CN105634593B - A kind of indoor visible light communication LED array layout optimization method based on genetic algorithm - Google Patents

A kind of indoor visible light communication LED array layout optimization method based on genetic algorithm Download PDF

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CN105634593B
CN105634593B CN201510957604.XA CN201510957604A CN105634593B CN 105634593 B CN105634593 B CN 105634593B CN 201510957604 A CN201510957604 A CN 201510957604A CN 105634593 B CN105634593 B CN 105634593B
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刘焕淋
代洪跃
夏培杰
陈勇
刘保林
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CHONGQING INFORMATION TECHNOLOGY DESIGNING CO.,LTD.
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
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Abstract

The present invention relates to the LED array layout optimization method based on genetic algorithm, this method in a kind of indoor visible light communication to comprise the following steps：One, according to the array number of LED and the length and width information structuring chromosome in room.Two, create initial population using chromosome obtained in the previous step.Three, the fitness value of each individual in population is calculated, judges whether to meet algorithm termination condition.Four, selection, intersection and mutation operation are performed to each individual in population, to generate new population.Five, the LED array after optimization is parsed according to the optimum individual drawn of genetic algorithm and is laid out.The algorithm of the present invention can solve the problems, such as the reception power skewness received under existing LED layouts in receiving plane, and the LED array layout after optimization can cause the reception power in receiving plane to be evenly distributed, solve the integrity problem of LED communication.

Description

A kind of indoor visible light communication LED array layout optimization method based on genetic algorithm
Technical field
The invention belongs to indoor visible light communication technical field, is related in a kind of visible light communication based on genetic algorithm LED array layout optimization method.
Background technology
With visible light communication technology (VLC, VisibleLightCommunication) with its good confidentiality, frequency spectrum resource The features such as abundant, harmless, progressively becomes the hot-candidate technology of following the short distance wireless communication technology.VLC systems indoors In system, due to factors such as the reflection characteristics of room-sized, the Multipath Transmission of signal and body surface, in the difference of communication layer There were significant differences for the luminous power meeting that reception position receives, so as to seriously restrict the communication performance of VLC systems.Environment indoors Under, how in all positions that receive consistent power and illumination profile are obtained, there is weight for the reliability for improving communication Want meaning.
Indoor white light LEDs (Light Emitting Diode) array of source layout determines indoor light intensity and luminous power Spatial distribution.So during design white light LEDs visible light communication system, the double requirements of illumination and communication should be taken into account.Rationally design Array of source, makes indoor illuminance meet lighting criteria, while causes optical power distribution is presented indoors to be uniformly distributed.According to Internationalize the standard illuminated, and the illuminance requirement of office is between 300-1500 luxs.
The content of the invention
In view of this, it is an object of the invention to provide a kind of indoor visible light communication LED array based on genetic algorithm Layout optimization method, the algorithm can be solved under existing indoor LED array layout, and it is uneven that power is received in receiving plane The problem of.LED array layout after optimization can reduce the reception power swing in receiving plane, improve the reliable of communication system Property.
To reach above-mentioned purpose, the present invention provides following technical solution：Based on genetic algorithm in a kind of visible light communication LED array layout optimization method, the algorithm comprise the following steps：
(1) coordinate system is established for y-axis with a length of x-axis in room, width, builds gene.According to the length information L and width in room Degree information W comes structural gene storehouse, and then builds chromosome, and creates initial population.
(2) it is t to set evolutionary generation, and initializes evolutionary generation t=0；
(3) according to formula F (i)=dif (i, j)/E { [Pr-E(Pr)]2Calculate in population the fitness of each individual Value；Wherein, i is i-th of individual in population, and dif (i, j) represents two individual difference values, E (Pr) represent to receiving plane The reception power of upper all receiving points asks expectation.
(4) according to each selected probability of individualPerform selection operation.
(5) judge whether current evolutionary generation t is more than maximum evolutionary generation tmax, or continuous tcontFor in population most Excellent individual fitness value does not change；Meet that one condition of any of the above then goes to step (8)；If it is unsatisfactory for any of the above one Condition, then make evolutionary generation t=t+1.
(6) to the individual in population according to crossover probability PcroPerform crossover operation；
(7) with the individual in population according to mutation probability PmutPerform mutation operation, return to step (3)；
(8) individual of fitness value maximum in population is found out, decoding the optimal of each LED array according to this individual sits Mark, the LED array layout after as optimizing.
