when the energy in the rechargeable battery is insufficient, a power grid serves as a standby battery to supply power to the system;
analyzing the communication system, representing the energy collection process and the change process of the channel state by Poisson distribution according to the time-varying characteristics of the channel state and the energy state, wherein the arrival time of the collected energy and the change time of the channel state are respectively obeyed by the rate of lambdaeAnd λfPoisson count distribution of (a);
designing an optimization model with the aim of maximizing system throughput, and optimizing data transmission power distributed by each user and circuit power consumed in the transmission process;
solving the established optimization problem, converting the original problem into a dual problem by utilizing Lagrange duality, and then performing iterative update on a Lagrange multiplier by adopting a gradient descent method to obtain the optimal solution of the original problem;
and step five, according to the optimal solution of the problem, the base station distributes power for each user from the rechargeable battery and the power grid respectively and transmits signals.
Further, the large-scale fading factor is expressed as:
Figure BDA0001273301650000032
wherein z iskIs a lognormal random variable, 10log (z)k) Is a mean of 0 and a variance of
Figure BDA0001273301650000033
Complex Gaussian random variable of rkIs the distance, r, from the base station to the kth userhV is the path loss for the reference distance;
the sum rate of downlink transmission users of the Massive MIMO system is as follows:
Figure BDA0001273301650000034
where G is the channel matrix, pkFor the transmission power of the link between the base station and the kth user, the noise vector is calculated from the mean value of 0 and the variance of
Figure BDA0001273301650000041
Complex gaussian random variables.
Further, the randomness of the energy harvesting makes it necessary to additionally satisfy energy cause and effect constraints and battery capacity constraints when using the radio resources, namely:
Figure BDA0001273301650000042
in the whole transmission process, the frequency of channel change is N times, the frequency of energy arrival is Q-1 times, and the arrival energy of the initial time is recorded as Ein(1)＝E10 for
Figure BDA0001273301650000048
At a moment in time, if
Figure BDA0001273301650000049
In order for the energy to reach the moment,
Figure BDA00012733016500000410
if it is not
Figure BDA00012733016500000411
Is the time at which the channel is changing,
Figure BDA00012733016500000412
further, the optimization problem can be modeled as:
Figure BDA0001273301650000043
where the c1 constraint indicates that the transmitted power is derived from the energy limit of the rechargeable battery, and c2 requires that the remaining energy of the rechargeable battery be less than the battery capacity to avoid energy overflow and waste
Figure BDA0001273301650000044
c3 denotes the power limit from the grid supply, the requested power being less than the upper power limit of the grid
Figure BDA0001273301650000045
c4 sets the maximum transmission power PmaxThe limit of (2).
Further, when i > 1,
Figure BDA0001273301650000046
the optimal value of (c) is determined by a feasible set of other constraints, i is 1,
Figure BDA0001273301650000047
thus, the following steps are obtained:
Figure BDA0001273301650000051
wherein
Figure BDA0001273301650000052
X is more than or equal to 0 and less than or equal to pc;
the lagrange multiplier is updated by the formula:
Figure BDA0001273301650000053
where j ∈ {1, … Q + N },
Figure BDA0001273301650000054
in order to be able to perform the number of iterations,
Figure BDA0001273301650000055
and
Figure BDA0001273301650000056
are all iteration step sizes.
The invention has the advantages and positive effects that: in the downlink transmission process of a single-cell multi-user Massive MIMO system, a base station serving as a sending end is powered by renewable energy and a power grid together, the renewable energy is collected and stored in a battery for data transmission, and when the battery is lack of electricity, the base station ensures service by utilizing the power grid energy. According to the invention, a hybrid energy acquisition mechanism is established based on the uncertainty of energy acquisition, and according to the time-varying characteristics of the channel state and the energy state and the coordination influence among users, the system information is updated iteratively to finally obtain the optimal power distribution for each user, so that the system can obtain the highest throughput, simultaneously, the utilization of non-renewable resources is saved to the maximum extent, and the purposes of green and energy saving are achieved.
