CN107506847A - Pricing method based on Stackelberg games in the extensive mimo system of energy acquisition - Google Patents

Pricing method based on Stackelberg games in the extensive mimo system of energy acquisition Download PDF

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CN107506847A
CN107506847A CN201710574615.9A CN201710574615A CN107506847A CN 107506847 A CN107506847 A CN 107506847A CN 201710574615 A CN201710574615 A CN 201710574615A CN 107506847 A CN107506847 A CN 107506847A
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万晓榆
魏霄
杨晓娜
王正强
樊自甫
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Chongqing University of Post and Telecommunications
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • 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

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Abstract

The pricing method based on Stackelberg games in a kind of extensive mimo system of energy acquisition is claimed in the present invention, including：Initialize systematic parameter；Judged whether to meet extensive MIMO zero forcing algorithms limitation according to the number of the antenna number of base station and sensor, if condition meets, provide optimal time and the suboptimum time of system capacity collection；Otherwise, method terminates；For systematic parameter and energy acquisition time, the optimal and hypo-optimal pricing method of pyramid of energy is provided；For systematic parameter and energy acquisition time, the optimal and suboptimum income of pyramid of energy is calculated.The present invention calculates the optimal income of the energy acquisition optimal time of system, the optimal pricing of pyramid of energy and pyramid of energy, while in order to reduce computational complexity, gives the closed solutions of the energy acquisition time of hypo-optimal pricing method.Two methods can increase the income of pyramid of energy, have the advantages of practicality and strong feasibility.

