CN111083786A - Power distribution optimization method of mobile multi-user communication system - Google Patents
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Abstract
The invention discloses a power distribution optimization method of a mobile multi-user communication system, which comprises the following steps: establishing mobile multi-user communication system model, mobile relay node MR j Mobile source MS using transcoding forwarding strategy i Is 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, and taking the interruption probability closed expression of the best transmitting antenna as a constraint optimization targetAnd the function is minimized by using an enhanced wolf optimization algorithm to obtain an optimal power distribution coefficient, so that the energy consumption of the mobile multi-user communication system is obviously reduced. And further provides a suboptimal transmitting antenna selection scheme, wherein a break probability closed expression is deduced to be used as a constraint optimization objective function, an enhanced wolf optimization algorithm is used for enabling the minimum value to be reached to obtain an optimal power distribution coefficient, and the calculation complexity and the energy consumption of the mobile multi-user communication system are reduced relative to the optimal scheme.
Description
Technical Field
The invention belongs to the technical field of mobile communication, and particularly relates to a power distribution optimization method of a mobile multi-user communication system.
Background
With the development of the fifth generation mobile communication technology, the requirements of mobile users on the data rate and the service quality of wireless transmission are continuously increasing, and mobile communication with higher quality, higher rate and more diversity is pursued.
The existing spectrum resources are almost distributed to the end, a large amount of energy resources are consumed to replace the improvement of mobile communication quality, the problem of more and more severe energy consumption is brought, more users can access to the network simultaneously by using limited resources, the capacity of system data transmission is further improved, the energy consumption is reduced, the energy efficiency is improved, and the key problem facing the 5G green mobile communication technology is solved.
The power allocation technology is an effective method for reducing the energy consumption of a mobile multi-user communication system, but the existing power allocation mechanism is designed under the traditional communication architecture, the real-time response requirement and the high-efficiency data acquisition requirement of the mobile communication system are not considered, the complexity is high, and the efficiency, the real-time performance and the applicability to application scenes need to be improved.
Disclosure of Invention
The invention aims to provide a power distribution optimization method of a mobile multi-user communication system, which comprises the steps of establishing a mobile multi-user communication system model under an N-Nakagami channel, designing two transmitting antenna selection schemes, deducing an interruption probability closed expression of the system respectively aiming at the two schemes, establishing a power constraint optimization objective function, obtaining an optimal solution of the power constraint optimization objective function based on an enhanced wolf optimization algorithm, obtaining an optimal power distribution coefficient of the system and remarkably reducing the energy consumption of the mobile multi-user communication system.
In order to solve the technical problems, the invention adopts the following technical scheme:
a power distribution optimization method of a mobile multi-user communication system is provided, which comprises the following steps: establishing a mobile multi-user communication system model; mobile relay node MRjMobile source MS using transcoding forwarding strategyiIs forwarded to the mobile user MUl(ii) a 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.
Compared with the prior art, the invention has the advantages and positive effects that: the power distribution optimization method of the mobile multi-user communication system establishes a mobile multi-user communication system model, designs two transmitting antenna selection schemes, deduces closed expressions of system interruption probability respectively aiming at the two transmitting antenna selection schemes, establishes a power constraint optimization objective function according to the closed expressions of the interruption probability, obtains the optimal solution of the power constraint optimization objective function based on an enhanced wolf optimization method, obtains the optimal power distribution coefficient of the system, and obviously reduces the energy consumption of the multi-user communication system.
Other features and advantages of the present invention will become more apparent from the detailed description of the embodiments of the present invention when taken in conjunction with the accompanying drawings.
