CN114302487A - Energy efficiency optimization method, device and equipment based on adaptive particle swarm power distribution - Google Patents

Abstract

The invention discloses an energy efficiency optimization method, device and equipment based on adaptive particle swarm power allocation. The method includes: initializing the positions of each base station and each user in a cell; Scale fading factor β and channel covariance matrix R; receive the uplink pilot signal, and combine the large-scale fading factor β and channel covariance matrix R, use MMSE method to estimate the channel; According to the estimated channel, the signal interference of the system is derived Noise ratio SINR expression, and calculate the frequency efficiency according to Shannon's capacity theorem, and establish the energy efficiency optimization model of the system in combination with the power consumption model of the system; for the energy efficiency optimization model, according to the optimization objective function, the adaptive particle swarm algorithm is used in the fixed derivative. On the basis of frequency power, the data power allocation of users is carried out. The invention can effectively improve the energy efficiency of the system and meet the requirements of green communication.

Description

Energy efficiency optimization method, device and equipment based on adaptive particle swarm power allocation

technical field

The invention belongs to the technical field of wireless communication of massive MIMO systems, and in particular relates to an energy efficiency optimization method, device and equipment based on adaptive particle swarm power allocation.

Background technique

Massive MIMO system configures hundreds of thousands of antennas at the base station and uses multipath scattering to obtain spatial diversity gain and spatial multiplexing gain, so that the system can greatly improve the frequency efficiency of the system under the original bandwidth. Energy efficiency and the reliability of wireless links have become one of the key technologies of 5G. There are generally two multiplexing and duplexing methods for data transmission in wireless communication systems—TDD (time division duplexing) and FDD (frequency division duplexing). Reciprocity, using TDD model. The location relationship between the user and the base station and the surrounding environment are different, so that the channel gain between the user and the base station is different, and the fading degree of the signal during the channel transmission process is also different. During uplink data transmission, due to the non-orthogonality of the channels between users, the signals transmitted between users will interfere with each other, which makes the signal received by the base station include both useful signals and interference signals. Affect the performance of the entire communication system. Power control has always been one of the hot issues in communication system research. In the energy efficiency research of massive MIMO systems, by controlling the power of pilot and data signals, the total power consumption of the system can be reduced under the condition of ensuring the data transmission rate, the energy efficiency of the system can be improved, and the performance of the system can be further improved to meet the requirements of green communication requirements.

At present, the power distribution problem has been widely studied, and the specific solutions are:

First, the equal power allocation algorithm, that is, without considering the channel gain, the total uplink power is evenly allocated to each user in the cell. This method will make the communication quality of users with good channel gain good, while the communication quality of users with poor channel gain will not be very bad, but this algorithm does not consider the minimum rate of users, and it is not optimal for energy efficiency and frequency efficiency. , because in reality the number of antennas configured by the base station cannot approach infinity.

2. The water injection power algorithm from the perspective of channel gain. The water injection power algorithm is to adaptively allocate the transmit power according to the channel state. Usually, when the channel state is good, more power is allocated, and when the channel is poor, less power is allocated, so as to achieve the maximum optimized transfer rate. This power allocation algorithm is simple in implementation, but does not consider the fairness of user quality, which will lead to users with good channels allocated more power and better communication quality, while users with poor channels are allocated less power, poor communication quality, and poor reception. Strong interference to other users.

3. According to the power allocation algorithm of convex optimization theory such as fractional programming theory, Lagrange multiplication combined with Dinkelbach algorithm, the energy efficiency optimization objective of massive MIMO system is non-convex, and it is generally necessary to relax and reshape the non-convex. The objective function is reconstructed to form a convex objective function, and then the power is allocated by convex optimization methods such as the interior point method. The local optimal power allocation vector obtained in this way is the optimal power allocation vector. For system energy efficiency and frequency efficiency Both have good performance, but the implementation process of this power distribution method is complicated, the algorithm calculation complexity is too high, and solid mathematical knowledge is required.

SUMMARY OF THE INVENTION

Aiming at various problems that are not fully considered in the existing power allocation scheme, the purpose of the present invention is to provide an energy efficiency optimization method, device and equipment based on adaptive particle swarm power allocation, by iterative optimization of user uplink data power , under the condition of low computational complexity, considering the fairness of each user in the cell, the frequency efficiency and energy efficiency of the system are greatly improved.

