CN106792451B - D2D communication resource optimization method based on multi-population genetic algorithm - Google Patents

D2D communication resource optimization method based on multi-population genetic algorithm Download PDF

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CN106792451B
CN106792451B CN201611171534.6A CN201611171534A CN106792451B CN 106792451 B CN106792451 B CN 106792451B CN 201611171534 A CN201611171534 A CN 201611171534A CN 106792451 B CN106792451 B CN 106792451B
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李旭杰
陈星�
孙颖
顾燕
胡居荣
胡吉明
郭洁
谭国平
李建霓
李黎
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Hohai University HHU
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
    • H04W72/542—Allocation or scheduling criteria for wireless resources based on quality criteria using measured or perceived quality
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

A D2D communication resource optimization method based on multi-population genetic algorithm relates to the technical field of D2D communication spectrum resource allocation in an LTE network. The invention respectively establishes a system model and a channel model, and adopts a resource allocation method comprising the following steps: (1) setting a coding mode of a chromosome; (2) initializing a population; (3) solving the number of D2D users in normal communication in the cell after the scene change, and setting the number as a fitness function of a genetic algorithm; (4) carrying out a breeding process on the population, wherein each breeding generation comprises four steps of selection, crossing, variation and correction; (5) introducing immigration operators, and introducing optimal individuals of various populations into other populations regularly in the evolution process; (6) introducing an essence population; (7) and stopping the algorithm after the propagation algebra is iterated to meet the termination condition, namely the optimal individual minimum kept algebra is reached. The invention can effectively reduce the transmitting power of the mobile terminal on the premise of meeting the service quality of the user and realize rapid scene change.

Description

D2D communication resource optimization method based on multi-population genetic algorithm
Technical Field
The invention relates to research on D2D communication spectrum resource allocation problem in an LTE network, in particular to a resource allocation method of a D2D communication system based on multi-population genetic algorithm.
Background
With the rapid development of modern communication technology, the demand of people for wireless data is rapidly increasing. The frequency band used by wireless communication belongs to a precious scarce resource, the higher frequency band used for communication is increasingly tense, the D2D technology is developed, and the frequency spectrum utilization rate can be effectively improved by reusing the frequency spectrum resources of cellular users under the authorized frequency band. D2D communication under the authorized frequency band makes communication quality better, more stable, safety. The session establishment of the D2D communication has two modes, one mode is centralized, the method is intervened by a base station, and the base station can control the interference between links in good power control, resource allocation and other modes to realize reasonable optimization. The other is distributed, and the D2D is connected autonomously, but this method needs the D2D device with higher complexity to realize the discovery and connection between the D2D, and can realize communication in areas with incomplete coverage of some base stations, and make up the deficiency that the cellular users transit in these areas through the base stations.
However, D2D introduces a series of challenges, and different terminals in a cell reuse the same channel resource, which may cause co-channel interference to other terminals in the cell. Most inventions are concerned with improving the overall throughput of the system. But ignores the number of normal communications available to the user. Considering that the user information transmission rate requirement in a cell is not constant, the D2D communication under this cellular user is affected when the CUE user is transmitting at a higher rate. Therefore, a reasonable multi-population resource allocation algorithm needs to be designed, and resource allocation is effectively performed on the premise that the service quality of all users is met. Aiming at the problems, the invention provides a resource allocation method of a D2D communication system based on multi-population genetic algorithm under the condition of rapid scene change, which efficiently utilizes the spectrum resources of a cellular network and improves the spectrum efficiency.
Disclosure of Invention
The invention aims to provide a D2D communication resource optimization method based on a multi-population genetic algorithm aiming at the change of the number of normal D2D communication under the condition of scene switching, so that the resource allocation is optimized quickly and effectively, and the number of D2D users in normal communication after the scene switching is effectively ensured.
