WO2023221365A1 - 多用户室内电力线通信的资源优化方法、系统和存储介质 - Google Patents

多用户室内电力线通信的资源优化方法、系统和存储介质 Download PDF

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WO2023221365A1
WO2023221365A1 PCT/CN2022/122547 CN2022122547W WO2023221365A1 WO 2023221365 A1 WO2023221365 A1 WO 2023221365A1 CN 2022122547 W CN2022122547 W CN 2022122547W WO 2023221365 A1 WO2023221365 A1 WO 2023221365A1
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optimal
network configuration
user equipment
sub
power line
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PCT/CN2022/122547
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English (en)
French (fr)
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逄林
李铮
洪海敏
汤志颖
袁望星
李祥
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深圳市国电科技通信有限公司
深圳智芯微电子科技有限公司
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Publication of WO2023221365A1 publication Critical patent/WO2023221365A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/54—Systems for transmission via power distribution lines
    • 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
    • H04L41/0803—Configuration setting
    • 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
    • H04L41/142—Network analysis or design using statistical or mathematical methods
    • 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
    • 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

Definitions

  • the present invention relates to the technical field of power line communication, and in particular to a resource optimization method for multi-user indoor power line communication, a computer-readable storage medium, a resource optimization system for multi-user indoor power line communication and a power line communication system.
  • Power line communication is an infrastructure that does not require rewiring.
  • Power line communication technology has the advantages of simple networking, low cost, high security, and easy implementation. It can be used for remote meter reading and home automation. It is also suitable for high-speed information transmission such as data, Internet, audio and video multimedia, etc.
  • Using power lines to transmit data information can not only reduce operating costs, but also reduce the cost of building new communication networks.
  • multi-user power line communication systems are usually deployed by centrally deploying user equipment.
  • centralized deployment requires the use of centralized servers, and the cost of centralized servers is very high.
  • the cost of centralized servers is very high.
  • a centrally deployed power line communication system will generate large signaling overhead and have poor scalability.
  • the present invention aims to solve one of the technical problems in the related art, at least to a certain extent.
  • the first purpose of the present invention is to propose a resource optimization method for multi-user indoor power line communication, which can enable each user equipment to use the optimal sub-channel for transmission most of the time, thereby making the power line communication system close to the optimal It provides the best performance, reduces the signaling overhead of the system, and enhances the scalability of the system.
  • the second object of the present invention is to provide a computer-readable storage medium.
  • the third object of the present invention is to propose a resource optimization system for multi-user indoor power line communications.
  • the fourth object of the present invention is to provide a power line communication system.
  • the first embodiment of the present invention proposes a resource optimization method for multi-user indoor power line communications, which includes: randomly allocating sub-channels to each user equipment, and determining the network configuration of each sub-channel; calculating each The optimal value of the transmit power of the user equipment under its corresponding current network configuration, and find the optimal network configuration based on the optimal value of the transmit power under the current network configuration; obtain the optimal value of the transmit power of each user equipment under the optimal network configuration Merit value; determine the optimal system network utility value based on the optimal value of the transmit power under the optimal network configuration, and determine whether to update the sub-channel set of the user equipment based on the optimal system network utility value.
  • sub-channels are randomly allocated to each user equipment, and the network configuration of each sub-channel is determined; the transmit power of each user equipment under its corresponding current network configuration is calculated The optimal value of , and find the optimal network configuration based on the optimal value of the transmit power under the current network configuration; obtain the optimal value of the transmit power of each user equipment under the optimal network configuration; according to the optimal value of the transmit power under the optimal network configuration
  • the optimal value determines the optimal system network utility value, and determines whether to update the sub-channel set of the user equipment based on the optimal system network utility value.
  • the resource optimization method for multi-user indoor power line communication may also have the following additional technical features:
  • calculating the optimal value of the transmit power of each user equipment under its corresponding current network configuration includes: establishing a mixed integer nonlinear programming model based on the network utility function; determining based on the mixed integer nonlinear programming model Power allocation model; keep the current network configuration unchanged, use the Lagrangian function to convert the power allocation model into an unconstrained optimization problem, and determine the optimal value of the Lagrangian multiplier; according to the optimal value of the Lagrangian multiplier
  • the merit value uses the Karush-Kuhn-Tucker condition to determine the optimal value of transmit power under the current network configuration.
  • determining the optimal network configuration based on the optimal value of the transmit power under the current network configuration includes: keeping the optimal value of the transmit power under the current network configuration unchanged, changing the optimal system value under the current network configuration
  • the network utility value is added with a weighted entropy term to obtain the network configuration model; the Karush-Kuhn-Tucker condition is used to solve the network configuration model and determine the optimal network configuration.
  • the mixed integer nonlinear programming model is as follows:
  • NU( ⁇ ) represents the system network utility value
  • ⁇ represents the weight, 0 ⁇ 1
  • R n,k represents the transmission rate of device n on sub-channel k
  • p n,k represents the allocated transmit power
  • Indicates the power consumption of the circuit Indicates the service quality requirements of device n; Indicates the maximum value of the transmit power of device n.
  • the power allocation model type is converted into an unconstrained optimization problem through the following formula:
  • ⁇ n and ⁇ n represent Lagrange multipliers, ⁇ represents the weight, 0 ⁇ 1, R n,k represents the transmission rate of device n on sub-channel k; p n,k represents the allocated transmission power; Indicates the power consumption of the circuit; Indicates the service quality requirements of device n; Indicates the maximum value of the transmit power of device n.
  • the network configuration model is obtained through the following formula:
  • NU(c) represents the weight of network configuration c
  • ⁇ represents the weight of the entropy term
  • pr c represents the percentage of time the system is in network configuration c
  • C represents the network configuration set.
  • determining whether to update the sub-channel set of the user equipment according to the optimal system network utility value includes: generating an exponential distribution timer according to the optimal system network utility value and the sub-channel set currently selected by the user equipment; When the exponential distribution timer of the current user equipment expires, the release probability of the subchannel in the subchannel set selected by the current user equipment is determined; when the release probability of the subchannel is greater than the preset threshold, the subchannel is released from the subchannel set. , and updates the subchannel set of the current user equipment, randomly allocates a new subchannel to the current user equipment from the idle subchannels, and adds the new subchannel to the subchannel set selected by the current user equipment.
  • an exponentially distributed timer is generated by the following formula:
  • timer represents an exponential distribution timer, Represents the optimal system network utility value of the current sub-channel of the current user equipment; ⁇ represents a constant, ⁇ represents the weight of the entropy term, k i represents any one of the set of sub-channels selected by the current user equipment, and K n represents the user equipment
  • the set of sub-channels selected by n, i is a positive integer less than or equal to K, and K represents the number of available sub-channels.
  • the release probability of the subchannel in the subchannel set currently selected by the user equipment is determined by the following formula:
  • pr n,k represents the release probability of sub-channel k in the sub-channel set selected by user equipment n.
  • a second embodiment of the present invention provides a computer-readable storage medium on which a resource optimization program for multi-user indoor power line communications is stored.
