CN109041195A - A kind of energy-collecting type wireless relay network througput maximization approach based on semi-supervised learning - Google Patents
A kind of energy-collecting type wireless relay network througput maximization approach based on semi-supervised learning Download PDFInfo
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- H—ELECTRICITY
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
- H04W52/267—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the information rate
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Abstract
A kind of energy-collecting type wireless relay network througput maximization approach based on semi-supervised learning, the following steps are included: 1) realize maximum throughput by rechargeable energy optimum management in energy-collecting type wireless relay network, wherein, optimization problem is described as a Multi-variables optimum design problem；2) problem P1 is decomposed into two parts optimization: the optimization of power and time slot optimize, that is, pass through half educational inspector's learning method optimized variable piWithTo obtain optimal ri.The present invention provides a kind of method for maximizing system benefit by joint time scheduling and power distribution realization in energy-collecting type wireless relay network with maximum throughput.
Description
Technical field
The present invention relates to energy-collecting type wireless relay network technical field, especially a kind of energy-collecting type based on semi-supervised learning
Wireless relay network througput maximization approach.
Background technique
Due to the surge of wireless device and Emerging multimedia business, mobile data flow exponentially increases always.Due to
Such as path loss, shade and the channel loss to decline on a small scale, more and more indoor and edge customers can be potentially encountered low
The service performance of quality.In order to overcome this obstacle, relaying auxiliary access technology have been proposed as exploitation energy efficiency and
Space diversity is to improve the indoor valuable solution with Cell Edge User service quality.Relay base station will be used as edge
The terminal communicated between user and macrocell base stations.
However, densely energy consumption caused by relay base station and therewith bring greenhouse gases (such as carbon dioxide) are set by portion
Discharge amount is also huge.It is considered for the dual of benefits of environment and economy, energy acquisition technology is introduced in wireless relay
In network, relay base station and wireless device pass through acquisition renewable energy (such as solar energy, wind energy, thermoelectricity, electromechanical and ambient radio-frequency
Energy etc.) it is powered the feasible skill for having become and improving green junction network energy efficiency and reducing greenhouse gas emission total amount
Art.However, due to the discontinuity that rechargeable energy reaches, in order to provide reliable data transmission and network throughput guarantee,
Particularly important is become to rechargeable energy optimum management.
Summary of the invention
The problem of in order to avoid causing QoS of customer to decline due to channel and rechargeable energy uncertainty, the present invention
A kind of energy-collecting type wireless relay network througput maximization approach based on semi-supervised learning is provided.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of energy-collecting type wireless relay network througput maximization approach based on semi-supervised learning, the method includes with
Lower step:
1) maximum throughput is realized by rechargeable energy optimum management in energy-collecting type wireless relay network, wherein optimization
Problem is described as a Multi-variables optimum design problem:
P1:
It is limited to:(constraint condition 1)
(constraint condition 2)
(constraint condition 3)
(constraint condition 4)
(constraint condition 5)
Here, each parameter definition of problem P1 is as follows:
pi: transimission power of the relay node in time slot i；
ri: data transfer rate of the relay node in time slot i；
τi: transmission time of the source node in time slot i；
Transmission time of the relay node in time slot i；
ui: data transfer rate of the source node in time slot i；
hi: the channel gain of relay node to destination node；
Ei: relay node energy collected in time slot i；
Emax: the battery maximum capacity of relay node；
Qmax: the data buffer storage capacity of relay node；
L: single time slot length；
T: transmission time slot number；
W: network bandwidth；
2) problem P1 is decomposed into two parts optimization: the optimization of power and time slot optimize, that is, pass through optimized variable piWithTo obtain optimal ri, wherein optimize transimission power of the relay node on each time slot i by the method for semi-supervised learning
piAnd transmission timeTo the data transfer rate r of each time slot i in final decision problem P1iThe sum of maximization；
By the data transfer rate r of each time slot iiAs the input of neural network in semi-supervised learning, then generating one can make respectively
The data transfer rate r of time slot iiThe sum of maximum transimission power piAnd transmission timeThat is power distribution and time scheduling；Semi-supervised
Neural network in habit is used for transmission power piAnd transmission timeThe core of prediction and learning process, it receives each time slot i
Data transfer rate riThen a power distribution and time scheduling are predicted, but this power distribution and time scheduling are not necessarily most
It is excellent, thus will be regenerated on the basis of predicting the power distribution come and time scheduling later another power distribution and when
Between dispatch, then by calculate select best one in the two power distributions and time scheduling, finally with that good
Power distribution and time scheduling carry out optimization neural network as the prediction target of neural network, make it can be pre- in prediction next time
It measures more quasi-；During constantly repeating this, neural network will measure increasingly standard in advance, until convergence.
