CN112188428A - Energy efficiency optimization method for Sink node in sensing cloud network - Google Patents
Energy efficiency optimization method for Sink node in sensing cloud network Download PDFInfo
- Publication number
- CN112188428A CN112188428A CN202011037390.1A CN202011037390A CN112188428A CN 112188428 A CN112188428 A CN 112188428A CN 202011037390 A CN202011037390 A CN 202011037390A CN 112188428 A CN112188428 A CN 112188428A
- Authority
- CN
- China
- Prior art keywords
- sink node
- sensor
- deep neural
- neural network
- network model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000005457 optimization Methods 0.000 title claims abstract description 22
- 238000003062 neural network model Methods 0.000 claims abstract description 65
- 238000012360 testing method Methods 0.000 claims abstract description 29
- 238000004891 communication Methods 0.000 claims abstract description 20
- 238000012549 training Methods 0.000 claims abstract description 20
- 239000011159 matrix material Substances 0.000 claims description 27
- 238000009826 distribution Methods 0.000 claims description 15
- 230000004913 activation Effects 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 8
- 239000000126 substance Substances 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 4
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 description 12
- 230000005540 biological transmission Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000001186 cumulative effect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012804 iterative process Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 230000001413 cellular effect Effects 0.000 description 1
- 238000002790 cross-validation Methods 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005315 distribution function Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005562 fading Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/38—TPC being performed in particular situations
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention relates to the technical field of wireless communication, in particular to an energy efficiency optimization method of a Sink node in a sensing cloud network, which comprises the following steps: s1, constructing a sensing cloud network, and forming a plurality of sensor nodes into sensor areas of a honeycomb structure in the bottom layer of the sensing cloud network, wherein each sensor area is provided with a Sink node; s2, constructing a reference algorithm model to obtain a test optimal beam forming vector of the Sink node; s3, constructing a deep neural network model, and training and testing the deep neural network model through data of the reference algorithm model; and S4, optimizing energy efficiency, and obtaining the optimal beam forming vector of the Sink node through the trained and tested deep neural network model. The method and the device can improve the energy efficiency of the Sink node, reduce the decision time of the sensing cloud network and improve the real-time performance of the system.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to an energy efficiency optimization method for a Sink node in a sensing cloud network.
Background
With the popularization and development of communication engineering facilities, people have higher and higher requirements on high-energy-efficiency wireless communication, and how to improve the energy efficiency of the wireless communication transmission device and the energy transmission device again in the process of transmitting information and energy in the wireless communication becomes a key problem of the next generation of wireless communication.
At present, a sensing cloud technology is a sensor and application service platform integrating transmission, storage, acquisition, visualization, interface, APP and WeChat, and supports multi-mode platform privatized deployment. Due to the strong hardware product access capacity of the sensing cloud, the internet of things access cost of the hardware product is effectively reduced, and the system is a verification, display and application platform of hardware products such as a sensor and a controller. The traditional wireless network resource management strategy designed based on the optimization method is high in complexity and poor in real-time performance, and is not beneficial to making an online decision, and the decision time of the sensing cloud network is long.
Disclosure of Invention
In order to solve the problems, the invention provides an energy efficiency optimization method for a Sink node in a sensing cloud network, which can improve the energy efficiency of the Sink node, reduce the decision time of the sensing cloud network and improve the real-time performance of the system.
In order to achieve the purpose, the invention adopts the technical scheme that:
a method for optimizing energy efficiency of Sink nodes in a sensing cloud network comprises the following steps:
s1, constructing a sensing cloud network, wherein a plurality of sensor nodes form a sensor area of a honeycomb structure in the bottom layer of the sensing cloud network, each sensor area is provided with a Sink node, and the Sink nodes are provided with multiple antennas, so that the Sink nodes provide wireless energy-carrying communication service for the sensor nodes in the corresponding sensor areas;
s2, constructing a reference algorithm model, and inputting test channel state information of the Sink node into the reference algorithm model to obtain a test optimal beam forming vector of the Sink node;
s3, constructing a deep neural network model, and training and testing the deep neural network model through data of the reference algorithm model;
and S4, optimizing energy efficiency, namely replacing the reference algorithm model with the trained and tested deep neural network model, and inputting channel state information of the Sink node into the trained and tested deep neural network model to obtain an output optimal beam forming vector of the Sink node.