In order to achieve the purpose, the invention adopts the following technical scheme:
a multi-population genetic algorithm based D2D communication resource optimization method, the terminals of the D2D communication system including cellular network terminals (CUEs) and D2D mobile terminals (DUE), a pair of DUE including D2D transmitting mobile terminals (DTUEs) and D2D receiving mobile terminals (DRUEs), wherein there are M pairs of DUE and N CUEs sharing uplink resources, M and N being integers greater than 0, the method comprising the steps of:
(1) initializing system parameters, wherein the parameters comprise a signal-to-noise ratio threshold, power of D2D transmitting terminals, the number of mobile terminals and distance between the terminals;
(2) the resource allocation is subjected to chromosome coding in a way that G ═ G1,…,gj,…,gM) Each chromosome has a total of M loci, each locus gjIs 1 … N;
(3) randomly generating populations of a certain scale, wherein each population comprises a certain number of chromosomes;
(4) with the maximum number of normal communications of the D2D users in the scene switching situation as a target, calculating the number C (U) of D2D users in normal communications in N-m sub-channels after the scene switchingx) And is used as a fitness function value of the multi-population genetic algorithm;
(5) breeding the population, wherein each breeding generation comprises the processes of selection, crossing, variation and correction;
(6) introducing immigration operators, and introducing optimal individuals of various populations into other populations regularly in the evolution process;
(7) and (4) introducing an essence population, and selecting other optimal population individuals in each generation of evolution through an artificial selection operator, and putting the optimal population individuals in the essence population for storage.
(8) And repeating the propagation process until an iteration termination condition is met, namely the iteration times reach the optimal individual minimum kept algebra, and stopping propagation.
The threshold for the signal-to-noise ratio needs to satisfy the following condition: i.e., the CUE and DUE signal-to-noise ratios are greater than or equal to the signal-to-noise threshold.
The calculation formula of the signal-to-noise ratio is respectively as follows:
Figure GDA0002298258950000021
Figure GDA0002298258950000022
wherein, SINRciSINR, SINRd, received for CUEijSINR received for DRUE j, where PiIs the transmit power of CUEi, riIs the distance, P, between CUE i and the base stationTFor the transmission power of DTUE, dk,iDistance between DTUEk and CUE i, α is the path loss exponent, N0In order to be able to measure the power of the noise,
Figure GDA0002298258950000031
a terminal set corresponding to the ith sub-channel;
wherein ljIs the distance, P, between DTUEj and DRUEjmIs the transmit power of CUE m, dm,jIs the distance between CUE m and dreej,
Figure GDA0002298258950000032
and the terminal set corresponding to the mth sub-channel.
The transmission rates of cellular users and D2D users can be calculated according to shannon's formula:
Figure GDA0002298258950000033
Figure GDA0002298258950000034
when the information transmission rate becomes high, a higher signal-to-noise ratio is required under the condition that the communication frequency band bandwidth is constant, the signal-to-noise ratio is related to the interference of the accessed D2D to the base station, and the accumulated interference to the base station is increased by the D2D user pairs with the larger number. High rate user data transmission will result in non-multiplexing of D2D users in this band. Based on the above information transmission rate formula, we can calculate the number of D2D users that can not be accessed currently due to high-rate communication, and then perform resource reallocation.
The invention has the following advantages: compared with the prior art, the resource allocation method of the D2D communication system based on the multi-population genetic algorithm under the rapid scene change can effectively realize the normal communication of all users after the scene change, has high convergence speed, reduces the transmitting power, has excellent performance and is easy to realize.
Drawings
FIG. 1 is a flow chart of the optimization method of the present invention.
Fig. 2 is an initial scenario diagram of a D2D communication system.
Fig. 3 is a diagram of a scenario after a handover is required for the high rate communication of the CUE user in the D2D communication system.
Fig. 4 is a diagram of initial communication resource allocation in the D2D communication system.
Fig. 5 is an illustration of the allocation of D2D communication resources based on a multi-population genetic algorithm.
Fig. 6 is a graph of the number of D2D users that can communicate normally in a cell during a change in the D2D communication scenario, using a multi-population genetic algorithm and a genetic algorithm.
Fig. 7 is a plot of the mean transmit power of the CUE for a D2D communication system using a multi-population genetic algorithm and other algorithms.
Detailed Description
The present invention is further illustrated by the following description in conjunction with the accompanying drawings and the specific embodiments, it is to be understood that these examples are given solely for the purpose of illustration and are not intended as a definition of the limits of the invention, since various equivalent modifications will occur to those skilled in the art upon reading the present invention and fall within the limits of the appended claims.
The selection of the D2D user resource reuse which cannot normally communicate after the scene switch directly affects the performance of resource allocation, and the setting of the scene and the setting of the parameters are analyzed in detail below.