Description

Price based on Stackelberg games in the extensive mimo system of energy acquisition Method
Technical field
The invention belongs to green communications field of energy-saving technology, is specifically based in the extensive mimo system of energy acquisition The pricing method of Stackelberg games.
Background technology
In wireless energy gathers scene, three kinds of deployment base station, sensor, pyramid of energy equipment.Sensor is not any In the case of internal electric source, it then follows first gather the scheme transmitted afterwards and communicated with base station, i.e., sensor is first adopted at pyramid of energy Collect energy, then transmit information to base station using the energy of collection.In practice, sensor and pyramid of energy need one it is mutually beneficial Scheme ensures the transaction of the bipartite energy.First, the data throughout that the income of sensor includes obtaining, which subtracts, to be paid The cost of pyramid of energy and caused income.The income that the income of pyramid of energy should include selling energy production to sensor subtracts operation Cost.This interactive relation between pyramid of energy and sensor is modeled as Stackelberg games.In order to ensure to maximize certainly Body income, pyramid of energy need to control the energy that sensor gathers using rational pricing strategy.If pyramid of energy price is too low, So self benefits are smaller；And pyramid of energy price is too high, the collecting energy demand of sensor is caused to reduce, so as to also cause itself Income reduce.Therefore, it is necessary to which a kind of preferably pricing strategy is ensureing between sensor and base station on the premise of communication quality, The price of pyramid of energy is rationally set, maximizes the income of pyramid of energy.
In recent years, in energy acquisition based on Stackelberg game methods come control the Pricing Research of pyramid of energy just by To increasing concern.Existing literature is retrieved and found, pertinent literature is as follows：
Siddhartha SarmaEt al.《2016IEEE Wireless Communications Letters, Jun.2016,vol.5,no.3,pp.336-339.》On delivered entitled " Robust Energy Harvesting Based On a Stackelberg Game " article.This article have studied base station and multiple antennas energy in multisensor node scene Wireless energy acquisition technique between tower, the interactive relation between pyramid of energy and sensor node is designed as Stackelberg Game, this multiaerial system put forward the efficiency of high energy transmission by using beam forming.Article assume sensor node and Possess incomplete channel condition information between power beacon, it is proposed that in the effectiveness of non-interrupted probability of the base station end based on handling capacity Function.
Henry Chen et al. exist《2015IEEE International Conference on Acoustics, 2015, pp.3177-3181.》On delivered entitled " A stackelberg game-based energy trading scheme for Power beacon-assisted wireless-powered communication " article.This article have studied by one The pyramid of energy auxiliary wireless energy communication network of individual access point, an information source and multiple pyramid of energies composition.In the downlink, Information source gathers RF energy from access point, and pyramid of energy aids in access point to be charged for information source, and then information source is in up-link It is middle to send information to access point.However, in practice, access point and pyramid of energy may belong to different operators.Therefore, under Row energy transfer phase (i.e. " energy transaction "), this power exchange process modeling is Stackelberg games by model, wherein connecing Access point is leader, and pyramid of energy is follower, has shown that the Stackelberg of game is balanced.
From correlative study, in order to maximize the income of pyramid of energy and meet the telecommunication service quality of whole system, energy Amount tower needs to use a more rational pricing method to sensor.Revenue function of the invention based on pyramid of energy, according to convex excellent Change interior-point algohnhm, draw the best price factor for maximizing pyramid of energy income and optimal collecting energy time.While in order to subtract The complexity of small convex optimized algorithm, it is proposed that a kind of hypo-optimal pricing algorithm for maximizing pyramid of energy income, be derived suboptimum energy The closed set expression formula of acquisition time.
The content of the invention
Present invention seek to address that above problem of the prior art.Propose one kind and reduce algorithm complex, ensure simultaneously The pricing method based on Stackelberg games of preferable pyramid of energy income.Technical scheme is as follows：
Pricing method based on Stackelberg games in a kind of extensive mimo system of energy acquisition, it include with Lower step：
1), initialize and extensive mimo system parameter is set, including M, K, βk,gk,ξk,σ2,Pmax, k=1 ..., K, its In, M be base station antenna number, K be sensor number, βkIt is sensor k large scale fading factor, gkIt is pyramid of energy to passing Sensor k channel gain, ξkIt is sensor k energy conversion efficiency, σ2It is the ambient noise of base station end, PmaxBe pyramid of energy most Big transimission power；U is setPB,Initial value be 0, UPBWithIt is the optimal and suboptimum income of pyramid of energy respectively；
2), judged whether to meet extensive MIMO zero forcing algorithms according to the number of the antenna number of base station and sensor
Limitation, if condition meets, calculate optimal time and the suboptimum time of system capacity collection；Otherwise,
Method terminates；
3), the energy acquisition optimal time and suboptimum time of the systematic parameter for step 1) and step 2),
The optimal pricing and hypo-optimal pricing of pyramid of energy corresponding to calculating respectively；
