CN112969164B - Intelligent substation communication wireless resource allocation method based on D2D assistance in 5G network - Google Patents

Intelligent substation communication wireless resource allocation method based on D2D assistance in 5G network Download PDF

Info

Publication number
CN112969164B
CN112969164B CN202110208132.3A CN202110208132A CN112969164B CN 112969164 B CN112969164 B CN 112969164B CN 202110208132 A CN202110208132 A CN 202110208132A CN 112969164 B CN112969164 B CN 112969164B
Authority
CN
China
Prior art keywords
channel
sensor
edge gateway
max
formula
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.)
Active
Application number
CN202110208132.3A
Other languages
Chinese (zh)
Other versions
CN112969164A (en
Inventor
李奇越
唐皓辰
丁津津
徐晓冰
高博
孙伟
汪玉
李帷韬
李远松
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Electric Power Research Institute of State Grid Anhui Electric Power Co Ltd
Hefei University of Technology
Original Assignee
Electric Power Research Institute of State Grid Anhui Electric Power Co Ltd
Hefei University of Technology
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Electric Power Research Institute of State Grid Anhui Electric Power Co Ltd, Hefei University of Technology filed Critical Electric Power Research Institute of State Grid Anhui Electric Power Co Ltd
Priority to CN202110208132.3A priority Critical patent/CN112969164B/en
Publication of CN112969164A publication Critical patent/CN112969164A/en
Application granted granted Critical
Publication of CN112969164B publication Critical patent/CN112969164B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
    • 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
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/53—Allocation or scheduling criteria for wireless resources based on regulatory allocation policies
    • 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 discloses an intelligent substation communication wireless resource allocation method based on D2D assistance in a 5G network, which comprises the following steps: 1. constructing an optimal cluster division algorithm, and performing cluster division on sensors in the intelligent substation; 2. performing initial power distribution on the sensor, the edge gateway and the inspection robot; 3. channel allocation is carried out on the sensor, the edge gateway and the inspection robot; 4. power distribution to sensors, edge gateways, and inspection robots is improved. The invention can support cooperative data acquisition and resource allocation of the sensor and the inspection robot in the intelligent substation, and maximizes the spectrum efficiency while ensuring the system throughput.