  • the resource optimization program for multi-user indoor power line communications is executed by a processor.
  • the above-mentioned resource optimization method for multi-user indoor power line communication is realized.
  • the power line communication system can be brought close to optimal effectiveness, the signaling overhead of the system can be reduced, and the scalability of the system can be enhanced .
  • the third embodiment of the present invention proposes a resource optimization system for multi-user indoor power line communication.
  • the first determination module is used to randomly allocate sub-channels to each user equipment and determine the network of each sub-channel. Configuration;
  • the calculation module is used to calculate the optimal value of the transmit power of each user equipment under its corresponding current network configuration;
  • the second determination module is used to find the optimal network configuration based on the optimal value of the transmit power under the current network configuration. ;
  • the acquisition module is used to obtain the optimal value of the transmit power of each user equipment under the optimal network configuration;
  • the third determination module is used to determine the optimal system network utility value based on the optimal value of the transmit power under the optimal network configuration. , and determine whether to update the sub-channel set of the user equipment according to the optimal system network utility value.
  • the first determination module randomly allocates sub-channels to each user equipment and determines the network configuration of each sub-channel.
  • the calculation module calculates the time of each user equipment in its corresponding The optimal value of the transmit power under the current network configuration.
  • the second determination module finds the optimal network configuration based on the optimal value of the transmit power under the current network configuration.
  • the acquisition module obtains the optimal transmit power of each user equipment under the optimal network configuration.
  • the third determination module determines the optimal system network utility value according to the optimal value of the transmit power under the optimal network configuration, and determines whether to update the sub-channel set of the user equipment according to the optimal system network utility value.
  • the fourth embodiment of the present invention proposes a power line communication system, which includes a memory, a processor, and a resource optimization program for multi-user indoor power line communication stored in the memory and runable on the processor.
  • the processor When executing the resource optimization program for multi-user indoor power line communications, the above resource optimization method for multi-user indoor power line communications is implemented.
  • each user equipment can use the optimal sub-channel for transmission most of the time, thereby bringing the power line communication system close to the optimal It provides the best performance, reduces the signaling overhead of the system, and enhances the scalability of the system.
  • Figure 1 is a flow chart of a resource optimization method for multi-user indoor power line communications according to an embodiment of the present invention
  • Figure 2 is a flow chart for calculating the optimal value of user equipment transmit power according to an embodiment of the present invention
  • Figure 3 is a flow chart for determining optimal network configuration according to an embodiment of the present invention.
  • Figure 4 is a flow chart for determining whether to change the current sub-channel of the user equipment according to an embodiment of the present invention
  • Figure 5 is a block diagram of a resource optimization system for multi-user indoor power line communications according to an embodiment of the present invention
  • Figure 6 is a block diagram of a power line communication system according to an embodiment of the present invention.
  • Figure 1 is a flow chart of a resource optimization method for multi-user indoor power line communications according to an embodiment of the present invention.
  • the resource optimization method for multi-user indoor power line communication may include the following steps:
  • Step S101 Randomly assign sub-channels to each user equipment and determine the network configuration of each sub-channel.
  • Step S102 Calculate the optimal value of the transmit power of each user equipment under its corresponding current network configuration, and find the optimal network configuration based on the optimal value of the transmit power under the current network configuration.
  • Step S103 Obtain the optimal value of the transmit power of each user equipment under the optimal network configuration.
  • Step S104 Determine the optimal system network utility value based on the optimal value of the transmit power under the optimal network configuration, and determine whether to update the sub-channel set of the user equipment based on the optimal system network utility value.
  • the number of available sub-channels is K
  • the number of sub-channels is greater than the number of devices, that is, K>N.
  • each sub-channel can only be used by one device. For example, in the initial state, user equipment n randomly selects sub-channel k, where n ⁇ N and k ⁇ K, and can determine the network configuration of sub-channel k. When all user equipments in the system have completed selecting subchannels, the subchannel set of user equipment n is Kn.
  • the optimal value of the transmit power of the user equipment n is calculated. Then, the optimal network configuration of user equipment n in sub-channel k is found based on the optimal value of the transmit power of user equipment n. After finding the optimal network configuration of user equipment n in sub-channel k, the optimal value of the transmit power of user equipment n under the optimal network configuration can be obtained. Based on the same principle, the optimal value of the transmit power of each user equipment in the system under the optimal network configuration can be obtained.
  • the optimal system network utility value can be determined based on the optimal value of the transmit power of each user equipment under the optimal network configuration, and based on the optimal system
  • the network utility value calculates the release probability of the subchannel in the subchannel set Kn selected by the current user. When the release probability of a subchannel is greater than a certain value, the subchannel set of the current user equipment is updated.
  • the optimal value of the transmit power of each user equipment under its corresponding current network configuration is calculated, and the optimal network configuration is found based on the optimal value of the transmit power under the current network configuration, and the optimal value of each user equipment is obtained.
  • the optimal value of the transmit power under the optimal network configuration determine the optimal system network utility value based on the optimal value of the transmit power under the optimal network configuration, and determine whether to update the sub-channel set of the user equipment based on the optimal system network utility value, which can
  • Each user equipment will use the optimal sub-channel for transmission most of the time, thereby making the power line communication system close to optimal effectiveness, reducing the signaling overhead of the system, and enhancing the scalability of the system.
  • calculating the optimal value of the transmit power of each user equipment under its corresponding current network configuration may include the following steps:
  • Step S201 Establish a mixed integer nonlinear programming model based on the network utility function.
  • the mixed integer nonlinear programming model is as follows:
  • NU( ⁇ ) represents the system network utility value
  • ⁇ represents the weight, 0 ⁇ 1
  • R n,k represents the transmission rate of device n on sub-channel k
  • p n,k represents the allocated transmit power
  • Indicates the power consumption of the circuit Indicates the service quality requirements of device n; Indicates the maximum value of the transmit power of device n.
  • the energy supply is sufficient in power line communication networks, it is still important to improve the energy efficiency of the network in order to avoid wasting energy.
  • the energy efficiency of the entire system can be reflected through the network utility function (NU).
  • NU network utility function
  • the set C is used to represent the set of possible network configurations of the power line communication system, and a certain sub-channel in the system is Assign the result x as a network configuration c such that c ⁇ C.
  • Step S202 Determine the power allocation model according to the mixed integer nonlinear programming model.
  • Step S203 Keep the current network configuration unchanged, use the Lagrangian function to convert the power allocation model into an unconstrained optimization problem, and determine the optimal value of the Lagrangian multiplier.
  • the power allocation model is converted into an unconstrained optimization problem through the following formula:
  • ⁇ n and ⁇ n represent Lagrange multipliers, ⁇ represents the weight, 0 ⁇ 1, R n,k represents the transmission rate of device n on sub-channel k; p n,k represents the allocated transmission power; Indicates the power consumption of the circuit; Indicates the service quality requirements of device n; Indicates the maximum value of the transmit power of device n.