Further, the initialization of population scheme described in step (1) comprises the following steps：
(1.1) each individual (also referred to as chromosome) in population has following form：
CS=((x1,y1),(x2,y2),...,(xN,yN))
Wherein, N represents the LED array number on ceiling, (x in roomi,yi) representing a gene, x and y are represented respectively The abscissa and ordinate of LED array, and the value range of x and y is xi∈geneLibx,yi∈ geneLiby, meet as follows Formula：
Wherein ξ and μ represents to need the grid number for being subdivided into the length L in room and width W.
(1.2) when population is initialized, according to (1.1), N is builtpopuIndividual.Wherein, each individual base Randomly selected because the abscissa x in position randomly selects the ordinate y in gene pool geneLibx, gene position from gene pool geneLiby。
Further, the fitness computational methods detailed process described in step (3) is：
(3.1) length in room is L, width W, is highly H, and receiving plane is highly h.Receiving plane is divided into α × β grid.The reception power on each mesh point is calculated according to the following formula：
Wherein E be PD receive the inside detector surface area, DdIt is the air line distance between access point and PD, Ts(ψ) optics The gain of wave filter, ψ are the incidence angles of PD,It is the LED angles of departure, g (ψ) is optical concentrator gain.LED half-power angles φ1/2 Determine lambert's exponent m of light source, the conversion relation between them is：M=-ln2/ln (cos (φ1/2))。
(3.2) expectation is asked to obtain E (P all mesh pointsr).Dif (i, j) represents two individual difference values, and j Individual has highest fitness value in current population.Dif (i, j) is calculated according to the following formula:
(3.3) weighting function of the individual difference degree function as fitness function is introduced, can effectively prevent Evolution of Population During effective gene missing and be absorbed in local optimum.
Further, " the continuous t described in step (5)contDo not change for the fitness value of optimum individual in population " tool Body process is：
(5.1) design variable FlastRepresent the best individual fitness value in previous generation populations, FcrrtRepresent this generation population Best ideal adaptation angle value.
(5.2) if FcrrtMore than Flast, then t is madecont=0, Flast=Fcrrt；Otherwise, t is madecont=tcont+1,Flast= Fcrrt；
Further, " to the individual in population according to crossover probability P in step (6)croThe specific mistake of execution crossover operation " Cheng Wei：
(6.1) two chromosomes CS1 and CS2 are selected at random from population, then according to the following formula to each Chromosome segment：
Segment1=(x1,y1)...(xN/4,yN/4)
Segment2=(xN/4+1,yN/4+1)...(xN/2,yN/2)
Segment3=(xN/2+1,yN/2+1)...(x3N/4,y3N/4)
Segment4=(x3N/4,y3N/4)...(xN,yN)
(6.2) one section of Segment is randomly selected, then exchanges the gene on correspondence position.Need to carry out altogether above-mentioned The number of crossover operation is Npopu*Pcro。
Further, " with the individual in population according to mutation probability P in step (7)mutThe specific mistake of execution mutation operation " Cheng Wei：
(7.1) search space (plane where ceiling) of genetic algorithm is divided into Nar*NarA uniform cell Domain, each region are expressed as Ar (k)；
(7.2) a chromosome CS is randomly choosedmut, and count the chromosome CS in region Ar (k)mutGene Number is set to n (k)；
(7.3) sort to n (k), ArmaxN is corresponding with regionmaxA gene, ArminN is corresponding with regionminA gene；
(7.4) random erasure one belongs to ArmaxGene, and according to described in step (1) method generate one belong to ArminGene, and be inserted into corresponding position in chromosome.
(7.5) the operation N of the above is in repetitionpopu*PmutIt is secondary, that is, complete mutation operation.
The beneficial effects of the present invention are：The algorithm of the present invention can be solved under existing LED layouts in receiving plane The problem of reception power skewness received, the LED array layout after optimization can cause the reception in receiving plane Power is evenly distributed, and solves the integrity problem of LED communication.
Brief description of the drawings
In order to make the purpose of the present invention, technical solution and beneficial effect clearer, the present invention provides drawings described below and carries out Explanation：
Fig. 1 is the LED array layout optimization method flow chart based on genetic algorithm；
Fig. 2 is typical visible ray indoor communication system；
Fig. 3 is laid out for typical rectangular LED arrays and its power distribution；
Fig. 4 is the LED array layout and its power distribution after optimization.