Description

Intelligent substation communication wireless resource allocation method based on D2D assistance in 5G network
Technical Field
The invention relates to the field of uplink communication data transmission of intelligent substations, in particular to a communication wireless resource allocation method of an intelligent substation based on D2D assistance in a 5G network, which is suitable for an environment where a mass sensor and an inspection robot in the substation cooperate to acquire and transmit data, jointly executes sensor cluster division and wireless resource allocation, and maximizes spectral efficiency while ensuring system throughput.
Background
The intelligent transformer substation is a novel transformer substation which adopts advanced and reliable intelligent equipment such as an intelligent sensor, an intelligent inspection robot and a mobile intelligent terminal, automatically completes functions such as information measurement, acquisition, control and monitoring and the like by using total-station information digitization, communication platform networking and information sharing standardization as basic requirements and has advanced functions such as real-time automatic control, fault monitoring, cooperative interaction and the like. As an important link of an intelligent power grid, bidirectional real-time data acquisition and communication are the basis of scheduling control and intelligent monitoring of an intelligent substation.
The fifth generation mobile communication technology (5G) is a latest generation cellular mobile communication technology with high speed, low time delay, large connection and high reliability, and can meet three application scenes of enhanced mobile broadband, mass machine communication and high-reliability low-time delay communication. The 5G network introduces a large-scale multi-input multi-output antenna system, a Device-to-Device (D2D) communication technology, an in-band full duplex communication technology and other new wireless transmission technologies, and can support the requirements of mass sensor data transmission in an intelligent substation, and large data communication such as inspection robot fault images.
The university of southeast schroe is designed and realized a data communication gateway in an intelligent substation monitoring system (university of southeast schroe academic thesis, 2018, "design and realization of a data communication gateway in an intelligent substation monitoring system"). The author provides a general design scheme of the data communication gateway by analyzing the requirements of the data communication gateway, and discusses a protocol conversion model based on a shared memory. Finally, the author gives the service processing flow and the concrete implementation method of each functional module, and performs the function and performance test of the data gateway. The test result shows that the software, the hardware and the transmission method of the data communication gateway provided by the article can sufficiently support the real-time multitask application function of the data communication gateway, and the performance requirement of the data communication gateway is ensured. However, the communication gateway does not consider the channel gain when the mass sensor is accessed, and the number of measurement and control devices is small during performance test, so that the access condition of the mass sensor cannot be met.
An intelligent substation wireless heterogeneous network resource sharing strategy (power grid and clean energy, 2016 and intelligent substation wireless heterogeneous network resource sharing strategy) is proposed by koreans and the like of the power science research institute of the power company in the south of the Henan province of the State grid. An author divides the service requirements of the intelligent transformer substation into local services and wide area services, and provides a hierarchical wireless heterogeneous communication network structure of the intelligent transformer substation; aiming at the hierarchical intelligent substation wireless heterogeneous network, a D2D multiplexing uplink communication link resource algorithm is researched to relieve the problem of shortage of wireless frequency resources of an intelligent power grid. Simulation results show that the network structure and the resource sharing algorithm can well support service access of the intelligent substation, and meanwhile higher local communication frequency multiplexing gain is provided, so that the network service carrying capacity is integrally improved. However, the resource sharing strategy only considers the problem of spectrum and power joint allocation, does not consider cluster division of the sensors and the edge gateways, cannot provide the highest channel gain for the mass sensors, and cannot guarantee the requirement of the minimum throughput of the system.