In order to achieve the above object, the present invention provides the following technical solutions:

An energy efficiency optimization method based on adaptive particle swarm power distribution, comprising the following steps:

S1, initialize the location of each base station in the cell and the location of each user;

S2, according to the position of each base station and the position of each user, calculate the large-scale fading factor β and the covariance matrix R of the channel;

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;

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;

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 power.

表示为：Preferably, in step S2, the large-scale fading factor

Expressed as:

表示阴影衰落，其对数

服从高斯分布

表示小区l中第i个用户到小区j中心的基站的距离，r₀表示小区的半径；α表示信号传输过程中的路径损耗系数。in,

represents shadow fading, its logarithm

follow a Gaussian distribution

Represents the distance from the ith user in cell l to the base station in the center of cell j, r ₀ represents the radius of the cell; α represents the path loss coefficient in the signal transmission process.

The covariance matrix R of the channel is obtained according to the approximate solution of the Gaussian local scattering model and satisfies:

为用户的到达角。Among them, β represents the large-scale fading factor, d _H is the spatial distance of the antenna, M is the number of antennas configured by the base station,

is the angle of arrival of the user.

Preferably, step S3 specifically includes:

表示为:During the pilot transmission phase, the signal received by the base station in cell j will be

Expressed as:

为小区j中的第k个用户发射的导频序列；

表示基站接收端的加性高斯白噪声，其服从独立同分布CN～(0，σ_p)；Among them, p _jk represents the power of the kth user in cell j to transmit the pilot frequency,

is the pilot sequence transmitted for the kth user in cell j;

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 );

得到：Multiply both sides of the above equation by

get:

值为0；where, since the pilots are orthogonal,

value is 0;

进行信道估计，其中，小区l中的第k个用户与基站j之间的信道估计值可以表示为：Take the method of least mean square error estimation, according to

Perform channel estimation, where 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:

The received signal is received by maximum ratio combining, that is, the received vector is:

Therefore, the transmitted signal of user k in cell j is expressed by the following formula:

Among them, the first item is the useful signal; the second item is intra-cell interference; the remaining part is inter-cell interference and other non-correlated noises, and s _jk is the transmitted signal of user k in cell j.

Preferably, in step S4, the signal-to-interference noise ratio SINR of the system is

Through Shannon's capacity theorem, we know that the lower bound of the frequency effect of the system is expressed as:

The optimization problem is expressed as:

表示小区j中的基站接收到的小区j内第k个用户信号的信干噪比，p_li表示分配给小区j中用户i的数据功率，V_jk表示接收组合矢量，

表示小区j的基站与小区内第k个用户间信道的估计值，

表示信道矩阵的估计误差矩阵，

表示噪声功率，

为M_j阶的单位矩阵，EE^UL表示大规模MIMO上行传输过程的系统能效，τ_u表示传输上行数据的相干块长度，τ_c表示相干块的总长度，M为基站配备的天线数量，p_r为上行数据传输时天线的射频链路消耗的功率，p_s为大规模MIMO系统传输数据过程中消耗的静态电功耗，p_max是每个小区中所有用户在上行传输过程中的最大的电功率。R_min是在考虑最小速率约束下设定的值。in,

is 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 is the receiving combination 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,

is the identity matrix of order M _j , 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 radio frequency link of the antenna during uplink data transmission, p _s is the static electrical power consumption consumed in the process of data transmission in the massive MIMO system, and _pmax is the maximum power consumption of all users in each cell during the uplink transmission process. Electric power. _Rmin is the value set taking into account the minimum rate constraint.

Preferably, step S5 specifically includes:

S51, using the rand function and the upper and lower bounds of the set user data power to initialize the parameters of the particle;

S52, taking the energy efficiency function as the fitness function, calculating the fitness of the initialized particles, and initializing the local optimal solution and the global optimal solution;

S53, the judgment of the iteration termination condition is performed, and the iteration is terminated when the iteration reaches the initially set maximum number of iterations or the fitness value tends to be stable, otherwise, the following steps are continued;

S54, update the speed and position of the particles, and at the same time, perform boundary processing for the particles that exceed the boundary range, and recalculate the fitness value for the updated particles;

S55, save the results of each iteration, compare the results after each iteration, and update the local optimal solution and the global optimal solution.

S56, when the iteration termination condition is reached, the obtained global optimal value, that is, the required optimal user data power allocation vector.