4), the energy acquisition optimal time and suboptimum time of the systematic parameter for step 1) and step 2),
The optimal income and suboptimum income of pyramid of energy corresponding to calculating respectively.
Further, in step 2), if antenna for base station number M and number of probes K cause extensive MIMO ZFs to calculate Law part is set up, i.e. M >=K+1, then the optimal energy acquisition time of unit interval inner sensor is obtained by convex optimization interior point method τoptimal, and suboptimum energy acquisition time τsuboptimalFor：Wherein parameter PmaxIt is the maximum transmitted work(of pyramid of energy Rate；If condition is invalid, method terminates.
Further, the optimal or hypo-optimal pricing of the step 3) pyramid of energyFor： ParameterEnergy acquisition time τ=τoptimalOr τsuboptimal, by parameter alpha, rkWith τoptimalSubstitute into respectivelyCalculate the optimal pricing of pyramid of energy；Similarly, by α, rkAnd τsuboptimalSubstitute into respectivelyCalculate energy The hypo-optimal pricing of tower.
Further, the optimal income U of the step 4) pyramid of energyPBFor：Energy Measure the suboptimum income of towerFor：The theoretical limit of pyramid of energy income ValueFor：Wherein parameter τoptimal、τsuboptimalThe respectively optimal and suboptimum energy acquisition time of unit interval inner sensor.
Advantages of the present invention and have the beneficial effect that：
The present invention is directed to the extensive mimo system of energy acquisition, there is provided a kind of price side for maximizing pyramid of energy income Method.The present invention enables to system in the case where knowing channel condition information, is maximized by convex optimization interior point method to realize Pyramid of energy income.The pricing method finds the optimal solution for maximizing pyramid of energy income on the premise of system communication quality is ensured, But algorithm complex is higher.Therefore, the present invention has also been proposed a kind of hypo-optimal pricing algorithm for reducing algorithm complex, find most The suboptimum energy acquisition time of bigization pyramid of energy income, compared to optimal algorithm, algorithm complex is reduced, while ensure that preferably Pyramid of energy income.
The present invention proposes a kind of deployment base station, the new wireless energy collection field of three kinds of equipment of sensor and pyramid of energy Scape.The pricing strategy of maximization pyramid of energy income is searched for by the optimality condition of pyramid of energy, finds and maximizes pyramid of energy receipts The price of benefit, while propose a kind of hypo-optimal pricing strategy for reducing computational complexity.Two methods provided by the present invention are equal Improve the income of pyramid of energy.Because algorithm has analytical expression, therefore execution speed is fast, has preferable feasibility and reality The property used.
The step of specific innovation of the invention：First, multi-antenna base station is disposed in multisensor node and is adopted for energy The pyramid of energy of collection；Secondly, in the case where ensureing service quality QoS, it is assumed that known channel state information, derive that pyramid of energy is received Beneficial function, the energy acquisition time optimal solution for maximizing pyramid of energy income is found by convex optimization interior point method, obtains pyramid of energy Optimal pricing, and calculate the optimal income of pyramid of energy；Then, in order to reduce algorithm complex, the suboptimum energy of pyramid of energy is found Acquisition time, the hypo-optimal pricing of pyramid of energy is obtained, and calculate the suboptimum income of pyramid of energy；Finally, simulation comparison two kinds of algorithms Performance.
Brief description of the drawings
Fig. 1 is based in the extensive mimo system of the energy acquisition for the offer that the present invention provides preferred embodiment The flow chart of the pricing method of Stackelberg games；
Fig. 2 is the energy under different antennae number of the present invention when the maximum transmission power of pyramid of energy increases to 30dbm from 0 Tower yield curve figure；
Fig. 3 is the energy under different user number of the present invention when the maximum transmission power of pyramid of energy increases to 30dbm from 0 Tower yield curve figure；
Fig. 4 is pyramid of energy yield curve figure of the present invention when antenna for base station number increases to 100 from 10.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, detailed Carefully describe.Described embodiment is only the part of the embodiment of the present invention.
The present invention solve above-mentioned technical problem technical scheme be：
The present embodiment is the pricing scheme based on Stackelberg games, and ambient noise is the white Gaussian noise of zero-mean Value σ2=10-10W, base station and pyramid of energy are respectively distributed at (- 20,0) m, (20,0) m coordinate, and sensor is randomly dispersed in In rectangular area [- 10,10] × [- 10,10] m areal extent, channel gain of the pyramid of energy at sensor kPass The channel gain of sensor k to base station receiving terminal isWherein dkAnd mkIt is sensor k respectively to pyramid of energy and base station Distance, energy conversion efficiency value ξk=0.6.Fig. 2 and Fig. 4 number of sensors is that 9, Fig. 3 antenna for base station number is 100, Fig. 4 energy The maximum transmission power value P of towermax=20dBm, as a result passes through 103Secondary emulation is averaged.
The first step, initialize and each systematic parameter is set：M,K,βk,gk,ξk,σ2,Pmax, k=1 ..., K.U is setPB,Initial value be 0.