The invention discloses a combined optimization method for data security and resource allocation in a distributed wireless network of a transformer substation (publication number: CN112148478A) by the land country and the like of an ultra-high voltage transmission company, which comprises the following steps: firstly, a distributed system formed by scattered substations is used as a block chain to store communication data of the substations; secondly, achieving data consistency based on a Byzantine fault-tolerant protocol, and designing communication delay parameters consumed in the data consistency achieving process; and finally, the throughput function of the system is maximized to be a deep reinforcement learning target, and the deep reinforcement learning is adopted for learning training, so that the throughput and the communication delay of the system are optimal, and the aims of reducing the communication delay while optimally distributing the communication channel resources, the block chain block number and the capacity of the distributed system formed by the transformer substation are fulfilled. However, the method does not consider the problems of cluster division and power constraint of the sensors, and cannot maximize the spectral efficiency of the system.
Disclosure of Invention
In order to avoid the defects of the prior art, the invention provides the intelligent substation communication wireless resource allocation method based on D2D assistance in the 5G network, so that the sensor cluster division and the wireless resource allocation can be cooperatively executed, and the frequency spectrum efficiency is maximized while the system throughput is ensured.
The invention adopts the following technical scheme for solving the technical problems:
the invention discloses an intelligent substation communication wireless resource allocation method based on D2D assistance in a 5G network, which is characterized by comprising the following steps of:
step one, constructing an uplink network environment;
the uplink network environment includes: a sensor set M consisting of M | sensors, an edge gateway set H consisting of H | edge gateways, an inspection robot set C consisting of C | inspection robots and 1 wireless private network base station of 5G; any current active sensor in the sensor set M is marked as M, any current active edge gateway in the edge gateway set H is marked as H, any current active inspection robot in the inspection robot set C is marked as C, S is defined as a channel set used in a 5G network uplink, and any channel is marked as S; definition of
Figure GDA0003483104180000021
All active user sets of the uplink channel are provided, and any one active user is marked as u;
step two, assuming that each sensor can be connected with only one edge gateway, constructing an optimal cluster division algorithm, and performing cluster division on the sensor set M;
thirdly, performing initial equal power distribution on the sensor m, the edge gateway h and the inspection robot c by using the formula (1);
Figure GDA0003483104180000031
in the formula (1), the reaction mixture is,
Figure GDA0003483104180000032
average transmission power over channel s for active user u; puTotal transmission power for active user u; | S | is the total number of channels used in the uplink;
Figure GDA0003483104180000033
average transmit power over channel s for sensor m; pmIs the total transmission power of sensor m;
constructing a sensor channel allocation algorithm, an edge gateway channel allocation algorithm and an inspection robot channel allocation algorithm, and performing iterative allocation until the channel allocation processes of all the sensors, the edge gateways and the inspection robot converge, so as to obtain a sensor channel allocation scheme, an edge gateway channel allocation scheme and an inspection robot channel allocation scheme;
step five, under each channel allocation scheme, respectively improving initial equal power allocation of the sensor, the edge gateway and the inspection robot, namely taking the maximum value of throughput in the uplink transmission process as a target function, and establishing a series of constraint conditions according to power control and throughput requirements under different service qualities so as to form an optimal power allocation model;
and step six, solving the optimal power distribution model by adopting a CPLEX solver, thereby obtaining an optimal wireless resource distribution scheme.
The intelligent substation communication wireless resource allocation method based on D2D assistance in the 5G network is also characterized in that the second step is carried out according to the following process:
step 2.1, establishing the position l of the edge gateway h by using the formula (2)h：
Figure GDA0003483104180000034
In the formula (1), lcRepresenting the two-dimensional position of the base station of the 5G wireless private network; lmRepresents the two-dimensional position of sensor m; y ish,mIndicating whether the sensor m is connected with the edge gateway h or not, if yh,m1, it means connected, otherwise, it means unconnected;
step 2.2, establishing the maximum connection constraint of the edge gateway h by using the formula (3):
Figure GDA0003483104180000035
in the formula (3), the reaction mixture is,
Figure GDA0003483104180000036
represents the maximum number of sensors connected by the edge gateway h;
step 2.3, constructing an optimal cluster division algorithm:
step 2.3.1, connecting the selected sensor with the edge gateway with the highest channel gain;
step 2.3.2, determining the position of the connected edge gateway according to the formula (2);