Drawings
Fig. 1 is a flow chart of a power allocation optimization method for a mobile multi-user communication system according to the present invention;
fig. 2 is a diagram illustrating an architecture of the mobile multi-user communication system established in step S1 according to 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 a graph of outage probability performance for a suboptimal transmit antenna selection scheme proposed by the present invention;
FIG. 5 is a graph of the optimal K value obtained by the enhanced grayish optimization algorithm of the present invention;
FIG. 6 is the optimal K value obtained by GA algorithm;
FIG. 7 is an optimal K value obtained using the PSO algorithm;
FIG. 8 is an optimal K value obtained using the CS algorithm;
FIG. 9 is an optimal K value obtained by using the FA algorithm;
FIG. 10 is the optimal K value obtained using the DE algorithm;
FIG. 11 is the optimal K value obtained using the GS algorithm;
FIG. 12 shows OP performance comparison of seven GHO, GA, PSO, CS, FA, DE, GS algorithms.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
The N-Nakagami channel can more flexibly characterize the fading characteristics of mobile communication and better conform to the actual complex and variable mobile communication environment, and the N-Nakagami channel comprises the communication environments of Rayleigh, Nakagami and other traditional channels, the invention establishes a mobile multi-user communication system model under the N-Nakagami channel, designs two optimal and suboptimal Transmitting Antenna Selection (TAS) schemes, respectively deduces closed expressions of system interruption probability, establishes a power constraint optimization objective function by the closed expressions, provides an enhanced grayling optimization algorithm, obtains the optimal solution of the power constraint optimization objective function, obtains the optimal power distribution coefficient of the system, and combines with a differential evolution algorithm (DE), a golden section method (GS), a particle swarm optimization algorithm (PSO), a Genetic Algorithm (GA), the cuckoosearch algorithm (CS), the Firefly Algorithm (FA), and the like are compared, and simulation results show that the optimization method provided by the invention has better performance and can be conveniently applied to performance calculation and analysis of a mobile communication network in a complex environment.
As shown in fig. 1, the power allocation optimization method for a mobile multi-user communication system proposed by the present invention comprises the following steps:
step S1: and establishing a mobile multi-user communication system model.
As shown in the mobile multi-user cooperative communication system model shown in fig. 2, a mobile source MS sends information to L mobile users MU through a mobile relay node MR.
The communication channel is an N-Nakagami channel, and h is defined as hgG SR, SU, RU, representing the channel gain of the MS → MR, MS → MU, MR → MU link, the total transmit power of the MS and MR is E, and V is used to represent the relative position of the MS, MR and MU, respectivelySR,VSU, VRURepresents the position gain of the MS → MR, MS → MU, MR → MU link.
Step S2: mobile relay node MRjMobile source MS using transcoding forwarding strategyiIs forwarded to the mobile user MUl。
In two time slots, the total transmitting power of the system is E, K is the power distribution coefficient of the total transmitting power, and the ith transmitting antenna of the MS is represented as MSiThe j-th antenna of the MR is denoted as MRj。
In the first time slot, the MSiSending information x, rSRij,rSUilAre respectively MRjAnd MUlReceived signal of
Wherein n isSUilAnd nSRijHas a mean value of 0 and a variance of N0/2。
In the second time slot, MRjMobile source MS using decoding forwarding cooperation strategyiThe transmitted information x is transmitted to the mobile user MUlWhich receives a signal of
Wherein n isRUjlHas a mean value of 0 and a variance of N 02; if MRjIf the demodulation can be correctly carried out, β is equal to 1, otherwise β is equal to 0.
MSi→MRjThe instantaneous information rate of the link can be expressed as
γSRijIs MSi→MRjSignal-to-noise ratio of link
For a predetermined threshold information rate R0When I isSRij<R0Timely relay node MRjFailure to achieve full decoding, i.e. an interruption, can be expressed as
Wherein
The embodiment of the invention uses the combined receiving and the mobile user MUlCan be expressed as
Step S3: selecting the best transmitting antenna to maximize the receiving signal-to-noise ratio of the best mobile user; and the optimal mobile user is the user with the largest receiving signal-to-noise ratio.
For L mobile users, the best mobile user is selected, and the 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, i.e.
Where | C | represents the potential of the coding set C, which can be expressed as
C＝{1≤j≤Nt|γSRj≥Rth} (12)，
Step S4: 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.
A closed expression for deriving the optimal transmit antenna outage probability is as follows:
wherein,
Q1is calculated as follows
Q2Is calculated as follows
In the application of the invention, the deduced interrupt probability closed expression is used as a constraint optimization objective function to enable the constraint optimization objective function to reach the minimum value, and the optimal power distribution coefficient K is obtained, namely
Wherein, P1For transmission power of mobile sources, P2For the transmission power of the mobile relay node, PAIs the maximum power of the system, PDFor maximum power of the mobile source, PEIs the maximum power of the mobile relay node.
In order to obtain the optimal power distribution coefficient K, the invention uses an enhanced wolf algorithm for optimization.
The steps of the reinforced gray wolf algorithm in the invention are as follows:
step S31: the initial wolf pack is optimized.
