CN108810885A - Uplink dual-connection data distribution method based on joint confidentiality degree and power consumption optimization - Google Patents

Uplink dual-connection data distribution method based on joint confidentiality degree and power consumption optimization Download PDF

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Publication number
CN108810885A
CN108810885A CN201810364696.4A CN201810364696A CN108810885A CN 108810885 A CN108810885 A CN 108810885A CN 201810364696 A CN201810364696 A CN 201810364696A CN 108810885 A CN108810885 A CN 108810885A
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optimal solution
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optimization
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吴远
吴伟聪
倪克杰
毛浩伟
石佳俊
钱丽萍
黄亮
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Zhejiang University of Technology ZJUT
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Zhejiang University of Technology ZJUT
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/02—Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/10—Flow control between communication endpoints
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. Transmission Power Control [TPC] or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0209—Power saving arrangements in terminal devices
    • 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

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Computer Security & Cryptography (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

An uplink dual-connection data distribution method based on joint confidentiality degree and power consumption optimization is characterized in that an optimization problem of minimizing total power consumption of an MU under the condition of meeting data confidentiality requirements and energy effectiveness is described as a multivariable non-convex optimization problem; converting the problem P1 into a bottom Sub-problem P1-Sub and a Top problem P1-Top for optimization solution; converting the bottom Sub-problem P1-Sub into a P2-E problem through multiple equivalent; obtaining an optimized solution of the problem under the condition that the control variable range is given in the P2-E problem; enumerating the control variable range of the P2-E problem under different conditions, and substituting the control variable range into the optimized solution of the P2-E problem obtained when the control variable range is given; obtaining an optimal solution of the bottom Sub-problem P1-Sub; after the optimal solution of the bottom Sub-problem P1-Sub is obtained, the Top problem P1-Top is solved by using a linear search method to obtain the optimal solution of the Top problem, and finally the optimal solution of the whole optimization problem is obtained. The invention has higher efficiency and higher flexibility.

Description

A kind of uplink dual link data point based on joint privacy degrees and power consumption optimization Stream method
Technical field
The present invention relates to wireless network, especially a kind of uplink doubly-linked based on joint privacy degrees and power consumption optimization Connect data distribution method.
Background technology
In past ten years, the pouplarity of the explosive growth of intelligent mobile terminal, mobile network service constantly carried Height produces the huge traffic in Cellular Networks.On the multilayered structure of radio access network, a large amount of small base station of isomery is intensive It is covered in the unit of macro base station, mobile communication amount is diverted to small base station by macro base station, and this mode is exactly data distribution.Data As method that is a kind of effective and having an economic benefit, the congestion for alleviating the traffic in macro base station Cellular Networks rises for shunting Prodigious effect.But this single mode efficiency and flexibility ratio have larger shortcoming.In order to can preferably streamed data Resource is managed for greater flexibility, and third generation cooperative partner program proposes " dual link " technology, can make user (Mobile Users, MUs) it is exchanged by using two different radio interface and macro base station (macro Base Station, BS), and And the data of shunting are transmitted to small base station (small-cell Access Point, AP) simultaneously.
Invention content
In order to overcome the lower deficiency of the less efficient of the prior art, flexibility ratio, it is higher, clever that the present invention provides a kind of efficiency Uplink dual link data distribution method based on joint privacy degrees and power consumption optimization in the higher wireless network of activity.