step 2.3.3, verifying whether the maximum connection constraint of the connected edge gateway is established by using the formula (3); if yes, executing the step 2.3.4, otherwise, after the sensor is connected with the edge gateway with the second highest channel gain again, returning to execute the step 2.3.2;
and 2.3.4, sequentially executing the rest sensors which are not connected with the edge gateway in the sensor set M according to the step 2.3.1 until all the edge gateways finish the connection of | M | sensors, thereby outputting a final connection result and taking the final connection result as a cluster division result.
The fourth step is carried out according to the following processes:
step 4.1, constructing a sensor channel allocation algorithm:
step 4.1.1, calculating the throughput of each sensor on all channels, and storing the sensor throughput value in a sensor channel matrix with the dimension of M x S;
step 4.1.2, find in the sensor channel matrixTo the element (m) with the largest throughput valuemax,smax) And will channel smaxIs assigned to the sensor mmax；
Step 4.1.3, remove mth in the sensor channel matrixmaxRow and smaxColumn, thereby converting a sensor channel matrix of dimension | M | × | S | into a sensor channel matrix of dimension | M-1| × | S-1 |;
step 4.1.4, switching to step 4.1.2 until the sensor channel matrix becomes a 0 x 0 dimensional matrix;
step 4.2, constructing an edge gateway channel allocation algorithm:
step 4.2.1, calculating the throughput of each edge gateway on all channels, and storing the throughput of the edge gateway in an edge gateway channel matrix with dimension | H | × | S |;
step 4.2.2, finding the element (h) with the maximum throughput value of the edge gateway in the channel matrix of the edge gatewaymax,smax) And will channel smaxAssigned to edge gateway hmax；
Step 4.2.3, the current edge gateway hmaxCumulative throughput increase of (h)max,smax) The corresponding element value;
step 4.2.4, remove the s th in the edge gateway channel matrixmaxConverting the | H | × | S | dimensional edge gateway channel matrix into | H | × | S-1| dimensional edge gateway channel matrix;
step 4.2.5, if the edge gateway hmaxIs greater than or equal to its required throughput, then the h-th throughput in the edge gateway channel matrix is removedmaxThe method comprises the steps of converting an edge gateway channel matrix with dimension | H | × | S-1| into an edge gateway channel matrix with dimension | H-1| × | S-1 |; otherwise, reserving an edge gateway channel matrix with dimension | H | × | S-1 |;
step 4.2.6, go to step 4.2.2 until the edge gateway channel matrix becomes a 0 x 0 dimensional matrix;
and 4.3, judging whether idle channels are not distributed, if so, distributing channels to the inspection robot by using a formula (4), otherwise, finishing the channel distribution of the inspection robot:
Figure GDA0003483104180000051
in the formula (4), the reaction mixture is,
Figure GDA0003483104180000052
a variable 0-1, indicating whether channel s is allocated to active user u;
Figure GDA0003483104180000053
represents the throughput of the active user u transmission on channel s; s' represents the current remaining free channel.
The fifth step is carried out according to the following processes:
step 5.1, establishing an objective function by using the formula (5):
Figure GDA0003483104180000054
in the formula (5), the reaction mixture is,
Figure GDA0003483104180000055
and
Figure GDA0003483104180000056
for solving variables, respectively representing the transmission power of the active user u on the channel s and the transmission power of the sensor m on the channel s; w represents the bandwidth of each channel;
Figure GDA0003483104180000057
channel gain of a channel s is formed between a user u and a 5G wireless private network base station;
Figure GDA0003483104180000058
channel gain of a channel s is formed between the sensor m and the 5G wireless private network base station; sigma is additive white Gaussian noise;
step 5.2, establishing a series of constraint conditions by using the formula (6) to the formula (9):
Figure GDA0003483104180000059
Figure GDA00034831041800000510
Figure GDA00034831041800000511
Figure GDA00034831041800000512
in formula (6) -formula (9), SuAnd SmRespectively representing the set of channels allocated to user u and sensor m;
Figure GDA00034831041800000513
representing the channel gain between sensor m and edge gateway h on channel s;
Figure GDA00034831041800000514
representing the channel gain between the active user u on channel s and the edge gateway h;
Figure GDA00034831041800000515
represents the throughput required to support user u;
Figure GDA00034831041800000516
representing the throughput required to support sensor m.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention designs a framework for jointly applying D2D terminal direct communication and 5G cellular network communication in an intelligent substation, and applies D2D terminal direct communication when small data of a sensor and an edge gateway are transmitted in an uplink manner, so that cellular network resources can be shared, and the frequency spectrum utilization rate is improved; and 5G cellular network communication is applied during the uplink transmission of the big data of the edge gateway and the inspection robot, so that the low time delay and the reliability of data transmission are ensured.