In this embodiment, a best point set theory is adopted to generate an initial population of wolfs with a size of N, and then the best three wolfs are selected from the population, wherein the wolfs are α and delta wolfs respectively, and the wolfs are omega wolfs respectively.
Step S32: the wolf group surrounds.
The wolf pack firstly surrounds the target in the hunting process:
wherein t is the current iteration number,
in the form of a vector of coefficients,
indicating 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 wolf set is located).
Is shown as
Step S33: the wolf colony is hunted.
Guided by α, δ wolf, the other ω wolfs should update their respective positions according to the current α, δ wolf's position:
wherein,
step S34: a wolf pack attack.
The wolf colony attacks the prey, and the optimal solution is obtained. Mainly by a decreasing value of a.
In the application of the invention, compared with the selection scheme of the optimal transmitting antenna, the selection scheme of the sub-optimal transmitting antenna is also provided, and the interruption probability closed expression of the sub-optimal transmitting antenna is used as a constraint optimization objective function to reach the minimum value so as to obtain the minimum valueThe optimal power distribution coefficient is used to reduce the computational complexity. When the optimization performance can be properly reduced and the calculation complexity is considered, a sub-optimal scheme can be selected to maximize the MSi→MUlThe received signal-to-noise ratio of (a).
Selecting a sub-optimal transmit antenna as
The closed expression of the probability of interruption is:
(34) wherein
similarly, the derived interruption probability closed expression of the suboptimal antenna selection scheme is used as a constraint optimization objective function to reach the minimum value, and the corresponding optimal power distribution coefficient K is obtained, namely, the optimal solution is obtained by solving the equations (20) - (32).
Next, the present application simulates the power allocation optimization method of the mobile multi-user communication system, which is proposed above, to verify the performance of the optimization method of the present application.
Definition of μ ═ VSU/VRUFor the relative position gain, E ═ 1, and the simulation parameters were set to 10000 times per simulation.
In fig. 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 following table one gives the simulation coefficients:
| Parameter(s) | Numerical value |
| γth | 5dB |
| Rth | 5dB |
| Nt | 1,2,3 |
| Nr | 2 |
| L | 2 |
| m | 1 |
| K | 0.5 |
| N | 2 |
| u | 0dB |
As can be seen from FIGS. 3 and 4, the simulated values closely match the theoretical values, verifying the correctness of the derived theoretical closed expression, SNR and NtThe increase in (c) may continually improve interrupt probability performance.
In fig. 5 to 11, the optimum K values of seven algorithms of GWO (gray wolf optimization algorithm), GA (genetic algorithm), PSO (particle swarm optimization algorithm), CS (cuckoo search algorithm), FA (firefly algorithm), DE (differential evolution algorithm), and GS (golden section method) are compared, and the simulation coefficients are shown in the following table two:
watch two
As shown in table three below, comparing the running time, K and the interrupt probability performance OP of the seven algorithms, we can get, comparing with GS, GA, CS, PSO, FA, DE, GWO optimize better, running time shorter, get the best K value, and OP performance best.
Watch III
Fig. 12 shows a comparison of OP performance of the seven algorithms, and it can be seen from fig. 12 that the optimization effect of the reinforced grayish optimization algorithm is better and the OP performance is better.
In the method, a mobile multi-user communication system model is established under an N-Nakagami channel, two TAS schemes are designed, the OP performance of the mobile multi-user communication system is researched, a closed expression of the OP is deduced, then, an intelligent power distribution optimization mechanism based on an enhanced GWO algorithm is provided, and compared with GS, GA, CS, PSO, FA and DE, the intelligent optimization mechanism provided by the method obtains a better OP performance effect.
It should be noted that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art should also make changes, modifications, additions or substitutions within the spirit and 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 MRjMobile source MS using transcoding forwarding strategyiIs forwarded to the mobile user MUl；
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 ≦ Nt|γSRj≥RthThe potential of { C }; n is a radical oftFor the number of transmitting antennas, L is the number of mobile users, gammaSRjIs MSi→MRjReceived signal-to-noise ratio, gamma, of the linkSUilIs MSi→MUlReceived signal-to-noise ratio, gamma, of the linkRUjlIs MRj→MUlThe received signal-to-noise ratio of the link;
R0is MSi→MRjThe 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,
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:
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:
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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