Preferably, in step S52, the fitness function is set as an energy efficiency expression:

By taking the position parameter of each particle as the input parameter, and substituting it into the above formula to solve the corresponding fitness value, the first iteration process ends, and the initialized particle parameters are set to the local optimal value and the global optimal value.

Preferably, in step S54, the speed and position of the particles are updated and the boundary processing is performed as follows:

The update formulas for particle velocity and position are:

V _j (t+1)=V _j (t)+c1*rand*(Gbest _j -pop _j (t))+c2*rand*(Zbest-pop _j (t))

pop _j (t+1)=pop _j (t)+ω*V _j (t+1)

In the formula, V _j (t+1) represents the search speed of the j-th particle at the (t+1)th iteration, V _j (t) represents the search speed of the j-th particle at the t-th iteration, and c1 is the individual Learning factor, which indicates that the iteration of velocity is only related to the historical position of the particle itself, c2 is the social learning factor, which indicates the relationship between the iteration of velocity and the historical position of the entire particle swarm, ω represents the weight factor, when ω is larger, it is beneficial to Jumping out of the local minimum is convenient for global search, and when the inertia factor ω is small, it is conducive to accurate search for local tendencies. 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 j-th particle, Zbest represents the optimal particle position under the current number of iterations of the entire particle swarm, and pop _j (t) represents the position parameter of the j-th particle at the t-th 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 transition speed of the particle in the solution space, and can 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 movement in the solution space.

The embodiment of the present invention also provides an energy efficiency optimization device based on adaptive particle swarm power allocation, which includes:

an initialization unit, used to initialize the location of each base station in the cell and the location of each user;

a calculation unit, used for calculating 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 is used 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 building unit is used to derive the SINR expression of the system according to the estimated channel, calculate the frequency efficiency according to Shannon's capacity theorem, and establish the energy efficiency optimization model of the system combined with the power consumption model of the system;

The power allocation unit is configured to use the adaptive particle swarm algorithm to allocate the data power of the user on the basis of the fixed pilot power according to the optimization objective function of the energy efficiency optimization model.

Embodiments of the present invention also provide 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 to The energy efficiency optimization method based on adaptive particle swarm power allocation as described above is realized.

As mentioned above, the present invention can fully consider the communication quality fairness of each user under the condition of low computational complexity, effectively improve the energy efficiency of the system, and meet the requirements of green communication.

Description of drawings

FIG. 1 is a schematic work flow of an energy efficiency optimization method based on adaptive particle swarm power allocation provided by a first embodiment of the present invention.

FIG. 2 is a schematic block diagram of an adaptive particle swarm algorithm flow according to an embodiment of the present invention.

FIG. 3 is a schematic diagram showing the comparison of the average frequency efficiency performance of a cell based on the adaptive particle swarm algorithm and other algorithms provided by the present invention.

FIG. 4 is a schematic diagram showing the comparison of the average energy efficiency performance of a cell based on the adaptive particle swarm algorithm and other algorithms provided by the present invention.

FIG. 5 is a schematic structural diagram of an energy efficiency optimization apparatus based on adaptive particle swarm power distribution provided by a second embodiment of the present invention.

Detailed ways

The solution of the present invention will be further elaborated below with reference to specific embodiments and 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, initialize the location of each base station in the cell and the location of each user.

在每个小区中，用户通过随机分布的方式进行设置，同时约束用户到基站的距离最小距离，并针对每个用户计算周围基站的拓扑距离。Among them, in an implementation manner, a total of 16 cells are set in this embodiment, and they are arranged according to 4*4. In order to overcome the interference between cells, the system adopts orthogonal pilots for users in the cells. Meanwhile, in order not to lose generality, the pilot frequency reuse factor of this system is set to f=4. Therefore, the pilot sequence used in this system can be expressed as

In each cell, users are set by random distribution, while the minimum distance from the user to the base station is constrained, and the topological distance of the surrounding base stations is calculated for each user.

S2, 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.

可以表示为：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<<M). Without loss of generality, the channel gain from the ith user in cell l to cell base station j

It can be expressed as:

表示小尺度衰落，服从于圆对称复高斯分布，即CN～(0，I_M)。in,

Represents small-scale fading and obeys a circularly symmetric complex Gaussian distribution, namely CN～(0, _IM ).