The Massive MIMO system power distribution method based on hybrid energy acquisition provided by the invention improves the maximum throughput of the system by taking the maximum reachable rate of the system as a target. The method is simple and convenient to operate, and further saves the conventional energy resources. In fig. 3, the comparison algorithm adopted by the present invention is: the average power distribution under hybrid energy collection and the power distribution only with energy collection are shown, and under the same condition, the proposed optimization algorithm is superior to the average power distribution due to the consideration of different channel state information between users and base stations, while when only the energy collection is considered and no power of a power grid is available, the throughput of the system is far inferior to the proposed optimization algorithm due to the time-varying characteristic of energy arrival.
Drawings
Fig. 1 is a flowchart of a power allocation method for a Massive MIMO system based on hybrid energy acquisition according to an embodiment of the present invention.
Fig. 2 is a schematic model diagram of a downlink communication system using a Massive MIMO system according to an embodiment of the present invention.
Fig. 3 is a schematic diagram illustrating a comparison between throughput of an optimal power allocation algorithm based on hybrid energy acquisition and other algorithms in a Massive MIMO system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
As shown in fig. 1, a power distribution method based on hybrid energy harvesting provided by an embodiment of the present invention includes the following steps:
s101: in a Massive MIMO downlink system, a base station stores collected energy in a rechargeable battery, and when the energy in the rechargeable battery is insufficient, a power grid serves as a backup battery to supply power to the system;
s102: analyzing a communication system, representing an energy collection process and a change process of a channel state by adopting Poisson distribution according to time-varying characteristics of the channel state and the energy state, wherein the arrival time of the collected energy and the change time of the channel state respectively obey a rate of lambdaeAnd λfPoisson count distribution of (a);
s103: designing an optimization model with the aim of maximizing system throughput, and optimizing data transmission power distributed by each user and circuit power consumed in the transmission process;
s104: solving the established optimization problem, converting the original problem into a dual problem by utilizing Lagrange duality, and then performing iterative update on a Lagrange multiplier by adopting a gradient descent method to obtain the optimal solution of the original problem;
s105: and according to the optimal solution of the problem, the base station distributes power for each user from the rechargeable battery and the power grid respectively and carries out signal transmission.
The application of the principles of the present invention will now be described in further detail with reference to the accompanying drawings.
As shown in fig. 2, the system includes a base station with M antennas, which provides service for K single-antenna user terminals simultaneously, wherein the base station has an energy harvesting function, and it is assumed that a channel matrix is represented as
Figure BDA0001273301650000071
Sends a signal of
Figure BDA0001273301650000072
Then the received signal at the receiving end is:
Figure BDA0001273301650000073
wherein Y represents a received signal vector, pkIs the transmission power of the link between the base station and the k-th user, n is the mean value of 0 and the variance is
Figure BDA0001273301650000074
The channel matrix G ═ D, and the noise vector consisting of complex gaussian random variables1/2H，The elements in H are subject to independent and identically distributed complex Gaussian random variables with the mean value of 0 and the variance of 1. Diagonal matrix
Figure BDA0001273301650000076
Representing a large scale fading from the base station to the user, whichIn [ D ]kk]＝βk，βkβ because the distance between the user and the base station is much greater than the distance between the antennas, which is a large scale fading factorkChange more slowly, thereby having a large scale fading factor βkRegardless of frequency, the large-scale fading factor is:
Figure BDA0001273301650000077
wherein z iskIs a lognormal random variable, 10log (z)k) Is a mean of 0 and a variance of
Figure BDA0001273301650000078
Complex Gaussian random variable of rkIs the distance, r, from the base station to the kth userhFor reference distance, v is the path loss.