Embodiment
Below in conjunction with attached drawing, the preferred embodiment of the present invention is described in detail：
Fig. 2 is typical visible ray indoor communication system, and Fig. 1 is the LED array layout optimization method based on genetic algorithm Flow chart, as shown in the figure, a kind of indoor visible light communication LED array layout optimization side based on genetic algorithm provided by the invention Method, the algorithm comprise the following steps：
(1) typical indoor visible light communication system is as shown in Fig. 2, L=5 meters of the length information in room, width information W= 5 meters, be highly 3 meters, and receiving plane is 0.85 meter of height.According to reference axis is built shown in Fig. 2, x-axis is alongst identified as, Y-axis is identified as along width.
(2) chromosome is built using the information in step (1), each individual (also referred to as chromosome) in population has such as Under form：
CS=((x1,y1),(x2,y2),...,(xN,yN))
Wherein, N represents the LED array number on ceiling in room, N=16 in this example.(xi,yi) represent a gene, X and y represents the abscissa and ordinate of LED array respectively, and the value range of x and y is xi∈geneLibx,yi∈ GeneLiby, meets following formula：
Wherein ξ and μ expressions need the grid number for being subdivided into the length L in room and width W, it can influence to search The precision of LED array layout, it is contemplated that be actually needed, it is all 50 to make them respectively here.
(3) when population is initialized, according to step (2), N is builtpopuIndividual.The size of population can influence Optimal solution can be converged to genetic algorithm and converges to the speed of optimal solution.In view of being actually needed, here by NpopuIf It is set to 200.Wherein, the abscissa x in each genes of individuals position randomly selects vertical in gene pool geneLibx, gene position Coordinate y is randomly selected from gene pool geneLiby.
(4) according to each selected probability of individualPerform selection operation.
(5) it is t to set evolutionary generation, and initializes evolutionary generation t=0.
(6) length in room is L, width W, is highly H, and receiving plane is highly h.Receiving plane is divided into α × β grid.The division of sizing grid will influence whether the search precision of genetic algorithm, it is contemplated that the size in the room of this example is 5 meters * 5 meters, therefore room is divided into 50*50 grids.The reception power on each mesh point is calculated according to the following formula：
Wherein E be PD receive the inside detector surface area, DdIt is the air line distance between access point and PD, Ts(ψ) optics The gain of wave filter, ψ are the incidence angles of PD,It is the LED angles of departure, g (ψ) is optical concentrator gain.LED half-power angles φ1/2 Determine lambert's exponent m of light source, the conversion relation between them is：M=-ln2/ln (cos (φ1/2)).Used in this example Shown in parameter table specific as follows：
The parameter used in 1 VISIBLE LIGHT SYSTEM of table
Symbol Explanation Value
L*W*H Room-sized 5*5*3m
h Receiving plane height 0.85m
N LED array number 16
Pt LED array transmit power 452mW
Φ1/2 Half-power angle 80deg.
– LED chip number in LED array 7×7
– It is spaced between LED chip 0.01m
Ts(ψ) Filter gain 1.0
ψc Field angle 55deg.
A Detector surface is accumulated 1.0cm2
– Receiver photoelectric conversion efficiency 0.53A/W
n PD lens reflex coefficients 1.5
I(0) Central illumination intensity 23.81cd
(7) expectation is asked to obtain E (P all mesh pointsr).Dif (i, j) represents two individual difference values, and j Body has highest fitness value in current population.Dif (i, j) is calculated according to the following formula:
(8) weighting function of the individual difference degree function as fitness function is introduced, can effectively prevent Evolution of Population mistake The missing of effective gene in journey and be absorbed in local optimum.
(9) according to formula F (i)=dif (i, j)/E { [Pr-E(Pr)]2Calculate in population the fitness of each individual Value；Wherein, i is i-th of individual in population, and dif (i, j) represents two individual difference values, E (Pr) represent to receiving plane The reception power of upper all receiving points asks expectation.
(10) design variable FlastRepresent the best individual fitness value in previous generation populations, FcrrtRepresent this generation population Best ideal adaptation angle value.
(11) if FcrrtMore than Flast, then t is madecont=0, Flast=Fcrrt；Otherwise, t is madecont=tcont+1,Flast= Fcrrt；
(12) judge whether current evolutionary generation t is more than maximum evolutionary generation tmax=100, or continuous tcont=15 Do not change for the fitness value of optimum individual in population；Meet that one condition of any of the above then goes to step (20)；If it is unsatisfactory for One condition of any of the above, then make evolutionary generation t=t+1.tmaxAnd tcontInfluence genetic algorithm is converged to the speed of optimal solution Degree, it is contemplated that they are respectively set to 100 and 15 by problem scale here.
