CN110350960A - The power distribution method of large-scale antenna array based on hybrid power supply - Google Patents
The power distribution method of large-scale antenna array based on hybrid power supply Download PDFInfo
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- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
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Abstract
The invention discloses a kind of power distribution methods of large-scale antenna array based on hybrid power supply, and distributing first for aerial array can valid value q0With power factor w0, and calculate corresponding power distribution table p0；Power factor is iterated using subgradient algorithm, until meeting | wn+1‑wn|≤a, by wnCorresponding energy valid value is as suboptimum energy valid value；The handling capacity of corresponding power distribution table and aerial array is calculated according to suboptimum energy valid value and corresponding power factor；Calculate suboptimum energy valid value and the product value with its power distribution table, when the difference of product value and handling capacity is less than or equal to second threshold, using the corresponding power distribution table of suboptimum energy valid value as optimal power allocation table, power consumption distribution is carried out to aerial array according to optimal power allocation table；The rational allocation of different-energy source of supply may be implemented in the present invention, reduces traditional energy usage amount.
Description
[technical field]
The invention belongs to extensive multi-input/output antenna powering arrays technical fields, more particularly to one kind is based on mixing
The power distribution method of the large-scale antenna array of power supply.
[background technique]
Massive MIMO technology is by configuring ultra-large aerial array, Lai Tigao power system capacity and frequency spectrum in base station end
Efficiency.With the continuous increase of base station number and antenna scale, total energy consumption and carbon emission amount are also being continuously increased, easily
Cause shortage of energy and problem of environmental pollution.
However, not studying different-energy source of supply in the power distribution system of existing large-scale antenna array to carry out
Power distribution, it is difficult to accomplish the whole allotment energy, energy utilization rate is low.
[summary of the invention]
The object of the present invention is to provide a kind of power distribution methods of large-scale antenna array based on hybrid power supply, realize
The rational allocation of different-energy source of supply reduces traditional energy usage amount.
The invention adopts the following technical scheme: the power distribution method of the large-scale antenna array based on hybrid power supply,
It is characterized in that, comprising the following steps:
Distributing for aerial array can valid value q0With power factor w0, and calculate corresponding power distribution table p0；Wherein, function
Rate allocation table is made of initial green energy supply source power table and initial conventional energy source of supply power meter；
Power factor is iterated using subgradient algorithm, until meeting | wn+1-wn|≤a, by wnCorresponding energy valid value is made
For suboptimum energy valid value；Wherein, n is the number of iterations, wnFor the power factor after nth iteration, wn+1After (n+1)th iteration
Power factor, a are first threshold；
The handling capacity of corresponding power distribution table and aerial array is generated according to suboptimum energy valid value and corresponding power factor；
The product value for generating suboptimum energy valid value and corresponding power distribution table, when the difference of product value and handling capacity is small
In being equal to second threshold, using the corresponding power distribution table of suboptimum energy valid value as optimal power allocation table, according to optimal power point
Power consumption distribution is carried out to aerial array with table.
Preferably, green energy source of supply power meter is made of the green energy source of supply power of K user, each user
Green energy source of supply power calculation algorithms are as follows:
Wherein,It is the power that k-th of user provides, q for the i-th time slot green energy source of supplynFor nth iteration
Power exponentαnCorresponding energy valid value, i and l respectively indicate two time slots, and Q is total timeslot number, αiPower when for the i-th time slot
The factor, μlFor the Lagrange multiplier of l time slot, αi、ψiFor the Lagrange multiplier of the i-th time slot, N0For the side of noise vector n
Difference, M are the antenna amount of aerial array, and K is the number of users of aerial array service, βkFor the slow of aerial array to k-th user
Fading coefficients, [x]+=max [0, x].
Preferably, during being iterated using subgradient algorithm to power factor, when | wn+1-wn| when > a, specific iteration
Method is；
Step a: according to wn+1And qnGenerate updated power distribution table Pn+1；
According to wn+1And Pn+1Generate power factor wn+2；
Step b: judge whether to meet | wn+2-wn+1|≤a:
If so, by qn+1As suboptimum energy valid value；
If it is not, step a is repeated, and until being met | wn+2-wn+1The power factor of |≤a, and by the power factor
Corresponding energy valid value is as suboptimum energy valid value.