  • the user equipment n keeps the current network configuration c unchanged, and the power allocation model is converted into an unconstrained optimization problem using the above formula. Since the optimal value of the transmit power of user equipment n under the current network configuration c is related to the values of Lagrange multipliers ⁇ n and ⁇ n , it is necessary to first find the optimal value of the Lagrange multiplier and In one embodiment of the present invention, the optimal value of the Lagrange multiplier can be solved by the steepest descent method.
  • the specific formula is as follows:
  • Step S204 Determine the optimal value of the transmit power under the current network configuration by using the Karush-Kuhn-Tucker condition based on the optimal value of the Lagrange multiplier.
  • the optimal value of the Lagrange multiplier After obtaining the optimal value of the Lagrange multiplier through the above step S203, and Finally, using the Karush-Kuhn-Tucker condition, the optimal value of the Lagrange multiplier and Entering the following formula, the optimal value of the transmit power of device n under the current network configuration can be found
  • the specific formula is as follows:
  • ⁇ n is the network efficiency value
  • g n,k is the signal-to-noise ratio of device n on channel k.
  • determining the optimal network configuration based on the optimal value of the transmit power under the current network configuration may include the following steps:
  • Step S301 Keep the optimal value of the transmit power under the current network configuration unchanged, and add a weighted entropy term to the optimal system network utility value under the current network configuration to obtain a network configuration model.
  • the network configuration model is obtained through the following formula:
  • NU(c) represents the weight of network configuration c
  • ⁇ represents the weight of the entropy term
  • pr c represents the percentage of time the system is in network configuration c
  • C represents the network configuration set.
  • Step S302 Use Karush-Kuhn-Tucker conditions to solve the network configuration model and determine the optimal network configuration.
  • the Karush-Kuhn-Tucker condition is used to solve the network configuration model in step S301, and the optimal solution for the percentage of time that the system is in network configuration c can be obtained
  • the expression is as follows:
  • the product form of the optimal solution can be abstracted as the stationary distribution of the Markov chain at a certain time.
  • the optimal network configuration of the system can be determined by the optimal solution for the percentage of time the system is in network configuration c.
  • the optimal value of the transmit power of each user equipment under the optimal network configuration of the system can be obtained respectively.
  • determining whether to update the sub-channel set of the user equipment according to the optimal system network utility value may include the following steps:
  • Step S401 Generate an exponential distribution timer based on the optimal system network utility value and the subchannel set currently selected by the user equipment.
  • an exponentially distributed timer is generated by the following formula:
  • timer represents an exponential distribution timer, Represents the optimal system network utility value of the current sub-channel of the current user equipment; ⁇ represents a constant, ⁇ represents the weight of the entropy term, k i represents any one of the set of sub-channels selected by the current user equipment, and K n represents the user equipment
  • the set of sub-channels selected by n, i is a positive integer less than or equal to K, and K represents the number of available sub-channels.
  • user equipment n randomly selects a sub-channel k to add to the set K n and generates the current configuration c.
  • the network utility of user equipment n is z n,k , k ⁇ K n .
  • user equipment n can determine the optimal system network utility value of the current subchannel based on the optimal value of transmit power under the optimal network configuration. Will By introducing the above formula, an exponentially distributed timer timer for user device n can be generated and start counting down.
  • Step S402 When the exponential distribution timer of the current user equipment expires, determine the release probability of the subchannel in the subchannel set selected by the current user equipment.
  • the release probability of the subchannel in the subchannel set currently selected by the user equipment is determined by the following formula:
  • pr n,k represents the release probability of sub-channel k in the sub-channel set selected by user equipment n.
  • the release probability of sub-channel k selected by user equipment n can be determined.
  • Step S403 When the release probability of the subchannel is greater than the preset threshold, release the subchannel from the subchannel set, update the subchannel set of the current user equipment, and randomly allocate a subchannel to the current user equipment from the idle subchannels. The new sub-channel is added to the set of sub-channels selected by the current user equipment.
  • sub-channel k is released from the sub-channel set Kn, and the sub-channel set Kn of user equipment n is updated.
  • User equipment n releases the current sub-channel k and enters the hopping state. It can randomly select a new sub-channel for user equipment n from the idle sub-channels that remain active and add it to the sub-channel set Kn, and then broadcast a reset message to the power line communication system.
  • the power line communication system receives the reset message, after other devices complete the current timing process, they calculate their network utility values in the new network configuration and generate new timing variables, and then enter the waiting state.
  • the release probability of sub-channel k selected by user equipment n is less than the preset threshold, user equipment n remains in the current sub-channel k. As a result, the user equipment in the system can use the optimal subchannel for transmission.
  • the resource optimization method of the above embodiment can be deployed in a large-scale power line communication system without causing a lot of network utility performance degradation, and under large-scale network configuration, the resulting network approximation error upper bound is lower .
  • sub-channels are randomly allocated to each user equipment, and the network configuration of each sub-channel is determined; each user equipment is calculated in its corresponding current The optimal value of the transmit power under the network configuration, and determine the optimal network configuration based on the optimal value of the transmit power under the current network configuration; obtain the optimal value of the transmit power of each user equipment under the optimal network configuration; based on the optimal network configuration The optimal value of the transmit power under the configuration determines the optimal system network utility value, and determines whether to change the current sub-channel of the user equipment based on the optimal system network utility value.
  • this method enables each user equipment to use the optimal subchannel for transmission most of the time, thereby bringing the power line communication system close to optimal effectiveness, reducing the signaling overhead of the system, and enhancing the scalability of the system.
  • the present invention also provides a computer-readable storage medium.
  • the computer-readable storage medium of the embodiment of the present invention stores thereon a resource optimization program for multi-user indoor power line communication.
  • the resource optimization program for multi-user indoor power line communication is executed by a processor, the above-mentioned resources for multi-user indoor power line communication are realized. Optimization.
  • the power line communication system can be brought close to optimal effectiveness, the signaling overhead of the system can be reduced, and the scalability of the system can be enhanced .
  • the present invention also proposes a resource optimization system for multi-user indoor power line communication.
  • Figure 5 is a block diagram of a resource optimization system for multi-user indoor power line communications according to an embodiment of the present invention.
  • the resource optimization system 500 for multi-user indoor power line communication may include: a first determination module 510, a calculation module 520, a second determination module 530, an acquisition module 540 and a third determination module 550 .
  • the first determination module 510 is used to randomly allocate sub-channels to each user equipment and determine the network configuration of each sub-channel.
  • the calculation module 520 is used to calculate the optimal value of the transmit power of each user equipment under its corresponding current network configuration.
  • the second determination module 530 is used to find the optimal network configuration according to the optimal value of the transmit power under the current network configuration.
  • the obtaining module 540 is used to obtain the optimal value of the transmit power of each user equipment under the optimal network configuration.
  • the third determination module 550 is configured to determine the optimal system network utility value according to the optimal value of the transmit power under the optimal network configuration, and determine whether to update the sub-channel set of the user equipment according to the optimal system network utility value.