2. The invention jointly makes the problems of sensor cluster division and wireless resource allocation in D2D communication, so as to maximize the total rate in a cellular uplink, simultaneously ensure the minimum throughput requirement of each sensor and an edge gateway, provide the highest channel gain for massive sensors and improve the spectrum utilization rate.
3. The wireless resource allocation method provided by the invention can find a feasible allocation scheme in real time through the CPLEX solver, and reduces the operation time of wireless resource allocation in the application field.
Drawings
Fig. 1 is a system architecture diagram of a resource allocation method according to the present invention.
Detailed Description
In this embodiment, as shown in fig. 1, a method for allocating communication wireless resources of an intelligent substation based on D2D assistance in a 5G network is performed according to the following steps:
step one, constructing an uplink network environment;
the uplink network environment includes: a sensor set M composed of M | sensors, an edge gateway set H composed of H | edge gateways, an inspection robot set C composed of C | inspection robots, 1 5G wireless private network base station, and 1 metering data management system, where M | is 80, | H | 20, and | C | 10 in this embodiment; any current active sensor in the sensor set M is marked as M, any current active edge gateway in the edge gateway set H is marked as H, any current active inspection robot in the inspection robot set C is marked as C, S is defined as a channel set used in a 5G network uplink, and any channel is marked as S; definition of
Figure GDA0003483104180000061
All active user sets of the uplink channel are provided, and any one active user is marked as u; the data transmission of the sensor m and the edge gateway h adopts D2D terminal direct connection communication, the edge gateway h collects the data of the sensor and collects the collected dataThe data is transmitted to a 5G wireless private network base station through a 5G cellular network uplink; the inspection robot c transmits monitoring pictures, videos and other big data in the inspection process to a 5G wireless private network base station through a 5G cellular network uplink, and the base station transmits the data to a cloud metering data management system;
step two, assuming that each sensor can be connected with only one edge gateway, constructing an optimal cluster division algorithm, and performing cluster division on the sensor set M;
step 2.1, establishing the position l of the edge gateway h by using the formula (2)h：
Figure GDA0003483104180000071
In the formula (1), lcRepresenting the two-dimensional position of the base station of the 5G wireless private network; lmRepresents the two-dimensional position of sensor m; y ish,mIndicating whether the sensor m is connected with the edge gateway h or not, if yh,m1, it means connected, otherwise, it means unconnected;
step 2.2, establishing the maximum connection constraint of the edge gateway h by using the formula (3):
Figure GDA0003483104180000072
in the formula (3), the reaction mixture is,
Figure GDA0003483104180000073
indicating the maximum number of sensors connected by the edge gateway h,
Figure GDA0003483104180000074
step 2.3, constructing an optimal cluster division algorithm according to the improved K-Means algorithm:
step 2.3.1, connecting the selected sensor with the edge gateway with the highest channel gain;
step 2.3.2, determining the position of the connected edge gateway according to the formula (2);
step 2.3.3, verifying whether the maximum connection constraint of the connected edge gateway is established by using the formula (3); if yes, executing the step 2.3.4, otherwise, after the sensor is connected with the edge gateway with the second highest channel gain again, returning to execute the step 2.3.2;
and 2.3.4, sequentially executing the rest sensors which are not connected with the edge gateway in the sensor set M according to the step 2.3.1 until all the edge gateways finish the connection of | M | sensors, thereby outputting a final connection result and taking the final connection result as a cluster division result.
Thirdly, under a given cluster division scheme, performing initial equal power distribution on the sensor m, the edge gateway h and the inspection robot c by using a formula (1);
Figure GDA0003483104180000075
in the formula (1), the reaction mixture is,
Figure GDA0003483104180000076
average transmission power over channel s for active user u; puFor the total transmission power of active users u, Pu400 mW; | S | is the total number of channels used in the uplink, | S | ═ 100;
Figure GDA0003483104180000077
average transmit power over channel s for sensor m; pmIs the total transmission power, P, of the sensor mm＝200mW；
