CN110519833A - A kind of system energy consumption minimum method of the narrowband the NOMA Internet of Things based on MEC - Google Patents
A kind of system energy consumption minimum method of the narrowband the NOMA Internet of Things based on MEC Download PDFInfo
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- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
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- H04W52/18—TPC being performed according to specific parameters
- H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
- H04W52/241—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account channel quality metrics, e.g. SIR, SNR, CIR, Eb/lo
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Abstract
A kind of system energy consumption minimum method of the non-orthogonal multiple access narrowband Internet of Things based on mobile edge calculations, realize that system energy consumption minimizes by combined optimization transmission power, computational resource allocation and decoding sequence in the narrowband internet of things for having both MEC and NOMA technology, which is described as a Multi-variables optimum design；P1 is decomposed into two sub- optimization problems: optimized allocation of resources and optimization decoding sequence, wherein, the problem of for the optimization of resource allocation, P1 problem being converted into the convex problem of P2 according to transformation of variables in pairs first, then having optimized by gradient descent algorithm transmission power and computational resource allocation；Secondly for the optimization of equipment decoding sequence, using tabu search algorithm come optimized variable.The present invention, which provides, a kind of realizes the energy consumption minimized method of whole system by designing a kind of joint transmission power, computational resource allocation and the optimization algorithm of equipment decoding sequence in the narrowband Internet of Things for having both MEC and NOMA technology.
Description
Technical field
It is especially a kind of based on mobile edge calculations (Mobile Edge the invention belongs to wireless communication field
Computing, MEC) non-orthogonal multiple access (Non-orthogonal Multiple Access, NOMA) narrowband Internet of Things
The optimization method of system energy consumption is minimized by optimization computational resource allocation, user's transimission power and decoding sequence.
Background technique
With the development of narrowband technology of Internet of things, the data volume in narrowband Internet of Things is just in rapid growth.In narrowband Internet of Things
In net, non-orthogonal multiple (NOMA) and mobile edge calculations (MEC) have become two kinds of most popular technologies.Currently, communication common carrier
Substantial contribution is put into base station machine room construction, but investment in terms of energy-saving and emission-reduction and control do not give full play to it also really
Effect and advantage, existing high energy consumption, low efficiency problem be still puzzlement communications industry fast development technical barrier.
Summary of the invention
For the problem for causing system total energy consumption larger of existing network, the present invention proposes that a kind of NOMA based on MEC is narrow
The optimization method of system energy consumption is minimized by optimization computational resource allocation, user's transimission power and decoding sequence with Internet of Things.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of system energy consumption minimum method of the narrowband the NOMA Internet of Things based on MEC, the described method comprises the following steps:
1) in the narrowband the NOMA Internet of Things based on MEC by combined optimization computational resource allocation, user's transimission power and
Decoding sequence realizes that system energy consumption minimizes, which is described as a Multi-variables optimum design:
It is limited to:
Here, each parameter definition of problem P1 is as follows:
The successive interference cancellation SIC of user data signal decodes sequence；
dn: n-th of decoded user in decoding sequence；
User dnTransmission energy consumption, unit is joule；
User dnCalculating energy consumption, unit is joule；
User dnCalculative task data amount, unit are bits；
User dnTransimission power；
User dnTransmission rate；
R: the calculating energy consumption utilization coefficient of edge mobile computing unit；
Edge mobile computing unit calculates user dnTask when the computing resource distributed；
User dnFor transformation task data to the transmission delay of mobile edge calculations unit, unit is the second；
Mobile edge calculations unit calculates user dnTask data calculating it is time-consuming, unit is the second；
C: total calculating capacity of mobile edge calculations unit, unit is Mbps；
T: the maximum upper limit of each user task execution time delay, unit are the seconds；
User's collection { 1, N } in narrowband Internet of Things；
For the arrangement set of set { 1, N }；
2) since problem P1 is mixed integer nonlinear optimization problem, therefore P1 is decomposed into two sub- optimization problems: optimization
Resource allocation and optimization decoding sequence, wherein for the optimization of resource allocation, the resource allocation include user's transimission power and
