CN114302487A - Energy efficiency optimization method, device and equipment based on adaptive particle swarm power distribution - Google Patents
Energy efficiency optimization method, device and equipment based on adaptive particle swarm power distribution Download PDFInfo
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Abstract
The invention discloses an energy efficiency optimization method, device and equipment based on self-adaptive particle swarm power distribution, wherein the method comprises the following steps: initializing the positions of all base stations and the positions of all users in a cell; calculating a large-scale fading factor beta and a covariance matrix R of a channel according to the position of each base station and the position of each user; receiving an uplink pilot signal, and estimating a channel by adopting an MMSE (minimum mean square error) method by combining a large-scale fading factor beta and a covariance matrix R of the channel; deducing a signal to interference plus noise ratio (SINR) expression of the system according to the estimated channel, calculating frequency efficiency according to Shannon capacity theorem, and establishing an energy efficiency optimization model of the system by combining a power consumption model of the system; and for the energy efficiency optimization model, according to an optimization objective function, carrying out data power distribution on a user by using a self-adaptive particle swarm algorithm on the basis of fixed pilot frequency power. The invention can effectively improve the energy efficiency of the system and meet the requirement of green communication.
Description
Technical Field
The invention belongs to the technical field of wireless communication of large-scale MIMO systems, and particularly relates to an energy efficiency optimization method, device and equipment based on adaptive particle swarm power distribution.
Background
In a large-scale MIMO system, hundreds of thousands of antennas are configured at a base station, and spatial diversity gain and spatial multiplexing gain are obtained by using multipath scattering, so that the system can greatly improve the system frequency efficiency, energy efficiency and reliability of a wireless link under the condition of an original bandwidth, and the system becomes one of 5G key technologies. In a large-scale MIMO system, a TDD mode is adopted in order to utilize reciprocity of uplink and downlink channels within the same coherence time. The position relationship between the user and the base station and the surrounding environment are different, so that the channel gains between the user and the base station are different, and the fading degrees of signals in the channel transmission process are also different. When uplink data transmission is performed, due to the non-orthogonality of channels between users, signals transmitted between users interfere with each other, so that signals received by a base station end include useful signals and interference signals, and the performance of the whole communication system is affected. Power control has always been one of the hot problems in communication system research. In the energy efficiency research of a large-scale MIMO system, the total power consumption of the system can be reduced under the condition of ensuring the data transmission rate by controlling the power of the pilot frequency and the data signal, the energy efficiency of the system is improved, the performance of the system is further improved, and the requirement of green communication is met.
At present, the power distribution problem has been widely studied, and the specific scheme is as follows:
the first and the second power distribution algorithms are that the total uplink power is distributed to each user in the cell averagely without considering the channel gain. This approach can make the communication quality of the user with good channel gain good, and the communication quality of the user with poor channel gain not so poor, but this algorithm does not consider the user minimum rate, and is not optimal for energy efficiency and frequency efficiency, because the number of antennas configured by the base station cannot approach infinity in reality.
And secondly, a water injection power algorithm from the angle of channel gain, wherein the water injection power algorithm is used for adaptively distributing the transmission power according to the channel state, and usually, the transmission power is distributed more at the moment when the channel state is good, and the transmission power is distributed less at the moment when the channel state is poor, so that the maximum transmission rate is achieved. The power allocation algorithm is simple in implementation, but fairness of user quality is not considered, so that users with good channels allocate more power and have good communication quality, users with poor channels allocate less power and have poor communication quality, and strong interference of other users is received.
According to a power distribution algorithm of a convex optimization theory such as a fractional programming theory, Lagrange multiplication and Dinkelbach algorithm and the like, the energy efficiency optimization target of the large-scale MIMO system is non-convex, generally, the non-convex target function is reconstructed to form a convex target function through relaxation and reforming operations, then, power is distributed through a convex optimization method such as an interior point method and the like, so that the obtained local optimal power distribution vector is the optimal power distribution vector, and the system has good performance for energy efficiency and frequency efficiency.
Disclosure of Invention
Aiming at various problems which are not fully considered in the existing power distribution scheme, the invention aims to provide an energy efficiency optimization method, device and equipment based on adaptive particle swarm power distribution.
In order to achieve the purpose, the invention provides the following technical scheme:
an energy efficiency optimization method based on adaptive particle swarm power distribution comprises the following steps:
s1, initializing each base station position and each user position in the cell;
s2, calculating a large-scale fading factor beta and a covariance matrix R of a channel according to the positions of all base stations and the positions of all users;
s3, receiving an uplink pilot signal, and estimating a channel by adopting an MMSE (minimum mean square error) method by combining a large-scale fading factor beta and a covariance matrix R of the channel;
s4, deducing a signal to interference plus noise ratio (SINR) expression of the system according to the estimated channel, calculating frequency efficiency according to Shannon capacity theorem, and establishing an energy efficiency optimization model of the system by combining a power consumption model of the system;
and S5, according to the optimization objective function, the energy efficiency optimization model carries out data power distribution of users by using a self-adaptive particle swarm algorithm on the basis of fixed pilot frequency power.
Preferably, in step S2, the large scale fading factor
Expressed as:
wherein the content of the first and second substances,
representing shadow fading, logarithm thereof
Obeying a Gaussian distribution
Denotes the distance r from the ith user in cell l to the base station at the center of cell j0Represents the radius of the cell; α represents a path loss coefficient during signal transmission.
The covariance matrix R of the channel is obtained according to the approximate solution of a Gaussian local scattering model, and the following conditions are met:
where β represents a large scale fading factor, dHM is the number of antennas configured for the base station,
is the angle of arrival of the user.
Preferably, step S3 specifically includes:
in the pilot transmission phase, the signal received by the base station in cell j is transmitted
Expressed as:
wherein p isjkRepresenting the power at which the kth user in cell j transmits a pilot,
a pilot sequence transmitted for the kth user in cell j;
representing additive white Gaussian noise at the receiving end of the base stationFrom independent and identically distributed CN to (0, sigma)p)；
Multiplication of both sides of the above formula
Obtaining:
wherein, since the pilots are orthogonal,
a value of 0;
using a minimum mean square error estimation method based on
Performing channel estimation, wherein the channel estimation value between the kth user in the cell l and the base station j can be expressed as:
therefore, in the uplink data transmission phase, the data received by the base station j can be represented as:
maximum ratio combining reception is adopted for the received signals, namely the received vector is as follows:
therefore, the transmitted signal of user k in cell j is represented by:
wherein the first term is a useful signal; the second term is intra-cell interference; the remaining part is inter-cell interference and other uncorrelated noise, sjkIs the transmitted signal of user k in cell j.
Preferably, in step S4, the signal to interference plus noise ratio SINR of the system
The lower bound for knowing the frequency efficiency of the system by the shannon capacity theorem is expressed as:
the optimization problem is represented as:
wherein the content of the first and second substances,
represents the signal-to-interference-and-noise ratio, p, of the kth user signal in cell j received by the base station in cell jliDenotes the data power, V, allocated to user i in cell jjkIndicating that the combined vector is received,
representing the estimated value of the channel between the base station of cell j and the kth user in the cell,
an estimation error matrix representing a channel matrix,
which is indicative of the power of the noise,
is MjIdentity matrix of order, EEULRepresenting system energy efficiency, τ, in a massive MIMO uplink transmission processuIndicating the length of the coherent block, τ, for transmitting uplink datacDenotes the total length of the coherent block, M being the number of antennas with which the base station is equipped, prPower consumed by the radio link of the antenna for uplink data transmission, psStatic electrical power consumption, p, consumed during data transmission for large-scale MIMO systemsmaxIs the maximum electrical power during uplink transmission for all users in each cell. RminIs a value set under consideration of the minimum rate constraint.
Preferably, step S5 specifically includes:
s51, initializing the parameters of the particles by using the rand function and the upper and lower limits of the set user data power;
s52, taking the energy efficiency function as a fitness function, calculating the fitness of the initialized particles, and initializing a local optimal solution and a global optimal solution;
s53, judging the iteration termination condition, terminating the iteration when the iteration reaches the initial set maximum iteration number or the fitness value tends to be stable, otherwise, continuing the following steps;
s54, updating the speed and the position of the particle, simultaneously performing boundary processing on the particle beyond the boundary range, and recalculating the fitness value of the updated particle;
and S55, storing the result of each iteration, comparing the results after each iteration, and updating the local optimal solution and the global optimal solution.
And S56, when the iteration termination condition is reached, obtaining the global optimal value, namely the required optimal user data power allocation vector.
Preferably, in step S52, the fitness function is set as the energy efficiency expression:
and substituting the position parameter of each particle as an input parameter into the formula to solve a corresponding fitness value, finishing the first iteration process, and setting the initialized particle parameters as a local optimal value and a global optimal value.
Preferably, in step S54, the velocity and position of the particle are updated and boundary processing is performed as follows:
the update formula of the particle speed and the position is as follows:
Vj(t+1)＝Vj(t)+c1*rand*(Gbestj-popj(t))+c2*rand*(Zbest-popj(t))
popj(t+1)＝popj(t)+ω*Vj(t+1)
in the formula, Vj(t +1) represents the search speed, V, of the jth particle at the (t +1) th iterationj(t) represents the search speed of the jth particle in the tth iteration, c1 is an individual learning factor, the iteration representing the speed is only related to the historical position of the particle, c2 is a social learning factor, the relation between the iteration representing the speed and the historical position of the whole particle swarm is represented, omega represents a weight factor, when omega is larger, the local minimum value can be skipped, the global search is facilitated, and when the inertia factor omega is smaller, the local trend can be accurately searched. Thus, when the number of iterations is small, the value of ω is large, and when the number of iterations is largeWhen large, the value of ω is small. GbestjZbest represents the optimal particle position, pop, for the current position of the jth particle for the current iteration number of the entire particle swarmj(t) represents a position parameter representing the jth particle at the tth iteration.
The boundary processing method of the particle speed and the particle position comprises the following steps:
v (i, j) represents the velocity of the ith particle in the j-dimension direction, the transition speed of the particle in the solution space can be limited by processing the boundary of the particle velocity, and the solution space can be searched sufficiently. pop (i, j) indicates the displacement size of the ith particle in the j-dimension direction, and the position is subjected to boundary processing, so that the particle can be strictly limited to move in the solution space.
The embodiment of the present invention further provides an energy efficiency optimization device based on adaptive particle swarm power distribution, which includes:
the initialization unit is used for initializing the positions of all base stations and the positions of all users in a cell;
a calculating unit, configured to calculate a large-scale fading factor β and a covariance matrix R of a channel according to the positions of the base stations and the positions of the users;
the channel estimation unit is used for receiving the uplink pilot signal, and simultaneously estimating the channel by adopting an MMSE (minimum mean square error) method in combination with the large-scale fading factor beta and the covariance matrix R of the channel;
the model establishing unit is used for deducing a signal to interference plus noise ratio (SINR) expression of the system according to the estimated channel, calculating frequency efficiency according to the Shannon capacity theorem, and establishing an energy efficiency optimization model of the system by combining a power consumption model of the system;
and the power distribution unit is used for carrying out data power distribution on the user on the basis of fixed pilot frequency power by utilizing a self-adaptive particle swarm algorithm according to the optimization objective function for the energy efficiency optimization model.
The embodiment of the invention also provides energy efficiency optimization equipment based on adaptive particle swarm power distribution, which comprises a memory and a processor, wherein a computer program is stored in the memory, and the computer program can be executed by the processor so as to realize the energy efficiency optimization method based on adaptive particle swarm power distribution.
In the invention, the communication quality fairness of each user can be fully considered under the condition of low calculation complexity, the energy efficiency of the system is effectively improved, and the requirement of green communication is met.
Drawings
Fig. 1 is a schematic flowchart of a work flow of an energy efficiency optimization method based on adaptive particle swarm power allocation according to a first embodiment of the present invention.
Fig. 2 is a schematic block diagram of a flow of an adaptive particle swarm algorithm provided by an embodiment of the present invention.
Fig. 3 is a schematic diagram illustrating comparison of average frequency efficiency performance of cells based on adaptive particle swarm optimization and other algorithms provided by the present invention.
Fig. 4 is a schematic diagram illustrating comparison of average energy efficiency performance of a cell based on an adaptive particle swarm algorithm and other algorithms according to the present invention.
Fig. 5 is a schematic structural diagram of an energy efficiency optimization device based on adaptive particle swarm power allocation according to a second embodiment of the present invention.
Detailed Description
The invention will be further elucidated with reference to the embodiments and the accompanying drawings.
Referring to fig. 1, a first embodiment of the present invention provides an energy efficiency optimization method based on adaptive particle swarm power allocation, which includes the following steps:
s1, the location of each base station and the location of each user in the cell are initialized.
In one implementation manner, the present embodiment provides 16 cells in total, and the number of the cells is 4 × 4The arrangement is made such that the system employs orthogonal pilots for users within a cell in order to overcome inter-cell interference. Meanwhile, in order to avoid loss of generality, the pilot multiplexing factor of the system is set to be 4. Thus, the pilot sequence employed by the present system may be represented as
In each cell, users are set in a random distribution mode, the minimum distance from the users to the base station is constrained, and the topological distance of the surrounding base stations is calculated for each user.
S2, calculating a large-scale fading factor β and a covariance matrix R of the channel according to the positions of the base stations and the positions of the users.
In this example, consider a multi-cell, multi-user massive MIMO system, which consists of L square cells. In each cell, a base station equipped with M antennas is set up in the center of the cell, and serves K single-antenna users randomly distributed in the cell (where K is<<M). Without loss of generality, the channel gain from the ith user to cell base station j in cell l
Can be expressed as:
wherein the content of the first and second substances,
representing small scale fading, obeying a circularly symmetric complex Gaussian distribution, i.e. CN — (0, I)M)。
Wherein the content of the first and second substances,
is a large scale fading factor, generally expressed as
Herein, the
Representing shadow fading, logarithm thereof
Obeying a Gaussian distribution
Wherein
The distance from the ith user in the cell l to the base station at the center of the cell j is shown, r0 shows the radius of the cell, and alpha shows the path loss coefficient in the signal transmission process; in particular, it is possible to use, for example,
slowly varying and easily tracking over the coherence time of several channels, so that within one coherence time
Considered as a constant.
In this embodiment, according to the set base station and the user position, the arrival angle between the user and the base station is calculated, and the covariance matrix R of the channel is solved according to the approximate solution of the gaussian local scattering model:
where β represents a large scale fading factor, dHFor spatial distance of antenna (generally set as half wavelength), M is allocated to base stationThe number of the antennas to be placed is,
is the angle of arrival of the user.
And S3, receiving the uplink pilot signal, and estimating the channel by adopting an MMSE (minimum mean square error) method by combining the large-scale fading factor beta and the covariance matrix R of the channel.
Wherein, in the pilot frequency transmitting phase, the signal received by the base station in the cell j
Can be expressed as:
where p isjkIndicating the power at which the kth user in cell j transmits a pilot,
a pilot sequence transmitted for the kth user in cell j.
It is the additive white Gaussian noise at the receiving end of the base station, which is subject to independent same distribution CN (0-sigma)p)。
Multiplying both sides of the equation (4) simultaneously
It is possible to obtain:
due to the orthogonality of the pilot frequency, the third term on the right side of the above equation (5) is 0, so that the inter-cell interference can be removed, which is beneficial to channel estimation.
Through the above operations, after the base station in the cell receives the pilot signal sent by the user, the base stationCan be based on
For channel estimation, the present embodiment adopts a Minimum Mean Square Error (MMSE) estimation method. Thus, the channel estimate between the kth user in cell i and base station j can be expressed as:
therefore, in the uplink data transmission phase, the data received by the base station j can be represented as:
in this embodiment, maximum ratio combining reception is applied to the received signals, that is, the received vector is:
therefore, the transmitted signal for user k in cell j can be represented by the following sub-expression:
wherein the first term is a useful signal; the second term is intra-cell interference; the remaining portion is inter-cell interference and other uncorrelated noise.
And S4, deducing a signal to interference plus noise ratio (SINR) expression of the system according to the estimated channel, calculating frequency efficiency according to the Shannon capacity theorem, and establishing an energy efficiency optimization model of the system by combining a power consumption model of the system.
And deducing SINR of the system and expressions of frequency efficiency SE and energy efficiency EE according to the known process. Wherein:
the lower bound of the frequency efficiency of the system known by the shannon capacity theorem can be expressed as:
the purpose of this embodiment is to try to improve the average energy efficiency in the system, and to realize the development of green communication once. The optimization problem can therefore be expressed as:
wherein the content of the first and second substances,
represents the signal-to-interference-and-noise ratio, p, of the kth user signal in cell j received by the base station in cell jliDenotes the data power, V, allocated to user i in cell jjkIndicating that the combined vector is received,
representing the estimated value of the channel between the base station of cell j and the kth user in the cell,
an estimation error matrix representing a channel matrix,
which is indicative of the power of the noise,
is MjIdentity matrix of order, EEULRepresenting system energy efficiency, τ, in a massive MIMO uplink transmission processuIndicating the length of the coherent block, τ, for transmitting uplink datacDenotes the total length of the coherent block, M being the number of antennas with which the base station is equipped, prPower consumed by the radio link of the antenna for uplink data transmission, psStatic electrical power consumption, p, consumed during data transmission for large-scale MIMO systemsmaxIs the maximum electrical power during uplink transmission for all users in each cell. RminIs a value set under consideration of the minimum rate constraint.
And S5, according to the optimization objective function, the energy efficiency optimization model carries out data power distribution of users by using a self-adaptive particle swarm algorithm on the basis of fixed pilot frequency power.
Preferably, step S5 specifically includes:
s51, initializing the particles by using the rand function and the upper and lower limits of the set user data power,
wherein is at
The upper mark is iteration times, and the lower mark is particle number; p is a radical of12The first numerical subscript represents the cell number and the second numerical subscript represents the user number in the cell.
S52, taking the energy efficiency function EE as a fitness function, calculating the fitness of all initialized particles, and initializing local optimal
And global optimum
S53, updating the speed and position of the particle and performing boundary processing:
calculating the updated fitness value EE corresponding to the particles and updating the local optimal solution
And global optimal solution
And S54, processing boundary conditions, wherein the power constraint condition is required to be used as the boundary condition of the particle position in the iteration process, and the motion of the particle is constrained within a certain range.
The updating formula of the particle speed and the position is as follows:
Vj(t+1)＝Vj(t)+c1*rand*(Gbestj-popj(t))+c2*rand*(Zbest-popj(t)) (15)
in the formula, Vj(t +1) represents the velocity value of the jth particle at the (t +1) th iteration, Vj(t) represents the speed value of the jth particle in the tth iteration, c1 is an individual learning factor, the iteration representing the speed is only related to the historical position of the particle, c2 is a social learning factor, the iteration representing the speed is related to the historical position of the whole particle swarm, and omega represents a weight factorLocal search is facilitated, and when the inertia factor omega is small, accurate search tends to be conducted on local parts. Therefore, when the number of iterations is small, the value of ω is large, and when the number of iterations is large, the value of ω is small. GbestjThe optimal position of the jth particle's current position, Zbest is the optimal particle position so far, pop, of the entire particle populationj(t) represents a position parameter representing the jth particle at the tth iteration.
The boundary processing method of the particle speed and the particle position comprises the following steps:
v (i, j) represents the velocity of the ith particle in the j-dimension direction, and boundary processing is performed on the particle velocity, so that the search velocity of the particle in the solution space can be limited, and the solution space can be sufficiently searched. pop (i, j) indicates the displacement size of the ith particle in the j-dimension direction, and the particle motion can be strictly limited in the solution space by performing boundary processing on the position.
In step S54, after the speed and position of each particle are updated, the fitness value of the newly generated particle group is calculated, and according to the comparison between the fitness value of the current particle and the fitness value of the previous particle, the optimal position of each particle is selected as the local optimal position, and the optimal position of the whole particle group is selected as the global optimal position. While the boundary processing for particle velocity and position can be more efficient and reliable.
S55, in order to avoid the particle swarm optimization falling into a local extremum, the embodiment combines the mutation operation of the genetic algorithm, and processes the positions of the particles with a variation probability of twenty percent:
k＝ceil(LK*rand) (19)
pop(i,k)＝rand*(popmax-popmin)+popmin (20)
and S56, when the iteration termination condition is reached, the global optimal value is the optimal user data power allocation vector to be selected.
Fig. 3 and 4 are graphs of performance simulation results of embodiments of the present invention. It can be seen from the figure that, under the same condition, compared with the conventional power allocation algorithm, the method of the present invention has a certain improvement in frequency efficiency and a great improvement in energy efficiency, and as the number of antennas increases, the performance of several methods finally gradually decreases. Therefore, compared with the traditional allocation algorithm, the method has obvious performance improvement.