And step four, assuming that the channel s is a resource unit which can be allocated, and at the moment of each resource allocation, each sensor can allocate at most one channel for transmission. Constructing a sensor channel allocation algorithm, an edge gateway channel allocation algorithm and an inspection robot channel allocation algorithm, and performing iterative allocation until the channel allocation processes of all the sensors, the edge gateway and the inspection robot converge, thereby obtaining a sensor channel allocation scheme, an edge gateway channel allocation scheme and an inspection robot channel allocation scheme;
step 4.1, constructing a sensor channel allocation algorithm:
step 4.1.1, calculating the throughput of each sensor on all channels, and storing the sensor throughput value in a sensor channel matrix with the dimension of M x S;
step 4.1.2, find the element (m) with the largest throughput value in the sensor channel matrixmax,smax) And will channel smaxIs assigned to the sensor mmax；
Step 4.1.3, remove mth in the sensor channel matrixmaxRow and smaxColumn, thereby converting a sensor channel matrix of dimension | M | × | S | into a sensor channel matrix of dimension | M-1| × | S-1 |;
step 4.1.4, the step 4.1.2 is carried out until the sensor channel matrix becomes a 0 x 0 dimensional matrix;
step 4.2, constructing an edge gateway channel allocation algorithm:
step 4.2.1, calculating the throughput of each edge gateway on all channels, and storing the throughput of the edge gateway in an edge gateway channel matrix with dimension | H | × | S |;
step 4.2.2, finding the element (h) with the maximum throughput value of the edge gateway in the channel matrix of the edge gatewaymax,smax) And will channel smaxAssigned to edge gateway hmax；
Step 4.2.3, the current edge gateway hmaxCumulative throughput increase of (h)max,smax) The corresponding element value;
step 4.2.4, remove the s-th in the edge gateway channel matrixmaxConverting the | H | × | S | dimensional edge gateway channel matrix into | H | × | S-1| dimensional edge gateway channel matrix;
step 4.2.5, if the edge gateway hmaxIs greater than or equal to its required throughput, then the h-th throughput in the edge gateway channel matrix is removedmaxThe method comprises the steps of converting an edge gateway channel matrix with dimension | H | × | S-1| into an edge gateway channel matrix with dimension | H-1| × | S-1 |; otherwise, reserving an edge gateway channel matrix with dimension | H | × | S-1 |;
step 4.2.6, go to step 4.2.2 until the edge gateway channel matrix becomes 0 x 0 dimensional matrix;
and 4.3, judging whether idle channels are not distributed, if so, distributing channels to the inspection robot by using a formula (4), otherwise, finishing the channel distribution of the inspection robot:
Figure GDA0003483104180000091
in the formula (4), the reaction mixture is,
Figure GDA0003483104180000092
a variable 0-1, indicating whether channel s is allocated to active user u;
Figure GDA0003483104180000093
represents the throughput of the active user u transmission on channel s; s' represents the current remaining free channel.
Step five, under each channel allocation scheme, respectively improving initial equal power allocation of the sensor, the edge gateway and the inspection robot, namely taking the maximum value of throughput in the uplink transmission process as a target function, and establishing a series of constraint conditions according to power control and throughput requirements under different service qualities so as to form an optimal power allocation model;
step 5.1, establishing an objective function by using the formula (5):
Figure GDA0003483104180000094
in the formula (5), the reaction mixture is,
Figure GDA0003483104180000095
and
Figure GDA0003483104180000096
for solving variables, respectively representing the transmission power of the active user u on the channel s and the transmission power of the sensor m on the channel s; w represents the bandwidth of each channel, w is 200 kHz;
Figure GDA0003483104180000097
the channel gain of the channel s between the user u and the 5G wireless private network base station is measured by the base station;
Figure GDA0003483104180000098
the channel gain of the channel s between the sensor m and the 5G wireless private network base station is measured by the base station; sigma is additive white Gaussian noise, and the mean value of the additive white Gaussian noise follows Gaussian distribution with the standard deviation of 8 dB;
step 5.2, establishing a series of constraint conditions by using the formula (6) to the formula (9):
Figure GDA0003483104180000099
Figure GDA00034831041800000910
Figure GDA00034831041800000911
Figure GDA00034831041800000912
in formula (6) -formula (9), SuAnd SmRespectively representing the set of channels allocated to user u and sensor m;
Figure GDA00034831041800000913
representing the channel gain between the sensor m and the edge gateway h on the channel s, which can be measured by the base station;
Figure GDA00034831041800000914
representing the channel gain between the active user u on the channel s and the edge gateway h, which can be measured by the base station;
Figure GDA00034831041800000915
representing the throughput required to support user u,
Figure GDA0003483104180000101
Figure GDA0003483104180000102
representing the throughput required to support sensor m,
Figure GDA0003483104180000103
and step six, solving the optimal power distribution model by adopting a CPLEX solver, thereby obtaining an optimal wireless resource distribution scheme.