1. Description of System model
In the D2D communication system, mobile terminals are classified into two types: a conventional cellular network mobile terminal CUE and a D2D mobile terminal DUE. The DUE is in pair form, one pair of DUE includes D2D transmitting mobile terminal DTUE and D2D receiving mobile terminal DRUE. In an FDD-LTE network, one subchannel is allocated to one CUE, and multiple DUE pairs may simultaneously share a certain subchannel resource. In the present invention, N CUEs and M-pair DUEs share all channel resources. Fig. 2 is an initial scene diagram of a D2D communication system, in which N CUEs and M DTUEs are uniformly distributed in a cell with a radius R, and a dre is distributed in a circle with its corresponding DTUE as the center and L as the radius. The same color in the figure represents that the spectrum resources of the same sub-channel are used.
The invention provides a resource allocation method of a D2D communication system based on multi-population genetic algorithm based on scene switching.
2. Establishment of channel model
In a conventional cellular network, the CUE employs a strict power control approach. However, in D2D communication, the DTUEs of the D2D transmitting terminals generally use the same transmission power, denoted as PT. We assume that the channel model between the transmitting and receiving terminals is a free space fading model, i.e., Pr/Pt＝1/rαIn which P isrIs the power received by the receiving terminal, PtIs the transmit power of the transmitting terminal, r is the distance between the terminals, and α is the path loss factor.
3. Resource allocation method
In the analysis of scene cuts, the increase of data rate required by users and the requirement of voice quality or video calls in communication systems lead to the need for fast and accurate reallocation of communication resources. As shown in fig. 3, when the CUE4 is due to high rate information transmission requirements, the pair of DTUE7 and dree 7 that multiplexes D2D users of the CUE4 spectrum resources will be squeezed out. DTUE4 sends a communication request to the base station, which reallocates the other subchannel resources that are reusable to the pair of DUE. But since multiple DUE pairs have been multiplexed under the spectrum of the CUE1, CUE2, and CUE 3. The non-optimized allocation method can prevent all cellular users and D2D users under the spectrum resources from normally communicating, and by using the method of multi-population genetic algorithm, D2D users, which cannot be accessed due to high-quality communication of some cellular users in the cell, will reuse other cellular users without high-quality requirement, for example, the DTUE7 and the dree 7 which are denied access in fig. 3 ensure normal communication of D2D users by multiplexing the CUE 1.
Based on the theoretical basis, the D2D communication resource allocation method based on the multi-population genetic algorithm under the rapid scene change is designed.
The symbols or parameters used in the present invention are first described as follows:
and (4) CUE: a conventional cellular network mobile terminal;
DUE: D2D mobile terminal;
and DTUE: a transmitting mobile terminal of the D2D mobile terminal pair;
DRUE: a receiving mobile terminal of the pair of D2D mobile terminals;
m: the number of D2D mobile terminal pairs in the cell;
n: the number of CUE mobile terminals in a cell;
m: CUE number required for high rate communication in a cell
R: a cell radius;
BS: a base station;
l: a maximum communicable distance between the DTUE and the DRUE in the pair of DUEs;
Pi: the transmit power of CUE i;
ri: distance between CUE i and the base station;
PT: the transmit power of the DTUE;
dk,i: distance between DTUEk and CUE i;
α path loss exponent;
N0: a noise power;
lj: distance between DTUEj and DRUEj;
dm,j: distance between CUE m and DRUEj;
dk,j: distance between DTUEk and dreej;
b: a subchannel bandwidth;
Figure GDA0002298258950000051
a terminal set corresponding to the ith sub-channel;
P0: cross probability;
P1: the probability of variation.
The embodiment of the invention discloses a D2D communication resource allocation method based on multi-population genetic algorithm under rapid scene change, which comprises the following steps:
(1) initialization:
1) initializing system parameters, wherein the parameters comprise a signal-to-noise ratio threshold value and the power of a D2D transmitting terminal;
2) obtaining number of mobile terminals M, CUE number of mobile terminals N and various distance variables D in D2D communication systemk,i、dm,jAnd dk,jA value of (d); the invention assumes that the base station can obtain the current channel states of all cellular communication links and D2D communication links and the QoS (quality of service) requirements of all users by utilizing channel estimation and some feedback information, and the position information of each device can be directly initialized in a simulation environment;
3) for each CUE, only one channel is occupied, and no loss of generality exists, the number of the CUE terminal allocated to the 1 st channel is assumed to be 1, and sequential series pushing is performed, that is, the number of the CUE terminal allocated to the ith channel is assumed to be i.
(2) Setting the code of chromosome as G ═ (G)1,…,gj,…,gM) Each chromosome has a total of M loci, representing M pairs of DUE terminals, each locus gjIs 1 … N, which represents the sequence number of the DUE shared channel resource.