Preferably, when the difference of product value and handling capacity is greater than second threshold, method particularly includes:
Pass throughNew energy valid value q ' is generated, new energy valid value q ' as initially energy valid value and is combined into initial energy
The corresponding power factor of valid value is iterated new power factor using subgradient algorithm, until meeting | w 'n+1-w′n|≤a,
Then by w 'nCorresponding energy valid value q 'nAs suboptimum energy valid value；
According to suboptimum energy valid value q 'nAnd corresponding power factor w 'nCalculate corresponding power distribution table and aerial array
Handling capacity；
Calculate suboptimum energy valid value q 'nWith the product value with its power distribution table, when the difference of the product value and the handling capacity
Less than or equal to second threshold, by suboptimum energy valid value q 'nCorresponding power distribution table is as optimal power allocation table；Otherwise, it repeats
Above-mentioned steps are executed, until obtaining optimal power allocation table；
Power consumption distribution is carried out to aerial array according to the optimal power allocation table.
Preferably, throughput calculation methods are as follows:
Wherein, LiFor the time span of an event in data transmission procedure.
The beneficial effects of the present invention are: present invention assumes that energy acquisition process and channel variation process are it is known that establish maximum
Change system energy efficiency be target, using signal transmission power and circuit consumption power as optimized variable, while have energy causality constraint,
The restrictive conditions such as battery capacity constraint and user's minimum speed limit, are supplied using the mixed tensor that collecting energy and power grid combine
The mode answered come for base station power supply, which is the mode based on collecting energy, supplemented by power grid, in conjunction with Dinkelbach,
Lagrange it is effective and reasonable to the method for even summation KKT condition realize the power resource allocation of large-scale antenna array, and adopt
The interference between user can be eliminated well with the mode of ZF precoding.
[Detailed description of the invention]
Fig. 1 is the flow diagram of the embodiment of the present invention；
Fig. 2 is single cell downlink model of communication system figure based on energy acquisition in the prior art；
Fig. 3 is transmitting terminal radio frequency link illustraton of model in the prior art with M root antenna；
Fig. 4 is the efficiency comparison diagram in comparative example of the present invention.
[specific embodiment]
The following describes the present invention in detail with reference to the accompanying drawings and specific embodiments.
In the prior art, as shown in Fig. 2, single cell downlink model of communication system figure of the position based on energy acquisition, considers
Be Massive mimo system downlink communication mode under single cell.In this system, in single cell, there is K
A single-antenna subscriber is uniformly distributed, and base station installation M root omnidirectional antenna is communicated with all users in cell simultaneously.If being
Channel matrix in system model between base station and user is G, and it is X, pre-coding matrix A that base station, which sends signal, then user terminates
Collection of letters vector representation isWherein, Y=[y1, y2... yk]TTo receive signal phasor, X=[x1,
x2... xk]TTo send signal phasor, n is noise vector, element be independent identically distributed mean value be zero, variance N0Answer
Gaussian random variable, A are M × K rank pre-coding matrix, and G is K × M rank channel matrix, and meets G=D1/2H.Wherein, M × K rank
MatrixRepresent the rapid fading coefficient matrix between base station and user, wherein hkRepresent base station and K
Rapid fading coefficient vector between a user, and assume hkIt is 0 that element, which obeys mean value, the multiple Gauss stochastic variable that variance is 1.D
For K × K rank diagonal matrix, indicate base station to the slow fading coefficient matrix between user, diagonal entry [D]kk=βkRepresent base
It stands to the slow fading coefficient of k-th of user, including path loss and shadow fading etc..Then between base station BS and k-th of user
Channel coefficients gk, can be write asWherein βk=ζk*(dk/r0)-α, dkFor the distance between BS and k-th of user, α
For path loss index, α=3.7, ζ are generally takenkObey 10log10ζk~N (0, δ2) Lognormal shadowing distribution.Under then
The signal from base station that k-th of user receives in row scene is
Wherein, user i represents other users in addition to user k, ρkAnd ρiIt is allocated under user k and user i respectively
Row sends power, the rank vector of M × 1, akAnd aiRespectively represent the downlink precoding vector of k-th of user and i-th of user, xkWith
xiRepresent k-th and i-th of user needed for data information, and E | xk|2=1.βkAnd hkIt is base station declining slowly to user k respectively
Fall coefficient and rapid fading coefficient vector, nkFor the noise at k-th of user.Then the SINR of k-th of user can be indicated are as follows:
Based on the precoding algorithms of ZF criterion, can directly be realized by the operation of channel regurgitation.It is big meeting transmitting antenna
When total receiving antenna, ZF algorithm can eliminate the interference between transmitting antenna, to eliminate the interference between intra-cell users.