  • the calculation module 520 calculates the optimal value of the transmit power of each user equipment under its corresponding current network configuration, specifically for establishing a mixed integer nonlinear programming model based on the network utility function; according to the mixed integer
  • the nonlinear programming model determines the power allocation model; keeping the current network configuration unchanged, using the Lagrangian function to convert the power allocation model into an unconstrained optimization problem, and determining the optimal value of the Lagrangian multiplier; according to the Lagrangian
  • the optimal value of the daily multiplier uses the Karush-Kuhn-Tucker condition to determine the optimal value of the transmit power under the current network configuration.
  • the second determination module 530 determines the optimal network configuration according to the optimal value of the transmit power under the current network configuration. Specifically, it is used to keep the optimal value of the transmit power under the current network configuration unchanged, and change the current
  • the optimal system network utility value under network configuration adds a weighted entropy term to obtain the network configuration model; the Karush-Kuhn-Tucker condition is used to solve the network configuration model and determine the optimal network configuration.
  • the mixed integer nonlinear programming model is as follows:
  • NU( ⁇ ) represents the system network utility value
  • ⁇ represents the weight, 0 ⁇ 1
  • R n,k represents the transmission rate of device n on sub-channel k
  • p n,k represents the allocated transmit power
  • Indicates the power consumption of the circuit Indicates the service quality requirements of device n; Indicates the maximum value of the transmit power of device n.
  • the calculation module 520 converts the power allocation model into an unconstrained optimization problem through the following formula:
  • ⁇ n and ⁇ n represent Lagrange multipliers, ⁇ represents the weight, 0 ⁇ 1, R n,k represents the transmission rate of device n on sub-channel k; p n,k represents the allocated transmission power; Indicates the power consumption of the circuit; Indicates the service quality requirements of device n; Indicates the maximum value of the transmit power of device n.
  • the second determination module 530 obtains the network configuration model through the following formula:
  • NU(c) represents the weight of network configuration c
  • ⁇ represents the weight of the entropy term
  • pr c represents the percentage of time the system is in network configuration c
  • C represents the network configuration set.
  • the third determination module 550 determines whether to update the sub-channel set of the user equipment according to the optimal system network utility value, specifically, based on the optimal system network utility value and the sub-channel set selected by the current user equipment.
  • Generate an exponential distribution timer when the exponential distribution timer of the current user equipment ends, determine the release probability of the subchannel in the subchannel set selected by the current user equipment; when the release probability of the subchannel is greater than the preset threshold, release the subchannel
  • the channel is released from the sub-channel set and merged with the sub-channel set of the current user equipment.
  • a new sub-channel is randomly assigned to the current user equipment from the idle sub-channel and the new sub-channel is added to the sub-channel set selected by the current user equipment. .
  • the third determination module 550 generates an exponential distribution timer through the following formula:
  • timer represents an exponential distribution timer, Represents the optimal system network utility value of the current sub-channel of the current user equipment; ⁇ represents a constant, ⁇ represents the weight of the entropy term, k i represents any one of the set of sub-channels selected by the current user equipment, and K n represents the user equipment
  • the set of sub-channels selected by n, i is a positive integer less than or equal to K, and K represents the number of available sub-channels.
  • the third determination module 550 determines the release probability of the subchannel in the subchannel set currently selected by the user equipment through the following formula:
  • pr n,k represents the release probability of sub-channel k in the sub-channel set selected by user equipment n.
  • the resource optimization system 500 for multi-user indoor power line communications includes: a processor, wherein the processor is configured to execute the above program modules and units in a memory, including: a first determination module 510 and a calculation module 520 , the second determination module 530, the acquisition module 540 and the third determination module 550.
  • the first determination module randomly allocates sub-channels to each user equipment and determines the network configuration of each sub-channel.
  • the calculation module calculates the time of each user equipment in its corresponding The optimal value of the transmit power under the current network configuration.
  • the second determination module determines the optimal network configuration based on the optimal value of the transmit power under the current network configuration.
  • the acquisition module obtains the optimal transmit power of each user equipment under the optimal network configuration.
  • the third determination module determines the optimal system network utility value according to the optimal value of the transmit power under the optimal network configuration, and determines whether to change the current sub-channel of the user equipment according to the optimal system network utility value.
  • the present invention also provides a power line communication system.
  • Figure 6 is a block diagram of a power line communication system according to an embodiment of the present invention.
  • the power line communication system 600 of the embodiment of the present invention includes a memory 610, a processor 620, and a multi-user indoor power line communication resource optimization program stored in the memory 610 and runable on the processor 620.
  • the processor When 620 executes the resource optimization program for multi-user indoor power line communication, the above resource optimization method for multi-user indoor power line communication is implemented.
  • each user equipment can use the optimal sub-channel for transmission most of the time, thereby bringing the power line communication system close to the optimal It provides the best performance, reduces the signaling overhead of the system, and enhances the scalability of the system.
  • a "computer-readable medium” may be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
  • Non-exhaustive list of computer readable media include the following: electrical connections with one or more wires (electronic device), portable computer disk cartridges (magnetic device), random access memory (RAM), Read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM).
  • the computer-readable medium may even be paper or other suitable medium on which the program may be printed, as the paper or other medium may be optically scanned, for example, and subsequently edited, interpreted, or otherwise suitable as necessary. process to obtain the program electronically and then store it in computer memory.
  • various parts of the present invention may be implemented in hardware, software, firmware, or a combination thereof.
  • various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system.
  • a logic gate circuit with a logic gate circuit for implementing a logic function on a data signal.
  • Discrete logic circuits application specific integrated circuits with suitable combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.
  • first and second are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Therefore, features defined as “first” and “second” may explicitly or implicitly include at least one of these features.
  • “plurality” means at least two, such as two, three, etc., unless otherwise expressly and specifically limited.