ZF precoding is to multiply the transmission signal by a weighting matrix to make the interference on each transmission antenna be 0 to eliminate the interference between users in the cell, and the precoding matrix is A-GH(GGH)-1The transmission signal is changed into a transmission signal
Figure BDA0001273301650000079
Can be expressed as x ═ GH(GGH)-1s, at which time the received signal becomes:
Figure BDA00012733016500000710
the received signal of the kth user is obtained as follows:
Figure BDA0001273301650000081
akand gkFor the k-th column elements of matrix a and matrix G, the signal to interference plus noise ratio is thus found to be:
Figure BDA0001273301650000082
wherein
Figure BDA0001273301650000083
Wherein g isiak＝δikThe signal to interference plus noise ratio is:
Figure BDA0001273301650000084
therefore, the resulting system sum rate is:
Figure BDA0001273301650000085
since the energy collection process is highly uncertain both temporally and spatially, the collection process is random and temporally independent, and in order to not lose generality, the present invention models the energy collection process using poisson distribution. The arrival time of energy collection and the time of channel fading variation are respectively obeyed by the rate of lambdaeAnd λfThe distribution of poisson counts, the time of arrival of the energy and the time of fading variation of the channel being countable, respectively
Figure BDA0001273301650000086
And
Figure BDA0001273301650000087
represents a sequence of energy arrival times and a sequence of channel variation times, and
Figure BDA0001273301650000088
the interval of the time sequence according to the properties of the Poisson distribution
Figure BDA0001273301650000089
And
Figure BDA00012733016500000810
respectively obey mean value of 1/lambdaeAnd 1/lambdafIs used as the index distribution of (1). During the whole transmission process, each timeThe energy arrival time or the channel state change time is recorded as a time breakpoint, the time from the previous time to the time breakpoint is recorded as a time period, wherein the time period i is recorded as [ t ]i,ti+1) Wherein t isiAnd ti+1The time interval of the time period i is L for two adjacent time breakpointsi＝ti+1-tiDuring each period of time, no energy arrives nor is there any change in the channel, so the transmit power of the signal is assumed to be the same. Suppose the energy collected at time i is EiIs represented by the formula (I) in which EiFor power transmission at the next moment, the energy collected only at the moment of energy arrival is charged into the battery, when the collected energy is not enough to meet the energy required by signal transmission, the energy required by residual transmission is provided by the power grid, and when the newly arrived energy exceeds the residual capacity of the battery
Figure BDA00012733016500000811
In time, the energy will overflow and cannot be reused, and energy waste will also result. The randomness of the energy harvesting therefore makes it necessary to additionally satisfy energy causal and battery capacity constraints when using the radio resources, namely:
Figure BDA0001273301650000091
in the present invention, consider the case where [0, T ] is]In time, the number of transmitted bits is maximized, the number of channel changes is N, the number of energy arrivals is Q-1, and the energy arrival at the initial time is denoted as Ein(1)＝E10 for
Figure BDA00012733016500000913
At a moment in time, if
Figure BDA00012733016500000914
In order for the energy to reach the moment,
Figure BDA00012733016500000915
if it is not
Figure BDA00012733016500000916
Is the time at which the channel is changing,
Figure BDA00012733016500000917
in summary, the optimization problem can be modeled as:
Figure BDA0001273301650000092
wherein
Figure BDA0001273301650000093
And
Figure BDA0001273301650000094
respectively the transmission power of the signals provided by the energy harvesting and the grid,
Figure BDA0001273301650000095
and
Figure BDA0001273301650000096
consuming power for the circuits provided by the energy harvesting and the grid respectively,
Figure BDA0001273301650000097
is the battery capacity of the energy harvester,
Figure BDA0001273301650000098
for maximum power supplied by the grid, PmaxIs the maximum value of the signal transmission power. In which the transmission power pi,kConsisting of two parts, one part resulting from energy harvesting, i.e.
Figure BDA0001273301650000099
Another part of the transmitted power coming from the signal supplied by the network, i.e.
Figure BDA00012733016500000910
The c1 constraint is expressed as originating from a rechargeable batteryPower limitation, c2, requires that the remaining energy of the rechargeable battery be less than the battery capacity to avoid wasting energy by spilling over
Figure BDA00012733016500000911
c3 denotes the limitation of the power fraction from the grid supply, the required power being less than the upper power limit of the grid
Figure BDA00012733016500000912
c4 sets the maximum transmission power PmaxThe limit of (2).
To solve the above optimization problem, first, the lagrangian function of the above problem needs to be solved, and the following steps are obtained:
Figure BDA0001273301650000101
where α, τ, γ, μ are lagrange multipliers, where τ is the lagrange multiplier corresponding to constraint c2, on the first epoch, when the energy capture is 0,
Figure BDA0001273301650000102
the corresponding dual optimization problem is obtained as follows:
Figure BDA0001273301650000103
transmission power pi,kFrom energy harvesting
Figure BDA0001273301650000104
And the transmission power from the signal supplied by the power grid
Figure BDA0001273301650000105
Composition from which
Figure BDA0001273301650000106
The power allocation at each epoch is found using optimization theory and the KKT condition.