(3) The population is initialized, the larger the population size, the more likely it is to find a global solution, but the run time is also relatively long. In the example, 3 × N chromosomes are generated at random to form a population, the population number is 10, the population number can be properly adjusted according to the scale of the terminal number and the efficiency requirement of an algorithm in specific application, the gene position of each chromosome is also generated at random, the value is 1 … N, and the probability is 1/N;
(4) number C (U) of D2D users solving for normal communication in D2D communication systemx) Are combined withPut it as a fitness function of the genetic algorithm, where UxIs the x-th chromosome, wherein C (U)x) The solution process of (2) is as follows:
according to the cellular network basic theory, the SINR (signal to interference plus noise ratio) received by the cellular network terminal CUEi may be expressed as:
Figure GDA0002298258950000061
likewise, the SINR received by the D2D receiving terminal dree may be expressed as
Figure GDA0002298258950000071
Obviously, the CUE and D2D users in the D2D communication system meet the signal-to-noise ratio requirement in the resource allocation process first. And finding the optimal chromosome in a plurality of populations, wherein the number of D2D users multiplexing the cellular users with low speed requirements on the chromosome is the fitness function.
(5) The population is propagated, and each propagation generation comprises six steps of selection, crossing, variation, correction, introduction of immigration operators, selection of optimal individuals and entering of elite population:
1) selecting
In the present invention, a classical roulette selection method is used to select chromosomes from a population, chromosome x UxThe probability of being selected is:
Figure GDA0002298258950000072
2) crossover and mutation
The effect of crossover is to get the better next generation, we use a single point crossover pattern, crossover points are randomly selected, and then two chromosomes are interchanged based on the left and right parts of the crossover points. We set the crossover probability to PcThis value varies from population to population. The variation is in the population according to the variation probability P1Optionally, several gene loci change their place value, and the difference in mutation probability will also result in different optimized structures, therefore, the method of the present invention is designedThe value of the gene position is 1 … N according to different variation probabilities of the population, so the value after gene variation is the complement of the value.
4) Introduction of correction and immigration operators
In order to ensure the QoS of each terminal, the SINR of each terminal must be higher than a threshold of SINR, but sometimes the channel allocation status corresponding to the chromosome generated in the population initialization, crossing, and mutation processes does not satisfy that the SINR of each terminal is higher than a certain threshold, and therefore a modification process is required. In the present invention, we use simple process iterations (re-select, cross, mutate process from parent) to correct. Meanwhile, the multi-population genetic algorithm utilizes immigration operators to introduce the optimal individuals of each population into other populations regularly in the evolution process, and information exchange among the populations is realized.
5) Introduction of elite population
Multi-population genetic algorithms introduce elite populations that differ greatly from other populations. In each generation of evolution, other population optimal individuals are selected through an artificial selection operator and are put into the essence population for storage, and the essence population is not subjected to a series of operations such as crossing and variation, so that the optimal individuals are not damaged or lost in the following variation process.
(6) Judging whether the propagation algebra iterates to the set optimal individual minimum kept algebra Num or not, and if so, stopping the algorithm; if not, continue to execute step (5) after Num + 1. And finally, calculating a final objective function value, and allocating resources according to the individual with the maximum fitness function value.
Fig. 4 and 5 are specific examples of the present invention, and are illustrations of allocation of D2D communication resources based on multi-population genetic algorithm under rapid scene change, and cellular users are respectively labeled as 1, 2, 3 and 4 by using real number coding. For example, (1, 2, 1, 2, 2, 3, 4, 3) means that the first and third users multiplex the first CUE, the second, fourth and fifth DUEs multiplex the second CUE, the sixth and eighth DUE users multiplex the third CUE, and the seventh DUE multiplexes the fourth CUE.
FIG. 6 shows details ofThe normal number of communications of D2D users using the multi-population genetic algorithm and the genetic algorithm were compared. In order to verify the advantages of the method of the invention over the prior art, the invention sets the following simulation parameters: the cell radius R is 600m, the maximum distance L of the DUE terminal pairs is 20m, the number of the DUE terminal pairs is 30, the number of the CUE terminals is 4, and the number of the CUE mobile terminals needing high-rate transmission is 1 (the number of users needing high-rate communication is changed according to actual conditions). The maximum transmitting power of CUE is 2W, the transmitting power of DUE is 0.001W, and the noise power N0The channel path loss coefficient is 4, the iteration times are 40, the optimal individual minimum preserving algebra is 20, and the threshold value of SINR is 6dB, wherein the channel path loss coefficient is-105 dBm. It can be seen from the figure that the multi-population genetic algorithm can more quickly find a method for enabling all D2D users to normally communicate after scene switching, and compared with the genetic algorithm, the resource allocation method based on the multi-population genetic algorithm, which is adopted in the invention, has the advantages of small calculation amount, short time consumption and quick convergence.
Fig. 7 compares the mean CUE transmit power of D2D communication systems using various population genetic algorithms and other algorithms. It can be seen from the figure that the average transmission power of the CUE of the proposed resource allocation method based on multi-population genetic algorithm is lower, because the CUE still adopts power control in the multi-population genetic algorithm, the transmission power can be effectively reduced, and the energy consumption of the mobile terminal can be reduced.

Claims (3)

1. A D2D communication resource optimization method based on multi-population genetic algorithm, the scene model of the D2D communication system comprises M pairs of DUEs and N traditional cellular network mobile terminals CUEs which share uplink resources, one pair of DUEs comprises D2D transmitting mobile terminal DTUE and D2D receiving mobile terminal DRUE, one sub-channel is allocated to one CUE, and a plurality of pairs of DUEs can simultaneously share a certain sub-channel resource; the optimization method is characterized by comprising the following steps:
(1) initializing system parameters, wherein the parameters comprise a signal-to-noise ratio threshold, power of D2D transmitting terminals, the number of mobile terminals and distance between terminals;
(2) the resource allocation scheme is chromosomally encoded usingReal number encoding scheme, G ═ G1,…,gj,…,gM) Each chromosome has a total of M loci, each locus gjIs 1 … N;
(3) randomly generating an initial population number of a certain scale, wherein each population comprises a certain number of chromosomes;
(4) with the maximum number of normal communications of the D2D users in the scene switching situation as a target, calculating the number C (U) of D2D users in normal communications in N-m sub-channels after the scene switchingx) And is used as a fitness function value of the multi-population genetic algorithm; wherein m: number of CUEs, U, required for high rate communication within a cellxIs the x-th chromosome;
(5) breeding the population, wherein each breeding generation comprises the processes of selection, crossing, variation and correction;
(6) introducing immigration operators, and introducing optimal individuals of various populations into other populations regularly in the evolution process;
(7) introducing an essence population, selecting other population optimal individuals in each generation of evolution through an artificial selection operator, and putting the other population optimal individuals into the essence population for storage;
(8) and repeating the propagation process until an iteration termination condition is met, namely the iteration times reach the optimal individual minimum kept algebra, and stopping propagation.
2. The multi-population genetic algorithm-based D2D communication resource optimization method according to claim 1, wherein the threshold of signal-to-noise ratio in step (1) is required to satisfy the following condition: that is, the signal-to-noise ratio of CUE and DUE is greater than or equal to the signal-to-noise ratio threshold;
the signal-to-noise ratio calculation formulas of CUE and DUE are respectively as follows:
Figure FDA0002298258940000011
Figure FDA0002298258940000021
wherein, SINRciFor CUEi to receiveSignal to noise ratio of (1), SINRdjFor the received signal-to-noise ratio, P, of DRUEjiIs the transmit power of CUE i, riIs the distance between CUE i and BS, BS is the base station, PTFor the transmission power of DTUE, dk,iDistance between DTUEk and CUE i, α is the path loss exponent, N0In order to be able to measure the power of the noise,
Figure FDA0002298258940000022
a terminal set corresponding to the ith sub-channel; and ljIs the distance, P, between DTUEj and DRUEjmIs the transmit power of CUE m, dm,jIs the distance between CUE m and DRUEj, dk,jAs the distance between DTUE k and dreej,
Figure FDA0002298258940000023
a terminal set corresponding to the mth sub-channel; and if the signal-to-noise ratio of the user is greater than or equal to the threshold value, the user can normally communicate.
3. The multi-population genetic algorithm-based D2D communication resource optimization method according to claim 1, wherein in step (5) chromosomes are selected from the population by classical roulette selection and chromosome Xth U is selected from chromosome XMxThe probability of being selected is:
Figure FDA0002298258940000024
q is the number of chromosomes in the first generation population, C (U)x) Is UxThe number of D2D users capable of normally communicating in the corresponding resource allocation mode.
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