This algorithm uses ZF precoding, ZF weighting matrix are as follows:
A=GH(GGH)-1
It is not difficult to find out GA=GGH(GGH)-1=IK, i.e. gkai=δkiWherein δkiMeet: when k=i, δki=1；Work as k
When ≠ i, δki=1, this illustrates ZF precodings can eliminate the interference between community user.
Therefore, it can be obtained according to shannon formula, the traversal achievable rate of k-th of user can be expressed as Rk=E { log2(1+
SINR)}。
Before carrying out Energy Efficiency Analysis, need first to define the power consumption model of Massive mimo system, it is assumed that system
Base station end have M root antenna, and this M root antenna is used to send data entirely, then Fig. 3 indicates the transmitting terminal radio frequency with M root antenna
Link model, therefore, circuit power consumption model are expressed as follows:
PC≈M(PADC+Pmix+Pfilt)+Psvn,
Total system power consumption by base station overall transmission power PtxWith circuit power consumption PCTwo parts composition, i.e. Ptotal=Ptx+PC, fixed
The bit number that adopted system energy efficiency is transmitted by every Joule energy, i.e.,
Foregoing description solves the model of efficiency, the presence interfered between community user, and since objective function is
The form of score, thus optimization problem be it is non-convex, be difficult to solve the globally optimal solution of optimization problem in this way.It is based on
The energy collecting system power distribution of Massive mimo system carries out precoding to signal using ZF precoder in transmitting terminal
The interference between user is eliminated, to improve energy efficiency as target, using a kind of algorithm (Dinkelbach) of iteration come most
Bigization efficiency.The purpose of this design is the fractional form of efficiency to be converted to the form of subtraction, and combine Lagrange duality method
The performance that algorithm is improved with KKT condition reaches maximum energy valid value.
The embodiment of the invention discloses a kind of power distribution methods of large-scale antenna array based on hybrid power supply, including
Following steps:
Step 1 gives initial efficiency qnThe initial value of (the wherein the number of iterations that n is efficiency), Lagrange multiplier.Step
Rapid two, go out to optimize performance number according to the calculation of initial value of given efficiency initial value and Lagrange multiplier.Step 3, according to drawing
The iterative formula of Ge Lang multiplier updates Lagrange multiplier, and calculates corresponding optimization performance number P*Step 4 judges R
(P*)-qn*Ptotal(P*) value whether be less than Δ, if so, havingOtherwise, it enablesN=n
+ 1, until n=N.
Method particularly includes: distributing for aerial array can valid value q0With power factor w0, and calculate corresponding power distribution table
p0；Wherein, power distribution table is made of initial green energy supply source power table and initial conventional energy source of supply power meter.
Power factor is iterated using subgradient algorithm, until meeting | wn+1-wn|≤a, by wnCorresponding energy valid value qn
As suboptimum energy valid value；Wherein, n is the number of iterations, wnFor the power factor after nth iteration, wn+1After (n+1)th iteration
Power factor, a is first threshold.
Handling up for corresponding power distribution table and aerial array is calculated according to suboptimum energy valid value and corresponding power factor
Amount.Suboptimum energy valid value and the product value with its power distribution table are calculated, when the difference of product value and handling capacity is less than or equal to second
Threshold value, using the corresponding power distribution table of suboptimum energy valid value as optimal power allocation table, according to optimal power allocation table to antenna
Array carries out power consumption distribution.
In embodiments of the present invention, when the difference of product value and handling capacity is greater than second threshold, method particularly includes:
Pass throughNew energy valid value q ' is calculated, new energy valid value q ' as initially energy valid value and is combined initial
The corresponding power factor of energy valid value, is iterated new power factor using subgradient algorithm, until meeting | w 'n+1-w′n|≤
A, w 'nCorresponding energy valid value q 'nAs suboptimum energy valid value.