The technical solution adopted by the present invention to solve the technical problems is：
A kind of uplink dual link data distribution method based on joint privacy degrees and power consumption optimization, the method packet Include following steps：
(1) there are one mobile subscriber MU under the coverage area of base station BS, while deploying a cellulor auxiliary network Access point AP provides data distribution service by " dual link " for MU；
In the wireless network, the total work of MU is minimized in the case where meeting data security requirement and energy efficiency The optimization problem of rate consumption describes the nonconvex property optimization problem P1 problems being as follows, and the problem representation is as follows：
min piA+piB
Restrictive condition:
xiA≥0
xiB≥0
Control variable:(xiA,piA) and (xiB,piB)
In P1 problems, xiBIndicate the attainable maximum data demand volume of BS side MU institutes, piBIndicate BS side MU consumption Energy；xiAIndicate the attainable maximum data demand volume of AP side MU institutes, piAIndicate the energy of AP side MU consumption；PoutBe about piAAnd xiAFunction, be expressed as Pout(piA,xiA), formula (1-5) is obtained by Shannon's theorems；
The meaning of each variable in problem is described as follows：
piA：Energy/W of the sides AP MU consumption；
piB：Energy/W of the sides BS MU consumption；
xiB：The attainable maximum data demand volume of the sides BS MU institutes；
xiA：The attainable maximum data demand volume of the sides AP MU institutes；
WB：Channel width/HZ of MU to BS；
WA：Channel width/HZ of MU to AP；
giA：The channel gain of MU to AP；
giB：The channel gain of MU to BS；
giE：Channel gains of the MU to listener-in；
nA：Background Noise Power/W of MU to AP；
nB：Background Noise Power/W of MU to BS；
nE：Background Noise Power/Ws of the MU to listener-in；
The maximum private data handling capacity that MU to AP can be obtained；
Pout：The probability that confidentiality of the AP when providing data distribution service to MU is overflowedMU to AP is most Big consumption energy/W；
Maximum consumption energy/W of MU to BS；
The upper bound of the confidentiality overflow probability of MU；
∈i:The confidentiality overflow probability of MU；
αi：Average values of the MU to listener-in's channel gain；
(2) it is that a bottom subproblem P1-Sub and a top layer are asked by P1 PROBLEM DECOMPOSITIONs by the analysis to P1 problems Topic P1-Top optimizes solution, and bottom subproblem P1-Sub therein is as follows：
V(∈i)=minpiA+piB
Restrictive condition:Pout(piA,xiA)=∈i (2-1)
xiA≥0
xiB≥0
Control variable:(xiA,piA) and (xiB,piB)
Top layer problem P1-Top is as follows:
min V(εi)
Restrictive condition：
Control variable：εi
During the Optimization Solution of P1 problems, Optimization Solution gradually first is carried out to bottom subproblem P1-Sub；
(3) the probability function P that confidentiality is overflowedout(piA,xiA) expression formula is as follows：
In above formulaIndicate that the maximum private data handling capacity that MU to AP can be obtained, expression formula are as follows：
Formula (3-2) substitution (3-1) is obtained into Pout(piA,xiA) expression formula is as follows：
Define an auxiliary quantityIndicate that the efficient channel power gain of MU to AP, expression formula are as follows：
Convolution (3-4) obtains Pout(piA,xiA) expression formula is as follows：
(4) by carrying out simultaneous analysis to (1-1) and (3-5), the limiting expression formula for obtaining (1-1) is as follows：
Define a new variable θiATo quantify the influence of confidentiality demand, θiAExpression formula it is as follows：
By the further conversion to (4-1), the equivalent expression for obtaining (1-1) is as follows：
And in the optimization scheme of P1 problems, above formula is a hard constraints of problem, and in case study, MU's Streamed data flow rate meets following expression：
Following expression is obtained by the analysis of (2-2) and (4-4)：
Therefore, by simultaneous (2-5) and (4-5), it is as follows expression formula can be obtained：
(5) equivalent conversion of P1-Sub problems, substitute into (4-5), (4-6) or more is respectively related to P1-Sub problems, obtains It is as follows to P2 problem representations：
Restrictive condition:
Control variable:piA
Equivalent conversion is carried out to (5-1), it is as follows to obtain expression formula：
Equivalent conversion is also equally carried out to (5-2), it is as follows to obtain expression formula：
Convert P2 problems progress equivalence to P2-E problems by (5-3) and (5-4), what " E " was indicated be it is of equal value, such as Under：
Restrictive condition:Condition (1-3)
Condition (5-3)
Condition (5-4)
Control variable:piA
Restrictive condition (5-3) and (5-4) in P2-E problems all with piAIt is linear, so in parameter setting, three A restrictive condition (5-3), (5-4), (1-3) produce one about piAFeasible section, i.e.,
(6) P2-E problems regard a convexity optimization problem as, carry out first derivation to the object function in P2-E, obtain Its first derivative expression formula is as follows：
Known by analysisIt is about piAIncreasing function；
