CN115001975A - 5G slice access power distribution network protection optimization method and system - Google Patents
5G slice access power distribution network protection optimization method and system Download PDFInfo
- Publication number
- CN115001975A CN115001975A CN202110449049.5A CN202110449049A CN115001975A CN 115001975 A CN115001975 A CN 115001975A CN 202110449049 A CN202110449049 A CN 202110449049A CN 115001975 A CN115001975 A CN 115001975A
- Authority
- CN
- China
- Prior art keywords
- slice
- accessed
- constraint
- distribution network
- power distribution
- 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 74
- 238000009826 distribution Methods 0.000 title claims abstract description 53
- 238000005457 optimization Methods 0.000 title claims abstract description 35
- 230000005540 biological transmission Effects 0.000 claims abstract description 33
- 238000004891 communication Methods 0.000 claims abstract description 28
- 230000006870 function Effects 0.000 claims description 86
- 238000005070 sampling Methods 0.000 claims description 63
- 239000008186 active pharmaceutical agent Substances 0.000 claims description 6
- 238000005520 cutting process Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 3
- 229910052739 hydrogen Inorganic materials 0.000 claims description 2
- 239000001257 hydrogen Substances 0.000 claims description 2
- 125000004435 hydrogen atom Chemical class [H]* 0.000 claims description 2
- 230000009471 action Effects 0.000 abstract description 12
- 238000013178 mathematical model Methods 0.000 abstract description 4
- 230000008569 process Effects 0.000 abstract description 4
- 230000000694 effects Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000035772 mutation Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 229910052731 fluorine Inorganic materials 0.000 description 1
- 125000001153 fluoro group Chemical group F* 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0893—Assignment of logical groups to network elements
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
-
- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/20—Systems supporting electrical power generation, transmission or distribution using protection elements, arrangements or systems
Abstract
The invention provides a power distribution network protection optimization method for 5G slice access. And obtaining a corresponding mathematical model by adopting a data transmission scheme with minimized time delay, and then carrying out model solution by adopting a Lagrange multiplier method and a branch-and-bound method according to the scheme so as to uniformly distribute 5G network resources. By adopting the method, the time delay generated in the process of protecting communication of the power distribution network can be reduced. The external equipment can be regarded as part of interference, is integrated into a formula of data transmission rate, can optimize jitter delay generated when external movable equipment is accessed into the 5G network, and has certain robustness to the external interference. According to the invention, the power distribution network protection optimization method adopting 5G slice access overcomes the problems of power distribution network protection action maloperation and refusal action caused by 5G low delay and jitter, realizes further precision of power distribution network protection action, and enables the whole power distribution network system to run more stably and safely.
Description
Technical Field
The invention relates to the technical field of power distribution network protection, in particular to a power distribution network protection optimization method and system for 5G slice access.
Background
In recent years, 5G technology has been developed rapidly, and ultra-reliable and low-latency communication services (URLLC) brought by the technology have the advantages of high bandwidth, low latency, and the like. The 5G technology is applied to differential protection of the power distribution network, and technical advantages are provided for fault location, isolation and recovery. However, the transmission delay and jitter of 5G communication still have adverse effects on the calculation of the differential protection criterion, and problems of protection maloperation, protection refusal operation and the like are generated, which are not favorable for the operation of the whole power distribution network system. The main reason for the large variation of the 5G delay jitter is caused by the uneven allocation of network resources.
In order to solve the above problems, the invention discloses a distributed differential protection method and a distributed differential protection system [ ZH ] of a power distribution network based on a 5G network, which is disclosed by application number CN201911175988.4, and the invention discloses a distributed differential protection method and a distributed differential protection system of a power distribution network based on a 5G network, and the distributed differential protection method and the distributed differential protection system comprise: when the digital protection devices transmit signals, a communication channel based on a 5G network is used for communication, so that the data interaction of the two digital protection devices is realized; the 5G slice network is used as a communication channel, so that the communication safety is ensured, and on the basis of the low time delay characteristic of the 5G communication, the data link is optimized through the slice network so as to minimize the hop count of link transmission and further reduce the time delay of data transmission; the method comprises the steps that the current at two ends of a power distribution network line is simultaneously subjected to mutation detection by utilizing a digital protection device, respective mutation moments are used as synchronous time references of the digital protection devices at two ends of the line, once a mutation starting criterion is met, the digital protection device is started and sends a starting signal to an opposite end, after the digital protection device is started, a current fault component value after fault starting is calculated, and current fault component data are sent to the opposite end after calculation is completed. The data link is optimized through the slice network so as to minimize the hop count of link transmission and reduce the time delay of data transmission. But this method is not robust to external disturbances.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a power distribution network protection optimization method for 5G slice access, so that delay jitter generated by access of external mobile equipment has certain robustness, and the protection action of the power distribution network is more accurate.