(13) two chromosomes CS1 and CS2 are selected at random from population, then according to the following formula to each Chromosome segment：
Segment1=(x1,y1)...(xN/4,yN/4)
Segment2=(xN/4+1,yN/4+1)...(xN/2,yN/2)
Segment3=(xN/2+1,yN/2+1)...(x3N/4,y3N/4)
Segment4=(x3N/4,y3N/4)...(xN,yN)
(14) one section of Segment is randomly selected, then exchanges the gene on correspondence position.Need to carry out above-mentioned friendship altogether The number of fork operation is Npopu*Pcro.In order to meet the needs of genetic algorithm convergence rate, the value range of crossover probability is general Between 0.6~0.9.Crossover probability P is taken in this examplecro=0.6.
(15) search space (plane where ceiling) of genetic algorithm is divided into Nar*NarA uniform zonule, Each region is expressed as Ar (k).Depending on the division in region can be according to the size of search space.Search space in this example is 5 meters of * 5 meters, therefore the division region of search space is set as 5*5；
(16) a chromosome CS is randomly choosedmut, and count the chromosome CS in region Ar (k)mutGene Number is set to n (k)；
(17) sort to n (k), ArmaxN is corresponding with regionmaxA gene, ArminN is corresponding with regionminA gene；
(18) random erasure one belongs to ArmaxGene, and according to described in step (1) method generate one belong to ArminGene, and be inserted into corresponding position in chromosome.
(19) the operation N of the above is in repetitionpopu*PmutIt is secondary, that is, complete mutation operation.Searched in order to stablize genetic algorithm Rope cannot be obtained too greatly, generally below 0.01 to a feasible solution, mutation probability.Mutation probability P is taken in this examplemut= 0.01.Return to step (8).
(20) individual of fitness value maximum in population is found out, the optimal of each LED array is decoded according to this individual Coordinate, the LED array layout after as optimizing.
(21) the reception power in common indoor LED array layout and its receiving plane is as shown in Figure 3.As seen from the figure, Under the layout, the reception power distribution in receiving plane is extremely uneven, shows the characteristics of middle high surrounding is low.By losing LED array layout and its power distribution after propagation algorithm optimization is as shown in Figure 4.In Fig. 4, hence it is evident that as it can be seen that the reception after optimization With the fluctuation of smaller and more balanced, this is of great significance to improving the reliability to communicate for power distribution.
Finally illustrate, preferred embodiment above is merely illustrative of the technical solution of the present invention and unrestricted, although logical Cross above preferred embodiment the present invention is described in detail, however, those skilled in the art should understand that, can be Various changes are made to it in form and in details, without departing from claims of the present invention limited range.

Claims (4)

  1. A kind of 1. indoor visible light communication LED array layout optimization method based on genetic algorithm, it is characterised in that：This method bag Include following steps：
    (1) coordinate system is established for y-axis with a length of x-axis in room, width, builds gene；Believed according to the length information L and width in room Breath W comes structural gene storehouse, and then builds chromosome, and creates initial population；
    (2) it is t to set evolutionary generation, and initializes evolutionary generation t=0；
    (3) according to formula F (i)=dif (i, j)/E { [Pr-E(Pr)]2Calculate in population the fitness value of each individual； Wherein, i is i-th of individual in population, and dif (i, j) represents two individual difference values, E (Pr) represent in receiving plane The reception power of all receiving points asks expectation；
    (4) according to each selected probability of individualPerform selection operation；
    (5) judge whether current evolutionary generation t is more than maximum evolutionary generation tmax, or continuous tcontFor optimum individual in population Fitness value do not change；Meet that one condition of any of the above then goes to step (8)；If being unsatisfactory for one condition of any of the above, Then make evolutionary generation t=t+1；
    (6) to the individual in population according to crossover probability PcroPerform crossover operation；
    (7) with the individual in population according to mutation probability PmutPerform mutation operation, return to step (3)；
    (8) individual of fitness value maximum in population is found out, the best coordinates of each LED array are decoded according to this individual, LED array layout after as optimizing；
    In the step (1), each individual in population has following form：
    CS=((x1,y1),(x2,y2),...,(xN,yN))