(7) givenWithIn the case of, according to the monotonicity of the first derivative of object function in P2-E problems The algorithm SolP2E for solving the problem is as follows：
Step 7.1：It is arranged and calculates the tolerance value of error as γ, flag=1；
Step 7.2：IfIt sets up, thenExecute step 7.6；If It sets up, thenStep 7.6 is executed, it is no to then follow the steps 7.3；
Step 7.3 is arranged
Step 7.4：As flag=1, obtainIfIt sets up, thenFlag=0 is set simultaneously, executes step 7.6；
Step 7.5：IfIt sets up, works as satisfactionWhen, update Return to step 7.4；Work as satisfactionWhen, updateReturn to step 7.4；
Step 7.6：End loop；
Step 7.7：Output P2-E problems current optimal solution be
(8) it is in given p in algorithm SolP2EiAThe upper boundWith lower boundIn the case of calculate, so It will be to piAThe upper boundWith lower boundSolved, need to consider it is a variety of in the case ofWithDefine two New parameter K and L, expression formula are as follows：
By parameter K and L, (5-3) and (5-4) is converted into following expression：
It is obtained based on above (8-3) and (8-4) two formulaWithIt needs to consider the K and L under different situations, it is first Analysis (8-3) is first passed through, two different situations, i.e. Case I are obtained：KL≥1；CaseⅡ：KL<1；
Case I is the situation under KL >=1, in this case, is met It indicates that BS can meet whole flow demands of MU, does not need AP and carry out data distribution；On the contrary, Case II is in KL<Situation under 1 Under, BS cannot meet the data traffic demand of MU wholes, and therefore, P2-E problems may be infeasible in Case II；
If KL >=1, as Case I obtains two sub-cases by analysis, as follows：
CaseⅠ.1：WhenWhen, it obtains
CaseⅠ.2：WhenWhen, it obtains
If KL<1, as Case II obtain five sub-cases by analysis, as follows：
CaseⅡ.1：WhenP2-E problems are infeasible；
CaseⅡ.2a：WhenAndWhen, it obtains
CaseⅡ.2b：WhenAndWhen, P2-E problems are infeasible；
CaseⅡ.3a：WhenAndWhen, it obtains It arrives
CaseⅡ.3b：WhenAndWhen, P2- E problems are infeasible；
Finally obtain following situation：
CaseⅠ.1
Case
Ⅰ.2
CaseⅡ.1P2-E problems are infeasible；
CaseⅡ.2a(And):
CaseⅡ.2b(And):P2-E problems are infeasible；Case Ⅱ.3a(And):
CaseⅡ.3b(And):
P2-E problems are infeasible；
It is obtained by above procedureWithIt substitutes into algorithm SolP2E and obtains optimal solutionBy obtaining most Excellent solutionObtain other corresponding three optimal solutions of P2-E problems It is as follows：
It is the optimal solution of P2-E problems above, as in P1-Sub problems, MU consumes the optimal solution of energy in the sides AP MU shunts demand optimal solution in AP side datasMU consumes the optimal solution of energy in the sides BSMU is in BS side data demands Optimal solution
(9) Optimization Solution of top layer problem P1-Top, by the analysis to bottom subproblem, top layer problem P1-Top is indicated As follows：
Restrictive condition：
Control variable：∈i
According to ∈iLinear search method in feasible region is as follows come the algorithm SolP1Top for solving P1-Top：
Step 9.1：It is empty set, current optimum energy consumption value CBV=∞, while ∈ is arranged that current optimal solution CBS, which is arranged,i Initial value be Δ, step-length also be Δ；
Step 9.2：If ∈iMeetThen follow the steps 9.3；It is no to then follow the steps 9.6；
Step 9.3：By ∈iIt brings into the object function of top layer problem P1-Top, judges obtained V (∈i) whether be less than Current optimum energy consumption value CBV；
Step 9.4：If V (∈i) >=CBV is set up, then updating ∈i=∈i+ Δ, return to step 9.2；
Step 9.5：If V (∈i)<CBV is set up, then updating current optimal solutionCurrent optimal energy disappears Consumption value is V*(∈i), while updating ∈i=∈i+ Δ, return to step 9.2；
Step 9.6：End loop；
Step 9.7：If current optimum energy consumption value CBV is ∞, P1 problems are infeasible, and otherwise output is current most Excellent solutionCurrent optimum energy consumption value is V*(∈i)；
(10) it by the hierarchical solving to P1 problems, obtains MU and consumes the optimal solution of energy in the sides APMU is in the sides AP number According to shunting demand optimal solutionMU consumes the optimal solution of energy in the sides BSOptimal solutions of the MU in BS side data demands The confidentiality degree optimal solution of MUThe optimum energy consumption value of MU is V*(∈i)。
The present invention is the optimization design of the wireless network data shunting based on dual link.In view of in the wireless network, being The AP that MU provides data distribution service is operated in unauthorized frequency range, this results in a listener-in that can be stolen in unauthorized frequency range Listen the data traffic for being diverted to AP.So the present invention study be under dual link combine privacy degrees and power consumption optimization, The conceptual design that the data distribution service of AP is optimized.The present invention is directed to above-mentioned proposed imagination, has studied and is based on The uplink dual link data distribution conceptual design of joint privacy degrees and power consumption.