Further, in the step 2), the iterative process of semi-supervised learning are as follows:
Step 2.1: the assessment neural network in initialization semi-supervised learning, the number of iterations k are initialized as 1；
Step 2.2: when k is less than or equal to given the number of iterations K, by the data transfer rate r of each time slot iiAs neural network
Input, predict preliminary power distribution and time scheduling；
Step 2.3: on the basis of the power distribution of tentative prediction and time scheduling, then generating another different power
Distribution and time scheduling；
Step 2.4: by calculating, selecting that function that can make problem P1 bigger in both power distributions and time scheduling
Rate distribution and time scheduling；
Step 2.5: by the data transfer rate r of the better power distribution of effect and time scheduling and each time slot i of inputiPairing,
The data for forming one group of tape label, for neural network learning；
Step 2.6: with the data of gradient descent algorithm and a upper process, constantly reducing the error of neural network, make it
It is constantly optimised, it measures in advance more quasi-, with season k=k+1, returns to step 2.2；
Step 2.7: when k is greater than given the number of iterations K, learning process terminates, and obtains optimal power distribution and time
Scheduling.
Technical concept of the invention are as follows: first, we are using power distribution and time scheduling as two kinds of controllable network resources
It joins together to consider, realizes and system benefit is maximized with maximum throughput end to end.In other words, it is desirable to obtain one it is optimal
Transimission power and time scheduling scheme make network throughput maximumlly simultaneously, and overall transmission power consumption is minimum.Then, pass through
Semi-supervised learning obtains optimal transmission power piAnd transmission timeTo obtain optimal transimission power and time scheduling, realize
With the maximization system benefit of maximize handling capacity.
Beneficial effects of the present invention are mainly manifested in: 1, for entire energy-collecting type wireless relay network system, optimizing function
Rate distribution and time scheduling can reduce the capital cost of system, and energy consumption caused by relay base station and bring therewith
Greenhouse gases (such as carbon dioxide) discharge amount can also decrease.Energy-collecting type wireless relay network can not only reduce general power
Consumption, and the transmission rate of network can be improved, reach maximize handling capacity end to end, increases the system benefit of network；
2, for network operator, optimal time slot and power distribution can make the more users of network system service, and reduce by
In path loss, the probability of lower quality of service caused by the reasons such as shade and the channel loss to decline on a small scale, to increase
User's prestige further increases its profit.
Detailed description of the invention
Fig. 1 is the schematic diagram of energy-collecting type wireless relay network.
Specific embodiment
Present invention is further described in detail with reference to the accompanying drawing.