According to suboptimum energy valid value q 'nAnd corresponding power factor w 'nCalculate corresponding power distribution table and aerial array
Handling capacity.
Calculate suboptimum energy valid value q 'nWith the product value with its power distribution table, when the difference of the product value and the handling capacity
Less than or equal to second threshold, by suboptimum energy valid value q 'nCorresponding power distribution table is as optimal power allocation table, otherwise, repeats
Above-mentioned steps are executed, until obtaining optimal power allocation table；Power consumption point is carried out to aerial array according to the optimal power allocation table
Match.
Energy acquisition process and channel variation process, which are assumed, by the above method maximizes system energy efficiency it is known that establishing as mesh
Mark, using signal transmission power and circuit consumption power as optimized variable, while there is energy causality constraint, battery capacity to constrain,
And the restrictive conditions such as user's minimum speed limit, the effective and reasonable power resource allocation for realizing large-scale antenna array.
In the embodiment of the present invention, green energy source of supply power meter is made of the green energy source of supply power of K user,
The green energy source of supply power calculation algorithms of each user are as follows:
Wherein,It is the power that k-th of user provides, q for the i-th time slot green energy source of supplynIt changes for n-th
The power factor w in generationnCorresponding energy valid value, i and l respectively indicate two time slots, and Q is total timeslot number, μlFor the glug of l time slot
Bright day multiplier, αi、ψiFor the Lagrange multiplier of the i-th time slot, N0For the variance of noise vector n, M is the antenna number of aerial array
Amount, K are the number of users of aerial array service, βkFor aerial array to the slow fading coefficient of k-th of user, [x]+=max [0, x].
Throughput calculation methods are as follows:
Wherein, LiFor the time span of an event in data transmission procedure.
In the embodiment of the present invention, during being iterated using subgradient algorithm to power factor, when | αn+1-αn| when > a,
Specifically alternative manner is；
Step a: according to wn+1And qnCalculate updated power distribution table Pn+1；
According to wn+1And Pn+1Update its corresponding power factor wn+2；
Step b: judge whether to meet | wn+2-wn+1|≤a:
If so, by qn+1As suboptimum energy valid value；
If it is not, step a is repeated, and until being met | wn+2-wn+1The power factor of |≤a, and by the power factor
Corresponding energy valid value is as suboptimum energy valid value.
Detailed process of the embodiment of the present invention are as follows:
This algorithm carries out precoding to signal using ZF precoder, and according to ZF criterion, the pre-coding matrix of base station can be with
It is expressed asBase station can be obtained under desirable channel conditions according to Random Matrices Theory using ZF pre-coding scheme
The traversal achievable rate lower bound of k-th of user isTherefore, with the Massive of K user
The power system capacity lower bound of MIMO downlink communication system is
Behavior is reached to the random energies in energy collecting device using Poisson distribution in energy acquisition model to model.Energy
It is λ that the process of amount acquisition, which obeys rate,ePoisson counting process.The time interval that adjacent energy twice reaches isWherein b ∈ { 1,2 ... }.The process of channel state variations describes the characteristic of channel using coherence time.
Within coherence time, the state of channel is identical.In entire transmission process, it is assumed that energy each time reaches
Or CSI (channel status) changes into an event, the time of adjacent events is denoted as an epoch, length Li=ti-ti-1。
In adjacent epoch, the also not variation of channel is reached without energy, so the hair power invariability of time slot i is Pi.Due to energy
Arrival random time and battery storage capacity it is limited, so power distribution strategies are deposited by the causality constraint and battery of energy
Store up capacity-constrained.Energy causality constraint and battery storage capacity constraint.Energy causality constraint refers to the chargeable of current time consumption
Energy in battery is less than the gross energy currently acquired.Battery storage capacity refers to that the capacity for being stored in rechargeable battery is less than
The maximum capacity limit of rechargeable battery.It is formulated as
In consecutive hours [0, T], it is assumed that channel variation number is N, and it is D that energy, which reaches number, then total timeslot number Q=N+D+1, then
Overall system throughput R (P) and total system power consumption P in T time sectiontotal(P) it can be expressed as
So that the mathematical model of the maximized power distribution strategies of efficiency are as follows:
S.t.c1:
C2:
C3:
C4:
C5:
C6:
C7:
Wherein,For optimized variable.WithIt is in time slot i respectively by acquisition energy
Amount and power grid are the transmission power that user k is provided；WithIt is in time slot i respectively by collecting energy and power grid is user k
The circuit power of offer.