The present invention technical concept be：First, it is contemplated that AP and BS passes through the data for MU in heterogeneous wireless network Shunting, which is realized, minimizes power to obtain certain economic benefit.In the present invention, by the way that problem specificity analysis, problem is turned It is changed to a bottom subproblem and a top layer problem.By equivalency transform, bottom subproblem is converted to be easily solved it is convex Property optimization problem.Later, by analyzing obtained equivalent problems, variable is first controlled in hypothesis problem and has been given, Further according to the monotonicity and binary chop of the first derivative of object function, to obtain the feelings of current given control range of variables Bottom subproblem optimum solution under condition.Later, by the analysis to problem, the control variable model under different situations is enumerated It encloses, then different control ranges of variables is substituted into obtained optimum solution.Final solve obtains the optimal of bottom subproblem Solution.Finally, top layer problem is solved by linear search method, obtains the optimum solution of top layer problem.It is final to propose one kind The optimization solution of uplink dual link data distribution method based on joint privacy degrees and power consumption optimization.
Beneficial effects of the present invention are mainly manifested in：1, for total system, doubly-linked connection technology substantially increases pair In the utilization rate of radio resource；2, it for MU, in joint privacy degrees and power consumption optimization, minimizes in dual link Under total-power loss, both obtained more good upstream data flow service, and ensure that the confidentiality demand of MU, and improved The utilization rate of energy.
Description of the drawings
Fig. 1 is the field of a user MU, a macro base station BS, base station AP one small and a listener-in in wireless network Scape schematic diagram.
Specific implementation mode
The present invention is described in further detail below in conjunction with the accompanying drawings.
Referring to Fig.1, a kind of uplink dual link data distribution method based on joint privacy degrees and power consumption optimization, it is real Existing this method can keep the total energy consumption of MU minimum, improve the nothing of whole system under the premise of meeting MU confidentiality demands appropriate Line resource utilization and energy utilization rate, present invention could apply to wireless network, in scene as shown in Figure 1.For the mesh Mark design mainly includes the following steps the optimization method of problem：
(1) there are one mobile subscriber MU under the coverage area of base station BS, while deploying a cellulor auxiliary network Access point AP provides data distribution service by " dual link " for MU；
In the wireless network, the total work of MU is minimized in the case where meeting data security requirement and energy efficiency The optimization problem of rate consumption describes the nonconvex property optimization problem P1 problems being as follows, and the problem representation is as follows：
min piA+piB
Restrictive condition:
xiA≥0
xiB≥0
Control variable:(xiA,piA) and (xiB,piB)
In P1 problems, xiBIndicate the attainable maximum data demand volume of BS side MU institutes, piBIndicate BS side MU consumption Energy；xiAIndicate the attainable maximum data demand volume of AP side MU institutes, piAIndicate the energy of AP side MU consumption；PoutBe about piAAnd xiAFunction, be expressed as Pout(piA,xiA), formula (1-5) is obtained by Shannon's theorems；
The meaning of each variable in problem is described as follows：
piA：Energy/W of the sides AP MU consumption；
piB：Energy/W of the sides BS MU consumption；
xiB：The attainable maximum data demand volume of the sides BS MU institutes；
xiA：The attainable maximum data demand volume of the sides AP MU institutes；
WB：Channel width/HZ of MU to BS；
WA：Channel width/HZ of MU to AP；
giA：The channel gain of MU to AP；
giB：The channel gain of MU to BS；
giE：Channel gains of the MU to listener-in；
nA：Background Noise Power/W of MU to AP；
nB：Background Noise Power/W of MU to BS；
nE：Background Noise Power/Ws of the MU to listener-in；
The maximum private data handling capacity that MU to AP can be obtained；