    Wherein, N represents the LED array number on ceiling, (x in roomi,yi) representing a gene, x and y represent LED respectively The abscissa and ordinate of array, and the value range of x and y is xi∈geneLibx,yi∈ geneLiby, meet following Formula：
    <mrow> <mi>g</mi> <mi>e</mi> <mi>n</mi> <mi>e</mi> <mi>L</mi> <mi>i</mi> <mi>b</mi> <mi>x</mi> <mo>=</mo> <mo>{</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>&amp;times;</mo> <mfrac> <mi>L</mi> <mi>&amp;xi;</mi> </mfrac> <mo>,</mo> <mn>2</mn> <mo>&amp;times;</mo> <mfrac> <mi>L</mi> <mi>&amp;xi;</mi> </mfrac> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>&amp;xi;</mi> <mo>&amp;times;</mo> <mfrac> <mi>L</mi> <mi>&amp;xi;</mi> </mfrac> <mo>}</mo> </mrow>
    <mrow> <mi>g</mi> <mi>e</mi> <mi>n</mi> <mi>e</mi> <mi>L</mi> <mi>i</mi> <mi>b</mi> <mi>y</mi> <mo>=</mo> <mo>{</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>&amp;times;</mo> <mfrac> <mi>W</mi> <mi>&amp;mu;</mi> </mfrac> <mo>,</mo> <mn>2</mn> <mo>&amp;times;</mo> <mfrac> <mi>W</mi> <mi>&amp;mu;</mi> </mfrac> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>&amp;mu;</mi> <mo>&amp;times;</mo> <mfrac> <mi>W</mi> <mi>&amp;mu;</mi> </mfrac> <mo>}</mo> </mrow>
    Wherein ξ and μ represents to need the grid number for being subdivided into the length L in room and width W；
    When population is initialized, N is builtpopuIndividual；Wherein, the abscissa x in each genes of individuals position, which is randomly selected, comes From gene pool geneLibx, the ordinate y in gene position is randomly selected from gene pool geneLiby.
  2. 2. a kind of indoor visible light communication LED array layout optimization method based on genetic algorithm according to claim 1, It is characterized in that：" continuous t described in step (5)contDo not change for the fitness value of optimum individual in population " specific mistake Cheng Wei：
    Design variable FlastRepresent the best individual fitness value in previous generation populations, FcrrtRepresent best of this generation population Body fitness value；
    If FcrrtMore than Flast, then t is madecont=0, Flast=Fcrrt；Otherwise, t is madecont=tcont+1,Flast=Fcrrt。
  3. 3. a kind of indoor visible light communication LED array layout optimization method based on genetic algorithm according to claim 1, It is characterized in that：" to the individual in population according to crossover probability PcroPerform crossover operation " detailed process be：It is general according to intersecting Rate selects two chromosomes from population at random, and then the geometrical feature according to room selects wherein Chromosome segment One section of chromosome, exchange the gene on corresponding allele；
    " continuous t described in step (5)contDo not change for the fitness value of optimum individual in population " detailed process be：
    Design variable FlastRepresent the best individual fitness value in previous generation populations, FcrrtRepresent best of this generation population Body fitness value；
    If FcrrtMore than Flast, then t is madecont=0, Flast=Fcrrt；Otherwise, t is madecont=tcont+1,Flast=Fcrrt；
    " to the individual in population according to crossover probability P in step (6)croPerform crossover operation " detailed process be：
    Two chromosomes CS1 and CS2 are selected at random from population, then according to the following formula to each Chromosome segment：
    Segment1=(x1,y1)...(xN/4,yN/4)
    Segment2=(xN/4+1,yN/4+1)...(xN/2,yN/2)
    Segment3=(xN/2+1,yN/2+1)...(x3N/4,y3N/4)
    Segment4=(x3N/4,y3N/4)...(xN,yN)
    One section of Segment is randomly selected, then exchanges the gene on correspondence position；Need to carry out above-mentioned crossover operation altogether Number is Npopu*Pcro。
  4. 4. a kind of indoor visible light communication LED array layout optimization method based on genetic algorithm according to claim 1, It is characterized in that：
    " with the individual in population according to mutation probability PmutPerform mutation operation " detailed process be：Search to genetic algorithm Space divides region, and counts the gene number on each region, finds out comprising some most region of gene number, and stochastic censored Except one of gene；Some region for including gene number minimum is found out, is inserted into a gene for belonging to the region；
    Detailed process is：
    By the search space of genetic algorithm, the plane where ceiling, is divided into Nar*NarA uniform zonule, each region It is expressed as Ar (k)；
    Randomly choose a chromosome CSmut, and count the chromosome CS in region Ar (k)mutGene number be set to n (k)；
    Sort to n (k), ArmaxN is corresponding with regionmaxA gene, ArminN is corresponding with regionminA gene；
    Random erasure one belongs to ArmaxGene, and according to described in step (1) method generate one belong to ArminBase Cause, and it is inserted into corresponding position in chromosome；
    The operation N of the above is in repetitionpopu*PmutIt is secondary, that is, complete mutation operation.
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