Therefore, the energy efficiency of the Sink node in the sensing cloud network is optimized.
Further, the construction method of the reference algorithm model comprises the following steps:
A1. initialization
A2. Computing
A3. Computing
A4. Setting t to 0, looping steps a1-A3, and accumulating 1 for each loop t;
A5. updating
A6. Updating
A7. Updating
A8. Computing
A9. When in use
Then, obtaining the experimental optimal beam forming matrix W of the Sink nodeiOptimizing a beamforming matrix W through testing the Sink nodeiPerforming eigenvector calculation to obtain experimental optimal beamforming vector of the ith Sink node
Wherein the content of the first and second substances,
representing the parameters of the Sink node in the ith sensor region in the iteration process of the reference algorithm model for three parameters, wherein t is the iteration frequency; piIs the transmit power of the Sink node; rhoiA power cut ratio for the sensor node when accepting wireless energy-carrying communication; etaiThe time length ratio of the downlink phase occupying one system period is defined; h isikData that is a channel state;
random noise at the Sink node; alpha is alphaikWeighting coefficients for the kth sensor node in the ith sensor region; pmaxIs the maximum transmit power of the sensor node; is an error parameter; t isiRepresenting the duration of one system cycle in the ith said sensor zone; wiA test beamforming matrix W for the Sink nodei；
Optimal beamforming for the ith trial of the Sink nodeThe vector of the vector is then calculated,
is 1 line MiA vector of columns.
Further, the experimental optimal beamforming matrix W of the Sink nodeiThe calculation method comprises the following steps:
wherein the content of the first and second substances,
Eik＝pik(1-ηi)Tiformula (10)
Further, the deep neural network model comprises an input layer, a plurality of hidden layers and an output layer, and the input layer, the hidden layers and the output layer are formed in a fully connected mode; the activation function of the hidden layer adopts a ReLU function, and the activation function of the output layer is as follows:
y＝min(max(x,0),1)
formula (11)
Wherein x is the output of the hidden layer and y is the output of the activation function of the output layer.
Further, the input of the deep neural network model is (I, K, M) in scalei) The output of the deep neural network model is the optimal beam forming matrix of the I Sink nodes
And the data size output by the deep neural network is (I, M)i,Mi) The three-dimensional array of (a) is,
wherein I is the number of the sensor regions, K is the number of the sensor nodes existing in the sensor region where each Sink node is located, and MiThe number of the Sink node antennas is shown;
the training method of the deep neural network model comprises the following steps:
B1. generating data of channel state based on deterministic standard normal distribution
B2. Setting the value of the step A9 according to the reference algorithm model to obtain the optimal beam forming matrix
B3. Repeating the steps B1-B2 for multiple times, and obtaining the data h of the channel stateikConversion to scale (I, K, M)i) As input values for the deep neural network model, the three-dimensional array of (A) obtained in step (B2)
As an output value of the deep neural network model;
B4. taking the input values and the output values of the deep neural network model of the step B3 as a data sample set, and slicing the data sample set into a training set and a testing set for training and testing the deep neural network model.
Further, calculating a model of a difference between the beam forming matrix of the Sink node output by the deep neural network model and the test beam forming matrix of the Sink node output by the reference algorithm model to obtain a loss function, and finishing training of the deep neural network model when the loss function is lower than a threshold value.
Further, the training set optimizes the weights of the deep neural network model by adopting a gradient descent algorithm.
Furthermore, the Sink node is powered by a stable power supply, and the energy of the sensor node is collected from the energy in the downlink wireless energy-carrying communication service process.
Further, the sensor node is equipped with a single antenna.
The invention has the beneficial effects that:
1. channel state information and a corresponding test optimal beamforming vector of a Sink node can be obtained by constructing a reference algorithm model, so that optimized input and output data can be provided for a subsequent deep neural network model; the deep neural network model is trained and tested by using data of the reference algorithm model, the reference algorithm model is replaced by the deep neural network model after training is completed, and therefore operation planning of the Sink node can be achieved, energy efficiency of the Sink node can be improved, decision time of a sensing cloud network is shortened, instantaneity of a system is improved, energy efficiency optimization of the Sink node can be achieved, and better instantaneity and lower complexity are provided.