是大尺度衰落因子，在一般情况下表示为in,

is the large-scale fading factor, which is generally expressed as

表示阴影衰落，其对数

服从高斯分布

其中

表示小区l中第i个用户到小区j中心的基站的距离，r0表示小区的半径，α表示信号传输过程中的路径损耗系数；特别的，

在若干个信道的相干时间内变化缓慢并且容易追踪，所以将一个相干时间内的

视为常数。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:

为用户的到达角。Among them, β represents 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.

可以表示为:Among them, in the pilot transmission stage, the signal received by the base station in cell j

It can be expressed as:

为小区j中的第k个用户发射的导频序列。

表示的是基站接收端的加性高斯白噪声，其服从独立同分布CN(0～σ_p)。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 ).

可以得到：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.

来进行信道估计，本实施例采取最小均方误差(MMSE)估计的方法。因此，小区l中的第k个用户与基站j之间的信道估计值可以表示为：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:

表示小区j中的基站接收到的小区j内第k个用户信号的信干噪比，p_li表示分配给小区j中用户i的数据功率，V_jk表示接收组合矢量，

表示小区j的基站与小区内第k个用户间信道的估计值，

表示信道矩阵的估计误差矩阵，

表示噪声功率，

为M_j阶的单位矩阵，EE^UL表示大规模MIMO上行传输过程的系统能效，τ_u表示传输上行数据的相干块长度，τ_c表示相干块的总长度，M为基站配备的天线数量，p_r为上行数据传输时天线的射频链路消耗的功率，p_s为大规模MIMO系统传输数据过程中消耗的静态电功耗，p_max是每个小区中所有用户在上行传输过程中的最大的电功率。R_min是在考虑最小速率约束下设定的值。in,

is 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 is the receiving combination 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,

is the identity matrix of order M _j , 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 radio frequency link of the antenna during uplink data transmission, p _s is the static electrical power consumption consumed in the process of data transmission in the massive MIMO system, and _pmax is the maximum power consumption of all users in each cell during the uplink transmission process. Electric power. _Rmin 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 power.

Preferably, step S5 specifically includes:

其中在

中，上标为迭代次数，下标为粒子编号；p₁₂为中前一个数字下标代表小区编号，第二个数字下标代表小区中的用户编号。S51, use the rand function and the upper and lower bounds of the set user data power to initialize the particles,

of which in

In , the superscript is the number of iterations, and the subscript is the particle number; p ₁₂ is the subscript of the first number in the middle, which represents the cell number, and the second subscript of the number represents the user number in the cell.

以及全局最优

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:

以及全局最优解

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 j-th particle at the (t+1)th iteration, V _j (t) represents the velocity value of the j-th particle at the t-th iteration, and c1 is the individual Learning factor, the iteration of speed is only related to the historical position of the particle itself, c2 is the social learning factor, the iteration of speed is related to the historical position of the entire particle swarm, ω represents the weight factor, when ω is large, it is conducive to jumping out The local minimum value is convenient for the global search, and when the inertia factor ω is small, it is conducive to the accurate search of the local tendency. 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 j-th particle, Zbest is the optimal particle position of the entire particle swarm so far, and pop _j (t) represents the position parameter of the j-th particle at the t-th 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.

3 and 4 are performance simulation result diagrams of an 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.

Referring to FIG. 5 , the second embodiment of the present invention further 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.

Claims (9)

1. an energy efficiency optimization method based on adaptive particle swarm power distribution, is characterized in that, comprises the following steps: S1, initialize the location of each base station in the cell and the location of each user; S2, according to the position of each base station and the position of each user, calculate the large-scale fading factor β and the covariance matrix R of the channel; 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; 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; 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 power. 2. The energy efficiency optimization method based on adaptive particle swarm power allocation according to claim 1, wherein in step S2, the large-scale fading factor

Expressed as:

in,

represents shadow fading, its logarithm

follow a Gaussian distribution

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; 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 the Gaussian local scattering model and satisfies:

Among them, β represents the large-scale fading factor, d _H is the spatial distance of the antenna, M is the number of antennas configured by the base station,

is the angle of arrival of the user. 3. The energy efficiency optimization method based on adaptive particle swarm power allocation according to claim 2, wherein step S3 specifically comprises: During the pilot transmission phase, the signal received by the base station in cell j will be

Expressed as:

Among them, p _jk represents the power of the kth user in cell j to transmit the pilot frequency,

is the pilot sequence transmitted for the kth user in cell j;

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 the above equation by

get:

Among them, due to the use of orthogonal pilots,

value is 0; Take the method of least mean square error estimation, according to

Perform channel estimation, where the channel estimation value between the kth user in cell l and base station j can be expressed as:

where P _li represents the set of users that transmit the same pilot as user i in cell 1. Therefore, in the uplink data transmission stage, the data received by base station j is expressed as:

The received signal is received by maximum ratio combining, that is, the received vector is:

Therefore, the transmitted signal of user k in cell j is 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; s _jk represents the transmitted signal of user k in cell j. 4. The energy efficiency optimization method based on adaptive particle swarm power allocation according to claim 3, wherein in step S4,

According to Shannon's capacity theorem, the lower bound of the frequency effect of the system can be expressed as:

The optimization problem is expressed as:

in,

is 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 is the receiving combination 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,

is the identity matrix of order M _j , EE ^UL represents the system energy efficiency of the massive MIMO uplink transmission process, τ _c represents the total length of the coherent block, τ _u represents the length of the coherent block for transmitting uplink data, M is the number of antennas equipped by the base station, p _r is the power consumed by the radio frequency link of the antenna during uplink data transmission, p _s is the static electrical power consumption consumed in the process of data transmission in the massive MIMO system, and _pmax is the maximum power consumption of all users in each cell during the uplink transmission process. Electric power, R _min is the minimum user transfer rate limit we consider. 5. The energy efficiency optimization method based on adaptive particle swarm power allocation according to claim 4, wherein step S5 specifically comprises: S51, using the rand function and the upper and lower bounds of the set user data power to initialize the parameters of the particle; S52, using the energy efficiency function as the fitness function, calculate the fitness value of the initialized particles, and initialize the local optimal solution and the global optimal solution; S53, the judgment of the iteration termination condition is performed, and the iteration is terminated when the iteration reaches the initially set maximum number of iterations or the fitness value tends to be stable, otherwise, the following steps are continued; S54, update the speed and position of the particles, and at the same time, perform boundary processing for the particles that exceed the boundary range, and recalculate the fitness value for the updated particles; S55, save the results of each iteration, compare the results after each iteration, and update the local optimal solution and the global optimal solution. S56, when the iteration termination condition is reached, the obtained global optimal value, that is, the required optimal user data power allocation vector. 6. The energy efficiency optimization method based on adaptive particle swarm power allocation according to claim 5, wherein, In step S52, the fitness function is set as the energy efficiency expression:

By taking the position parameter of each particle as the input parameter, and substituting it into the above formula to solve the corresponding fitness value, the first iteration process ends, and the initialized particle parameters are set to the local optimal value and the global optimal value. 7. The energy efficiency optimization method based on adaptive particle swarm power allocation according to claim 5, wherein in step S54, the speed and position of the particles are updated and boundary processing is performed as follows: The update formulas for particle velocity and position are: V _j (t+1)=V _j (t)+c1*rand*(Gbest _j -pop _j (t))+c2*rand*(Zbest-pop _j (t)) pop _j (t+1)=pop _j (t)+ω*V _j (t+1)

In the formula, V _j (t+1) represents the search speed of the j-th particle at the (t+1)th iteration, V _j (t) represents the search speed of the j-th particle at the t-th iteration, and c1 is the individual Learning factor, which indicates that the iteration of velocity is only related to the historical position of the particle itself, c2 is the social learning factor, which indicates the relationship between the iteration of velocity and the historical position of the entire particle swarm, ω indicates the weight factor; Gbest _j is the jth particle The current optimal position, Zbest is the optimal particle position of the entire particle swarm so far, and pop _j (t) represents the position parameter of the j-th particle at the t-th iteration; The boundary processing methods for particle velocity and position are:

V(i,j) represents the search velocity of the ith particle in the jth dimension; pop(i,j) represents the displacement of the ith particle in the jth dimension. 8. An energy efficiency optimization device based on adaptive particle swarm power distribution, characterized in that it comprises: an initialization unit, used to initialize the location of each base station in the cell and the location of each user; a calculation unit, 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 is used 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 building unit is used to deduce the SINR expression of the system according to the estimated channel, calculate the frequency efficiency according to Shannon's capacity theorem, and establish the energy efficiency optimization model of the system combined with the power consumption model of the system; The power allocation unit is configured to use the adaptive particle swarm algorithm to allocate the data power of the user on the basis of the fixed pilot power according to the optimization objective function of the energy efficiency optimization model. 9. An energy efficiency optimization device based on adaptive particle swarm power distribution, characterized in that it 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 to achieve the following: 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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