Wherein, M be base station antenna number, K be sensor number, βkIt is sensor k large scale fading factor, gkIt is Pyramid of energy is to sensor k channel gain, ξkIt is sensor k energy conversion efficiency, σ2It is the ambient noise of base station end, PmaxIt is The maximum transmission power of pyramid of energy, UPB、It is the optimal and suboptimum income of pyramid of energy respectively.
Second step, if antenna for base station number M and number of probes K cause extensive MIMO zero forcing algorithms condition (M >=K+1) Set up, then the optimal energy acquisition time τ of unit interval inner sensor is obtained by following convex optimization interior point methodoptimal, and it is secondary Excellent energy acquisition time τsuboptimalFor：Wherein parameter PmaxIt is the maximum transmission power of pyramid of energy, M is the antenna number of base station, and K is the number of sensor, βkIt is sensor k large scale Fading factor, gkIt is channel gain of the pyramid of energy to sensor k, ξkIt is sensor k energy conversion efficiency, σ2It is base station end Ambient noise；If condition is invalid, method terminates.
The convex optimization interior point method method for solving is as follows：
For the revenue function of pyramid of energyWherein, τ is energy Acquisition time,PmaxIt is the maximum transmission power of pyramid of energy, it is seen that UPB(τ) For convex function, orderSolve It is equivalent to seek following optimization problem：
Note feasible zone D=τ | and g (τ) >=0 }, the situation of the interior point set non-NULL of feasible zone is D0=τ | g (τ)>0}≠φ.
Construct augmented objective function：WhereinFor barrier function, Parameter μ>0 is penalty factor.
Penalty factor in interior point method requires μk→ 0, Solve problems formula (1), which can be converted into, solves sequence unconstrained optimization Problem：
The specific calculation procedure of interior point method is as follows：
Step1：Given initial point τ0∈D0, terminate error 0≤ε≤1.μ1>0,ρ∈(0,1).Make k:=1.
Step2:With τk-1Unconstrained optimization subproblem is solved for initial point, formula (2) is seen, obtains minimal point τk。
Step3:IfStop calculating, export τ*≈τkAs approximate minimal point.
Step4:Make μk+1:=ρ μk,k:=k+1, turns Step2.
By multiple loop iteration, optimal energy acquisition time τ is obtainedoptimal=τ*。
3rd step：The optimal or hypo-optimal pricing of pyramid of energyFor：ParameterEnergy acquisition time τ=τoptimalOr τsuboptimal, PmaxBe pyramid of energy most Big transimission power, M be base station antenna number, K be sensor number, βkIt is sensor k large scale fading factor, gkIt is energy Tower is measured to sensor k channel gain, ξkIt is sensor k energy conversion efficiency, σ2It is the ambient noise of base station end, generation respectively Enter τoptimalAnd τsuboptimalCalculate the optimal and hypo-optimal pricing of pyramid of energy.
4th step：The optimal income U of pyramid of energyPBFor：Energy Measure the suboptimum income of towerFor：The theoretical pole of pyramid of energy income Limit valueFor：Wherein parameter τoptimal、τsuboptimalThe respectively optimal and suboptimum energy acquisition time of unit interval inner sensor, PmaxBe pyramid of energy most Big transimission power, M be base station antenna number, K be sensor number, βkIt is sensor k large scale fading factor, gkIt is energy Tower is measured to sensor k channel gain, ξkIt is sensor k energy conversion efficiency, σ2It is the ambient noise of base station end.
In the present embodiment, the present embodiment hypo-optimal pricing side is respectively adopted in the case of giving base station different antennae number in Fig. 2 The pyramid of energy yield curve figure that method and the present embodiment optimal pricing method obtain；Fig. 3 is adopted respectively in the case of different sensors number The pyramid of energy yield curve figure obtained with the present embodiment hypo-optimal pricing method and the present embodiment optimal pricing method；Fig. 4 is different The energy that the present embodiment hypo-optimal pricing method and the present embodiment optimal pricing method obtain is respectively adopted in the case of the energy acquisition time Measure tower yield curve figure.As seen from Figure 2：Increase with the maximum transmission power of pyramid of energy, institute in the case of different base station antenna number The effect for putting forward hypo-optimal pricing implementation gradually approaches optimal pricing implementation, but second best measure greatly reduces computing Complexity, the gradual approximation theory limiting value of two methods.As seen from Figure 3：Increase with the maximum transmission power of pyramid of energy, no Optimal pricing implementation, but second best measure are gradually approached with the effect that hypo-optimal pricing implementation is put forward in the case of number of users Greatly reduce computational complexity, the gradual approximation theory limiting value of two methods.As seen from Figure 4：Carried suboptimum and optimal Pricing method obtains more excellent and optimal pyramid of energy income, the gradual approximation theory limiting value of two methods.With reference to Fig. 2, figure 3rd, Fig. 4 understands that carrying suboptimum and best practice improves pyramid of energy income, and second best measure gradually approaches best practice, but reduces Computational complexity.This method obtains the suboptimum and optimal pricing strategy of pyramid of energy, and institute's extracting method can efficiently solve energy Amount gathers the relevant issues such as resource allocation based on price in extensive mimo system.
The above embodiment is interpreted as being merely to illustrate the present invention rather than limited the scope of the invention. After the content for having read the record of the present invention, technical staff can make various changes or modifications to the present invention, these equivalent changes Change and modification equally falls into the scope of the claims in the present invention.