2. In reference algorithm model construction, obtaining according to channel state
The error parameter of the reference algorithm model can be calculated by expressing the parameter of the reference algorithm model in the iterative process and utilizing the iterative process parameter of the reference algorithm model and the parameter of the Sink node, and when the error parameter is smaller than a set error threshold value, the experimental beam forming matrix W of the Sink node is provediOptimizing beamforming matrix W for experimentationiForming a matrix W by testing the beam of the Sink nodeiPerforming eigenvector calculation to obtain the experimental optimal beamforming vector of the Sink node
In order to improve the performance of the sensing cloud network, the Sink node is provided with multiple antennas, so that the channel state is a vector of 1 row and Mi column, and therefore the output of the reference algorithm model is also a vector of 1 row and Mi column. Obtaining an optimal beam forming vector of the Sink node by using a reference algorithm model; the method is characterized in that the corresponding deep neural network model is designed to realize the approximation of the reference algorithm model, and the deep neural network model has a good approximation effect on the reference algorithm model, so that the sensing cloud network energy efficiency and the distribution thereof similar to the reference algorithm can be realized.
Drawings
Fig. 1 is a flowchart of an energy efficiency optimization method for a Sink node in a sensor cloud network according to a preferred embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a Sink node of the energy efficiency optimization method for the Sink node in the sensor cloud network according to a preferred embodiment of the present invention.
Fig. 3 is an operation timing chart of a method for optimizing energy efficiency of a Sink node in a sensor cloud network according to a preferred embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a deep neural network model of an energy efficiency optimization method for Sink nodes in a sensor cloud network according to a preferred embodiment of the present invention.
Fig. 5 is a cumulative comparison distribution diagram of energy efficiency of an energy efficiency optimization method for Sink nodes in a sensor cloud network according to a preferred embodiment of the present invention.
In the figure, 1 is a sensing cloud network, 2 is a sensor area, and 3 is a Sink node.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1 to 4, a method for optimizing energy efficiency of a Sink node in a sensor cloud network according to a preferred embodiment of the present invention includes the following steps:
s1, constructing a sensing cloud network, forming a plurality of sensor nodes into a sensor area 2 of a honeycomb structure in the bottom layer of the sensing cloud network 1, setting a Sink node 3 in each sensor area 2, and configuring the Sink node 3 with multiple antennas so that the Sink node 3 provides wireless energy-carrying communication service for the sensor nodes in the corresponding sensor areas; the sensor node is equipped with a single antenna.
In this embodiment, the Sink node 3 is powered by a stable power supply, and energy of the sensor node is collected from energy in a downlink wireless energy-carrying communication service process.
As shown in FIG. 1, the energy of the sensor node is all from energy collection in the wireless energy-carrying communication service process. The sensing cloud network 1 is periodically divided into two stages of downlink wireless energy-carrying communication and uplink WIT. In a downlink stage, the Sink node 3 provides wireless energy-carrying communication service for the sensor nodes in the sensor area 2, the sensor nodes realize synchronous reception of energy and task information in a Power Split (PS) working mode and assume that the duration of the downlink stage is sufficient for receiving the task information, and the sensor nodes in an uplink stage upload acquired data to the Sink node 3 by using the energy collected in the downlink stage.
As shown in fig. 2, where the system period of the ith cell is denoted as TiThe duration factor of the downlink phase is ηi. In order to further improve the performance of the sensing cloud network 1, the Sink node 3 is provided with multiple antennas to enhance the wireless energy-carrying communication efficiency, and meanwhile, the sensor node is provided with a single antenna. And then, constructing a system energy efficiency optimization problem, and designing a reference algorithm model and a deep neural network model respectively to solve the optimal beamforming vector.
And S2, constructing a reference algorithm model, and inputting the test channel state information of the Sink node 3 into the reference algorithm model to obtain the test optimal beam forming vector of the Sink node 3.