Pout：The probability that confidentiality of the AP when providing data distribution service to MU is overflowedMU to AP is most Big consumption energy/W；
Maximum consumption energy/W of MU to BS；
The upper bound of the confidentiality overflow probability of MU；
∈i:The confidentiality overflow probability of MU；
αi：Average values of the MU to listener-in's channel gain；
(2) it is that a bottom subproblem P1-Sub and a top layer are asked by P1 PROBLEM DECOMPOSITIONs by the analysis to P1 problems Topic P1-Top optimizes solution, and bottom subproblem P1-Sub therein is as follows：
V(∈i)=minpiA+piB
Restrictive condition:Pout(piA,xiA)=εi (2-1)
xiA≥0
xiB≥0
Control variable:(xiA,piA) and (xiB,piB)
Top layer problem P1-Top is as follows:
min V(∈i)
Restrictive condition：
Control variable：∈i
During the Optimization Solution of P1 problems, Optimization Solution gradually first is carried out to bottom subproblem P1-Sub；
(3) the probability function P that confidentiality is overflowedout(piA,xiA) expression formula is as follows：
In above formulaIndicate that the maximum private data handling capacity that MU to AP can be obtained, expression formula are as follows：
Formula (3-2) substitution (3-1) is obtained into Pout(piA,xiA) expression formula is as follows：
Define an auxiliary quantityIndicate that the efficient channel power gain of MU to AP, expression formula are as follows：
Convolution (3-4) obtains Pout(piA,xiA) expression formula is as follows：
(4) by carrying out simultaneous analysis to (1-1) and (3-5), the limiting expression formula for obtaining (1-1) is as follows：
Define a new variable θiATo quantify the influence of confidentiality demand, θiAExpression formula it is as follows：
By the further conversion to (4-1), the equivalent expression for obtaining (1-1) is as follows：
And in the optimization scheme of P1 problems, above formula is a hard constraints of problem, and in case study, MU's Streamed data flow rate meets following expression：
Following expression is obtained by the analysis of (2-2) and (4-4)：
Therefore, by simultaneous (2-5) and (4-5), it is as follows to obtain expression formula：
(5) equivalent conversion of P1-Sub problems, substitute into (4-5), (4-6) or more is respectively related to P1-Sub problems, obtains It is as follows to P2 problem representations：
Restrictive condition:
Control variable:piA
Equivalent conversion is carried out to (5-1), it is as follows to obtain expression formula：
Equivalent conversion is also equally carried out to (5-2), it is as follows to obtain expression formula：
Convert P2 problems progress equivalence to P2-E problems by (5-3) and (5-4), what " E " was indicated be it is of equal value, such as Under：
Restrictive condition:Condition (1-3)
Condition (5-3)
Condition (5-4)
Control variable:piA
Restrictive condition (5-3) and (5-4) in P2-E problems all with piAIt is linear, so in parameter setting, three A restrictive condition (5-3), (5-4), (1-3) produce one about piAFeasible section, i.e.,
(6) P2-E problems regard a convexity optimization problem as, carry out first derivation to the object function in P2-E, obtain Its first derivative expression formula is as follows：
Known by analysisIt is about piAIncreasing function；
(7) givenWithIn the case of, according to the monotonicity of the first derivative of object function in P2-E problems The algorithm SolP2E for solving the problem is as follows；
Step 7.1：It is arranged and calculates the tolerance value of error as γ, flag=1；
Step 7.2：IfIt sets up, thenExecute step 7.6；If It sets up, thenStep 7.6 is executed, it is no to then follow the steps 7.3；
Step 7.3 is arranged
Step 7.4：As flag=1, obtainIfIt sets up, thenFlag=0 is set simultaneously, executes step 7.6；
Step 7.5：IfIt sets up, works as satisfactionWhen, update Return to step 7.4；Work as satisfactionWhen, updateReturn to step 7.4；
Step 7.6：End loop；
Step 7.7：Output P2-E problems current optimal solution be
(8) it is in given p in algorithm SolP2EiAThe upper boundWith lower boundIn the case of calculate, so It will be to piAThe upper boundWith lower boundSolved, need to consider it is a variety of in the case ofWithDefine two New parameter K and L, expression formula are as follows：
By parameter K and L, (5-3) and (5-4) is converted into following expression：
It is obtained based on above (8-3) and (8-4) two formulaWithIt needs to consider the K and L under different situations, it is first Analysis (8-3) is first passed through, two different situations, i.e. Case I are obtained：KL≥1；CaseⅡ：KL<1；