  • connection In the present invention, unless otherwise clearly stated and limited, the terms “installation”, “connection”, “connection”, “fixing” and other terms should be understood in a broad sense. For example, it can be a fixed connection or a detachable connection. , or integrated into one; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be an internal connection between two elements or an interactive relationship between two elements, unless otherwise specified limitations. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

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Abstract

本发明公开了一种多用户室内电力线通信的资源优化方法、系统和存储介质，其中，方法包括：为每台用户设备随机分配子信道，并确定每个子信道的网络配置；计算每台用户设备在其对应的当前网络配置下发射功率的最优值，并根据当前网络配置下发射功率的最优值寻找最优网络配置；获取每台用户设备在最优网络配置下发射功率的最优值；根据最优网络配置下发射功率的最优值确定最优系统网络效用值，并根据最优系统网络效用值确定是否更新用户设备的子信道集合。该方法能够使每台用户设备在大部分时间都会使用最佳子信道进行传输，从而使电力线通信系统接近最佳效用，减少系统的信令开销，并增强系统的可扩展性。

Description

多用户室内电力线通信的资源优化方法、系统和存储介质 技术领域
本发明涉及电力线通信技术领域，尤其涉及一种多用户室内电力线通信的资源优化方法、一种计算机可读存储介质、一种多用户室内电力线通信的资源优化系统和一种电力线通信系统。
背景技术
随着物联网等新兴技术的发展，电力线通信作为一种无需重新布线的基础设施，并且电力线通信技术具有组网简单、成本低、安全性高、易于实现等优点，可用于远程抄表、家庭自动化等低速控制，也适用于数据、互联网、音视频多媒体等高速信息传输。利用电力线传输数据信息不仅可以降低运营成本，还可以减少建设新的通信网络的费用。
相关技术中，对于多用户电力线通信系统，通常采用将用户设备集中部署的方式进行部署。但是集中部署需要使用集中式服务器，而集中式服务器的成本非常高昂。另外，在集中式架构下，随着业务量的增长，当需要对系统进行扩展时，只能通过横向扩展同样架构的服务器的方式进行扩展。因此，集中部署的电力线通信系统会产生较大的信令开销，并且可扩展性差。
发明内容
本发明旨在至少在一定程度上解决相关技术中的技术问题之一。为此，本发明的第一个目的在于提出一种多用户室内电力线通信的资源优化方法，能够使每台用户设备在大部分时间都会使用最佳子信道进行传输，从而使电力线通信系统接近最佳效用，减少系统的信令开销，并增强系统的可扩展性。
本发明的第二个目的在于提出一种计算机可读存储介质。
本发明的第三个目的在于提出一种多用户室内电力线通信的资源优化系统。
本发明的第四个目的在于提出一种电力线通信系统。
为达到上述目的，本发明第一方面实施例提出了一种多用户室内电力线通信的资源优化方法，包括：为每台用户设备随机分配子信道，并确定每个子信道的网络配置；计算每台用户设备在其对应的当前网络配置下发射功率的最优值，并根据当前网络配置下发射功率的最优值寻找最优网络配置；获取每台用户设备在最优网络配置下发射功率的最优值；根据最优网络配置下发射功率的最优值确定最优系统网络效用值，并根据最优系统网络效用值确定是否更新用户设备的子信道集合。
根据本发明实施例的多用户室内电力线通信的资源优化方法，为每台用户设备随机分配子信道，并确定每个子信道的网络配置；计算每台用户设备在其对应的当前网络配置下发射功率的最优值，并根据当前网络配置下发射功率的最优值寻找最优网络配置；获取每台用户设备在最优网络配置下发射功率的最优值；根据最优网络配置下发射功率的最优值确定最优系统网络效用值，并根据最优系统网络效用值确定是否更新用户设备的子信道集合。由此，该方法能够使每台用户设备在大部分时间都会使用最佳子信道进行传输，从而使电力线通信系统接近最佳效用，减少系统的信令开销，并增强系统的可扩展性。
另外，根据本发明上述实施例的多用户室内电力线通信的资源优化方法，还可以具有如下的附加技术特征：
根据本发明的一个实施例，计算每台用户设备在其对应的当前网络配置下发射功率的最优值，包括：基于网络效用函数建立混合整数非线性规划模型；根据混合整数非线性规划模型确定功率分配模型；保持当前网络配置不变，采用拉格朗日函数将功率分配模型转换为无约束优化问题，并确定拉格朗日乘子的最优值；根据拉格朗日乘子的最优值采用Karush-Kuhn-Tucker条件确定当前网络配置下发射功率的最优值。
根据本发明的一个实施例，根据当前网络配置下发射功率的最优值确定最优网络配置，包括：保持当前网络配置下发射功率的最优值不变，将当前网络配置下的最优系统网络效用值增加加权熵项，以获得网络配置模型；采用Karush-Kuhn-Tucker条件对网络配置模型进行求解，确定最优网络配置。
根据本发明的一个实施例，混合整数非线性规划模型如下：
Figure PCTCN2022122547-appb-000001
Figure PCTCN2022122547-appb-000002
Figure PCTCN2022122547-appb-000003
Figure PCTCN2022122547-appb-000004
其中，NU(·)表示系统网络效用值；α表示权值，0≤α≤1；R n,k表示设备n在子信道k上的传输速率；x n,k表示一个二元变量，当设备n选择子信道k时，x n,k＝1，否则x n,k＝0；p n,k表示分配的发射功率；
Figure PCTCN2022122547-appb-000005
表示电路消耗功率；
Figure PCTCN2022122547-appb-000006
表示设备n的服务质量需求；
Figure PCTCN2022122547-appb-000007
表示设备n发送功率的最大值。
根据本发明的一个实施例，通过以下公式将功率分配模型型转换为无约束优化问题：
Figure PCTCN2022122547-appb-000008
其中，λ n和μ n表示拉格朗日乘子， α表示权值，0≤α≤1，R n,k表示设备n在子信道k上的传输速率；p n,k表示分配的发射功率；
Figure PCTCN2022122547-appb-000009
表示电路消耗功率；
Figure PCTCN2022122547-appb-000010
表示设备n的服务质量需求；
Figure PCTCN2022122547-appb-000011
表示设备n发送功率的最大值。
根据本发明的一个实施例，通过以下公式获得网络配置模型：
Figure PCTCN2022122547-appb-000012
Figure PCTCN2022122547-appb-000013
其中，NU(c)表示网络配置c的权重，
Figure PCTCN2022122547-appb-000014
表示加权熵项，β表示熵项的权值，pr c表示系统处于网络配置c的时间百分比，C表示网络配置集合。
根据本发明的一个实施例，根据最优系统网络效用值确定是否更新用户设备的子信道集合，包括：根据最优系统网络效用值和当前用户设备选择的子信道集合生成指数分布计时器；在当前用户设备的指数分布计时器计时结束时，确定当前用户设备选择的子信道集合中子信道的释放概率；在子信道的释放概率大于预设阈值时，将该子信道从子信道集合中释放，并更新当前用户设备的子信道集合，以及从空闲的子信道中为当前用户设备随机分配一条新的子信道，并将新的子信道加入当前用户设备选择的子信道集合。