And S3, constructing a deep neural network model, and training and testing the deep neural network model through data of the reference algorithm model.
And S4, optimizing energy efficiency, namely replacing the trained and tested deep neural network model with a reference algorithm model, and inputting channel state information of the Sink node 3 into the trained and tested deep neural network model to obtain an output optimal beam forming vector of the Sink node 3.
Channel state information and a corresponding test optimal beamforming vector of the Sink node 3 can be obtained by constructing a reference algorithm model, so that optimized input and output data can be provided for a subsequent deep neural network model. The deep neural network model is trained and tested by using data of the reference algorithm model, the reference algorithm model is replaced by the deep neural network model after training is completed, and therefore the Sink node 3 is planned to operate, the energy efficiency of the Sink node 3 can be improved, the decision time of the sensing cloud network 1 is shortened, the real-time performance of the system is improved, the energy efficiency optimization of the Sink node 3 can be achieved, and better real-time performance and lower complexity are provided.
In the step S2, in step S2,
the construction method of the benchmark algorithm model comprises the following steps:
A1. initialization
A2. Computing
A3. Computing
A4. Setting t to 0, looping steps a1-A3, and accumulating 1 for each loop t;
A5. updating
A6. Updating
A7. Updating
A8. Computing
A9. When in use
Then, the experimental optimal beam forming matrix W of the Sink node 3 is obtainediOptimizing the beamforming matrix W by testing the Sink node 3iPerforming eigenvector calculation to obtain the experimental optimal beamforming vector of the ith Sink node 3
Wherein the content of the first and second substances,
representing the parameters of the Sink node 3 in the ith sensor region 2 in the iteration process of the reference algorithm model by using three parameters, wherein t is the iteration frequency; piIs the transmit power of the Sink node 3; rhoiThe power cutting ratio when the sensor node receives wireless energy carrying communication is obtained; etaiThe time length ratio of the downlink phase occupying one system period is defined; h isikFor the data of channel state, h is because Sink node 3 is equipped with multiple antennasikIs one 1 line MiA vector of columns;
random noise at Sink node 3; alpha is alphaikWeighting coefficients of the kth sensor node in the ith sensor area 2; pmaxThe maximum transmission power of the sensor node; is an error parameter; t isiRepresents the duration of one system cycle in the i-th sensor zone 2; wiTest beamforming matrix W for Sink node 3i；
For the trial optimal beamforming vector for the ith Sink node 3,
is 1 line MiA vector of columns.
Experimental optimal beamforming matrix W of Sink node 3iThe calculation method comprises the following steps:
Wi＝hik -1Eik(hik H)-1(PiρiηiTi)-1formula (8)
Wherein the content of the first and second substances,
Eik＝pik(1-ηi)Tiformula (10)
In step S3, the deep neural network model includes an input layer, a plurality of hidden layers, and an output layer, and the input layer, the hidden layers, and the output layer are formed in a fully connected manner; the activation function of the hidden layer adopts a ReLU function, and the activation function of the output layer is as follows:
y is min (max (x,0),1) formula (11)
Where x is the output of the hidden layer and y is the output of the activation function of the output layer.
In this embodiment, the input of the deep neural network model is a scale of (I, K, M)i) The output of the deep neural network model is the optimal beam forming matrix of I Sink nodes 3
And the data size output by the deep neural network is (I, M)i,Mi) The three-dimensional array of (a) is,
wherein I is the number of the sensor regions 2, K is the number of the sensor nodes existing in the sensor region 2 where each Sink node 3 is located, and MiThe number of Sink node 3 antennas is shown;
the training method of the deep neural network model comprises the following steps:
B1. generating data of channel state based on deterministic standard normal distribution
The present embodiment employs a Rayleigh fading distribution with zero mean and unit variance.
B2. Setting the value of the step A9 according to the reference algorithm model to obtain the optimal beam forming matrix
The setting of this embodiment is 10-3。
B3. Repeating the steps B1-B2 for multiple times, and converting the data h of the channel stateikConversion to scale (I, K, M)i) As input values for the deep neural network model, the three-dimensional array of (A) obtained in step B2
As an output value of the deep neural network model.