Case I is that the situation under KL >=1 meets W in this caseBlog2(1+piBmaxgiBnB >=Rireq, table Show that BS can meet whole flow demands of MU, does not need AP and carry out data distribution；On the contrary, Case II is in KL<In the case of 1 time, BS cannot meet the data traffic demand of MU wholes, and therefore, P2-E problems may be infeasible in Case II；
If KL >=1, as Case I obtains two sub-cases by analysis, as follows：
CaseⅠ.1：WhenWhen, it obtains
CaseⅠ.2：WhenWhen, it obtains
If KL<1, as Case II obtain five sub-cases by analysis, as follows：
CaseⅡ.1：WhenP2-E problems are infeasible；
CaseⅡ.2a：WhenAndWhen, it obtains
CaseⅡ.2b：WhenAndWhen, P2-E problems are infeasible；
CaseⅡ.3a：WhenAndWhen, it obtains It arrives
CaseⅡ.3b：WhenAndWhen, P2-E problems are infeasible；
Finally obtain following situation：
CaseⅠ.1
Case
Ⅰ.2
CaseⅡ.1P2-E problems are infeasible；
CaseⅡ.2a(And):
CaseⅡ.2b(And):P2-E problems are infeasible；Case Ⅱ.3a(And):
CaseⅡ.3b(And):
P2-E problems are infeasible；
It is obtained by above procedureWithIt substitutes into algorithm SolP2E and obtains optimal solutionBy obtaining most Excellent solutionObtain other corresponding three optimal solutions of P2-E problems It is as follows：
It is the optimal solution of P2-E problems above, as in P1-Sub problems, MU consumes the optimal solution of energy in the sides AP MU shunts demand optimal solution in AP side datasMU consumes the optimal solution of energy in the sides BSMU is in BS side data demands Optimal solution
(9) Optimization Solution of top layer problem P1-Top, by the analysis to bottom subproblem, top layer problem P1-Top is indicated As follows：
Restrictive condition：
Control variable：∈i
According to ∈iLinear search method in feasible region is as follows come the algorithm SolP1Top for solving P1-Top：
Step 9.1：It is empty set, current optimum energy consumption value CBV=∞, while ∈ is arranged that current optimal solution CBS, which is arranged,i Initial value be Δ, step-length also be Δ；
Step 9.2：If ∈iMeetThen follow the steps 9.3；It is no to then follow the steps 9.6；
Step 9.3：By ∈iIt brings into the object function of top layer problem P1-Top, judges obtained V (∈i) whether be less than Current optimum energy consumption value CBV；
Step 9.4：If V (∈i) >=CBV is set up, then update ∈ i=∈i+ Δ, return to step 9.2；
Step 9.5：If V (∈i)<CBV is set up, then updating current optimal solutionCurrent optimal energy disappears Consumption value is V*(∈i), while updating ∈i=∈i+ Δ, return to step 9.2；
Step 9.6：End loop；
Step 9.7：If current optimum energy consumption value CBV is ∞, P1 problems are infeasible, and otherwise output is current most Excellent solutionCurrent optimum energy consumption value is V*(∈i)；
(10) it by the hierarchical solving to P1 problems, obtains MU and consumes the optimal solution of energy in the sides APMU is in the sides AP number According to shunting demand optimal solutionMU consumes the optimal solution of energy in the sides BSOptimal solutions of the MU in BS side data demands The confidentiality degree optimal solution of MUThe optimum energy consumption value of MU is V*(∈i)。
In the present embodiment, Fig. 1 be in the wireless network that considers of the present invention comprising a macro base station BS, a user MU, The system of base station AP one small and a listener-in.Within the system, what is mainly considered does not include interference, but be can take into account 1. channel circumstance, user MU between user MU and small base station AP and the channel circumstance between macro base station BS and user MU with steal Channel circumstance between hearer；2. the confidentiality demand of user MU；3. the total energy consumptions of user MU in varied situations.In order to enable MU obtains a target for meeting confidentiality demand while energy consumption minimum, proposes that the solution for the problem is realized in invention.
The implementation case is conceived to meet user MU confidentiality demands appropriate under the premise of, minimize under dual link The total energy consumption of MU combines privacy degrees and power consumption optimization, improves wireless resource utility efficiency and capacity usage ratio.The present invention Implementation during, have benefited from the reduction of the effective conversion and optimization algorithm of problem for computation complexity.