根据本发明的一个实施例，通过以下公式生成指数分布计时器：
Figure PCTCN2022122547-appb-000015
其中，timer表示指数分布计时器，
Figure PCTCN2022122547-appb-000016
表示当前用户设备的当前子信道的最优系统网络效用值；σ表示常数，β表示熵项的权值，k i表示当前用户设备选择的子信道的集合中的任一条，K n表示用户设备n选择的子信道的集合，i为小于等于K的正整数，K表示可用子信道的数目。
根据本发明的一个实施例，通过以下公式确定当前用户设备选择的子信道集合中子信道的释放概率：
Figure PCTCN2022122547-appb-000017
其中，pr n,k表示用户设备n选择的子信道集合中子信道k的释放概率。
为达到上述目的，本发明第二方面实施例提出了一种计算机可读存储介质，其上存储有多用户室内电力线通信的资源优化程序，该多用户室内电力线通信的资源优化程序被处理器执行时实现上述的多用户室内电力线通信的资源优化方法。
根据本发明实施例的计算机可读存储介质，通过执行上述的多用户室内电力线通信的资源优化方法，能够使电力线通信系统接近最佳效用，减少系统的信令开销，并增强系统的可扩展性。
为达到上述目的，本发明第三方面实施例提出了一种多用户室内电力线通信的资源优化系统，第一确定模块，用于为每台用户设备随机分配子信道，并确定每个子信道的网络配置；计算模块，用于计算每台用户设备在其对应的当前网络配置下发射功率的最优值；第二确定模块，用于根据当前网络配置下发射功率的最优值寻找最优网络配置；获取模块，用于获取每台用户设备在最优网络配置下发射功率的最优值；第三确定模块，用于根据最优网络配置下发射功率的最优值确定最优系统网络效用值，并根据最优系统网络效用值确定是否更新用户设备的子信道集合。
根据本发明实施例的多用户室内电力线通信的资源优化系统，第一确定模块为每台用户设备随机分配子信道，并确定每个子信道的网络配置，计算模块计算每台用户设备在其对应的当前网络配置下发射功率的最优值，第二确定模块根据当前网络配置下发射功率的最优值寻找最优网络配置，获取模块获取每台用户设备在最优网络配置下发射功率的最优值，第三确定模块根据最优网络配置下发射功率的最优值确定最优系统网络效用值，并根据最优系统网络效用值确定是否更新用户设备的子信道集合。由此，该系统能够使每台用户设备在大部分时间都会使用最佳子信道进行传输，从而使电力线通信系统接近最佳效用，减少系统的信令开销，并增强系统的可扩展性。
为达到上述目的，本发明第四方面实施例提出了一种电力线通信系统，包括存储器、处理器及存储在存储器上并可在处理器上运行的多用户室内电力线通信的资源优化程序，处理器执行多用户室内电力线通信的资源优化程序时，实现上述的多用户室内电力线通信的资源优化方法。
根据本发明实施例的电力线通信系统，通过执行上述的多用户室内电力线通信的资源优化方法，能够使每台用户设备在大部分时间都会使用最佳子信道进行传输，从而使电力线通信系统接近最佳效用，减少系统的信令开销，并增强系统的可扩展性。
本发明附加的方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本发明的实践了解到。
附图说明
图1为根据本发明实施例的多用户室内电力线通信的资源优化方法的流程图；
图2为根据本发明一个实施例的计算用户设备发射功率的最优值的流程图；
图3为根据本发明一个实施例的确定最优网络配置的流程图；
图4为根据本发明一个实施例的确定是否更换用户设备的当前子信道的流程图；
图5为根据本发明实施例的多用户室内电力线通信的资源优化系统的方框示意图；
图6为根据本发明实施例的电力线通信系统的方框示意图。
具体实施方式
下面详细描述本发明的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，旨在用于解释本发明，而不能理解为对本发明的限制。
下面参考附图描述本发明实施例提出的多用户室内电力线通信的资源优化方法、计算机可读存储介质、多用户室内电力线通信的资源优化系统和电力线通信系统。
图1为根据本发明实施例的多用户室内电力线通信的资源优化方法的流程图。
如图1所示，本发明实施例的多用户室内电力线通信的资源优化方法，可包括以下步骤：
步骤S101，为每台用户设备随机分配子信道，并确定每个子信道的网络配置。
步骤S102，计算每台用户设备在其对应的当前网络配置下发射功率的最优值，并根据当前网络配置下发射功率的最优值寻找最优网络配置。
步骤S103，获取每台用户设备在最优网络配置下发射功率的最优值。
步骤S104，根据最优网络配置下发射功率的最优值确定最优系统网络效用值，并根据最优系统网络效用值确定是否更新用户设备的子信道集合。
具体而言，在本发明实施例的多用户室内电力线通信系统中，假设有N台用户设备，采用分布式部署，可用子信道的数目为K个，并且子信道的数量大于设备的数量，即K＞N。为了避免不同设备之间的干扰，每个子信道只能由一台设备使用。例如，在初始状态下，用户设备n随机选择子信道k，其中n∈N，k∈K，并可以确定子信道k的网络配置。当系统中的所有用户设备均选择子信道完成后，用户设备n的子信道集合为Kn。
进一步地，根据用户设备n在其对应的当前网络配置下，计算用户设备n发射功率的最优值。然后再根据用户设备n发射功率的最优值寻找用户设备n在子信道k的最优网络配置。在寻找到用户设备n在子信道k的最优网络配置后，可以获取用户设备n在该最优网络配置下发射功率的最优 值。基于同样的原理，可以获取系统中的每台用户设备在最优网络配置下发射功率的最优值。
在获取每台用户设备在最优网络配置下发射功率的最优值以后，可以根据最优网络配置下每台用户设备发射功率的最优值确定最优系统网络效用值，并根据最优系统网络效用值计算当前用户选择的子信道集合Kn中子信道的释放概率。当子信道的释放概率大于一定值时，更新当前用户设备的子信道集合。
由此，通过计算每台用户设备在其对应的当前网络配置下发射功率的最优值，并根据当前网络配置下发射功率的最优值寻找最优网络配置，并获取每台用户设备在最优网络配置下发射功率的最优值，根据最优网络配置下发射功率的最优值确定最优系统网络效用值，并根据最优系统网络效用值确定是否更新用户设备的子信道集合，能够使每台用户设备在大部分时间都会使用最佳子信道进行传输，从而使电力线通信系统接近最佳效用，减少系统的信令开销，并增强系统的可扩展性。
下面详细描述本发明的资源优化方法的具体流程。
根据本发明的一个实施例，如图2所示，计算每台用户设备在其对应的当前网络配置下发射功率的最优值，可包括以下步骤：
步骤S201，基于网络效用函数建立混合整数非线性规划模型。
根据本发明的一个实施例，混合整数非线性规划模型如下：
Figure PCTCN2022122547-appb-000018
Figure PCTCN2022122547-appb-000019
Figure PCTCN2022122547-appb-000020
Figure PCTCN2022122547-appb-000021
其中，NU(·)表示系统网络效用值；α表示权值，0≤α≤1；R n,k表示设备n在子信道k上的传输速率；x n,k表示一个二元变量，当设备n选择子信道k时，x n,k＝1，否则x n,k＝0；p n,k表示分配的发射功率；
Figure PCTCN2022122547-appb-000022
表示电路消耗功率；
Figure PCTCN2022122547-appb-000023
表示设备n的服务质量需求；
Figure PCTCN2022122547-appb-000024
表示设备n发送功率的最大值。
具体而言，虽然在电力线通信网络中，能量供应是充足的，但是为了避免浪费能源，提高网络的能量效率依然十分重要。在本发明的实施例中，通过网络效用函数(NU)可以反映整个系统的能效，使用集合C来表示电力线通信系统的整个可能的网络配置的集合，并将系统中的一个子信道的某个分配结果x作为一个网络配置c，从而c∈C。当设备n选择子信道k，即x n,k＝1时，上述的混合整数非线性规划模型可以转化为：
Figure PCTCN2022122547-appb-000025
Figure PCTCN2022122547-appb-000026
Figure PCTCN2022122547-appb-000027
c∈C.