B4. And taking the input value and the output value of the deep neural network model of the step B3 as a data sample set, and slicing the data sample set into a training set and a testing set for training and testing the deep neural network model.
In step B4, a model of the difference between the beam forming matrix of the Sink node 3 output by the deep neural network model and the test beam forming matrix of the Sink node 3 output by the reference algorithm model is calculated to obtain a loss function, and when the loss function is lower than a threshold, the deep neural network model is trained.
In order to improve the performance of the deep neural network model training process, in this embodiment, the initialization of the weights and the side lengths obeys the truncated normal distribution, and simultaneously, the attenuation rate is set to be 0.9, and the appropriate learning rate and batch size are set through cross validation.
For a strong switch with a single antenna equipped in the Sink node, the application of the embodiment method can be realized by replacing the channel vector with the channel coefficient.
In the embodiment, the performance of the embodiment in the aspect of improving the energy efficiency of the sensing cloud network 1 is verified through four situations, namely a reference algorithm model (SWIPT-SWMME), a random power algorithm, a maximum power algorithm and a deep neural network model (DNN).
The system energy efficiency and its Cumulative Distribution Function (CDF) realized in 25000 computations for four cases are shown in fig. 5.
The best Sink node 3 energy efficiency is realized by adopting a system strategy of a reference algorithm model; meanwhile, compared with two strategies of random power distribution and maximum power distribution, the system energy efficiency realized by the reference algorithm model algorithm has a wider distribution domain value, because the interference management problem under different channel states is effectively considered in the algorithm calculation process, the compromise between the system performance and the energy consumption can be realized, otherwise, the random power distribution and maximum power distribution algorithm cannot make corresponding decisions according to the channel states, namely, the sending power cannot be increased in time to improve the system throughput when the channel states are good, and the power cannot be reduced in time to reduce the energy transmission loss when the channel states are poor, so that the distribution range of the system energy efficiency is narrower.
Further, although the maximum power allocation strategy cannot adapt to the situation of a poor channel state to a certain extent, the maximum power allocation strategy can improve the system performance under the situation of a good channel state, so that the realized system energy efficiency value and the distribution range thereof are slightly larger than the random power allocation decision. On the other hand, the deep neural network model realizes a good approximation effect on the reference algorithm and can realize system energy efficiency and distribution similar to the reference algorithm. This proves the effectiveness and performance of the Sink node 3 optimal energy efficiency method of the embodiment.
As shown in table 1, the comparison of the statistical data of the reference algorithm model and the deep neural network model shows that the deep neural network model can achieve better real-time performance and lower complexity than the reference algorithm model. Where K represents the number of sensor nodes present within the cellular sensor region 2 where each Sink node 3 is located. The comparison shows that the optimal energy efficiency algorithm based on the deep neural network model occupies lower CPU (Central processing Unit) time than the optimal energy efficiency algorithm based on the reference algorithm model, so that the deep neural network model has lower complexity and shorter time consumption, and is more favorable for making online decisions.
TABLE 1 comparison of statistical data for the baseline Algorithm model and the deep neural network model
Claims (9)
1. The energy efficiency optimization method for the Sink node in the sensing cloud network is characterized by comprising the following steps:
s1, constructing a sensing cloud network, wherein a plurality of sensor nodes form a sensor area of a honeycomb structure in the bottom layer of the sensing cloud network, each sensor area is provided with a Sink node, and the Sink nodes are provided with multiple antennas, so that the Sink nodes provide wireless energy-carrying communication service for the sensor nodes in the corresponding sensor areas;
s2, constructing a reference algorithm model, and inputting test channel state information of the Sink node into the reference algorithm model to obtain a test optimal beam forming vector of the Sink node;
s3, constructing a deep neural network model, and training and testing the deep neural network model through data of the reference algorithm model;
and S4, optimizing energy efficiency, namely replacing the reference algorithm model with the trained and tested deep neural network model, and inputting channel state information of the Sink node into the trained and tested deep neural network model to obtain an output optimal beam forming vector of the Sink node.