From L (X (p), α, tau, gammaMu) pair
Figure BDA0001273301650000107
Derivation is carried out to obtain the following formula and deduce
Figure BDA0001273301650000108
Figure BDA0001273301650000109
For convenience of calculation, order
Figure BDA00012733016500001010
This yields:
Figure BDA00012733016500001011
is composed of L (X (p), α, tau, gamma, mu) pairs
Figure BDA00012733016500001012
Derivation is carried out to obtain the following formula and deduce
Figure BDA00012733016500001013
Figure BDA00012733016500001014
Definition of the invention
Figure BDA0001273301650000111
For iteration times, the last time can be obtained from the Lagrange multiplier
Figure BDA0001273301650000112
The invention can thus be obtained by the above formula
Figure BDA0001273301650000113
Wherein
Figure BDA0001273301650000114
Is equal to
Figure BDA0001273301650000115
Then pass through
Figure BDA0001273301650000116
Is solved by the solution formula
Figure BDA0001273301650000117
Due to the L (X (p), α, tau, gamma, mu) pairs
Figure BDA0001273301650000118
And (5) derivation to obtain:
Figure BDA0001273301650000119
thus obtaining Li(X (p), α, gamma) about
Figure BDA00012733016500001110
Is an affine function, in short, when i > 1,
Figure BDA00012733016500001111
the optimal value of (c) is determined by a feasible set of other constraints, i is 1,
Figure BDA00012733016500001112
thus, the following steps are obtained:
Figure BDA00012733016500001113
wherein
Figure BDA00012733016500001114
X is more than or equal to 0 and less than or equal to pc.
The lagrange multiplier is updated by the formula:
Figure BDA00012733016500001115
where j ∈ {1, … Q + N },
Figure BDA00012733016500001116
in order to be able to perform the number of iterations,
Figure BDA00012733016500001117
and
Figure BDA00012733016500001118
are all iteration step sizes.
The application effect of the present invention will be described in detail with reference to the simulation.
Assuming that the number of users is 10, the maximum transmitting power provided by the power grid is
Figure BDA00012733016500001119
The acquisition rate is 2J/s, and in fig. 3, the comparison algorithm adopted by the invention is as follows: average power allocation under hybrid energy harvesting and power allocation for energy-only harvesting. It can be seen that under the same conditions, the proposed optimization algorithm is better than the average power distribution due to consideration of different channel state information between the user and the base station, while when only energy collection is considered and no power of the power grid is available, the throughput of the system is far inferior to the proposed optimization algorithm due to the time-varying characteristic of energy arrival.
The invention provides an optimal power distribution method aiming at maximizing the reachable rate of the system, and improves the maximum throughput of the system.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (6)

1. A Massive MIMO system power distribution method based on hybrid energy acquisition is characterized in that a hybrid energy acquisition model is established for the uncertainty of energy acquisition of the Massive MIMO system power distribution method based on hybrid energy acquisition, and according to the time-varying characteristics of a channel state and an energy state and the coordination influence among users, system information is updated in an iterative manner, and finally optimal power is distributed for each user, so that the highest throughput is obtained, and the utilization of non-renewable resources is saved to the maximum;
the energy collection model adopts Poisson distribution to analyze the energy collection process and the channel fading variation process, and the energy arrival time and the channel fading variation time are countable and are respectively used
Figure FDA0002396293420000011
And
Figure FDA0002396293420000012
represents a sequence of energy arrival times and a sequence of channel variation times, and
Figure FDA0002396293420000013
the interval of the time sequence according to the properties of the Poisson distribution
Figure FDA0002396293420000014
And
Figure FDA0002396293420000015
respectively obey mean value of 1/lambdaeAnd 1/lambdafThe distribution of indices;
the optimal power allocation is expressed as:
Figure FDA0002396293420000016
wherein
Figure FDA0002396293420000017
And
Figure FDA0002396293420000018
respectively the transmission power of the signals provided by the energy harvesting and the grid,
Figure FDA0002396293420000019
and
Figure FDA00023962934200000110
consuming power for the circuits provided by the energy harvesting and the grid respectively,
Figure FDA00023962934200000111
battery capacity as energy harvester, Ein(i) For the energy to be collected at time i,
Figure FDA00023962934200000112
for maximum power supplied by the grid, PmaxMaximum value of signal transmission power, N represents the number of channel changes, Q-1 represents the number of energy arrivals, η is power amplifier efficiency, LiIs the i ═ { 1., Q + N } time interval, and [ x ═ x]+＝max(0,x)，
Figure FDA00023962934200000113
Denotes that 0. ltoreq. x. ltoreq.pc，pcFor the total circuit power consumption, α, τ, γ, μ are lagrange multipliers, and G is the channel matrix.