Referring to Fig.1, a kind of energy-collecting type wireless relay network througput maximization approach based on semi-supervised learning, changes speech
It, i.e., realized by joint Power distribution and time scheduling with the maximization system benefit of end-to-end maximize handling capacity.This hair
It is bright to be based on a kind of energy-collecting type wireless relay network system (as shown in Figure 1).In energy-collecting type wireless relay network system, pass through half
Supervised learning optimizes power distribution and time scheduling, reaches peak transfer rate.Invention is in limited data buffer storage and energy storage electricity
Under the conditions of pond, for the time scheduling and Power Control Problem in energy-collecting type wireless relay network, propose based on semi-supervised
The througput maximization approach of habit, the described method comprises the following steps:
1) maximum throughput is realized by rechargeable energy optimum management in energy-collecting type wireless relay network, wherein optimization
Problem is described as a Multi-variables optimum design problem:
P1:
It is limited to:(constraint condition 1)
(constraint condition 2)
(constraint condition 3)
(constraint condition 4)
(constraint condition 5)
Here, each parameter definition of problem P1 is as follows:
pi: transimission power of the relay node in time slot i；
ri: data transfer rate of the relay node in time slot i；
τi: transmission time of the source node in time slot i；
Transmission time of the relay node in time slot i；
ui: data transfer rate of the source node in time slot i；
hi: the channel gain of relay node to destination node；
Ei: relay node energy collected in time slot i；
Emax: the battery maximum capacity of relay node；
Qmax: the data buffer storage capacity of relay node；
L: single time slot length；
T: transmission time slot number；
W: network bandwidth；
2) problem P1 is decomposed into two parts optimization: the optimization of power and time slot optimize, that is, pass through optimized variable piWith
To obtain optimal ri, wherein optimize transimission power p of the relay node on each time slot i by the method for semi-supervised learningi
And transmission timeTo the data transfer rate r of each time slot i in final decision problem P1iThe sum of maximization；
By the data transfer rate r of each time slot iiAs the input of neural network in semi-supervised learning, then generating one can make respectively
The data transfer rate r of time slot iiThe sum of maximum transimission power piAnd transmission timeThat is power distribution and time scheduling；Semi-supervised
Neural network in habit is used for transmission power piAnd transmission timeThe core of prediction and learning process, it receives each time slot i
Data transfer rate riThen a power distribution and time scheduling are predicted, but this power distribution and time scheduling are not necessarily most
It is excellent, thus will be regenerated on the basis of predicting the power distribution come and time scheduling later another power distribution and when
Between dispatch, then by calculate select best one in the two power distributions and time scheduling, finally with that good
Power distribution and time scheduling carry out optimization neural network as the prediction target of neural network, make it can be pre- in prediction next time
It measures more quasi-；During constantly repeating this, neural network will measure increasingly standard in advance, until convergence.
Further, in the step 2), the iterative process of semi-supervised learning are as follows:
Step 2.1: the assessment neural network in initialization semi-supervised learning, the number of iterations k are initialized as 1；
Step 2.2: when k is less than or equal to given the number of iterations K, by the data transfer rate r of each time slot iiAs neural network
Input, predict preliminary power distribution and time scheduling；
Step 2.3: on the basis of the power distribution of tentative prediction and time scheduling, then generating another different power
Distribution and time scheduling；
Step 2.4: by calculating, selecting that function that can make problem P1 bigger in both power distributions and time scheduling
Rate distribution and time scheduling；
Step 2.5: by the data transfer rate r of the better power distribution of effect and time scheduling and each time slot i of inputiPairing,
The data for forming one group of tape label, for neural network learning；
Step 2.6: with the data of gradient descent algorithm and a upper process, constantly reducing the error of neural network, make it
It is constantly optimised, it measures in advance more quasi-, with season k=k+1, returns to step 2.2；
Step 2.7: when k is greater than given the number of iterations K, learning process terminates, and obtains optimal power distribution and time
Scheduling.
In the present embodiment, Fig. 1 is the wireless relay network of the invention in relation to energy-collecting type relay base station.It is wireless in the energy-collecting type
In relay network system, the capital cost of system, and relay base station can be reduced by optimization time scheduling and power distribution
Generated energy consumption and bring greenhouse gases (such as carbon dioxide) discharge amount can also decrease therewith.During energy-collecting type is wireless
After network system, total power consumption can be not only reduced, but also the transmission rate of network can be improved, reaches maximum end to end
Change handling capacity, increases the system benefit of network.