2. The energy efficiency optimization method for the Sink node in the sensor cloud network according to claim 1, characterized in that: the construction method of the reference algorithm model comprises the following steps:
A1. initialization
A2. Computing
A3. Computing
A4. Setting t to 0, looping steps a1-A3, and accumulating 1 for each loop t;
A5. updating
A6. Updating
A7. Updating
A8. Computing
A9. When in use
Then, obtaining the experimental optimal beam forming matrix W of the Sink nodeiOptimizing a beamforming matrix W through testing the Sink nodeiPerforming eigenvector calculation to obtain experimental optimal beamforming vector of the ith Sink node
Wherein the content of the first and second substances,
representing parameters of the Sink node in the ith sensor region in the iteration process of the reference algorithm model for three parametersNumber, t is the number of iterations; piIs the transmit power of the Sink node; rhoiA power cut ratio for the sensor node when accepting wireless energy-carrying communication; etaiThe time length ratio of the downlink phase occupying one system period is defined; h isikData that is a channel state;
random noise at the Sink node; alpha is alphaikWeighting coefficients for the kth sensor node in the ith sensor region; pmaxIs the maximum transmit power of the sensor node; is an error parameter; t isiRepresenting the duration of one system cycle in the ith said sensor zone; wiA test beamforming matrix W for the Sink nodei；
For the trial optimal beamforming vector for the ith said Sink node,
is 1 line MiA vector of columns.
3. The energy efficiency optimization method for the Sink node in the sensor cloud network according to claim 2, characterized in that: the experimental optimal beamforming matrix W of the Sink nodeiThe calculation method comprises the following steps:
wherein the content of the first and second substances,
Eik＝pik(1-ηi)Tiformula (10)
4. The energy efficiency optimization method for the Sink node in the sensor cloud network according to claim 2, characterized in that: the deep neural network model comprises an input layer, a plurality of hidden layers and an output layer, and the input layer, the hidden layers and the output layer are formed in a full-connection mode; the activation function of the hidden layer adopts a ReLU function, and the activation function of the output layer is as follows:
y＝min(max(x,0),1)
formula (11)
Wherein x is the output of the hidden layer and y is the output of the activation function of the output layer.
5. The energy efficiency optimization method for the Sink node in the sensor cloud network according to claim 4, characterized in that:
the input of the deep neural network model is (I, K, M) in scalei) The output of the deep neural network model is the optimal beam forming matrix of the I Sink nodes
And the data size output by the deep neural network is (I, M)i,Mi) The three-dimensional array of (a) is,
wherein I is the number of the sensor regions, K is the number of the sensor nodes existing in the sensor region where each Sink node is located, and MiThe number of the Sink node antennas is shown;
the training method of the deep neural network model comprises the following steps:
B1. generating data of channel state based on deterministic standard normal distribution
B2. Setting the value of the step A9 according to the reference algorithm model to obtain the optimal beam forming matrix
B3. Repeating the steps B1-B2 for multiple times, and obtaining the data h of the channel stateikConversion to scale (I, K, M)i) As input values for the deep neural network model, the three-dimensional array of (A) obtained in step (B2)
As an output value of the deep neural network model;
B4. taking the input values and the output values of the deep neural network model of the step B3 as a data sample set, and slicing the data sample set into a training set and a testing set for training and testing the deep neural network model.
6. The energy efficiency optimization method for the Sink node in the sensor cloud network according to claim 5, characterized in that: and calculating a model of the difference between the beam forming matrix of the Sink node output by the deep neural network model and the test beam forming matrix of the Sink node output by the reference algorithm model to obtain a loss function, and finishing training of the deep neural network model when the loss function is lower than a threshold value.
7. The energy efficiency optimization method for the Sink node in the sensor cloud network according to claim 5, characterized in that: and the training set adopts a gradient descent algorithm to optimize the weight of the deep neural network model.
8. The energy efficiency optimization method for the Sink node in the sensor cloud network according to claim 1, characterized in that: the Sink node is powered by a stable power supply, and the energy of the sensor node is collected from the energy in the downlink wireless energy-carrying communication service process.