Computational resource allocation is firstly introduced into new variablesIt is defined as narrowband internet of things equipment dnThe signal-to-noise ratio received calculates
Formula are as follows:
Logarithmic transformation is carried out to the variable that needs optimize, even In the case where sorting to definite decoding, problem P1 is equally converted to following problems P2:
It is limited to:
Wherein, parameter definition is as follows in formula:
N0: additive Gaussian noise power；
User dnChannel gain between base station；
W: the bandwidth that all protenchyma on-line customers share；
3) P1 problem has been converted into the convex problem of P2 according to transformation of variables in pairs, then by gradient descent algorithm come excellent
The problem of changing user's transimission power and computational resource allocation, which includes the following steps:
Step 3.1: since problem P2 is convex problem, then obtain a Lagrangian:
Wherein, parameter definition is as follows in formula:
Optimize parameterVector indicate；
Optimize parameterVector indicate；
Optimize parameterVector indicate；
Lagrangian multiplier coefficient and vector；
Step 3.2: for the objective function in P2 problem, enabling Local derviation is asked to obtain the following formula the Lagrangian again:
Step 3.3: gradient descent method is utilized, the descent direction of optimized variable is obtained:
The value that optimized variable is updated by k iteration, obtains:
Lagrangian coefficient is updated iteration by subgradient algorithm, obtains formula:
Step 3.4: giving parameter required for each parameter, i.e. signal-to-noise ratio at randomTransmission rateCalculate money
SourceAnd Lagrange multiplierPrimary iteration number k=1 is set；
Step 3.5: setting maximum tolerance error ε calculates two normsValue, as circulation
Stop condition updates the number of iterations k and is k=k+1 and comes back to the calculating that step 3.3 starts a new round, terminates until meeting
StandardProblem P1 obtains optimal solution signal-to-noise ratioRateComputing resource
4) optimization that sequence is secondly decoded for user, using tabu search algorithm come optimized variableThe process includes
Following steps:
Step 4.1: initial feasible solution is selected according to the parameter of convex optimizationAnd provide introduce taboo listSetting
Primary iteration number m=1；
Step 4.2: in the m times iteration, by the m-1 times sequence of exchange, i.e.,Middle any two protenchyma
The decoding of networked devices is sorted to generateIn the Candidate Set for meeting taboo requirementAnd T(m-1)In select one it is optimal
Solution and record current optimal solutionUpdate introduce taboo list T(m)；
Step 4.3: if meeting stop condition, that is, working asWhen intangibility, then stop calculating, otherwise repeatedly step 4.2,
And iteration update times m=m+1 is updated, finally obtain the optimal solution of decoding sequence
Technical concept of the invention are as follows: in view of the characteristic of NOMA technology, the present invention is mainly from resources configuration optimization and use
Family decoding sequence angle is set out, and using optimum theory as main method, establishes the basic framework of narrowband Internet of Things energy optimization.In
The narrowband NOMA Internet of Things proposed by the present invention based on MEC, for each narrowband internet of things equipment, if can reasonably optimize
Resource allocation and user decode ordering user decoding sequence to minimize the computing capability of system and execute the time delay of task, into
And can reduce the energy consumption of main equipment, then the energy efficiency of whole network will be substantially improved.Therefore, how research combines
Optimize user's transimission power, computational resource allocation and decoding sequence so that system total energy consumption minimum is significantly.
Get up to consider firstly, resource allocation and user are decoded sequence as controllable factors in combination by us, realize entire
The energy consumption (transmission energy consumption and calculating energy consumption) of system is minimum.In other words, it is desirable to by optimized allocation of resources, including transimission power and
The distribution of computing resource and the decoding sequence of equipment keep total energy consumption minimum.Then, we lead to initial non-convex optimization problem
Logarithm conversion is crossed into a convex optimization problem, according to convex optimization problem of equal value, we are based on method of Lagrange multipliers and gradient
Descent algorithm obtains optimal resource allocation, then obtains decoding sequencing schemes based on TABU search.
Beneficial effects of the present invention are mainly manifested in: resource (transimission power and meter by optimizing narrowband internet of things equipment
Calculate resource) sequence is distributed and decodes, we can reduce the total energy consumption of whole network system and promote the system benefit of network.
Detailed description of the invention
Fig. 1 is the narrowband Internet of things system schematic diagram for having both MEC and NOMA technology.
Specific embodiment
Present invention is further described in detail with reference to the accompanying drawing.