Power factor wnIncluding multiple factors, meet claimed below, the minimum speed limit requirement of c1 expression user respectively, wherein
RminIt is the minimum speed limit of user；The energy causality constraint of c2 expression energy acquisition；C3 indicates noenergy overflow condition；C4 is indicated
Power grid be the maximum power that provides of base station no more thanC5 is provided with the transimission power of base station no more than Pmax；C6 description
The component part of circuit power consumption；C7 is the nonnegativity restrictions to power distribution variable.
Above formula is observed, which is non-linear fractional programming problem, in order to obtain effective power distribution, can be by target letter
Number equivalence is converted to convex optimization problem, then is solved.Enable q=R (P)/Ptotal(P), new function is
When F (q)=0, maximum efficiency q is obtained*.Therefore the solution of objective function, which is converted to, asks F (q)=0
Root,
According to the prior art, Dinkelbach convergence speed of the algorithm is better than the convergence rate of dichotomy, so using
Dinkelbach algorithm finds the root.
In former optimization problem, since c6 is equality constraint, then it can useCarry out generationIt replaces；C7 is optimization
The nonnegativity restrictions of variable only need to be assigned a value of 0 in the negative power value finally found out, to omit constraint condition c6 and c7.This
When, optimized variable
The problem can be solved by Lagrangian and KKT condition, convert former optimization problem to without constraint
The optimization problem of condition, then the corresponding Lagrangian of optimization problem indicates are as follows:
Wherein, γ is Lagrange multiplier, corresponding constraint condition c1.α, μ, v are infused, ψ is corresponding constraint condition c2 respectively,
The Lagrange multiplier of c3, c4, c5.These Lagrange multipliers constitute impact factor wn, according to definition, former optimization problem
Dual problem is expressed as follows:
The solution of its dual problem is divided into two parts: first part is according to given efficiency qnIt is asked with Lagrange multiplier
Optimal P out；Second part is updated Lagrange multiplier according to subgradient algorithm, again by updated Lagrange multiplier
It brings first part into, given efficiency q is found by continuous iterationnUnder optimum allocation power P*.Asking for first part is carried out first
Solution solves optimization problem using KKT condition in the case where given Lagrange multiplier, enables:
According to above formula, the optimum transmission power of user k is supplied in time slot i collecting energy are as follows:
Wherein, [x]+=max [0, x].The present invention uses mixed tensor method of supplying, only when the energy in rechargeable battery
When amount is insufficient, it just will use power grid and be used as the supplement energy, therefore, ifThenExpression formula conversion are as follows:
Equally use KKT condition, to Lagrangian aboutOptimal value seek local derviation.
It enables:I.e.
According to above formula, in time slot i by the optimum transmission power that power grid is that user k is provided are as follows:
The transmission power that the power grid provides is limited to the transmission power provided by collecting energy, design of the embodiment of the present invention
The original intention of algorithm is: base station is powered first with collecting energy, when collecting energy deficiency, power grid as supplementary energy,
Continue to supply normal operation of the electricity to guarantee base station.
According to set energy using strategy it is found that collecting energy is for the optimal value of circuit power and for emitting function
The optimal value of rateIt is related, so, time slot i is respectively indicated by the optimum circuit power that collecting energy and power grid provide are as follows:
Wherein,It indicates: as x > PcWhen,As 0≤x≤PcWhen,As x < 0,If collecting energy is not enough to support circuits power consumption, i.e.,Then base station also will obtain energy from power grid
Amount.So far, all power optimized values are solved out.
Next, carrying out the solution of second part, i.e., Lagrange multiplier is updated using subgradient algorithm:
Wherein, l, i ∈ { 1 .., Q },Step-length is updated for iteration, t is the number of iterations.