The invention solves the technical problems through the following technical means:
a protection optimization method for a 5G slice-accessed power distribution network comprises the following steps:
setting I data sampling nodes in the power grid, S n (i) A slice ID currently serving the ith sampling node; among K accessible access points of the sampling node, the K-th access point deploys a slice set with a slice S m (k) M ∈ {1,2, …, M }, where M denotes the slice set size; b m (k) Is S m (k) A corresponding bandwidth; to make it possible toThe resource utilization rate of the system is highest, and resources are uniformly distributed to all the data sampling nodes; therefore, the data transmission rate of the ith sampling node is as shown in equation (1);
in the formula (1), a m (k) Represents the current slice S m (k) The number of accessed sampling nodes; d is a radical of ik Represents the distance between the sampling node i and the access point to be accessed, the propagation loss of which is PL (d) ik ) Representing; n is a radical of 0 Noise power per unit bandwidth; p k Representing the transmission power of the access point to be accessed; delta represents the total interference received by the AP to be accessed;
wherein, DS i The size of the data packet sampled for the ith sampling node;
the constraint conditions are as shown in formula (3) to formula (6):
S m (k)≥S n (i) (4)
r ik (m)≥R i ,i∈I,k∈K,m∈M (5)
in the formula (2), x ik (m) is a variable 0-1 to be solved when x ik When (m) is 1, the ith data sampling node accesses the mth slice of the kth AP, otherwise, the access is not performed; the constraint (3) indicates that a data sampling node can only access at a certain timeOne slice; the constraint condition (4) indicates that the QoS service level of the slice accessed by the data sampling node should be not less than the original service slice; the constraint condition (5) indicates that the data transmission rate of the data sampling node after being accessed into the slice should be greater than or equal to the minimum rate requirement R i (ii) a The constraint condition (6) represents that the number of nodes which can be accommodated by the slice to be accessed should be less than or equal to the number u of the remaining nodes before the access m (k)；
The invention provides a power distribution network protection optimization method for 5G slice access. And obtaining a corresponding mathematical model by adopting a data transmission scheme with minimized time delay, and then carrying out model solution by adopting a Lagrange multiplier method and a branch-and-bound method according to the scheme so as to uniformly distribute 5G network resources. By adopting the method, the time delay generated in the process of protecting communication of the power distribution network can be reduced. The external equipment can be regarded as part of interference, is integrated into a formula of data transmission rate, can optimize jitter delay generated when external movable equipment is accessed into the 5G network, and has certain robustness to the external interference. According to the invention, the power distribution network protection optimization method adopting 5G slice access overcomes the problems of power distribution network protection action maloperation and refusal action caused by 5G low delay and jitter, realizes further precision of power distribution network protection action, and enables the whole power distribution network system to run more stably and safely.
Further, the step 3 comprises the following specific steps: establishing a Lagrangian function of the nonlinear programming problem according to equation (7):
in the formula (7), L (x) ik (m, λ) represents with respect to x ik (m) and a Lagrangian function of the Lagrangian multiplier lambda,h 1 (x ik (m))，h 2 (x ik (m))，h 3 (x ik (m))，h 4 (x ik (m))，h 5 (x ik (m)) represent 5 constraint functions, respectively, λ 1 、λ 2 、λ 3 、λ 4 、λ 5 A lagrange multiplier representing a corresponding constraint function and having:
h 2 (x ik (m))＝x ik (m)-1 (9)
h 3 (x ik (m))＝S n (i)-S m (k) (10)
h 4 (x ik (m))＝R i -r ik (m) (11)
establishing a Carrocon-Kunn-Tak condition according to the formulas (13) to (16), thereby solving the optimal solution x of the relaxed nonlinear programming problem by combining the correlation equations of the Carrocon-Kunn-Tak condition relax ：
h 1 (x ik (m))＝0，h 2 (x ik (m))≤0，h 3 (x ik (m))≤0，h 4 (x ik (m))≤0，h 5 (x ik (m))≤0 (14)
λ 1 ，λ 2 ，λ 3 ，λ 4 ，λ 5 ≥0 (15)
Further, the step 4 comprises the following specific steps:
step 4.1, taking the formula (2) as a problem p-1; the inputs of the branch-and-bound method are: optimal solution x to the relaxation problem satisfying the Carlo-Cohen-Tak conditions relax Optimal objective function value Z of relaxation problem relax 0-1 any value epsilon; initialization k is 0, L is 0, U is Z relax ；
Step 4.2, from the optimal solution x relax Arbitrarily selecting a solution x which does not meet the constraint condition of 0-1 j Namely: x is the number of j ∈(0,1)；
Step 4.3, if x is more than or equal to 0 j <If ε is satisfied, the constraint x is satisfied j Adding 0 to question p-1 to form sub-question i; otherwise, the condition x will be constrained j 1 is added to the question p-1 to form a subproblem ii, epsilon representing any value from 0 to 1;
step 4.4, k + +, continue to solve the relaxation problem solution of sub-problem I or sub-problem II, which is marked as x k And the corresponding optimal objective function value is recorded as Z k ；
Step 4.5, finding out the maximum value U of the optimal target function as a new upper bound, namely:
U＝max{Z k′ |k′＝1,2,…,k},x k′ ∈[0,1]；
step 4.6, finding out the maximum value L of the target function as a new lower bound from the branches meeting the condition of 0-1, namely:
L＝max{Z k′ |k′＝1,2,…,k},x k′ ∈{0,1}；
step 4.7, if Z k′ <L, cutting off corresponding branch, otherwise Z k′ If the L value is more than L and the condition is not satisfied with 0-1, returning to the step 4.3;
step 4.8, otherwise it means that the optimal objective function values for all branches are equal to the lower bound: z k′ Is equal to L, mixing Z k′ Assignment Z 0-1 X is to k′ Is assigned to x 0-1 And as the optimal solution to the problem p-1, where x 0-1 Representing an optimal solution that meets the 0-1 constraint.
Corresponding to the method, the invention also provides a power distribution network protection optimization system accessed by the 5G slice, which comprises the following steps:
the modeling module is used for modeling the power distribution network system adopting the 5G communication network by considering communication delay:
setting I data sampling nodes in the power grid, S n (i) A slice ID currently serving the ith sampling node; among K accessible access points of the sampling node, the K-th access point deploys a slice set with a slice S m (k) M ∈ {1,2, …, M }, where M denotes the slice set size; b is m (k) Is S m (k) A corresponding bandwidth; in order to maximize the resource utilization rate of the system, resources are uniformly distributed to all the data sampling nodes; therefore, the data transmission rate of the ith sampling node is as shown in equation (1);
in the formula (1), a m (k) Representing the current slice S m (k) The number of accessed sampling nodes; d ik Represents the distance between the sampling node i and the access point to be accessed, the propagation loss of which is PL (d) ik ) Represents; n is a radical of hydrogen 0 Noise power per unit bandwidth; p k Representing the transmission power of the access point to be accessed; delta represents the total interference received by the AP to be accessed;
the target function establishing module takes the minimized time delay as an optimization target, and the target function is shown as a formula (2);
wherein, DS i The size of the data packet sampled for the ith sampling node;
the constraint conditions are as shown in formula (3) to formula (6):
S m (k)≥S n (i) (4)
r ik (m)≥R i ,i∈I,k∈K,m∈M (5)
in the formula (2), x ik (m) is a variable 0-1 to be solved when x ik When (m) is 1, the ith data sampling node accesses the mth slice of the kth AP, otherwise, the access is not performed; the constraint (3) indicates that a data sampling node can only access one slice at a time; the constraint condition (4) indicates that the QoS service level of the slice accessed by the data sampling node should be not less than the original service slice; the constraint condition (5) indicates that the data transmission rate of the data sampling node after being accessed into the slice should be greater than or equal to the minimum rate requirement R i (ii) a The constraint condition (6) represents that the number of nodes which can be contained in the slice to be accessed should be less than or equal to the number u of the residual nodes before access m (k)；
An objective function solving module for solving the objective function in the step 2 by using a Lagrange multiplier method,
a judging module, a solution of the objective function, and a 0-1 solving variable x of the objective function obtained by a branch-and-bound method 0-1 And determining whether the user accesses the corresponding slice or not according to the solving variable.
Further, the objective function solving module specifically comprises the following steps: establishing a lagrangian function of the nonlinear programming problem according to equation (7):
in the formula (7), L (x) ik (m), λ) denotes with respect to x ik (m) and Lagrangian function of the Lagrangian multiplier λ, h 1 (x ik (m))，h 2 (x ik (m))，h 3 (x ik (m))，h 4 (x ik (m))，h 5 (x ik (m)) represent 5 constraint functions, respectively, λ 1 、λ 2 、λ 3 、λ 4 、λ 5 A lagrange multiplier representing a corresponding constraint function and having:
h 2 (x ik (m))＝x ik (m)-1 (9)
h 3 (x ik (m))＝S n (i)-S m (k) (10)
h 4 (x ik (m))＝R i -r ik (m) (11)
establishing a Carrocon-Kunn-Tak condition according to the formulas (13) to (16), thereby solving the optimal solution x of the relaxed nonlinear programming problem by combining the correlation equations of the Carrocon-Kunn-Tak condition relax ：
h 1 (x ik (m))＝0，h 2 (x ik (m))≤0，h 3 (x ik (m))≤0，h 4 (x ik (m))≤0，h 5 (x ik (m))≤0 (14)
λ 1 ，λ 2 ，λ 3 ，λ 4 ，λ 5 ≥0 (15)
Further, the judging module specifically comprises the following steps:
step 4.1, taking the formula (2) as a problem p-1; the input of the branch-and-bound method is as follows: optimal solution x to the relaxation problem satisfying the Carlo-Cohen-Tak conditions relax Optimal objective function value Z of relaxation problem relax 0-1 any value epsilon; initialization k is 0, L is 0, U is Z relax ；
Step 4.2, from the optimal solution x relax Arbitrarily selecting a solution x which does not meet the constraint condition of 0-1 j Namely: x is a radical of a fluorine atom j ∈(0,1)；
Step 4.3, if x is more than or equal to 0 j <If ε is satisfied, the constraint x is satisfied j Adding 0 to question p-1 to form sub-question i; otherwise, the condition x will be constrained j 1 is added to the question p-1 to form a subproblem ii, epsilon representing any value from 0 to 1;
step 4.4, k + +, continue to solve the relaxation problem solution of sub-problem I or sub-problem II, which is marked as x k And the corresponding optimal objective function value is recorded as Z k ；
Step 4.5, finding out the maximum value U of the optimal target function as a new upper bound, namely:
U＝max{Z k′ |k′＝1,2,…,k},x k′ ∈[0,1]；
step 4.6, finding out the maximum value L of the target function from the branches meeting the condition of 0-1 as a new lower bound, namely:
L＝max{Z k′ |k′＝1,2,…,k},x k′ ∈{0,1}；
step 4.7, if Z k′ <L, cutting off corresponding branch, otherwise Z k′ If the value is more than L and the condition is not met with 0-1, returning to the step 4.3;
step 4.8, otherwise it means that the optimal objective function values for all branches are equal to the lower bound: z k′ Is equal to L, mixing Z k′ Assignment Z 0-1 X is to be k′ Is assigned to x 0-1 And as the optimal solution to the problem p-1, where x 0-1 Representing an optimal solution that meets the 0-1 constraint.
The present invention also provides a processing device comprising at least one processor, and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, which when called by the processor are capable of performing the methods described above.
The present invention also provides a computer-readable storage medium storing computer instructions that cause the computer to perform the above-described method.
The invention has the advantages that:
the invention provides a power distribution network protection optimization method for 5G slice access. And obtaining a corresponding mathematical model by adopting a data transmission scheme with minimized time delay, and then carrying out model solution by adopting a Lagrange multiplier method and a branch-and-bound method according to the scheme so as to uniformly distribute 5G network resources. By adopting the method, the time delay generated in the process of protecting communication of the power distribution network can be reduced. The external equipment can be regarded as part of interference, is integrated into a formula of data transmission rate, can optimize jitter delay generated when external movable equipment is accessed into the 5G network, and has certain robustness to the external interference. According to the invention, the power distribution network protection optimization method adopting 5G slice access overcomes the problems of power distribution network protection action maloperation and refusal action caused by 5G low delay and jitter, realizes further precision of power distribution network protection action, and enables the whole power distribution network system to run more stably and safely.
Drawings
Fig. 1 is a flow chart of a power distribution network protection optimization method of 5G slice access in the embodiment of the present invention;
fig. 2 is an application scenario of a power distribution network protection optimization method for 5G slice access in the embodiment of the present invention;
FIG. 3 is a comparison of the effects of example 1 without optimization and with the optimization method of the present invention;
fig. 4 is a comparison of the effect of example 25G transmission delay with or without user access in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
The embodiment provides a power distribution network protection optimization method for 5G slice access, which comprises the following steps:
the scenario of differential protection of a power distribution network by using a 5G communication network is considered in the present embodiment as shown in fig. 1. The 5G power distribution network protection comprises 1 5G base station and a plurality of protection devices accessed to the base station. Each protection device has current sampling capability and is arranged on two sides of the line to perform differential protection current data sampling. The 5G base station provides a 5G network, provides communication service for the protection equipment and also provides network service for mobile terminal equipment accessed by other users. The comparison of line current data among the protection devices can be realized by a 5G communication mode, so that whether the line needs to perform differential protection action or not is judged.
The distribution grid is provided with I data acquisition nodes, DS i Packet size, S, sampled for the ith node n (i) A slice ID currently serving the ith sampling node; in K accessible Access Points (APs) of an acquisition node, any type of slice S exists in a slice set deployed by the kth AP m (k) M ∈ {1,2, …, M }, where M denotes the slice set size; b is m (k) Is S m (k) The corresponding bandwidth. In order to ensure that the resource utilization rate of the system is the highest, all the data acquisition nodes are uniformly distributed with resources. Therefore, the data transmission rate of the ith node is as shown in equation (1).
In the formula (1), a m (k) Represents the current slice S m (k) The number of accessed nodes; d ik Represents the distance between node i and the AP to be accessed, whose propagation loss PL (d) ik ) Representing; n is a radical of 0 Noise power per unit bandwidth; p is k Indicating the transmit power of the AP to be accessed(ii) a δ represents the total interference received by the AP to be accessed.
And 2, in order to enable the data acquisition node to maximally utilize slice resources during data transmission, meet the requirements of time delay and throughput, and take the minimized time delay as an optimization target, wherein an objective function is shown as a formula (2).
The constraint conditions are as shown in formula (3) to formula (6):
S m (k)≥S n (i) (4)
r ik (m)≥R i ,i∈I,k∈K,m∈M (5)
in the formula (2), x ik (m) is a variable 0-1 to be solved when x ik When the (m) is 1, the ith data acquisition node is accessed to the mth slice of the kth AP, otherwise, the access is not performed; the constraint condition (3) indicates that one data acquisition node can only access one slice at a certain time; the constraint condition (4) indicates that the QoS service level of the slice accessed by the data acquisition node should be not less than the original service slice; the constraint condition (5) indicates that the data transmission rate of the data acquisition node after being accessed into the slice should be greater than or equal to the minimum rate requirement R i (ii) a The constraint condition (6) represents that the number of nodes which can be accommodated by the slice to be accessed should be less than or equal to the number u of the remaining nodes before the access m (k)。
in the formula (7), L (x) ik (m), λ) denotes with respect to x ik (m) and Lagrangian function of the Lagrangian multiplier λ, h 1 (x ik (m))，h 2 (x ik (m))，h 3 (x ik (m))，h 4 (x ik (m))，h 5 (x ik (m)) represent 5 constraint functions, respectively, λ 1 、λ 2 、λ 3 、λ 4 、λ 5 A lagrange multiplier representing the corresponding constraint function and having:
h 2 (x ik (m))＝x ik (m)-1 (9)
h 3 (x ik (m))＝S n (i)-S m (k) (10)
h 4 (x ik (m))＝R i -r ik (m) (11)
KKT Conditions (Karush-Kuhn-Tucker Conditions, Carrocon-Kuen-Tack Conditions) are established according to the formulas (13) to (16), so that the optimal solution x of the relaxed nonlinear programming problem is solved by combining related equations of the KKT Conditions relax ：
h 1 (x ik (m))＝0，h 2 (x ik (m))≤0，h 3 (x ik (m))≤0，h 4 (x ik (m))≤0，h 5 (x ik (m))≤0 (14)
λ 1 ，λ 2 ，λ 3 ，λ 4 ，λ 5 ≥0 (15)
and 4.1, taking the formula (2) as a problem p-1. The inputs of the branch-and-bound method are: optimal solution x to relaxation problem satisfying KKT condition relax Optimal objective function value Z of relaxation problem relax 0-1 arbitrary value epsilon. Initialization k is 0, L is 0, U is Z relax 。
Step 4.2, from the optimal solution x relax Arbitrarily selecting a solution x which does not meet the constraint condition of 0-1 j Namely: x is the number of j ∈(0,1)；
Step 4.3, if x is more than or equal to 0 j <If ε is satisfied, the constraint x is satisfied j Adding 0 to question p-1 to form sub-question i; otherwise, the condition x will be constrained j 1 is added to the question p-1 to form a subproblem ii, epsilon representing any value from 0 to 1;
step 4.4, k + +, continue to solve the relaxation problem solution of sub-problem I or sub-problem II, which is marked as x k And the corresponding optimal objective function value is recorded as Z k ；
Step 4.5, finding out the maximum value U of the optimal target function as a new upper bound, namely:
U＝max{Z k′ |k′＝1,2,…,k},x k′ ∈[0,1]；
step 4.6, finding out the maximum value L of the target function as a new lower bound from the branches meeting the condition of 0-1, namely:
L＝max{Z k′ |k′＝1,2,…,k},x k′ ∈{0,1}；
step 4.7, if Z k′ <L, cutting off corresponding branch, otherwise Z k′ If the L value is more than L and the condition is not satisfied with 0-1, returning to the step 4.3;
step 4.8, else means that the optimal objective function values for all branches are equal toLower bound: z is a linear or branched member k′ Is equal to L, mixing Z k′ Assignment Z 0-1 X is to be k′ Is assigned to x 0-1 And as the optimal solution to the problem p-1, where x 0-1 Representing an optimal solution that meets the 0-1 constraint.
The embodiment discloses a power distribution network protection optimization method based on 5G slice access, aiming at the optimization problem of time delay and jitter generated when power distribution network protection collected current data is transmitted through 5G, firstly analyzing the use scene of 5G communication in a power distribution network, and establishing a mathematical model of 5G communication access time delay; then, according to the model, an optimization objective function of minimizing the time delay of the differential signal is researched, 5G slice selection which maximally utilizes communication channel resources is solved by adopting a Lagrange multiplier method and a branch-and-bound method according to the optimized objective function, and the minimum time delay of differential signal transmission is realized, so that the safety of a power grid system is improved, and the stable operation of the power grid system is ensured.
The above steps are described below with reference to specific examples.
Example 1: comparing the delay curves generated by the optimization method and the delay curves generated by the optimization method, the comparison of the two conditions is shown in fig. 2, and under the condition that the minimum delay method is not adopted, the delay generated by 5G transmission randomly changes along with the change of time, which is not beneficial to the judgment of differential protection action. With the method used herein, the time delay generated by 5G transmission will gradually decrease with time and eventually approach a constant value. From the above comparison, the advantages of the minimization optimization method proposed herein can be embodied.
Example 2: under the optimization method provided by the invention, 100 mobile terminal devices are accessed twice in 1s and 7s respectively, the 5G base station can allocate certain network resources for use, the process can slightly increase the time delay of differential protection transmission, and the generated time delay tends to be stable due to the optimization function of the algorithm, is nearly the same as the condition without the mobile terminal device access, and shows the robustness of the method. The specific results are shown in FIG. 3.
The embodiment also provides a power distribution network protection optimization system for 5G slice access, which corresponds to the method described above, and includes:
the modeling module is used for modeling the power distribution network system adopting the 5G communication network by considering communication delay:
the configuration power grid is provided with I data sampling nodes, S n (i) A slice ID currently serving the ith sampling node; among K accessible access points of the sampling node, the K-th access point deploys a slice set with a slice S m (k) M ∈ {1,2, …, M }, where M denotes the slice set size; b is m (k) Is S m (k) A corresponding bandwidth; in order to maximize the resource utilization rate of the system, resources are uniformly distributed to all the data sampling nodes; therefore, the data transmission rate of the ith sampling node is as shown in equation (1);
in the formula (1), a m (k) Representing the current slice S m (k) The number of accessed sampling nodes; d ik Represents the distance between the sampling node i and the access point to be accessed, and its propagation loss is PL (d) ik ) Represents; n is a radical of 0 Noise power per unit bandwidth; p k Representing the transmission power of the access point to be accessed; delta represents the total interference received by the AP to be accessed;
the target function establishing module takes the minimized time delay as an optimization target, and the target function is shown as a formula (2);
wherein, DS i The size of the data packet sampled for the ith sampling node;
the constraint conditions are shown in formula (3) to formula (6):
S m (k)≥S n (i) (4)
r ik (m)≥R i ,i∈I,k∈K,m∈M (5)
in the formula (2), x ik (m) is a variable 0-1 to be solved when x ik When (m) is 1, the ith data sampling node accesses the mth slice of the kth AP, otherwise, the access is not performed; the constraint (3) indicates that one data sampling node can only access one slice at a certain time; the constraint condition (4) indicates that the QoS service level of the slice accessed by the data sampling node should be not less than the original service slice; the constraint condition (5) indicates that the data transmission rate of the data sampling node after being accessed into the slice should be greater than or equal to the minimum rate requirement R i (ii) a The constraint condition (6) represents that the number of nodes which can be contained in the slice to be accessed should be less than or equal to the number u of the residual nodes before access m (k)；
An objective function solving module for solving the objective function in the step 2 by using a Lagrange multiplier method,
a judging module, a solution of the objective function, and a 0-1 solving variable x of the objective function obtained by a branch-and-bound method 0-1 And determining whether the user accesses the corresponding slice or not according to the solving variable.
The objective function solving module comprises the following specific steps: establishing a Lagrangian function of the nonlinear programming problem according to equation (7):
in the formula (7), L (x) ik (m, λ) represents with respect to x ik (m) and Lagrangian function of Lagrangian multiplier λ, h 1 (x ik (m))，h 2 (x ik (m))，h 3 (x ik (m))，h 4 (x ik (m))，h 5 (x ik (m)) represent 5 constraint functions, respectively, λ 1 、λ 2 、λ 3 、λ 4 、λ 5 A lagrange multiplier representing the corresponding constraint function and having:
h 2 (x ik (m))＝x ik (m)-1 (9)
h 3 (x ik (m))＝S n (i)-S m (k) (10)
h 4 (x ik (m))＝R i -r ik (m) (11)
establishing a Carrocon-Kunn-Tak condition according to the formulas (13) to (16), thereby solving the optimal solution x of the relaxed nonlinear programming problem by combining the correlation equations of the Carrocon-Kunn-Tak condition relax ：
h 1 (x ik (m))＝0，h 2 (x ik (m))≤0，h 3 (x ik (m))≤0，h 4 (x ik (m))≤0，h 5 (x ik (m))≤0 (14)
λ 1 ，λ 2 ，λ 3 ，λ 4 ，λ 5 ≥0 (15)
Further, the judging module specifically comprises the following steps:
step 4.1, taking the formula (2) as a problem p-1; the inputs of the branch-and-bound method are: optimal solution x to the relaxation problem satisfying the Carlo-Cohen-Tak conditions relax Problem of loosenessOptimum objective function value Z relax 0-1 any value epsilon; initialization k is 0, L is 0, U is Z relax ；
Step 4.2, from the optimal solution x relax Arbitrarily selecting a solution x which does not meet the constraint condition of 0-1 j Namely: x is the number of j ∈(0,1)；
Step 4.3, if x is more than or equal to 0 j <If ε is satisfied, the constraint x is satisfied j Adding 0 to question p-1 to form sub-question i; otherwise, the condition x will be constrained j 1 is added to the question p-1 to form a subproblem ii, epsilon representing any value from 0 to 1;
step 4.4, k + +, continue to solve the relaxation problem solution of sub-problem I or sub-problem II, which is marked as x k And the corresponding optimal objective function value is recorded as Z k ；
Step 4.5, finding out the maximum value U of the optimal target function as a new upper bound, namely:
U＝max{Z k′ |k′＝1,2,…,k},x k′ ∈[0,1]；
step 4.6, finding out the maximum value L of the target function as a new lower bound from the branches meeting the condition of 0-1, namely:
L＝max{Z k′ |k′＝1,2,…,k},x k′ ∈{0,1}；
step 4.7, if Z k′ <L, cutting off corresponding branch, otherwise Z k′ If the L value is more than L and the condition is not satisfied with 0-1, returning to the step 4.3;
step 4.8, otherwise it means that the optimal objective function values for all branches are equal to the lower bound: z k′ Is equal to L, mixing Z k′ Assignment Z 0-1 X is to be k′ Is assigned to x 0-1 And as the optimal solution to the problem p-1, where x 0-1 Representing an optimal solution that meets the 0-1 constraint.
The present invention also provides a processing device comprising at least one processor, and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, which when called by the processor are capable of performing the methods described above.
The present invention also provides a computer-readable storage medium storing computer instructions that cause the computer to perform the above-described method.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A protection optimization method for a 5G slice-accessed power distribution network is characterized by comprising the following steps:
step 1, modeling considering communication delay for a power distribution network system adopting a 5G communication network:
the configuration power grid is provided with I data sampling nodes, S n (i) A slice ID currently serving the ith sampling node; among the K accessible access points of the sampling node, the K-th access point deploys a slice S in the slice set m (k) M ∈ {1,2, …, M }, where M denotes the slice set size; b is m (k) Is S m (k) A corresponding bandwidth; in order to ensure that the resource utilization rate of the system is the highest, uniformly distributing resources to all data sampling nodes; therefore, the data transmission rate of the ith sampling node is as shown in equation (1);
in the formula (1), a m (k) Represents the current slice S m (k) The number of accessed sampling nodes; d ik Represents the distance between the sampling node i and the access point to be accessed, the propagation loss of which is PL (d) ik ) Represents; n is a radical of hydrogen 0 Noise power per unit bandwidth; p k Representing the transmission power of the access point to be accessed; delta indicates received by AP to be accessedTotal interference;
step 2, taking the minimized time delay as an optimization target, wherein an objective function is shown as a formula (2);
wherein, DS i The size of the data packet sampled for the ith sampling node;
the constraint conditions are shown in formula (3) to formula (6):
S m (k)≥S n (i) (4)
r ik (m)≥R i ,i∈I,k∈K,m∈M (5)
in the formula (2), x ik (m) is a variable 0-1 to be solved, when x ik When (m) is 1, the ith data sampling node accesses the mth slice of the kth AP, otherwise, the access is not performed; the constraint (3) indicates that one data sampling node can only access one slice at a certain time; the constraint condition (4) indicates that the QoS service level of the slice accessed by the data sampling node should be not less than the original service slice; the constraint condition (5) indicates that the data transmission rate of the data sampling node after being accessed into the slice should be greater than or equal to the minimum rate requirement R i (ii) a The constraint condition (6) represents that the number of nodes which can be accommodated by the slice to be accessed should be less than or equal to the number u of the remaining nodes before the access m (k)；
Step 3, solving the optimal solution of the objective function in the step 2 by using a Lagrange multiplier method,
step 4, obtaining 0-1 solving variable x of the objective function by using a branch-and-bound method based on the optimal solution of the step 3 0-1 To decide the user according to the solution variableWhether to access the corresponding slice; said x 0-1 Representing an optimal solution that meets the 0-1 constraint.
2. The method for protecting and optimizing the power distribution network accessed by the 5G slice according to claim 1, wherein the step 3 comprises the following specific steps: establishing a lagrangian function of the nonlinear programming problem according to equation (7):
in the formula (7), L (x) ik (m, λ) represents with respect to x ik (m) and Lagrangian function of Lagrangian multiplier λ, h 1 (x ik (m))，h 2 (x ik (m))，h 3 (x ik (m))，h 4 (x ik (m))，h 5 (x ik (m)) represent 5 constraint functions, λ, respectively 1 、λ 2 、λ 3 、λ 4 、λ 5 A lagrange multiplier representing a corresponding constraint function and having:
h 2 (x ik (m))＝x ik (m)-1 (9)
h 3 (x ik (m))＝S n (i)-S m (k) (10)
h 4 (x ik (m))＝R i -r ik (m) (11)
establishing a Carrocon-Kunn-Tak condition according to the formulas (13) to (16), thereby solving a relaxed nonlinear programming question by combining the correlation equations of the Carrocon-Kunn-Tak conditionOptimal solution x of the problem relax ：
h 1 (x ik (m))＝0，h 2 (x ik (m))≤0，h 3 (x ik (m))≤0，h 4 (x ik (m))≤0，h 5 (x ik (m))≤0 (14)
λ 1 ，λ 2 ，λ 3 ，λ 4 ，λ 5 ≥0 (15)
3. The method for protecting and optimizing the power distribution network accessed by the 5G slice according to claim 1, wherein the step 4 comprises the following specific steps:
step 4.1, taking the formula (2) as a problem p-1; the input of the branch-and-bound method is as follows: optimal solution x to the relaxation problem satisfying the Carlo-Cohen-Tak conditions relax Optimal objective function value Z of relaxation problem relax 0-1 any value epsilon; initialization k is 0, L is 0, U is Z relax ；
Step 4.2, from the optimal solution x relax Arbitrarily selecting a solution x which does not meet the constraint condition of 0-1 j Namely: x is the number of j ∈(0,1)；
Step 4.3, if x is more than or equal to 0 j <If ε is satisfied, the constraint x is satisfied j Adding 0 to question p-1 to form sub-question i; otherwise, the condition x will be constrained j 1 to the question p-1 to form a subproblem ii, epsilon represents any value from 0 to 1;
step 4.4, k + +, continue to solve the relaxation problem solution of sub-problem I or sub-problem II, which is marked as x k And the corresponding optimal objective function value is recorded as Z k ；
Step 4.5, finding out the maximum value U of the optimal target function as a new upper bound, namely:
U＝max{Z k′ |k′＝1,2,…,k},x k′ ∈[0,1]；
step 4.6, finding out the maximum value L of the target function from the branches meeting the condition of 0-1 as a new lower bound, namely:
L＝max{Z k′ |k′＝1,2,…,k},x k′ ∈{0,1}；
step 4.7, if Z k′ <L, cutting off corresponding branch, otherwise Z k′ If the L value is more than L and the condition is not satisfied with 0-1, returning to the step 4.3;
step 4.8, otherwise it means that the optimal objective function values for all branches are equal to the lower bound: z k′ Is equal to L, mixing Z k′ Assignment Z 0-1 X is to k′ Is assigned to x 0-1 And as the optimal solution to the problem p-1, where x 0-1 Representing an optimal solution that meets the 0-1 constraint.
4. The utility model provides a distribution network protection optimization system of 5G section access which characterized in that includes:
the modeling module is used for modeling the power distribution network system adopting the 5G communication network by considering communication delay:
the configuration power grid is provided with I data sampling nodes, S n (i) A slice ID currently serving the ith sampling node; among K accessible access points of the sampling node, the K-th access point deploys a slice set with a slice S m (k) M ∈ {1,2, …, M }, where M denotes the slice set size; b is m (k) Is S m (k) A corresponding bandwidth; in order to ensure that the resource utilization rate of the system is the highest, uniformly distributing resources to all data sampling nodes; therefore, the data transmission rate of the ith sampling node is as shown in equation (1);
in the formula (1), a m (k) Represents the current slice S m (k) The number of accessed sampling nodes; d ik Indicating that sampling node i is to be connectedDistance between incoming access points, propagation loss of which is PL (d) ik ) Represents; n is a radical of 0 Noise power per unit bandwidth; p k Representing the transmission power of the access point to be accessed; delta represents the total interference received by the AP to be accessed;
the target function establishing module takes the minimized time delay as an optimization target, and the target function is shown as a formula (2);
wherein, DS i The size of the data packet sampled for the ith sampling node;
the constraint conditions are shown in formula (3) to formula (6):
S m (k)≥S n (i) (4)
r ik (m)≥R i ,i∈I,k∈K,m∈M (5)
in the formula (2), x ik (m) is a variable 0-1 to be solved when x ik When (m) is 1, the ith data sampling node accesses the mth slice of the kth AP, otherwise, the access is not performed; the constraint (3) indicates that a data sampling node can only access one slice at a time; the constraint condition (4) indicates that the QoS service level of the slice accessed by the data sampling node should be not less than the original service slice; the constraint condition (5) indicates that the data transmission rate of the data sampling node after being accessed into the slice should be greater than or equal to the minimum rate requirement R i (ii) a The constraint condition (6) represents that the number of nodes which can be accommodated by the slice to be accessed should be less than or equal to the number u of the remaining nodes before the access m (k)；
An objective function solving module for solving the objective function in the step 2 by using a Lagrange multiplier method,
a judging module, a solution of the objective function, and a 0-1 solving variable x of the objective function obtained by a branch-and-bound method 0-1 And thus, whether the user accesses the corresponding slice or not is determined according to the solving variable.
5. The power distribution network protection optimization system accessed by the 5G slice according to claim 4, wherein the objective function solving module comprises the following specific steps: establishing a Lagrangian function of the nonlinear programming problem according to equation (7):
in the formula (7), L (x) ik (m), λ) denotes with respect to x ik (m) and Lagrangian function of the Lagrangian multiplier λ, h 1 (x ik (m))，h 2 (x ik (m))，h 3 (x ik (m))，h 4 (x ik (m))，h 5 (x ik (m)) represent 5 constraint functions, λ, respectively 1 、λ 2 、λ 3 、λ 4 、λ 5 A lagrange multiplier representing the corresponding constraint function and having:
h 2 (x ik (m))＝x ik (m)-1 (9)
h 3 (x ik (m))＝S n (i)-S m (k) (10)
h 4 (x ik (m))＝R i -r ik (m) (11)
establishing a Carrocon-Kunn-Tak condition according to the formulas (13) to (16), thereby solving the optimal solution x of the relaxed nonlinear programming problem by combining the correlation equations of the Carrocon-Kunn-Tak condition relax ：
h 1 (x ik (m))＝0，h 2 (x ik (m))≤0，h 3 (x ik (m))≤0，h 4 (x ik (m))≤0，h 5 (x ik (m))≤0 (14)
λ 1 ，λ 2 ，λ 3 ，λ 4 ，λ 5 ≥0 (15)
6. The method for protecting and optimizing the distribution network accessed by the 5G slice according to claim 4, wherein the judging module comprises the following specific steps:
step 4.1, taking the formula (2) as a problem p-1; the inputs of the branch-and-bound method are: optimal solution x to the relaxation problem satisfying the Carlo-Cohen-Tak conditions relax Optimal objective function value Z of relaxation problem relax 0-1 any value epsilon; initialization k is 0, L is 0, U is Z relax ；
Step 4.2, from the optimal solution x relax In which one solution x not meeting the constraint condition of 0-1 is arbitrarily selected j Namely: x is the number of j ∈(0,1)；
Step 4.3, if x is more than or equal to 0 j <If ε is satisfied, the constraint x is satisfied j Adding 0 to question p-1 to form sub-question i; otherwise, the condition x will be constrained j 1 is added to the question p-1 to form a subproblem ii, epsilon representing any value from 0 to 1;
step 4.4, k + +, continue to find the childSolution to the relaxation problem of problem I or sub-problem II, denoted x k And the corresponding optimal objective function value is recorded as Z k ；
Step 4.5, finding out the maximum value U of the optimal target function as a new upper bound, namely:
U＝max{Z k′ |k′＝1,2,…,k},x k′ ∈[0,1]；
step 4.6, finding out the maximum value L of the target function as a new lower bound from the branches meeting the condition of 0-1, namely:
L＝max{Z k′ |k′＝1,2,…,k},x k′ ∈{0,1}；
step 4.7, if Z k′ <L, cutting off corresponding branch, otherwise Z k′ If the L value is more than L and the condition is not satisfied with 0-1, returning to the step 4.3;
step 4.8, otherwise it means that the optimal objective function values for all branches are equal to the lower bound: z k′ Is equal to L, mixing Z k′ Assignment Z 0-1 X is to be k′ Is assigned to x 0-1 And as the optimal solution to the problem p-1, where x 0-1 Representing an optimal solution that meets the 0-1 constraint.