Claims (1)

1. a kind of uplink dual link data distribution method based on joint privacy degrees and power consumption optimization, which is characterized in that It the described method comprises the following steps：
(1) there are one mobile subscriber MU under the coverage area of base station BS, while deploying a cellulor auxiliary network insertion Point AP provides data distribution service by " dual link " for MU；
In the wireless network, the general power that MU is minimized in the case where meeting data security requirement and energy efficiency disappears The optimization problem of consumption describes the nonconvex property optimization problem P1 problems being as follows, and the problem representation is as follows：
min piA+piB
Restrictive condition：
xiA≥0
xiB≥0
Control variable：(xiA, piA) and (xiB, piB)
In P1 problems, xiBIndicate the attainable maximum data demand volume of BS side MU institutes, piBIndicate the energy of BS side MU consumption； xiAIndicate the attainable maximum data demand volume of AP side MU institutes, piAIndicate the energy of AP side MU consumption；PoutIt is about piAWith xiAFunction, be expressed as Pout(piA, xiA), formula (1-5) is obtained by Shannon's theorems；
The meaning of each variable in problem is described as follows：
piA：Energy/W of the sides AP MU consumption；
piB：Energy/W of the sides BS MU consumption；
xiB：The attainable maximum data demand volume of the sides BS MU institutes；
xiA：The attainable maximum data demand volume of the sides AP MU institutes；
WB：Channel width/HZ of MU to BS；
WA：Channel width/HZ of MU to AP；
giA：The channel gain of MU to AP；
giB：The channel gain of MU to BS；
giE：Channel gains of the MU to listener-in；
nA：Background Noise Power/W of MU to AP；
nB：Background Noise Power/W of MU to BS；
nE：Background Noise Power/Ws of the MU to listener-in；
The maximum private data handling capacity that MU to AP can be obtained；
Pout：The probability that confidentiality of the AP when providing data distribution service to MU is overflowed
Maximum consumption energy/W of MU to AP；
Maximum consumption energy/W of MU to BS；
The upper bound of the confidentiality overflow probability of MU；
∈i：The confidentiality overflow probability of MU；
αi：Average values of the MU to listener-in's channel gain；
(2) it is an a bottom subproblem P1-Sub and top layer problem P1- by P1 PROBLEM DECOMPOSITIONs by the analysis to P1 problems Top optimizes solution, and bottom subproblem P1-Sub therein is as follows：
V(∈i)=min PiA+PiB
Restrictive condition：Pout(piA, xiA)=∈i (2-1)
xiA≥0
xiB≥0
Control variable：(xiA, PiA) and (xiB, PiB)
Top layer problem P1-Top is as follows：
min V(∈i)
Restrictive condition：
Control variable：∈i
During the Optimization Solution of P1 problems, Optimization Solution gradually first is carried out to bottom subproblem P1-Sub；
(3) the probability function P that confidentiality is overflowedout(piA, xiA) expression formula is as follows：
In above formulaIndicate that the maximum private data handling capacity that MU to AP can be obtained, expression formula are as follows：
Formula (3-2) substitution (3-1) is obtained into Pout(piA, xiA) expression formula is as follows：
Define an auxiliary quantityIndicate that the efficient channel power gain of MU to AP, expression formula are as follows：
Convolution (3-4) obtains Pout(piA, xiA) expression formula is as follows：
(4) by carrying out simultaneous analysis to (1-1) and (3-5), the limiting expression formula for obtaining (1-1) is as follows：
Define a new variable θiATo quantify the influence of confidentiality demand, θiAExpression formula it is as follows：
By the further conversion to (4-1), the equivalent expression for obtaining (1-1) is as follows：
And in the optimization scheme of P1 problems, above formula is a hard constraints of problem, and in case study, the shunting of MU Data traffic rate meets following expression：
Following expression is obtained by the analysis of (2-2) and (4-4)：
Therefore, by simultaneous (2-5) and (4-5), it is as follows to obtain expression formula：
(5) equivalent conversion of P1-Sub problems, substitute into (4-5), (4-6) or more is respectively related to P1-Sub problems, obtains P2 Problem representation is as follows：
Restrictive condition：
Control variable：PiA
Equivalent conversion is carried out to (5-1), it is as follows to obtain expression formula：
Equivalent conversion is also equally carried out to (5-2), it is as follows to obtain expression formula：
Convert P2 problems progress equivalence to P2-E problems by (5-3) and (5-4), what " E " was indicated be it is of equal value, it is as follows：
Restrictive condition：Condition (1-3)
Condition (5-3)
Condition (5-4)
Control variable：PiA
Restrictive condition (5-3) and (5-4) in P2-E problems all with PiAIt is linear, so in parameter setting, three limits Condition (5-3) processed, (5-4), (1-3) produce one about PiAFeasible section, i.e.,