Referring to Fig.1, a kind of narrowband NOMA Internet of Things based on MEC system energy consumption minimize method, have both MEC and
Make in the narrowband Internet of Things of NOMA technology system transmission energy consumption and calculate it is energy consumption minimized come optimized allocation of resources and decoding sequence,
This method can reduce total energy consumption of system, increase system benefit.The present invention is based on the network systems for having both MEC and NOMA technology
System, as shown in Figure 1.For the target, designs and a kind of enable the system to consumption in the narrowband Internet of Things for having both MEC and NOMA technology most
The method of smallization, the described method comprises the following steps:
1) in the narrowband the NOMA Internet of Things based on MEC by combined optimization computational resource allocation, user's transimission power and
Decoding sequence realizes that system energy consumption minimizes, which is described as a Multi-variables optimum design:
It is limited to:
Here, each parameter definition of problem P1 is as follows:
The successive interference cancellation SIC of user data signal decodes sequence；
dn: n-th of decoded user in decoding sequence；
User dnTransmission energy consumption, unit is joule；
User dnCalculating energy consumption, unit is joule；
User dnCalculative task data amount, unit are bits；
User dnTransimission power；
User dnTransmission rate；
R: the calculating energy consumption utilization coefficient of edge mobile computing unit；
Edge mobile computing unit calculates user dnTask when the computing resource distributed；
User dnFor transformation task data to the transmission delay of mobile edge calculations unit, unit is the second；
Mobile edge calculations unit calculates user dnTask data calculating it is time-consuming, unit is the second；
C: total calculating capacity of mobile edge calculations unit, unit is Mbps；
T: the maximum upper limit of each user task execution time delay, unit are the seconds；
User's collection { 1, N } in narrowband Internet of Things；
For the arrangement set of set { 1, N }；
2) since problem P1 is mixed integer nonlinear optimization problem, therefore P1 is decomposed into two sub- optimization problems: optimization
Resource allocation and optimization decoding sequence, wherein for the optimization of resource allocation, the resource allocation include user's transimission power and
Computational resource allocation is firstly introduced into new variablesIt is defined as narrowband internet of things equipment dnThe signal-to-noise ratio received calculates
Formula are as follows:
Logarithmic transformation is carried out to the variable that needs optimize, even In the case where sorting to definite decoding, problem P1 is equally converted to following problems P2:
It is limited to:
Wherein, parameter definition is as follows in formula:
N0: additive Gaussian noise power；
User dnChannel gain between base station；
W: the bandwidth that all protenchyma on-line customers share；
3) P1 problem has been converted into the convex problem of P2 according to transformation of variables in pairs, then by gradient descent algorithm come excellent
The problem of changing user's transimission power and computational resource allocation, which includes the following steps:
Step 3.1: since problem P2 is convex problem, then obtain a Lagrangian:
Wherein, parameter definition is as follows in formula:
Optimize parameterVector indicate；
Optimize parameterVector indicate；
Optimize parameterVector indicate；
Lagrangian multiplier coefficient and vector；
Step 3.2: for the objective function in P2 problem, enabling Local derviation is asked to obtain the following formula the Lagrangian again:
Step 3.3: gradient descent method is utilized, the descent direction of optimized variable is obtained:
The value that optimized variable is updated by k iteration, obtains:
Lagrangian coefficient is updated iteration by subgradient algorithm, obtains formula:
Step 3.4: giving parameter required for each parameter, i.e. signal-to-noise ratio at randomTransmission rateCalculate money
SourceAnd Lagrange multiplierPrimary iteration number k=1 is set；
Step 3.5: setting maximum tolerance error ε calculates two normsValue, as circulation
Stop condition updates the number of iterations k and is k=k+1 and comes back to the calculating that step 3.3 starts a new round, terminates until meeting
StandardProblem P1 obtains optimal solution signal-to-noise ratioRateComputing resource
4) optimization that sequence is secondly decoded for user, using tabu search algorithm come optimized variableThe process includes
Following steps:
Step 4.1: initial feasible solution is selected according to the parameter of convex optimizationAnd provide introduce taboo listSetting
Primary iteration number m=1；
Step 4.2: in the m times iteration, by the m-1 times sequence of exchange, i.e.,Middle any two protenchyma
The decoding of networked devices is sorted to generateIn the Candidate Set for meeting taboo requirementAnd T(m-1)In select one it is optimal
Solution and record current optimal solutionUpdate introduce taboo list T(m)；
Step 4.3: if meeting stop condition, that is, working asWhen intangibility, then stop calculating, otherwise repeatedly step 4.2, and
Iteration update times m=m+1 is updated, the optimal solution of decoding sequence is finally obtained
This implementation is conceived under conditions of guaranteeing each QoS of customer, passes through optimized allocation of resources (transimission power
And computing resource) and equipment decoding sequence realize system total energy consumption is minimized.Our work can reduce entire logical
The total energy consumption of communication network services user so that communication enterprise reduces communications cost as much as possible, saves Internet resources, increases
Network trap improves the performance of whole network.