2. A masiveMIMO system power distribution method based on hybrid energy acquisition is characterized by comprising the following steps:
step one, in a Massive MIMO downlink system, a base station stores collected energy in a rechargeable battery, and when the energy in the rechargeable battery is insufficient, a power grid serves as a standby power supply to supply power to the system;
analyzing the communication system, representing the energy collection process and the change process of the channel state by Poisson distribution according to the time-varying characteristics of the channel state and the energy state, wherein the arrival time of the collected energy and the change time of the channel state are respectively obeyed by the rate of lambdaeAnd λfPoisson count distribution of (a);
designing an optimization model with the aim of maximizing system throughput, and optimizing data transmission power distributed by each user and circuit power consumed in the transmission process;
solving the established optimization problem, converting the original problem into a dual problem by utilizing Lagrange duality, and then performing iterative update on a Lagrange multiplier by adopting a gradient descent method to obtain the optimal solution of the original problem;
and step five, according to the optimal solution of the problem, the base station distributes power for each user from the rechargeable battery and the power grid respectively and transmits signals.
3. The method for power allocation based on hybrid energy acquisition in a Massive MIMO system as claimed in claim 2, wherein the large scale fading factors are expressed as:
Figure FDA0002396293420000021
wherein z iskIs a lognormal random variable, 10log (z)k) Is a mean of 0 and a variance of
Figure FDA0002396293420000022
Complex Gaussian random variable of rkIs the distance, r, from the base station to the kth userhV is the path loss for the reference distance;
the sum rate of downlink transmission users of the Massive MIMO system is as follows:
Figure FDA0002396293420000023
where G is the channel matrix, pkFor the transmission power of the link between the base station and the kth user, the noise vector is calculated from the mean value of 0 and the variance of
Figure FDA0002396293420000024
Complex gaussian random variables.
4. The method for power allocation of Massive MIMO system based on hybrid energy harvesting according to claim 2, wherein the randomness of energy harvesting is such that energy causal constraint and battery capacity constraint must be additionally satisfied when using wireless resources, that is:
Figure FDA0002396293420000031
in the whole transmission process, the frequency of channel change is N times, the frequency of energy arrival is Q-1 times, and the arrival energy of the initial time is recorded as Ein(1)＝E1For time l, if l is the energy arrival time, Ein(l)＝EiIf l is the time of channel change, Ein(l)＝0。
5. The Massive MIMO system power allocation method based on hybrid energy acquisition as claimed in claim 2, wherein the optimization problem can be modeled as:
Figure FDA0002396293420000032
where the c1 constraint indicates that the transmitted power is derived from the energy limit of the rechargeable battery, and c2 requires that the remaining energy of the rechargeable battery be less than the battery capacity to avoid energy overflow and waste
Figure FDA0002396293420000033
c3 denotes the power limit from the grid supply, the requested power being less than the upper power limit of the grid
Figure FDA0002396293420000034
c4 sets the maximum transmission power PmaxThe limit of (2).
6. The Massive MIMO system power allocation method based on hybrid energy harvesting as claimed in claim 2, wherein when i isWhen the pressure is higher than 1,
Figure FDA0002396293420000035
the optimal value of (c) is determined by a feasible set of other constraints, i is 1,
Figure FDA0002396293420000041
thus, the following steps are obtained:
Figure FDA0002396293420000042
wherein
Figure FDA0002396293420000043
Denotes that 0. ltoreq. x. ltoreq.pc；
The lagrange multiplier is updated by the formula:
Figure FDA0002396293420000044
where j ∈ {1, … Q + N },
Figure FDA0002396293420000045
in order to be able to perform the number of iterations,
Figure FDA0002396293420000046
and
Figure FDA0002396293420000047
are all iteration step sizes.
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