Claims (4)

1. the pricing method based on Stackelberg games in a kind of extensive mimo system of energy acquisition, it is characterised in that Comprise the following steps：
1), initialize and extensive mimo system parameter is set, including M, K, βk,gk,ξk,σ2,Pmax, k=1 ..., K, wherein, M For the antenna number of base station, K is the number of sensor, βkIt is sensor k large scale fading factor, gkIt is pyramid of energy to sensor k Channel gain, ξkIt is sensor k energy conversion efficiency, σ2It is the ambient noise of base station end, PmaxIt is the maximum biography of pyramid of energy Defeated power；U is setPB,Initial value be 0, UPBWithIt is the optimal and suboptimum income of pyramid of energy respectively；
2), judged whether to meet that extensive MIMO zero forcing algorithms limit according to the number of the antenna number of base station and sensor, if Condition meets, calculates optimal time and the suboptimum time of system capacity collection；Otherwise, method terminates；
3), the energy acquisition optimal time and suboptimum time of the systematic parameter for step 1) and step 2), is calculated pair respectively The optimal pricing and hypo-optimal pricing for the pyramid of energy answered；
4), the energy acquisition optimal time and suboptimum time of the systematic parameter for step 1) and step 2), is calculated pair respectively The optimal income and suboptimum income for the pyramid of energy answered.
2. the price based on Stackelberg games in the extensive mimo system of energy acquisition according to claim 1 Method, it is characterised in that in step 2), if antenna for base station number M and number of probes K cause extensive MIMO zero forcing algorithms Condition is set up, i.e. M >=K+1, then the optimal energy acquisition time of unit interval inner sensor is obtained by convex optimization interior point method τoptimal, and suboptimum energy acquisition time τsuboptimalFor：Wherein parameter PmaxIt is the maximum transmitted work(of pyramid of energy Rate；If condition is invalid, method terminates.
3. the price based on Stackelberg games in the extensive mimo system of energy acquisition according to claim 2 Method, it is characterised in that the optimal or hypo-optimal pricing of the step 3) pyramid of energyFor：Ginseng NumberEnergy acquisition time τ=τoptimalOr τsuboptimal, by parameter alpha, rkWith τoptimalSubstitute into respectivelyCalculate the optimal pricing of pyramid of energy；Similarly, by α, rkAnd τsuboptimalSubstitute into respectivelyCalculate energy The hypo-optimal pricing of tower.
4. the price based on Stackelberg games in the extensive mimo system of energy acquisition according to claim 3 Method, it is characterised in that the optimal income U of the step 4) pyramid of energyPBFor： The suboptimum income of pyramid of energyFor：The reason of pyramid of energy income By limiting valueFor：Wherein parameter τoptimal、τsuboptimalThe respectively optimal and suboptimum energy acquisition time of unit interval inner sensor.
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CN108337024A (en) * 2018-02-06 2018-07-27 重庆邮电大学 A kind of extensive mimo system efficiency optimization method based on energy acquisition
CN113382428A (en) * 2021-06-09 2021-09-10 中通服咨询设计研究院有限公司 Wireless energy supply Internet of things full-duplex base station association and cache joint allocation method
CN116050661A (en) * 2023-03-06 2023-05-02 湖南大学 5G base station and power grid collaborative scheduling method based on master-slave game

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Publication number Priority date Publication date Assignee Title
CN108337024A (en) * 2018-02-06 2018-07-27 重庆邮电大学 A kind of extensive mimo system efficiency optimization method based on energy acquisition
CN108337024B (en) * 2018-02-06 2021-02-09 重庆邮电大学 Large-scale MIMO system energy efficiency optimization method based on energy collection
CN113382428A (en) * 2021-06-09 2021-09-10 中通服咨询设计研究院有限公司 Wireless energy supply Internet of things full-duplex base station association and cache joint allocation method
CN113382428B (en) * 2021-06-09 2024-01-26 中通服咨询设计研究院有限公司 Wireless energy supply Internet of things full duplex base station association and cache joint allocation method
CN116050661A (en) * 2023-03-06 2023-05-02 湖南大学 5G base station and power grid collaborative scheduling method based on master-slave game
CN116050661B (en) * 2023-03-06 2023-06-20 湖南大学 5G base station and power grid collaborative scheduling method based on master-slave game

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