Claims (1)
1. a kind of system energy consumption of the narrowband NOMA Internet of Things based on MEC minimizes method, the described method comprises the following steps:
1) pass through combined optimization computational resource allocation, user's transimission power and decoding in the narrowband the NOMA Internet of Things based on MEC
Sequence realizes that system energy consumption minimizes, which is described as a Multi-variables optimum design:
It is limited to:
Here, each parameter definition of problem P1 is as follows:
The successive interference cancellation SIC of user data signal decodes sequence；
dn: n-th of decoded user in decoding sequence；
User dnTransmission energy consumption, unit is joule；
User dnCalculating energy consumption, unit is joule；
User dnCalculative task data amount, unit are bits；
User dnTransimission power；
User dnTransmission rate；
R: the calculating energy consumption utilization coefficient of edge mobile computing unit；
Edge mobile computing unit calculates user dnTask when the computing resource distributed；
User dnFor transformation task data to the transmission delay of mobile edge calculations unit, unit is the second；
Mobile edge calculations unit calculates user dnTask data calculating it is time-consuming, unit is the second；
C: total calculating capacity of mobile edge calculations unit, unit is Mbps；
T: the maximum upper limit of each user task execution time delay, unit are the seconds；
User's collection { 1, N } in narrowband Internet of Things；
For the arrangement set of set { 1, N }；
2) since problem P1 is mixed integer nonlinear optimization problem, therefore P1 is decomposed into two sub- optimization problems: optimization resource
Distribution and optimization decoding sequence, wherein for the optimization of resource allocation, the resource allocation includes user's transimission power and calculating
Resource allocation is firstly introduced into new variablesIt is defined as narrowband internet of things equipment dnThe signal-to-noise ratio received, calculation formula
Are as follows:
Logarithmic transformation is carried out to the variable that needs optimize, even In
In the case where to definite decoding sequence, problem P1 is equally converted to following problems P2:
It is limited to:
Wherein, parameter definition is as follows in formula:
N0: additive Gaussian noise power；
User dnChannel gain between base station；
W: the bandwidth that all protenchyma on-line customers share；
3) P1 problem has been converted into according to transformation of variables in pairs by the convex problem of P2, then use is optimized by gradient descent algorithm
The problem of family transimission power and computational resource allocation, the process include the following steps:
Step 3.1: since problem P2 is convex problem, then obtain a Lagrangian:
Wherein, parameter definition is as follows in formula:
Optimize parameterVector indicate；
Optimize parameterVector indicate；
Optimize parameterVector indicate；
μ, ψλ: Lagrangian multiplier coefficient and vector；
Step 3.2: for the objective function in P2 problem, enabling Local derviation is asked to obtain the following formula the Lagrangian again:
Step 3.3: gradient descent method is utilized, the descent direction of optimized variable is obtained:
The value that optimized variable is updated by k iteration, obtains:
Lagrangian coefficient is updated iteration by subgradient algorithm, obtains formula:
Step 3.4: giving parameter required for each parameter, i.e. signal-to-noise ratio at randomTransmission rateComputing resourceAnd Lagrange multiplier μ(0), ψ(0),λ(0), primary iteration number k=1 is set；
Step 3.5: setting maximum tolerance error ε calculates two normsValue, the stopping as circulation
Condition updates the number of iterations k and is k=k+1 and comes back to the calculating that step 3.3 starts a new round, until meeting termination criteriaProblem P1 obtains optimal solution signal-to-noise ratioRateComputing resource
4) optimization that sequence is secondly decoded for user, using tabu search algorithm come optimized variableThe process includes as follows
Step:
Step 4.1: initial feasible solution is selected according to the parameter of convex optimizationAnd provide introduce taboo listSetting is initial
The number of iterations m=1；
Step 4.2: in the m times iteration, by the m-1 times sequence of exchange, i.e.,Middle any two narrowband Internet of Things
The decoding of equipment is sorted to generateIn the Candidate Set for meeting taboo requirementAnd T(m-1)It is middle to select an optimal solution
Certainly scheme and the current optimal solution of recordUpdate introduce taboo list T(m)；
Step 4.3: if meeting stop condition, that is, working asWhen intangibility, then stop calculating, otherwise repeatedly step 4.2, and updates
Iteration update times m=m+1 finally obtains the optimal solution of decoding sequence
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| CN113727371A (en) * | 2021-08-06 | 2021-11-30 | 北京科技大学 | IRS (inter-Range instrumentation) assisted MEC (Multi-media communication) network wireless and computing resource allocation method and device |
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