(6) P2-E problems regard a convexity optimization problem as, carry out first derivation to the object function in P2-E, obtain one Order derivative expression formula is as follows：
Known by analysisIt is about PiAIncreasing function；
(7) givenWithIn the case of, it is solved according to the monotonicity of the first derivative of object function in P2-E problems The algorithm SolP2E of the problem is as follows；
Step 7.1：It is arranged and calculates the tolerance value of error as γ, flag=1；
Step 7.2：Such as bodyIt sets up, thenExecute step 7.6；If It sets up, thenStep 7.6 is executed, it is no to then follow the steps 7.3；
Step 7.3 is arranged
Step 7.4：As flag=1, obtainIfIt sets up, then Flag=0 is set simultaneously, executes step 7.6；
Step 7.5：IfIt sets up, works as satisfactionWhen, updateIt returns Step 7.4；Work as satisfactionWhen, updateReturn to step 7.4；
Step 7.6：End loop；
Step 7.7：Output P2-E problems current optimal solution be
(8) it is in given p in algorithm SolP2EiAThe upper boundWith lower boundIn the case of calculate, so right piAThe upper boundWith lower boundSolved, need to consider it is a variety of in the case ofWithDefine two it is new Parameter K and L, expression formula are as follows：
By parameter K and L, (5-3) and (5-4) is converted into following expression：
It is obtained based on above (8-3) and (8-4) two formulaWithIt needs to consider the K and L under different situations, it is logical first Analysis (8-3) is crossed, two different situations, i.e. Case I are obtained：KL≥1；
Case II：KL < 1；
Case I are that the situation under KL >=1 meets W in this caseB log2(1+piBmaxgiBnB >=Rireq is indicated BS can meet whole flow demands of MU, do not need AP and carry out data distribution；On the contrary, Case II are in the case of KL < 1 time, BS cannot meet the data traffic demand of MU wholes, and therefore, P2-E problems may be infeasible in Case II；
If KL >=1, as Case I obtain two sub-cases by analysis, as follows：
Case I.1：WhenWhen, it obtains
Case I.2：WhenWhen, it obtains
If KL < 1, as Case II obtain five sub-cases by analysis, as follows：
Case II.1：WhenP2-E problems are infeasible；
Case II.2a：WhenAndWhen, it obtains
Case II.2b：WhenAndWhen, P2-E problems are infeasible；
Case II.3a：WhenAndWhen, it obtains
Case II.3b：WhenAndWhen,
P2-E problems are infeasible；
Finally obtain following situation：
Case I.
Case
I.
Case II.P2-E problems are infeasible；
Case II.
Case II.P2-E problems are infeasible；
Case II.
Case II.
P2-E problems are infeasible；
It is obtained by above procedureWithIt substitutes into algorithm SolP2E and obtains optimal solutionPass through obtained optimal solutionObtain other corresponding three optimal solutions of P2-E problems It is as follows：
It is the optimal solution of P2-E problems above, as in P1-Sub problems, MU consumes the optimal solution of energy in the sides APMU exists AP side datas shunt demand optimal solutionMU consumes the optimal solution of energy in the sides BSMU is optimal BS side data demands Solution
(9) Optimization Solution of top layer problem P1-Top, by the analysis to bottom subproblem, top layer problem P1-Top indicates as follows It is shown：
Restrictive condition：
Control variable：∈i
According to ∈iLinear search method in feasible region is as follows come the algorithm SolP1Top for solving P1-Top：
Step 9.1：It is empty set, current optimum energy consumption value CBV=∞, while ∈ is arranged that current optimal solution CBS, which is arranged,iJust Value is Δ, and step-length is also Δ；
Step 9.2：If ∈iMeetThen follow the steps 9.3；It is no to then follow the steps 9.6；
Step 9.3：By ∈iIt brings into the object function of top layer problem P1-Top, judges obtained V (∈i) whether be less than currently Optimum energy consumption value CBV；
Step 9.4：If V (∈i) >=CBV is set up, then updating ∈i=∈i+ Δ, return to step 9.2；
Step 9.5：If V (∈i) < CBV establishments, then updating current optimal solutionCurrent optimum energy consumption value For V*(∈i), while updating ∈i=∈i+ Δ, return to step 9.2；
Step 9.6：End loop；
Step 9.7：If current optimum energy consumption value CBV is ∞, P1 problems are infeasible, otherwise export current optimal solutionCurrent optimum energy consumption value is V*(∈i)；
(10) it by the hierarchical solving to P1 problems, obtains MU and consumes the optimal solution of energy in the sides APMU is in AP side datas point Stream demand optimal solutionMU consumes the optimal solution of energy in the sides BSOptimal solutions of the MU in BS side data demandsMU's Confidentiality degree optimal solutionThe optimum energy consumption value of MU is V*(∈i)。
CN201810364696.4A 2018-04-23 2018-04-23 Uplink dual-connection data distribution method based on joint confidentiality degree and power consumption optimization Pending CN108810885A (en)

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Application publication date: 20181113