Claims (1)

1. A communication wireless resource allocation method of an intelligent substation based on D2D assistance in a 5G network is characterized by comprising the following steps:
step one, constructing an uplink network environment;
the uplink network environment includes: a sensor set M consisting of M | sensors, an edge gateway set H consisting of H | edge gateways, an inspection robot set C consisting of C | inspection robots and 1 wireless private network base station of 5G; any current active sensor in the sensor set M is marked as M, any current active edge gateway in the edge gateway set H is marked as H, any current active inspection robot in the inspection robot set C is marked as C, S is defined as a channel set used in a 5G network uplink, and any channel is marked as S; defining U-C-H as a set of all active users of an uplink channel, wherein any one active user is recorded as U;
step two, assuming that each sensor can be connected with only one edge gateway, constructing an optimal cluster division algorithm, and performing cluster division on the sensor set M;
step 2.1, establishing the position l of the edge gateway h by using the formula (2)h：
Figure FDA0003483104170000011
In the formula (1), lcRepresenting the two-dimensional position of the base station of the 5G wireless private network; lmRepresents the two-dimensional position of sensor m; y ish,mIndicating whether the sensor m is connected with the edge gateway h or not, if yh,m1, it means connected, otherwise, it means unconnected;
step 2.2, establishing the maximum connection constraint of the edge gateway h by using the formula (3):
Figure FDA0003483104170000012
in the formula (3), the reaction mixture is,
Figure FDA0003483104170000013
represents the maximum number of sensors connected by the edge gateway h;
step 2.3, constructing an optimal cluster division algorithm:
step 2.3.1, connecting the selected sensor with the edge gateway with the highest channel gain;
step 2.3.2, determining the position of the connected edge gateway according to the formula (2);
step 2.3.3, verifying whether the maximum connection constraint of the connected edge gateway is established by using the formula (3); if yes, executing the step 2.3.4, otherwise, after the sensor is connected with the edge gateway with the second highest channel gain again, returning to execute the step 2.3.2;
step 2.3.4, the rest sensors which are not connected with the edge gateway in the sensor set M are executed according to the sequence of the step 2.3.1 until all the edge gateways finish the connection of | M | sensors, so that the final connection result is output and is used as a cluster division result;
thirdly, performing initial equal power distribution on the sensor m, the edge gateway h and the inspection robot c by using the formula (1);
Figure FDA0003483104170000021
in the formula (1), the reaction mixture is,
Figure FDA0003483104170000022
average transmission power over channel s for active user u; puTotal transmission power for active user u; | S | is the total number of channels used in the uplink;
Figure FDA0003483104170000023
average transmit power over channel s for sensor m; pmIs the total transmission power of sensor m;
constructing a sensor channel allocation algorithm, an edge gateway channel allocation algorithm and an inspection robot channel allocation algorithm, and performing iterative allocation until the channel allocation processes of all the sensors, the edge gateways and the inspection robot converge, so as to obtain a sensor channel allocation scheme, an edge gateway channel allocation scheme and an inspection robot channel allocation scheme;
step 4.1, constructing a sensor channel allocation algorithm:
step 4.1.1, calculating the throughput of each sensor on all channels, and storing the sensor throughput value in a sensor channel matrix with the dimension of M x S;
step 4.1.2, find the element (m) with the largest throughput value in the sensor channel matrixmax,smax) And will channel smaxIs assigned to the sensor mmax；
Step 4.1.3, remove mth in the sensor channel matrixmaxRow and smaxColumn, thereby converting a sensor channel matrix of dimension | M | × | S | into a sensor channel matrix of dimension | M-1| × | S-1 |;
step 4.1.4, switching to step 4.1.2 until the sensor channel matrix becomes a 0 x 0 dimensional matrix;
step 4.2, constructing an edge gateway channel allocation algorithm:
step 4.2.1, calculating the throughput of each edge gateway on all channels, and storing the throughput of the edge gateway in an edge gateway channel matrix with dimension | H | × | S |;
step 4.2.2, finding the element (h) with the maximum throughput value of the edge gateway in the channel matrix of the edge gatewaymax,smax) And will channel smaxAssigned to edge gateway hmax；
Step 4.2.3, the current edge gateway hmaxCumulative throughput increase of (h)max,smax) The corresponding element value;
step 4.2.4, removing the s-th channel matrix of the edge gatewaymaxConverting the | H | × | S | dimensional edge gateway channel matrix into | H | × | S-1| dimensional edge gateway channel matrix;
step 4.2.5, if the edge gateway hmaxIs greater than or equal to its required throughput, then the h-th gateway channel matrix in the edge gateway channel matrix is removedmaxThe method comprises the steps of converting an edge gateway channel matrix with dimension | H | × | S-1| into an edge gateway channel matrix with dimension | H-1| × | S-1 |; otherwise, reserving an edge gateway channel matrix with dimension | H | × | S-1 |;
step 4.2.6, go to step 4.2.2 until the edge gateway channel matrix becomes a 0 x 0 dimensional matrix;
and 4.3, judging whether idle channels are not distributed, if so, distributing channels to the inspection robot by using a formula (4), otherwise, finishing the channel distribution of the inspection robot:
Figure FDA0003483104170000031
in the formula (4), the reaction mixture is,
Figure FDA0003483104170000032
a variable 0-1, indicating whether channel s is allocated to active user u;
Figure FDA0003483104170000033
represents the throughput of the active user u transmission on channel s; s' represents the current remaining idle channel;
step five, under each channel allocation scheme, respectively improving initial equal power allocation of the sensor, the edge gateway and the inspection robot, namely taking the maximum value of throughput in the uplink transmission process as a target function, and establishing a series of constraint conditions according to power control and throughput requirements under different service qualities so as to form an optimal power allocation model;
step 5.1, establishing an objective function by using the formula (5):
Figure FDA0003483104170000034
in the formula (5), the reaction mixture is,
Figure FDA0003483104170000035
and
Figure FDA0003483104170000036
for solving variables, respectively representing the transmission power of the active user u on the channel s and the transmission power of the sensor m on the channel s; w represents the bandwidth of each channel;
Figure FDA0003483104170000037
channel gain of a channel s is formed between a user u and a 5G wireless private network base station;
Figure FDA0003483104170000038
channel gain of a channel s is formed between the sensor m and the 5G wireless private network base station; sigma is additive white Gaussian noise;
step 5.2, establishing a series of constraint conditions by using the formula (6) to the formula (9):
Figure FDA0003483104170000039
Figure FDA00034831041700000310
Figure FDA00034831041700000311
Figure FDA0003483104170000041
in formula (6) -formula (9), SuAnd SmRespectively representing the set of channels allocated to user u and sensor m;
Figure FDA0003483104170000042
representing the channel gain between sensor m and edge gateway h on channel s;
Figure FDA0003483104170000043
representing the channel gain between the active user u on channel s and the edge gateway h;
Figure FDA0003483104170000044
represents the throughput required to support user u;
Figure FDA0003483104170000045
represents the throughput required to support sensor m;
and step six, solving the optimal power distribution model by adopting a CPLEX solver, thereby obtaining an optimal wireless resource distribution scheme.
CN202110208132.3A 2021-02-24 2021-02-24 Intelligent substation communication wireless resource allocation method based on D2D assistance in 5G network Active CN112969164B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110208132.3A CN112969164B (en) 2021-02-24 2021-02-24 Intelligent substation communication wireless resource allocation method based on D2D assistance in 5G network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110208132.3A CN112969164B (en) 2021-02-24 2021-02-24 Intelligent substation communication wireless resource allocation method based on D2D assistance in 5G network