This implementation is conceived under conditions of meeting each QoS of customer, passes through control user's transimission power and optimization
Time scheduling maximizes end-to-end handling capacity to realize with the consumption of minimum overall transmission power.Our work can make network transport
It seeks quotient and obtains maximum profit, service user as much as possible, save Internet resources, improve the performance of whole network, realize maximum
The network system benefit of change.
Claims (2)
1. a kind of energy-collecting type wireless relay network througput maximization approach based on semi-supervised learning, it is characterised in that: described
Method the following steps are included:
1) maximum throughput is realized by rechargeable energy optimum management in energy-collecting type wireless relay network, wherein optimization problem
It is described as a Multi-variables optimum design problem:
P1:
It is limited to:(constraint condition 1)
(constraint condition 2)
(constraint condition 3)
(constraint condition 4)
(constraint condition 5)
Here, each parameter definition of problem P1 is as follows:
pi: transimission power of the relay node in time slot i；
ri: data transfer rate of the relay node in time slot i；
τi: transmission time of the source node in time slot i；
Transmission time of the relay node in time slot i；
ui: data transfer rate of the source node in time slot i；
hi: the channel gain of relay node to destination node；
Ei: relay node energy collected in time slot i；
Emax: the battery maximum capacity of relay node；
Qmax: the data buffer storage capacity of relay node；
L: single time slot length；
T: transmission time slot number；
W: network bandwidth；
2) problem P1 is decomposed into two parts optimization: the optimization of power and time slot optimize, that is, pass through optimized variable piWithCome
To optimal ri, wherein optimize transimission power p of the relay node on each time slot i by the method for semi-supervised learningiAnd biography
The defeated timeTo the data transfer rate r of each time slot i in final decision problem P1iThe sum of maximization；
By the data transfer rate r of each time slot iiAs the input of neural network in semi-supervised learning, each time slot i can be made by then generating one
Data transfer rate riThe sum of maximum transimission power piAnd transmission timeThat is power distribution and time scheduling；In semi-supervised learning
Neural network is used for transmission power piAnd transmission timeThe core of prediction and learning process, it receives the data of each time slot i
Rate riThen predict a power distribution and time scheduling, but this power distribution and time scheduling be not necessarily it is optimal,
So later another power distribution and time tune will be regenerated on the basis of predicting the power distribution come and time scheduling
Then degree selects best one in the two power distributions and time scheduling by calculating, finally with that good power
Distribution and time scheduling carry out optimization neural network as the prediction target of neural network, measure it can in advance in prediction next time
It is more quasi-；During constantly repeating this, neural network will measure increasingly standard in advance, until convergence.
2. a kind of throughput-maximized side of energy-collecting type wireless relay network based on semi-supervised learning as described in claim 1
Method, it is characterised in that: in the step 2), the iterative process of semi-supervised learning are as follows:
Step 2.1: the assessment neural network in initialization semi-supervised learning, the number of iterations k are initialized as 1；
Step 2.2: when k is less than or equal to given the number of iterations K, by the data transfer rate r of each time slot iiAs the defeated of neural network
Enter, predicts preliminary power distribution and time scheduling；
Step 2.3: on the basis of the power distribution of tentative prediction and time scheduling, then generating another different power distribution
And time scheduling；
Step 2.4: by calculating, selecting that power point that can make problem P1 bigger in both power distributions and time scheduling
Match and time scheduling；
Step 2.5: by the data transfer rate r of the better power distribution of effect and time scheduling and each time slot i of inputiPairing, composition one
The data of group tape label, for neural network learning；
Step 2.6: with the data of gradient descent algorithm and a upper process, constantly reducing the error of neural network, make it constantly
It is optimised, it measures in advance more quasi-, with season k=k+1, returns to step 2.2；
Step 2.7: when k is greater than given the number of iterations K, learning process terminates, and obtains optimal power distribution and time scheduling.
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Application publication date: 20181218 |