9. The energy efficiency optimization method for the Sink node in the sensor cloud network according to claim 1, characterized in that: the sensor node is equipped with a single antenna.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011037390.1A CN112188428B (en) | 2020-09-28 | 2020-09-28 | Energy efficiency optimization method for Sink node in sensor cloud network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011037390.1A CN112188428B (en) | 2020-09-28 | 2020-09-28 | Energy efficiency optimization method for Sink node in sensor cloud network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112188428A true CN112188428A (en) | 2021-01-05 |
| CN112188428B CN112188428B (en) | 2024-01-30 |
Family
ID=73944365
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011037390.1A Active CN112188428B (en) | 2020-09-28 | 2020-09-28 | Energy efficiency optimization method for Sink node in sensor cloud network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112188428B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116111730A (en) * | 2023-04-13 | 2023-05-12 | 国网江西省电力有限公司信息通信分公司 | Power grid monitoring method based on electric power optical cable co-fiber transmission system |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103841571A (en) * | 2014-03-20 | 2014-06-04 | 吉林大学 | Wireless sensor network beam forming transmission array node selecting method |
| CN106888430A (en) * | 2017-04-17 | 2017-06-23 | 华侨大学 | A kind of believable sensing cloud Data Collection appraisal procedure |
| CN107426826A (en) * | 2017-04-27 | 2017-12-01 | 成都瑞沣信息科技有限公司 | The MAC protocol for wireless sensor networks design method collected based on RF energy |
| CN108924897A (en) * | 2018-06-30 | 2018-11-30 | 北京工业大学 | A kind of mobile sink paths planning method based on deeply learning algorithm |
| CN109617584A (en) * | 2019-01-08 | 2019-04-12 | 南京邮电大学 | A kind of mimo system beamforming matrix design method based on deep learning |
| CN110300380A (en) * | 2019-07-30 | 2019-10-01 | 电子科技大学 | The method for tracking target of balance system energy consumption and tracking precision in mobile WSN |
| CN110769444A (en) * | 2019-10-25 | 2020-02-07 | 东北大学 | Transmission method of wireless energy-carrying communication based on power distribution |
| WO2020048594A1 (en) * | 2018-09-06 | 2020-03-12 | Nokia Technologies Oy | Procedure for optimization of self-organizing network |
| WO2020098256A1 (en) * | 2018-11-14 | 2020-05-22 | 平安科技（深圳）有限公司 | Speech enhancement method based on fully convolutional neural network, device, and storage medium |
| US20200295993A1 (en) * | 2019-03-12 | 2020-09-17 | Cisco Technology, Inc. | Downlink performance optimizations in wireless networks |
-
2020
- 2020-09-28 CN CN202011037390.1A patent/CN112188428B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103841571A (en) * | 2014-03-20 | 2014-06-04 | 吉林大学 | Wireless sensor network beam forming transmission array node selecting method |
| CN106888430A (en) * | 2017-04-17 | 2017-06-23 | 华侨大学 | A kind of believable sensing cloud Data Collection appraisal procedure |
| CN107426826A (en) * | 2017-04-27 | 2017-12-01 | 成都瑞沣信息科技有限公司 | The MAC protocol for wireless sensor networks design method collected based on RF energy |
| CN108924897A (en) * | 2018-06-30 | 2018-11-30 | 北京工业大学 | A kind of mobile sink paths planning method based on deeply learning algorithm |
| CN109936865A (en) * | 2018-06-30 | 2019-06-25 | 北京工业大学 | A kind of mobile sink paths planning method based on deeply learning algorithm |
| WO2020048594A1 (en) * | 2018-09-06 | 2020-03-12 | Nokia Technologies Oy | Procedure for optimization of self-organizing network |
| WO2020098256A1 (en) * | 2018-11-14 | 2020-05-22 | 平安科技（深圳）有限公司 | Speech enhancement method based on fully convolutional neural network, device, and storage medium |
| CN109617584A (en) * | 2019-01-08 | 2019-04-12 | 南京邮电大学 | A kind of mimo system beamforming matrix design method based on deep learning |
| US20200295993A1 (en) * | 2019-03-12 | 2020-09-17 | Cisco Technology, Inc. | Downlink performance optimizations in wireless networks |