7. A processing device comprising at least one processor and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1 to 3.
8. A computer-readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110449049.5A CN115001975B (en) | 2021-04-25 | 2021-04-25 | Power distribution network protection optimization method and system for 5G slice access |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110449049.5A CN115001975B (en) | 2021-04-25 | 2021-04-25 | Power distribution network protection optimization method and system for 5G slice access |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115001975A true CN115001975A (en) | 2022-09-02 |
| CN115001975B CN115001975B (en) | 2023-11-03 |
Family
ID=83018654
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110449049.5A Active CN115001975B (en) | 2021-04-25 | 2021-04-25 | Power distribution network protection optimization method and system for 5G slice access |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115001975B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018120935A1 (en) * | 2016-12-31 | 2018-07-05 | 山东大学 | Resource allocation and energy management method for collaborative cellular network |
| CN110149646A (en) * | 2019-04-10 | 2019-08-20 | 中国电力科学研究院有限公司 | A kind of smart grid method for managing resource and system based on time delay and handling capacity |
| WO2019210946A1 (en) * | 2018-05-02 | 2019-11-07 | Huawei Technologies Co., Ltd. | Management device for slice management in a network, method and computer program for managing network slices |
| US20190394132A1 (en) * | 2017-03-31 | 2019-12-26 | Huawei Technologies Co., Ltd. | System and Method for Network Slicing for Service-Oriented Networks |
| CN111182076A (en) * | 2020-01-02 | 2020-05-19 | 合肥工业大学 | Cloud-edge cooperative smart power grid monitoring system and resource allocation and scheduling method thereof |
-
2021
- 2021-04-25 CN CN202110449049.5A patent/CN115001975B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018120935A1 (en) * | 2016-12-31 | 2018-07-05 | 山东大学 | Resource allocation and energy management method for collaborative cellular network |
| US20190394132A1 (en) * | 2017-03-31 | 2019-12-26 | Huawei Technologies Co., Ltd. | System and Method for Network Slicing for Service-Oriented Networks |
| WO2019210946A1 (en) * | 2018-05-02 | 2019-11-07 | Huawei Technologies Co., Ltd. | Management device for slice management in a network, method and computer program for managing network slices |
| CN110149646A (en) * | 2019-04-10 | 2019-08-20 | 中国电力科学研究院有限公司 | A kind of smart grid method for managing resource and system based on time delay and handling capacity |
| CN111182076A (en) * | 2020-01-02 | 2020-05-19 | 合肥工业大学 | Cloud-edge cooperative smart power grid monitoring system and resource allocation and scheduling method thereof |
Non-Patent Citations (1)
| Title |
|---|
| 吕玉祥;杨阳;董亚文;汪玉成;: "5G技术在配电网电流差动保护业务中的应用", 电信科学 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115001975B (en) | 2023-11-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109413724B (en) | MEC-based task unloading and resource allocation scheme | |
| Chen et al. | Joint offloading and resource allocation for computation and communication in mobile cloud with computing access point | |
| US7496367B1 (en) | Method of multi-carrier traffic allocation for wireless communication system | |
| WO1998030061A1 (en) | Method and system for quality of service assessment for multimedia traffic under aggregate traffic conditions | |
| CN111600967B (en) | Access pressure-based load balancing system among block chain nodes | |
| CN107210783B (en) | System and method for reducing interference using cooperative relaying for joint MIMO transmission and compression | |
| CN112533251B (en) | Cell interference assessment method and device | |
| CN110392079A (en) | The node calculating task dispatching method and its equipment calculated towards mist | |
| Moscholios et al. | QoS guarantee in a batched poisson multirate loss model supporting elastic and adaptive traffic | |
| CN109905864A (en) | A kind of cross-layer Resource Allocation Formula towards electric power Internet of Things | |
| US20180098332A1 (en) | Resource allocation method, apparatus, and system, and base station | |
| Gudkova et al. | Modeling the utilization of a multi-tenant band in 3GPP LTE system with Licensed Shared Access | |
| Abboud et al. | Distributed caching in 5G networks: An alternating direction method of multipliers approach | |
| CN103974426B (en) | Communication means and device and cordless communication network for cordless communication network | |
| CN113692058B (en) | Satellite Optical Network Spectrum Allocation Method and System Based on Spectrum Resource Evaluation Set | |
| WO2016101420A1 (en) | Vector calculation method and apparatus for removing far-end crosstalk of subscriber line | |
| CN108777857B (en) | Access control method and system under coexistence scene of URLLC and mMTC | |
| CN108834003B (en) | Electric power optical carrier communication multidimensional resource distribution optimization method for quantum communication service | |
| CN110536398B (en) | Average delay guarantee power control method and system based on multidimensional effective capacity | |
| CN116113039B (en) | Method, device, equipment and medium for optimizing power hybrid service resources | |
| CN115001975A (en) | 5G slice access power distribution network protection optimization method and system | |
| KR102135509B1 (en) | Multiple access control appratus in non-orthogonal multiple access system and method for adjusting overlap ratio of resource | |
| CN108834214B (en) | Time slot resource allocation method and device based on uplink and downlink queue balance | |
| CN112911708A (en) | Resource allocation method, server and storage medium | |
| CN107148078B (en) | User access control method and device for hybrid full-duplex and half-duplex network |
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 |