Verify embodiment:
The convergent of described this method is verified by Matlab emulation, it is assumed that number of users 10, frequency mixer power consumption Pmix
For 30.3mW, filter power consumption PfiltFor 2.5mW, noise power variance N0For -128dBm, analog-to-digital conversion power consumption PADCFor 6.7mW,
Frequency synthesizer power consumption PsynFor 50mW, shadow fading standard deviation δ2For 8dB.Fig. 4 is when average energy acquisition rate is 3J/s
Efficiency comparison diagram, the chart is bright: the energy valid value of mean power algorithm is first increases and then decreases, and algorithm proposed by the invention is most
Pipe is decline, but its energy valid value is better than the energy valid value of average power allocation.
Claims (5)
1. the power distribution method of the large-scale antenna array based on hybrid power supply, which comprises the following steps:
Distributing for aerial array can valid value q0With impact factor w0, and calculate corresponding power distribution table p0；Wherein, power point
It is made of with table initial green energy supply source power table and initial conventional energy source of supply power meter；
The impact factor is iterated using subgradient algorithm, until meeting | wn+1-wn|≤a, by wnCorresponding energy valid value is made
For suboptimum energy valid value；Wherein, n is the number of iterations, wnFor the power factor after nth iteration, wn+1After (n+1)th iteration
Power factor, a are first threshold；
The handling capacity of corresponding power distribution table and aerial array is generated according to the suboptimum energy valid value and corresponding power factor；
The product value for generating the suboptimum energy valid value and corresponding power distribution table, when the product value and the handling capacity
Difference be less than or equal to second threshold, using the corresponding power distribution table of the suboptimum energy valid value as optimal power allocation table, root
Power consumption distribution is carried out to the aerial array according to the optimal power allocation table.
2. the power distribution method of the large-scale antenna array based on hybrid power supply as described in claim 1, which is characterized in that
The green energy source of supply power meter is made of the green energy source of supply power of K user, the green of each user
Energy supply source power calculation method are as follows:
Wherein,It is the power that k-th of user provides, q for the i-th time slot green energy source of supplynFor the function of nth iteration
Rate factor wnCorresponding energy valid value, i and l respectively indicate two time slots, and Q is total timeslot number, μlLagrange for l time slot multiplies
Son, αi、ψiFor the Lagrange multiplier of the i-th time slot, N0For the variance of noise vector n, M is the antenna amount of aerial array, and K is
The number of users of aerial array service, βkFor aerial array to the slow fading coefficient of k-th of user, [x]+=max [0, x].
3. the power distribution method of the large-scale antenna array based on hybrid power supply as claimed in claim 2, which is characterized in that
During being iterated using subgradient algorithm to the impact factor, when | wn+1-wn| when > a, specific alternative manner is；
Step a: according to wn+1And qnGenerate updated power distribution table Pn+1；
According to wn+1And Pn+1Generate power factor wn+2；
Step b: judge whether to meet | wn+2-wn+1|≤a:
If so, by qn+1As suboptimum energy valid value；
If it is not, step a is repeated, and until being met | wn+2-wn+1The power factor of |≤a, and the power factor is corresponding
Energy valid value as suboptimum energy valid value.
4. the power distribution method of the large-scale antenna array based on hybrid power supply as claimed in claim 3, which is characterized in that
When the difference of the product value and the handling capacity is greater than second threshold, method particularly includes:
Pass throughNew energy valid value q ' is generated, new energy valid value q ' is used as initially can valid value and the initial energy valid value of combination
Corresponding power factor is iterated new power factor using subgradient algorithm, until meeting | w 'n+1-w′n|≤a then will
w′nCorresponding energy valid value q 'nAs suboptimum energy valid value；
According to the suboptimum energy valid value q 'nAnd corresponding power factor w 'nCalculate corresponding power distribution table and aerial array
Handling capacity；
Calculate the suboptimum energy valid value q 'nWith the product value with power distribution table described in its, when the difference of the product value and the handling capacity
Value is less than or equal to second threshold, by the suboptimum energy valid value q 'nCorresponding power distribution table is as optimal power allocation table；It is no
Then, above-mentioned steps are repeated, until obtaining optimal power allocation table；
Power consumption distribution is carried out to the aerial array according to the optimal power allocation table.
5. the power distribution method of the large-scale antenna array based on hybrid power supply as described in claim 2-4 is any, special
Sign is, the throughput calculation methods are as follows:
Wherein, LiFor the time span of an event in data transmission procedure.
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