其中，
Figure PCTCN2022122547-appb-000028
表示设备n在网络配置c下选择的活跃子信道的集合。
步骤S202，根据混合整数非线性规划模型确定功率分配模型。
步骤S203，保持当前网络配置不变，采用拉格朗日函数将功率分配模型转换为无约束优化问题，并确定拉格朗日乘子的最优值。
根据本发明的一个实施例，通过以下公式将功率分配模型转换为无约束优化问题：
Figure PCTCN2022122547-appb-000029
其中，λ n和μ n表示拉格朗日乘子， α表示权值，0≤α≤1，R n,k表示设备n在子信道k上的传输速率；p n,k表示分配的发射功率；
Figure PCTCN2022122547-appb-000030
表示电路消耗功率；
Figure PCTCN2022122547-appb-000031
表示设备n的服务质量需求；
Figure PCTCN2022122547-appb-000032
表示设备n发送功率的最大值。
具体而言，用户设备n保持当前网络配置c不变，采用上述公式将将功率分配模型转换为无约束优化问题。由于用户设备n在当前网络配置c下发射功率的最优值与拉格朗日乘子λ n、μ n的取值有关，因此需要先求出拉格朗日乘子的最优值
Figure PCTCN2022122547-appb-000033
和
Figure PCTCN2022122547-appb-000034
在本发明的一个实施例中，可以最速下降法求解出拉格朗日乘子的最优值，具体公式如下：
Figure PCTCN2022122547-appb-000035
Figure PCTCN2022122547-appb-000036
其中，κ和γ足够小的正步长，t是迭代索引值。由于上述的无约束优化问题的拉格朗日函数是凸函数，因此通过上述公式对
Figure PCTCN2022122547-appb-000037
进行收敛，可以求得拉格朗日乘子的最优值
Figure PCTCN2022122547-appb-000038
和
Figure PCTCN2022122547-appb-000039
步骤S204，根据拉格朗日乘子的最优值采用Karush-Kuhn-Tucker条件确定当前网络配置下发射功率的最优值。
在通过上述步骤S203求出拉格朗日乘子的最优值
Figure PCTCN2022122547-appb-000040
和
Figure PCTCN2022122547-appb-000041
后，采用Karush-Kuhn-Tucker条件，将拉格朗日乘子的最优值
Figure PCTCN2022122547-appb-000042
和
Figure PCTCN2022122547-appb-000043
带入下述公式，可以求出设备n在当前网络配置下发射功率的最优值
Figure PCTCN2022122547-appb-000044
具体公式如下：
Figure PCTCN2022122547-appb-000045
其中，ε n是网络效率值，g n,k是设备n在信道k的信噪比。
根据本发明的一个实施例，如图3所示，根据当前网络配置下发射功率的最优值确定最优网络配置，可包括以下步骤：
步骤S301，保持当前网络配置下发射功率的最优值不变，将当前网络配置下的最优系统网络效用值增加加权熵项，以获得网络配置模型。
根据本发明的一个实施例，通过以下公式获得网络配置模型：
Figure PCTCN2022122547-appb-000046
Figure PCTCN2022122547-appb-000047
其中，NU(c)表示网络配置c的权重，
Figure PCTCN2022122547-appb-000048
表示加权熵项，β表示熵项的权值，pr c表示系统处于网络配置c的时间百分比，C表示网络配置集合。
步骤S302，采用Karush-Kuhn-Tucker条件对网络配置模型进行求解，确定最优网络配置。
具体地，采用Karush-Kuhn-Tucker条件对步骤S301中的网络配置模型进行求解，可以得到统处于网络配置c的时间百分比的最优解
Figure PCTCN2022122547-appb-000049
表达式具体如下：
Figure PCTCN2022122547-appb-000050
其中，c'∈C，最优解的乘积形式可以抽象为某一时间下马尔科夫链的平稳分布。由此，通过系统处于网络配置c的时间百分比的最优解可以确定系统的最优网络配置。
进一步地，可以分别获取每台用户设备在系统的最优网络配置下发射功率的最优值。
根据本发明的一个实施例，如图4所示，根据最优系统网络效用值确定是否更新用户设备的子信道集合，可包括以下步骤：
步骤S401，根据最优系统网络效用值和当前用户设备选择的子信道集合生成指数分布计时器。
根据本发明的一个实施例，通过以下公式生成指数分布计时器：
Figure PCTCN2022122547-appb-000051
其中，timer表示指数分布计时器，
Figure PCTCN2022122547-appb-000052
表示当前用户设备的当前子信道的最优系统网络效用值；σ表示常数，β表示熵项的权值，k i表示当前用户设备选择的子信道的集合中的任一条，K n表示用户设备n选择的子信道的集合，i为小于等于K的正整数，K表示可用子信道的数目。
具体而言，在初始状态下，用户设备n随机选择一个子信道k加入到集合K n，并生成当前配置c，此时用户设备n的网络效用为z n,k，k∈K n。在获取到在最优网络配置下发射功率的最优值以后，用户设备n可以根据最优网络配置下发射功率的最优值确定在当前子信道的最优系统网络效用值
Figure PCTCN2022122547-appb-000053
将
Figure PCTCN2022122547-appb-000054
带入上述公式，可以生成一个用户设备n的指数分布计时器timer，并开始倒计时。
步骤S402，在当前用户设备的指数分布计时器计时结束时，确定当前用户设备选择的子信道集合中子信道的释放概率。
根据本发明的一个实施例，通过以下公式确定当前用户设备选择的子信道集合中子信道的释放概率：
Figure PCTCN2022122547-appb-000055
其中，pr n,k表示用户设备n选择的子信道集合中子信道k的释放概率。通过上述公式，可以确定用户设备n所选择的子信道k的释放概率。
步骤S403，在子信道的释放概率大于预设阈值时，将该子信道从子信道集合中释放，并更新当前用户设备的子信道集合，并从空闲的子信道中为当前用户设备随机分配一条新的子信道，并将新的子信道加入当前用户设备选择的子信道集合。
也就是说，当用户设备n所选择的子信道k的释放概率大于预设阈值时，将子信道k从子信道集合Kn中释放，并更新用户设备n的子信道集合Kn。用户设备n释放当前子信道k进入跳跃状态，可以为用户设备n从保持活跃的空闲子信道中随机选择一条新的子信道加入子信道集合Kn中，然后向电力线通信系统广播一条重置消息。当电力线通信系统收到重置消息后，待其他设备完成当前的计时过程后，在新的网络配置中计算其网络效用值并生成新的计时变量，然后进入等待状态。在用户设备n所选择的子信道k的释放概率小于预设阈值时，用户设备n保持在当前子信道k不变。 由此，可以使得系统中的用户设备使用最佳子信道进行传输。