Publications (2)

Publication Number Publication Date
CN112969164A CN112969164A (en) 2021-06-15
CN112969164B true CN112969164B (en) 2022-03-22

Family

ID=76285980

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110208132.3A Active CN112969164B (en) 2021-02-24 2021-02-24 Intelligent substation communication wireless resource allocation method based on D2D assistance in 5G network

Country Status (1)

Country Link
CN (1) CN112969164B (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015112068A1 (en) * 2014-01-27 2015-07-30 Telefonaktiebolaget L M Ericsson (Publ) Network node, wireless device, methods therein, computer programs and computer-readable mediums comprising the computer programs, for adapting and adopting, respectively, an allocation of radio resources
CN108134641A (en) * 2017-12-22 2018-06-08 南京邮电大学 A kind of base station spectral bandwidth distribution method based on SCMA multiple access access mechanisms

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9883523B2 (en) * 2013-06-03 2018-01-30 Lg Electronics Inc. Method for managing wireless resource and apparatus therefor
CN106507316B (en) * 2016-11-02 2019-03-26 西安邮电大学 User's sub-clustering and resource allocation methods under a kind of D2D multicast scene

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015112068A1 (en) * 2014-01-27 2015-07-30 Telefonaktiebolaget L M Ericsson (Publ) Network node, wireless device, methods therein, computer programs and computer-readable mediums comprising the computer programs, for adapting and adopting, respectively, an allocation of radio resources
CN108134641A (en) * 2017-12-22 2018-06-08 南京邮电大学 A kind of base station spectral bandwidth distribution method based on SCMA multiple access access mechanisms

Also Published As

Publication number Publication date
CN112969164A (en) 2021-06-15

Similar Documents

Publication Publication Date Title
US20220086052A1 (en) Intelligent management method and system based on edge computing
Wang et al. A deep learning based energy-efficient computational offloading method in Internet of vehicles
CN111475274B (en) Cloud collaborative multi-task scheduling method and device
CN111182076B (en) Cloud-edge cooperative smart power grid monitoring system and resource allocation and scheduling method thereof
CN113156992B (en) Three-layer architecture collaborative optimization method for unmanned aerial vehicle in edge environment
Ren et al. Collaborative edge computing and caching with deep reinforcement learning decision agents
CN113098711A (en) Power distribution Internet of things CPS (control system) management and control method and system based on cloud edge cooperation
CN113132945A (en) Energy-saving scheduling method and system for railway private network base station cell
CN112969164B (en) Intelligent substation communication wireless resource allocation method based on D2D assistance in 5G network
CN113821346A (en) Computation uninstalling and resource management method in edge computation based on deep reinforcement learning
CN112162837B (en) Edge calculation scheduling method and system based on software definition
CN113676357A (en) Decision method for edge data processing in power internet of things and application thereof
CN116684472A (en) Service deployment system and service deployment method for terminal-side computing network
CN116347522A (en) Task unloading method and device based on approximate computation multiplexing under cloud edge cooperation
CN116109058A (en) Substation inspection management method and device based on deep reinforcement learning
CN113037837B (en) Intelligent terminal facing comprehensive energy system and based on edge calculation and control method thereof
Shah Mobile edge cloud: Opportunities and challenges
CN109548055B (en) Autonomous energy management method in ultra-dense wireless network based on energy collection
CN102438325B (en) Resource scheduling method based on cognitive radio terminal reconfiguration system
Ma Communication Resource Allocation Strategy of Internet of Vehicles Based on MEC.
CN115065727B (en) Task unloading method based on edge computing scene
CN113485718B (en) Context-aware AIoT application program deployment method in edge cloud cooperative system
CN109413623B (en) Cooperative computing migration method between energy-starved terminal and flow-starved terminal
Liu et al. Workload Analysis and Microservice Placement of Energy Management Application based on Power Internet of Things
Liu et al. Cloud-edge collaboration-based data processing capability reconfiguration optimization in low voltage power distribution grid

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