In this example, consider a multi-cell multi-user massive MIMO system, which is composed 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<
 in, Represents small-scale fading and obeys a circularly symmetric complex Gaussian distribution, namely CN~(0,I M ).
 in, is the large-scale fading factor, which is generally expressed as
 here represents shadow fading, its logarithm follow a Gaussian distribution in represents the distance from the ith user in cell l to the base station in the center of cell j, r0 represents the radius of the cell, and α represents the path loss coefficient during signal transmission; in particular, The change is slow and easy to track in the coherence time of several channels, so the coherence time of one coherence time is regarded as a constant.
 In this embodiment, according to the set base station and user position, calculate the angle of arrival between the user and the base station, and obtain the covariance matrix R of the channel according to the approximate solution of the Gaussian local scattering model:
 where β is the large-scale fading factor, d H is the spatial distance of the antenna (generally set to half wavelength), M is the number of antennas configured by the base station, is the angle of arrival of the user.
 S3: Receive the uplink pilot signal, and use the MMSE method to estimate the channel by combining the large-scale fading factor β and the covariance matrix R of the channel.
 Among them, in the pilot transmission stage, the signal received by the base station in cell j It can be expressed as:
 here p jk represents the power of the kth user in cell j transmitting the pilot frequency, Pilot sequence transmitted for the kth user in cell j. It represents the additive white Gaussian noise at the receiving end of the base station, which obeys the independent and identical distribution CN (0~σ p ).
 Multiply both sides of equation (4) by You can get:
 Due to the orthogonality of pilots, the value of the third term on the right side of the above equation (5) is 0, so the inter-cell interference can be removed, which is beneficial to channel estimation.
 After the above operations, after the base station in the cell receives the pilot signal sent from the user, the base station can To perform channel estimation, this embodiment adopts the method of minimum mean square error (MMSE) estimation. Therefore, the channel estimation value between the kth user in cell l and base station j can be expressed as:
Therefore, in the uplink data transmission stage, the data received by base station j can be expressed as:
 In this embodiment, the received signal is received by using maximum ratio combining, that is, the received vector is:
 Therefore, the transmitted signal of user k in cell j can be expressed by the following formula:
 Among them, the first item is useful signal; the second item is intra-cell interference; the rest is inter-cell interference and other non-correlated noise.
 S4, derive the SINR expression of the signal-to-interference-noise ratio of the system according to the estimated channel, calculate the frequency efficiency according to Shannon's capacity theorem, and establish an energy efficiency optimization model of the system in combination with the power consumption model of the system.
 From the above-mentioned known process, the SINR, frequency efficiency SE, and energy efficiency EE expressions of the system are derived. in:
 Through Shannon's capacity theorem, it can be known that the lower bound of the frequency effect of the system can be expressed as:
 The purpose of this embodiment is to strive to improve the average energy efficiency in the system, once to achieve the development of green communication. So the optimization problem can be expressed as:
 in, Indicates the signal-to-interference-to-noise ratio of the kth user signal in cell j received by the base station in cell j, p li is the data power allocated to user i in cell j, V jk represents the received combined vector, represents the estimated value of the channel between the base station of cell j and the kth user in the cell, represents the estimated error matrix of the channel matrix, represents the noise power, for M j Unity matrix of order, EE UL Represents the system energy efficiency of the massive MIMO uplink transmission process, τ u Represents the length of the coherent block for transmitting uplink data, τ c represents the total length of the coherent block, M is the number of antennas equipped by the base station, p r is the power consumed by the RF link of the antenna during uplink data transmission, p s Static electrical power consumption during data transmission for massive MIMO systems, p max is the maximum electrical power of all users in each cell during uplink transmission. R min is the value set taking into account the minimum rate constraint.
 S5 , for the energy efficiency optimization model, according to the optimization objective function, the adaptive particle swarm algorithm is used to allocate the data power of the user on the basis of the fixed pilot frequency.
 Preferably, step S5 specifically includes:
 S51, use the rand function and the upper and lower bounds of the set user data power to initialize the particles, of which in , the superscript is the number of iterations, and the subscript is the particle number; p 12 The subscript of the first number in the middle represents the cell number, and the subscript of the second number represents the user number in the cell.
 S52, take the energy efficiency function EE as the fitness function, calculate the fitness of all the initialized particles, and initialize the local optimum and the global optimum
 S53, update the speed and position of the particle and perform boundary processing:
 Calculate the fitness value EE corresponding to the updated particle, and update the local optimal solution and the global optimal solution
 S54, perform boundary condition processing. In the iterative process, the power constraint condition needs to be used as the boundary condition of the particle position, and the movement of the particle is bound within a certain range.
 Among them, the update formula of particle velocity and position is:
 V j (t+1)=V j (t)+c1*rand*(Gbest j -pop j (t))+c2*rand*(Zbest-pop j (t)) (15)
 In the formula, V j (t+1) represents the velocity value of the jth particle at the (t+1)th iteration, V j (t) represents the velocity value of the j-th particle at the t-th iteration, c1 is the individual learning factor, the iteration of the velocity is only related to the historical position of the particle itself, c2 is the social learning factor, and the iteration of the velocity is related to the entire particle swarm ω represents the weight factor. When ω is large, it is conducive to jumping out of the local minimum, which is convenient for global search, and when the inertia factor ω is small, it is conducive to accurate local search. 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. Gbest j is the optimal position of the current position of the jth particle, Zbest is the optimal particle position of the entire particle swarm so far, pop j (t) represents the position parameter of the jth particle at the tth iteration.
 The boundary processing methods for particle velocity and position are:
 V(i,j) represents the velocity of the i-th particle in the j-th dimension. The boundary processing of the particle velocity can limit the particle's search speed in the solution space and fully search the solution space. pop(i,j) represents the displacement of the i-th particle in the j-th dimension, and the boundary processing of the position can strictly limit the particle motion in the solution space.
 Among them, in step S54, after each particle velocity and position are updated, the fitness value of the newly generated particle swarm will be calculated. The optimal position of each particle itself is regarded as the local optimum, and the optimum position of the entire particle swarm is regarded as the global optimum. Since each iteration selects the individual optimum and the optimum value of the population, each iteration process only needs to store two A location parameter, which greatly reduces the requirements for storage space. At the same time, the boundary processing of particle velocity and position can make the search more effective and reliable.
 S55, in order to prevent the particle swarm algorithm from being trapped in the local extreme value, the present embodiment combines the mutation operation of the genetic genetic algorithm, and adopts a 20% mutation probability to process the position of the particle:
 k=ceil(LK*rand) (19)
 pop(i,k)=rand*(pop max -pop min )+pop min (20)
 S56, when the iteration termination condition is reached, the global optimal value is the optimal user data power allocation vector to be selected.
 image 3 and Figure 4 It is the performance simulation result diagram of the embodiment of the present invention. As can be seen from the figure, under the same conditions, compared with the traditional power distribution algorithm, the present invention has a certain improvement in frequency efficiency, and also has a great improvement in energy efficiency. The performance of this method eventually shrinks gradually. Therefore, compared with the traditional allocation algorithm, the present invention has obvious performance improvement.
 see Figure 5 , the second embodiment of the present invention also provides an energy efficiency optimization device based on adaptive particle swarm power distribution, which includes:
 an initialization unit 210, configured to initialize the location of each base station and the location of each user in the cell;
 a calculation unit 220, configured to calculate the large-scale fading factor β and the covariance matrix R of the channel according to the position of each base station and the position of each user;
 The channel estimation unit 230 is configured to receive the uplink pilot signal, and at the same time, combine the large-scale fading factor β and the covariance matrix R of the channel, and use the MMSE method to estimate the channel;
 The model establishment unit 240 is used for deriving the SINR expression of the signal-to-interference-noise ratio of the system according to the estimated channel, and calculating the frequency efficiency according to Shannon's capacity theorem, and establishing an energy efficiency optimization model of the system in combination with the power consumption model of the system;
 The power allocation unit 250 is configured to use the adaptive particle swarm algorithm based on the fixed pilot power to allocate the data power of the user according to the optimization objective function for the energy efficiency optimization model.
 The third embodiment of the present invention also provides an energy efficiency optimization device based on adaptive particle swarm power distribution, 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 , in order to realize 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. Those skilled in the technical field of the present invention can make various modifications or supplements to the described specific embodiments or use similar methods to replace, but will not deviate from the spirit of the present invention or exceed the scope of the appended claims. defined range.