Referring to fig. 5, a second embodiment of the present invention further provides an energy efficiency optimization apparatus based on adaptive particle swarm power allocation, including:
an initializing unit 210, configured to initialize positions of each base station and each user in a cell;
a calculating unit 220, configured to calculate a large-scale fading factor β and a covariance matrix R of a channel according to the positions of the base stations and the positions of the users;
a channel estimation unit 230, configured to receive an uplink pilot signal, and estimate a channel by using an MMSE method in combination with a large-scale fading factor β and a covariance matrix R of the channel;
the model establishing unit 240 is configured to derive a signal to interference plus noise ratio SINR expression of the system according to the estimated channel, calculate frequency efficiency according to shannon capacity theorem, and establish an energy efficiency optimization model of the system in combination with a power consumption model of the system;
and the power distribution unit 250 is configured to distribute the data power of the user to the energy efficiency optimization model according to the optimization objective function by using a self-adaptive particle swarm algorithm on the basis of the fixed pilot power.
The third embodiment of the present invention further provides an energy efficiency optimization device based on adaptive particle swarm power allocation, which includes a memory and a processor, where a computer program is stored in the memory, and the computer program can be executed by the processor, so as to implement the energy efficiency optimization method based on adaptive particle swarm power allocation as described above.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or scope of the invention as defined in the appended claims.
Claims (9)
1. An energy efficiency optimization method based on adaptive particle swarm power distribution is characterized by comprising the following steps:
s1, initializing each base station position and each user position in the cell;
s2, calculating a large-scale fading factor beta and a covariance matrix R of a channel according to the positions of all base stations and the positions of all users;
s3, receiving an uplink pilot signal, and estimating a channel by adopting an MMSE (minimum mean square error) method by combining a large-scale fading factor beta and a covariance matrix R of the channel;
s4, deducing a signal to interference plus noise ratio (SINR) expression of the system according to the estimated channel, calculating frequency efficiency according to Shannon capacity theorem, and establishing an energy efficiency optimization model of the system by combining a power consumption model of the system;
and S5, according to the optimization objective function, the energy efficiency optimization model carries out data power distribution of users by using a self-adaptive particle swarm algorithm on the basis of fixed pilot frequency power.
2. The energy efficiency optimization method based on adaptive particle swarm power distribution according to claim 1, wherein in step S2, the large-scale fading factor
Expressed as:
wherein the content of the first and second substances,
representing shadow fading, logarithm thereof
Obeying a Gaussian distribution
Represents the distance from the ith user in cell l to the base station at the center of cell j, and r0 represents the radius of the cell; α represents the number of path losses during signal transmission.
The covariance matrix R of the channel is obtained according to the approximate solution of a Gaussian local scattering model, and the following conditions are met:
where β represents a large scale fading factor, dHM is the number of antennas configured for the base station,
is the angle of arrival of the user.
3. The energy efficiency optimization method based on adaptive particle swarm power distribution according to claim 2, wherein step S3 specifically includes:
in the pilot transmission phase, the signal received by the base station in cell j is transmitted
Expressed as:
wherein p isjkRepresenting the power at which the kth user in cell j transmits a pilot,
a pilot sequence transmitted for the kth user in cell j;
representing additive white Gaussian noise at the receiving end of the base station, which obeys independent same distribution CN to (0, sigma)p)；
Multiplication of both sides of the above formula
Obtaining:
wherein, due to the adoption of the orthogonal pilot frequency,
a value of 0;
using a minimum mean square error estimation method based on
Performing channel estimation, wherein the cellThe channel estimate between the kth user in l and base station j can be expressed as:
wherein P isliRepresenting the set of users transmitting the same pilot as user i in cell l.
Therefore, in the uplink data transmission phase, the data received by the base station j is represented as:
maximum ratio combining reception is adopted for the received signals, namely the received vector is as follows:
therefore, the transmitted signal of user k in cell j is represented by:
wherein the first term is a useful signal; the second term is intra-cell interference; the remaining part is inter-cell interference and other non-correlated noise; sjkRepresenting the transmitted signal of user k in cell j.