| CN110300380A (en) * | 2019-07-30 | 2019-10-01 | 电子科技大学 | The method for tracking target of balance system energy consumption and tracking precision in mobile WSN |
| CN110769444A (en) * | 2019-10-25 | 2020-02-07 | 东北大学 | Transmission method of wireless energy-carrying communication based on power distribution |
Non-Patent Citations (4)
| Title |
|---|
| ARIJIT ROY; AYAN MONDAL; SUDIP MISRA: "《Connectivity Re-establishment in the Presence of Dumb Nodes in Sensor-Cloud Infrastructure: A Game Theoretic Approach》", 《IEEE》 * |
| 徐荣: "《基于云计算的无线传感网络资源分配策略研究》", 《大庆师范学院学报》, vol. 37, no. 6 * |
| 李锐: "《基于物联网技术的无线型建筑环境监测系统设计与实现》", 《智能处理与应用》, no. 11 * |
| 饶芳，谭建军: "《无线传感网中基于sink节点的目标位置选择移动算法》", 《现代电子技术》, vol. 38, no. 19 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116111730A (en) * | 2023-04-13 | 2023-05-12 | 国网江西省电力有限公司信息通信分公司 | Power grid monitoring method based on electric power optical cable co-fiber transmission system |
| CN116111730B (en) * | 2023-04-13 | 2023-06-23 | 国网江西省电力有限公司信息通信分公司 | Power grid monitoring method based on electric power optical cable co-fiber transmission system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112188428B (en) | 2024-01-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Maksymyuk et al. | Deep learning based massive MIMO beamforming for 5G mobile network | |
| CN111800828B (en) | Mobile edge computing resource allocation method for ultra-dense network | |
| Liao et al. | Base station activation and linear transceiver design for optimal resource management in heterogeneous networks | |
| Li et al. | Downlink transmit power control in ultra-dense UAV network based on mean field game and deep reinforcement learning | |
| CN111182582B (en) | Multitask distributed unloading method facing mobile edge calculation | |
| Rajapaksha et al. | Deep learning-based power control for cell-free massive MIMO networks | |
| CN109714091B (en) | Iterative hybrid precoding method based on hierarchical design in millimeter wave MIMO system | |
| Van Chien et al. | Sum spectral efficiency maximization in massive MIMO systems: Benefits from deep learning | |
| Wang et al. | Robust smart-grid-powered cooperative multipoint systems | |
| CN109168197B (en) | Power distribution method based on heterogeneous small cellular network energy acquisition efficiency maximization | |
| CN103262593A (en) | Apparatus and method for determining a core network configuration of a wireless communication system | |
| Dai et al. | Energy‐efficient resource allocation for device‐to‐device communication with WPT | |
| CN112260733B (en) | Multi-agent deep reinforcement learning-based MU-MISO hybrid precoding design method | |
| CN110300417B (en) | Energy efficiency optimization method and device for unmanned aerial vehicle communication network | |
| Ma et al. | Cooperative beamforming for RIS-aided cell-free massive MIMO networks | |
| CN113473580B (en) | User association joint power distribution method based on deep learning in heterogeneous network | |
| CN104821838A (en) | Energy efficiency maximization-based multi-user information and energy simultaneous transmission transceiver design method | |
| CN113783593A (en) | Beam selection method and system based on deep reinforcement learning | |
| CN105792218A (en) | Optimization method of cognitive radio network with radio frequency energy harvesting capability | |
| Alameer et al. | Joint beamforming and network topology optimization of green cloud radio access networks | |
| CN112188428A (en) | Energy efficiency optimization method for Sink node in sensing cloud network | |
| Gupta et al. | LSTM-based energy-efficient wireless communication with reconfigurable intelligent surfaces | |
| Xu et al. | Deep reinforcement learning for communication and computing resource allocation in RIS aided MEC networks | |
| Mahmoud et al. | Federated learning resource optimization and client selection for total energy minimization under outage, latency, and bandwidth constraints with partial or no CSI | |
| Amiriara et al. | IRS-user association in IRS-aided MISO wireless networks: Convex optimization and machine learning approaches |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |