CN111083786A - A power allocation optimization method for a mobile multi-user communication system - Google Patents

Abstract

The invention discloses a power distribution optimization method for a mobile multi-user communication system, comprising: establishing a mobile multi-user communication system model, and a mobile relay node MR _j forwards the signal of the mobile source MS _i to the mobile user by using a decoding and forwarding strategy MU _l , select the best transmit antenna to maximize the received signal-to-noise ratio of the best mobile user, and take the closed expression of the outage probability of the best transmit antenna as the constrained optimization objective function, and use the enhanced gray wolf optimization algorithm to make it reach the minimum value. Obtaining the optimal power distribution coefficient significantly reduces the energy consumption of the mobile multi-user communication system. And further proposes a sub-optimal transmit antenna selection scheme, derives its outage probability closed expression as the constrained optimization objective function, and uses the enhanced gray wolf optimization algorithm to make it reach the minimum value to obtain the optimal power distribution coefficient, which reduces the computational cost of the optimal scheme. complexity and reduce the energy consumption of the mobile multi-user communication system.

Description

A power allocation optimization method for a mobile multi-user communication system

technical field

The invention belongs to the technical field of mobile communication, and in particular, relates to a power distribution optimization method of a mobile multi-user communication system.

Background technique

With the development of the fifth-generation mobile communication technology, mobile users' requirements for the data rate and service quality of wireless transmission are constantly improving, and they are pursuing higher-quality, higher-speed, and more diverse mobile communications.

Existing spectrum resources are almost exhausted, and a large amount of energy resources are consumed in exchange for the improvement of mobile communication quality, which brings more and more severe energy consumption problems. Using limited resources to enable more users to access the network at the same time, further Increasing the capacity of system data transmission, reducing energy consumption, and improving energy efficiency have become the key issues faced by 5G green mobile communication technology.

Power allocation technology is an effective method to reduce the energy consumption of mobile multi-user communication systems. However, the existing power allocation mechanism is designed under the traditional communication architecture, and the real-time response requirements and data efficient acquisition requirements of mobile communication systems are insufficiently considered. The complexity is high and needs to be improved in terms of efficiency, real-time performance and applicability to application scenarios.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a power distribution optimization method for a mobile multi-user communication system, establish a mobile multi-user communication system model under the N-Nakagami channel, design two transmission antenna selection schemes, and deduce respectively for these two schemes. The closed expression of the outage probability of the system is established, and the power constraint optimization objective function is established. Based on the enhanced gray wolf optimization algorithm, the optimal solution of the power constraint optimization objective function is obtained, and the optimal power distribution coefficient of the system is obtained, which significantly reduces the mobile multi-user communication. energy consumption of the system.

In order to solve the above-mentioned technical problems, the present invention adopts the following technical solutions to be realized:

A power allocation optimization method for a mobile multi-user communication system is proposed, including: establishing a mobile multi-user communication system model; and a mobile relay node _{MRj forwards the signal of the mobile source MS i to} the mobile user _MU1 using a decoding and forwarding strategy; Select the best transmit antenna to maximize the receiving signal-to-noise ratio of the best mobile user; wherein, the best mobile user is the user with the largest receive signal-to-noise ratio; take the closed expression of the outage probability of the best transmit antenna as the constraint optimization target function, using the enhanced gray wolf optimization algorithm to make it to the minimum value to obtain the optimal power distribution coefficient.

Compared with the prior art, the advantages and positive effects of the present invention are: the power allocation optimization method for a mobile multi-user communication system proposed by the present invention establishes a mobile multi-user communication system model, and designs two transmission antenna selection schemes. For two transmit antenna selection schemes, the closed expression of the system outage probability is deduced respectively, and then the power constraint optimization objective function is established according to the closed expression of the outage probability, and the optimal solution of the power constraint optimization objective function is obtained based on the enhanced gray wolf optimization method. , the optimal power distribution coefficient of the system is obtained, which significantly reduces the energy consumption of the multi-user communication system.

Other features and advantages of the present invention will become more apparent upon reading the detailed description of the embodiments of the present invention in conjunction with the accompanying drawings.

Description of drawings

Fig. 1 is the flow chart of the power allocation optimization method of the mobile multi-user communication system proposed by the present invention;

Fig. 2 is the model framework diagram of the mobile multi-user communication system established in step S1 of the present invention;

Fig. 3 is the outage probability performance of the optimal transmit antenna selection scheme proposed by the present invention;

FIG. 4 is the outage probability performance of the sub-optimal transmit antenna selection scheme proposed by the present invention;

Fig. 5 adopts the best K value that the enhanced gray wolf optimization algorithm obtains in the present invention;

Figure 6 is the best K value obtained by using the GA algorithm;

Fig. 7 is the optimal K value that adopts PSO algorithm to obtain;

Fig. 8 is the optimal K value obtained by CS algorithm;

Fig. 9 is the optimal K value that adopts FA algorithm to obtain;

Figure 10 shows the best K value obtained by using the DE algorithm;

Figure 11 shows the best K value obtained by using the GS algorithm;

Figure 12 shows the OP performance comparison of seven algorithms, GWO, GA, PSO, CS, FA, DE, and GS.

Detailed ways

The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

The N-Nakagami channel can more flexibly characterize the fading characteristics of mobile communication, and is more in line with the actual complex and changeable mobile communication environment, and the N-Nakagami channel includes the communication environment of traditional channels such as Rayleigh and Nakagami. A mobile multi-user communication system model is established under the channel, and two optimal and sub-optimal transmit antenna selection (TAS) schemes are designed. Objective function, an enhanced gray wolf optimization algorithm is proposed to obtain the optimal solution of the power constraint optimization objective function, and the optimal power distribution coefficient of the system is obtained. Differential evolution algorithm (differentialevolution, DE), golden section method (golden section, GS) , Particle swarm optimization (PSO), genetic algorithm (GA), cuckoo search algorithm (CS), firefly algorithm (FA), etc. have been compared, and the simulation results show that the The proposed optimization method has better performance and can be easily applied to the performance calculation and analysis of mobile communication networks in complex environments.

As shown in FIG. 1, the power allocation optimization method of the mobile multi-user communication system proposed by the present invention includes the following steps:

Step S1: Build a mobile multi-user communication system model.

As shown in the mobile multi-user cooperative communication system model shown in FIG. 2 , the mobile source MS sends information to L mobile users MU through a mobile relay node MR.

The communication channel is the N-Nakagami channel, and h=h _g , g SR, SU, RU are defined, indicating the channel gain of the MS→MR, MS→MU, MR→MU link, and the total transmit power of MS and MR is E, in order to Indicate the relative positions of MS, MR and MU, respectively use V _SR , V _SU , V _RU to denote the position gain of MS→MR, MS→MU, MR→MU link.

Step S2: The mobile relay node _MRj forwards the signal of the mobile source MS _i to the mobile user _MU1 by using the decoding and forwarding strategy.

In two time slots, the total transmit power of the system is E, K is the power distribution coefficient of the total transmit power, the ith transmit antenna of MS is represented as MS _i , and the jth antenna of MR is represented as MR _j .

In the first time slot, MS _i sends information x, r _SRij , r _SUil are the received signals of MR _j and MU _l , respectively

where n _SUil and n _SRij have a mean of 0 and a variance of N ₀ /2.

In the second time slot, MR _j uses the decoding and forwarding cooperative strategy to send the information x sent by the mobile source MS _i to the mobile user MU _l , and the received signal is

Among them, the mean value of n _RUjl is 0, and the variance is N ₀ /2; if MR _j can be correctly demodulated, then β=1; otherwise, β=0.

The instantaneous information rate of the MS _i → MR _j link can be expressed as

γ _SRij is the signal-to-noise ratio of MS _i → MR _j link

For a predetermined threshold information rate R ₀ , when I _SRij < R ₀ , the relay node MR _j cannot achieve complete decoding, that is, an interruption occurs, which can be expressed as

in

Combined reception is used in this embodiment of the present invention, and the received signal-to-noise ratio of mobile user MU ₁ can be expressed as

Step S3: Select the best transmit antenna to maximize the receiving signal-to-noise ratio of the best mobile user; wherein, the best mobile user is the user with the highest receiving signal-to-noise ratio.

For L mobile users, choose the best mobile user, and its received signal-to-noise ratio is expressed as

The best transmit antenna selection scheme is to select the transmit antenna w to maximize the received signal-to-noise ratio, that is,

where |C| represents the potential of the decoding set C, and the decoding set C can be expressed as

C={1≤j≤N _t |γ _{SRj ≥R th} } (12),

Step S4 : take the closed expression of the interruption probability of the optimal transmitting antenna as the constrained optimization objective function, and use the enhanced gray wolf optimization algorithm to make it reach the minimum value to obtain the optimal power distribution coefficient.

The closed expression for deriving the optimal transmit antenna outage probability is as follows:

in,

_Q1 is calculated as follows

_Q2 is calculated as follows

In the application of the present invention, the deduced closed expression of outage probability is used as a constraint optimization objective function to make it reach the minimum value, and the optimal power distribution coefficient K is obtained, that is,

Among them, P ₁ is the transmit power of the mobile source, P ₂ is the transmit power of the mobile relay node, P _A is the maximum power of the system, _PD is the maximum power of the mobile source, and P _E is the maximum power of the mobile relay node. power.

In order to obtain the optimal power distribution coefficient K, the application of the present invention uses the enhanced gray wolf algorithm for optimization.

The steps of enhancing the gray wolf algorithm in the present invention are:

Step S31: Optimizing the initial wolf pack.

In this embodiment, the good point set theory is used to generate an initial gray wolf population with a size of N, and then the best three wolves are selected from them, which are α, β, and δ wolves respectively, and the other wolves are ω wolves.

Step S32: surrounded by wolves.

The wolf pack first surrounds the target during the hunting process:

为系数向量,

表示猎物和灰狼之间的距离,

是为全局最优解向量(猎物所在位置)，

为潜在解向量(狼群所在位置)。

表示为where t is the current iteration number,

is the coefficient vector,

represents the distance between the prey and the gray wolf,

is the global optimal solution vector (where the prey is located),

is the potential solution vector (where the wolves are located).

Expressed as

为随机向量,取值范围为[0,1]；a的值随迭代数增加从2线性递减到0。

is a random vector, the value range is [0,1]; the value of a decreases linearly from 2 to 0 with the increase of the number of iterations.

Step S33: hunting by wolves.

Guided by the alpha, beta, and delta wolves, the other ω wolves should update their respective positions based on the current alpha, beta, and delta wolf positions:

in,

Step S34: attack by wolves.

When wolves attack their prey, the optimal solution is obtained. It is mainly achieved by decreasing the value of a.

In the application of the present invention, compared with the above-mentioned optimal transmission antenna selection scheme, a suboptimal transmission antenna selection scheme is also provided, and the closed expression of the interruption probability of the suboptimal transmission antenna is used as the constraint optimization objective function, so that the The minimum value is reached to obtain the best power distribution coefficient to reduce the computational complexity. When the optimization performance can be appropriately reduced and the computational complexity is mainly considered, the next best solution can be selected to maximize the received signal-to-noise ratio of MS _i →MU _l .

Choose the next best transmit antenna for

The closed expression for the probability of interruption is:

(34), where,

Similarly, the closed expression of the outage probability of the derived sub-optimal antenna selection scheme is used as the constraint optimization objective function to make it reach the minimum value, and the corresponding optimal power distribution coefficient K is obtained, that is, according to formulas (20)-(32) Find the optimal solution.

Next, the present application simulates the power allocation optimization method of the mobile multi-user communication system proposed above to verify the performance of the optimization method of the present application.

Define μ=V _SU /V _RU as the relative position gain, E=1, and each simulation parameter is set to 10,000 times.

In Figures 3 and 4, the outage probability performance for the best transmit antenna selection and the next best transmit antenna selection are given, respectively, and the simulation coefficients are given in Table 1 below:

Table I

parameter Numerical value γ_th 5dB R_th 5dB N_t 1,2,3 N_r 2 L 2 m 1 K 0.5 N 2 u 0dB

As can be seen from Figures 3 and 4, the simulated values are in good agreement with the theoretical values, verifying the correctness of the derived theoretical closed expressions, and the increase of SNR and _Nt can continuously improve the outage probability performance.

In Figure 5 to Figure 11, GWO (Grey Wolf Optimization Algorithm), GA (Genetic Algorithm), PSO (Particle Swarm Optimization Algorithm), CS (Cuckoo Search Algorithm), FA (Firefly Algorithm), DE (Differential Evolution) are compared The optimal K value of the seven algorithms of GS (Golden Section) and GS (Golden Section), the simulation coefficients are shown in Table 2 below:

Table II

As shown in Table 3 below, comparing the running time, K and interruption probability performance OP of the seven algorithms, it can be obtained that, compared with GS, GA, CS, PSO, FA, DE, GWO has better optimization effect and better running time. short, the best K value was obtained, and the OP performed the best.

Table 3

Figure 12 shows the OP performance comparison of the seven algorithms. It can be seen from Figure 12 that the enhanced gray wolf optimization algorithm has better optimization effect and better OP performance.

As mentioned above, this application establishes a mobile multi-user communication system model under the N-Nakagami channel, designs two TAS schemes, studies the OP performance of the mobile multi-user communication system, and derives the closed expression of OP. Then a Compared with GS, GA, CS, PSO, FA, DE, the intelligent optimization mechanism proposed in this paper obtains better OP performance.

It should be pointed out that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by those of ordinary skill in the art within the essential scope of the present invention, It should also belong to the protection scope of the present invention.

Claims (8)

1. A method for power allocation optimization in a mobile multi-user communication system, comprising:

establishing a mobile multi-user communication system model;

mobile relay node MR_jMobile source MS using transcoding forwarding strategy_iIs forwarded to the mobile user MU_l；

Selecting the best transmitting antenna to maximize the receiving signal-to-noise ratio of the best mobile user; the optimal mobile user is the user with the largest receiving signal-to-noise ratio;

and taking the interruption probability closed expression of the optimal transmitting antenna as a constraint optimization objective function, and using an enhanced wolf optimization algorithm to enable the interruption probability closed expression to reach the minimum value so as to obtain the optimal power distribution coefficient.

2. The method of claim 1 for optimizing power allocation in a mobile multi-user communication system,

the best transmitting antenna is selected as

Wherein,

received signal-to-noise ratio for the best mobile user; | C | is the decoding set C ═ 1 ≦ j ≦ N_t|γ_SRj≥R_thThe potential of { C }; n is a radical of_tFor the number of transmitting antennas, L is the number of mobile users, gamma_SRjIs MS_i→MR_jReceived signal-to-noise ratio, gamma, of the link_SUilIs MS_i→MU_lReceived signal-to-noise ratio, gamma, of the link_RUjlIs MR_j→MU_lThe received signal-to-noise ratio of the link;

R₀is MS_i→MR_jThe threshold information rate of the link.

3. The method of claim 2, further comprising:

the closed expression for deriving the optimal transmit antenna outage probability is:

；

wherein,

wherein N is_rFor the number of receiving antennas, m is the attenuation coefficient, Ω ═ E (| a | y²) E () represents an averaging operation; g [. C]Representing the Meijer's G function.

4. The method of claim 3, further comprising:

selecting a sub-optimal transmitting antenna, and taking an interruption probability closed expression of the sub-optimal transmitting antenna as a constraint optimization objective function to enable the interruption probability closed expression to reach a minimum value so as to obtain a sub-optimal power distribution coefficient;

wherein the sub-optimal transmitting antenna is selected to be

The closed expression of the probability of interruption is:

wherein,

5. the method according to claim 1 or 4, wherein the constraint condition using the closed expression of the outage probability as the constraint optimization objective function is:

wherein, P₁For transmission power of mobile sources, P₂For the transmission power of the mobile relay node, P_AIs the maximum power of the system, P_DFor maximum power of the mobile source, P_EMaximum power for the mobile relay node; e is the total transmission power of the system, and K is the power distribution coefficient of the total transmission power.

6. The method of claim 1 wherein the enhanced wolf optimization algorithm comprises:

the step of optimizing the initial wolf group is to select the best three wolfs α, delta wolfs and the other wolfs as omega wolfs;

the method comprises the following steps: based on

And

surrounding the target; where t is the current number of iterations,

in the form of a vector of coefficients,

indicating the distance between the prey and the gray wolf,

in order to obtain a global optimal solution vector,

is a potential solution vector;

the step of hunting the wolf pack is guided by α, delta wolf, omega wolf updating respective position according to current α, delta wolf position.

And (5) carrying out wolf pack attack to obtain an optimal solution.

7. The power allocation optimization method of claim 6, wherein in the wolf pack enclosing step:

wherein,

is a random vector with a value range of [0,1 ]](ii) a The value of a decreases linearly from 2 to 0 as the number of iterations increases.

8. The method as claimed in claim 6, wherein the ω wolf updates its position according to the current α, δ wolf position, specifically:

wherein,

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