4. The energy efficiency optimization method based on adaptive particle swarm power distribution according to claim 3, wherein in step S4,
the lower bound of the frequency efficiency of the system can be known by shannon capacity theorem as follows:
the optimization problem is represented as:
wherein the content of the first and second substances,
represents the signal-to-interference-and-noise ratio, p, of the kth user signal in cell j received by the base station in cell jliDenotes the data power, V, allocated to user i in cell jjkIndicating that the combined vector is received,
representing the estimated value of the channel between the base station of cell j and the kth user in the cell,
an estimation error matrix representing a channel matrix,
which is indicative of the power of the noise,
is MjIdentity matrix of order, EEULRepresenting system energy efficiency, τ, in a massive MIMO uplink transmission processcDenotes the total length of the coherent block, τuIndicating the length of the coherent block for transmitting uplink data, M being the number of antennas provided for the base station, prPower consumed by the radio link of the antenna for uplink data transmission, psStatic electrical power consumption, p, consumed during data transmission for large-scale MIMO systemsmaxIs the maximum electric power, R, of all users in each cell during uplink transmissionminIs the minimum user transmission rate limit we consider.
5. The energy efficiency optimization method based on adaptive particle swarm power distribution according to claim 4, wherein step S5 specifically includes:
s51, initializing the parameters of the particles by using the rand function and the upper and lower limits of the set user data power;
s52, taking the energy efficiency function as a fitness function, calculating the fitness value of the initialized particles, and initializing a local optimal solution and a global optimal solution;
s53, judging the iteration termination condition, terminating the iteration when the iteration reaches the initial set maximum iteration number or the fitness value tends to be stable, otherwise, continuing the following steps;
s54, updating the speed and the position of the particle, simultaneously performing boundary processing on the particle beyond the boundary range, and recalculating the fitness value of the updated particle;
and S55, storing the result of each iteration, comparing the results after each iteration, and updating the local optimal solution and the global optimal solution.
And S56, when the iteration termination condition is reached, obtaining the global optimal value, namely the required optimal user data power allocation vector.
6. The energy efficiency optimization method based on adaptive particle swarm power distribution according to claim 5,
in step S52, the fitness function is set as an energy efficiency expression:
and substituting the position parameter of each particle as an input parameter into the formula to solve a corresponding fitness value, finishing the first iteration process, and setting the initialized particle parameters as a local optimal value and a global optimal value.
7. The energy efficiency optimization method based on adaptive particle swarm power distribution according to claim 5, wherein in step S54, the particle velocity and position are updated and boundary processing is performed as follows:
the update formula of the particle speed and the position is as follows:
Vj(t+1)＝Vj(t)+c1*rand*(Gbestj-popj(t))+c2*rand*(Zbest-popj(t))
popj(t+1)＝popj(t)+ω*Vj(t+1)
in the formula, Vj(t +1) represents the search speed, V, of the jth particle at the (t +1) th iterationj(t) represents the search speed of the jth particle at the tth iteration, c1 is an individual learning factor, the iteration representing the speed is only related to the historical position of the particle, c2 is a social learning factor, the relation between the iteration representing the speed and the historical position of the whole particle swarm, and omega represents a weight factor; gbestjFor the current optimal position of the jth particle, Zbest is the so far optimal particle position, pop, of the entire populationj(t) denotes the time of the tth iteration representing the jth particleA location parameter of (a);
the boundary processing method of the particle speed and the particle position comprises the following steps:
v (i, j) represents the search speed of the ith particle in the j-dimension direction; pop (i, j) represents the displacement size of the ith particle in the j-dimension direction.
8. An energy efficiency optimization device based on adaptive particle swarm power distribution is characterized by comprising the following components:
the initialization unit is used for initializing the positions of all base stations and the positions of all users in a cell;
a calculating unit, configured to calculate a large-scale fading factor β and a covariance matrix R of a channel according to the positions of the base stations and the positions of the users;
the channel estimation unit is used for receiving the uplink pilot signal, and simultaneously estimating the channel by adopting an MMSE (minimum mean square error) method in combination with the large-scale fading factor beta and the covariance matrix R of the channel;
the model establishing unit is used for deducing a signal to interference plus noise ratio (SINR) expression of the system according to the estimated channel, calculating frequency efficiency according to the Shannon capacity theorem, and establishing an energy efficiency optimization model of the system by combining a power consumption model of the system;
and the power distribution unit is used for carrying out data power distribution on the user on the basis of fixed pilot frequency power by utilizing a self-adaptive particle swarm algorithm according to the optimization objective function for the energy efficiency optimization model.
9. An energy efficiency optimization device based on adaptive particle swarm power distribution, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement the energy efficiency optimization method based on adaptive particle swarm power distribution according to any one of claims 1 to 7.
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