由此，上述实施例的资源优化方法可以部署到大规模的电力线通信系统中，而不会导致很多网络效用性能下降，并且在大规模的网络配置下，所产生的网络近似误差上界更低。
综上所述，根据本发明实施例的多用户室内电力线通信的资源优化方法，为每台用户设备随机分配子信道，并确定每个子信道的网络配置；计算每台用户设备在其对应的当前网络配置下发射功率的最优值，并根据当前网络配置下发射功率的最优值确定最优网络配置；获取每台用户设备在最优网络配置下发射功率的最优值；根据最优网络配置下发射功率的最优值确定最优系统网络效用值，并根据最优系统网络效用值确定是否更换用户设备的当前子信道。由此，该方法能够使每台用户设备在大部分时间都会使用最佳子信道进行传输，从而使电力线通信系统接近最佳效用，减少系统的信令开销，并增强系统的可扩展性。
对应上述实施例，本发明还提出了一种计算机可读存储介质。
本发明实施例的计算机可读存储介质，其上存储有多用户室内电力线通信的资源优化程序，该多用户室内电力线通信的资源优化程序被处理器执行时实现上述的多用户室内电力线通信的资源优化方法。
根据本发明实施例的计算机可读存储介质，通过执行上述的多用户室内电力线通信的资源优化方法，能够使电力线通信系统接近最佳效用，减少系统的信令开销，并增强系统的可扩展性。
对应上述实施例，本发明还提出了一种多用户室内电力线通信的资源优化系统。
图5为根据本发明实施例的多用户室内电力线通信的资源优化系统的方框示意图。
如图5所示，本发明实施例的多用户室内电力线通信的资源优化系统500，可包括：第一确定模块510、计算模块520、第二确定模块530、获取模块540和第三确定模块550。
其中，第一确定模块510用于为每台用户设备随机分配子信道，并确定每个子信道的网络配置。计算模块520用于计算每台用户设备在其对应的当前网络配置下发射功率的最优值。第二确定模块530用于根据当前网络配置下发射功率的最优值寻找最优网络配置。获取模块540用于获取每台用户设备在最优网络配置下发射功率的最优值。第三确定模块550用于根据最优网络配置下发射功率的最优值确定最优系统网络效用值，并根据最优系统网络效用值确定是否更新用户设备的子信道集合。
根据本发明的一个实施例，计算模块520计算每台用户设备在其对应的当前网络配置下发射功率的最优值，具体用于，基于网络效用函数建立混合整数非线性规划模型；根据混合整数非线性规划模型确定功率分配模型；保持当前网络配置不变，采用拉格朗日函数将功率分配模型转换为无约束优化问题，并确定拉格朗日乘子的最优值；根据拉格朗日乘子的最优值采用Karush-Kuhn-Tucker条件确定当前网络配置下发射功率的最优值。
根据本发明的一个实施例，第二确定模块530根据当前网络配置下发射功率的最优值确定最优网络配置，具体用于，保持当前网络配置下发射功率的最优值不变，将当前网络配置下的最优系统网络效用值增加加权熵项，以获得网络配置模型；采用Karush-Kuhn-Tucker条件对网络配置模型进行求解，确定最优网络配置。
根据本发明的一个实施例，混合整数非线性规划模型如下：
Figure PCTCN2022122547-appb-000056
Figure PCTCN2022122547-appb-000057
Figure PCTCN2022122547-appb-000058
Figure PCTCN2022122547-appb-000059
其中，NU(·)表示系统网络效用值；α表示权值，0≤α≤1；R n,k表示设备n在子信道k上的传输速率；x n,k表示一个二元变量，当设备n选择子信道k时，x n,k＝1，否则x n,k＝0；p n,k表示分配的发射功率；
Figure PCTCN2022122547-appb-000060
表示电路消耗功率；
Figure PCTCN2022122547-appb-000061
表示设备n的服务质量需求；
Figure PCTCN2022122547-appb-000062
表示设备n发送功率的最大值。
根据本发明的一个实施例，计算模块520通过以下公式将功率分配模型转换为无约束优化问题：
Figure PCTCN2022122547-appb-000063
其中，λ n和μ n表示拉格朗日乘子，α表示权值，0≤α≤1，R n,k表示设备n在子信道k上的传输速率；p n,k表示分配的发射功率；
Figure PCTCN2022122547-appb-000064
表示电路消耗功率；
Figure PCTCN2022122547-appb-000065
表示设备n的服务质量需求；
Figure PCTCN2022122547-appb-000066
表示设备n发送功率的最大值。
根据本发明的一个实施例，第二确定模块530通过以下公式获得网络配置模型：
Figure PCTCN2022122547-appb-000067
Figure PCTCN2022122547-appb-000068
其中，NU(c)表示网络配置c的权重，
Figure PCTCN2022122547-appb-000069
表示加权熵项，β表示熵项的权值，pr c表示系统处于网络配置c的时间百分比，C表示网络配置集合。
根据本发明的一个实施例，第三确定模块550根据最优系统网络效用值确定是否更新用户设备的子信道集合，具体用于，根据最优系统网络效用值和当前用户设备选择的子信道集合生成指数分布计时器；在当前用户设备的指数分布计时器计时结束时，确定当前用户设备选择的子信道集合中子信道的释放概率；在子信道的释放概率大于预设阈值时，将该子信道从子信道集合中释放，并当前用户设备的子信道集合，从空闲的子信道中为当前用户设备随机分配一条新的子信道，并将新的子信道加入当前用户设备选择的子信道集合。
根据本发明的一个实施例，第三确定模块550通过以下公式生成指数分布计时器：
Figure PCTCN2022122547-appb-000070
其中，timer表示指数分布计时器，
Figure PCTCN2022122547-appb-000071
表示当前用户设备的当前子信道的最优系统网络效用值；σ表示常数，β表示熵项的权值，k i表示当前用户设备选择的子信道的集合中的任一条，K n表示用户设备n选择的子信道的集合，i为小于等于K的正整数，K表示可用子信道的数目。
根据本发明的一个实施例，第三确定模块550通过以下公式确定当前用户设备选择的子信道集合中子信道的释放概率：
Figure PCTCN2022122547-appb-000072
其中，pr n,k表示用户设备n选择的子信道集合中子信道k的释放概率。
需要说明的是，本发明实施例的多用户室内电力线通信的资源优化系统中未披露的细节，请参照本发明实施例的多用户室内电力线通信的资源优化方法中所披露的细节，具体这里不再赘述。
在另一个实施示例中，多用户室内电力线通信的资源优化系统500，包括：处理器，其中所述处理器用于执行存在存储器的上述程序模块和单元，包括：第一确定模块510、计算模块520、第二确定模块530、获取模块540和第三确定模块550。
根据本发明实施例的多用户室内电力线通信的资源优化系统，第一确定模块为每台用户设备随机分配子信道，并确定每个子信道的网络配置，计算模块计算每台用户设备在其对应的当前网络配置下发射功率的最优值，第二确定模块根据当前网络配置下发射功率的最优值确定最优网络配置，获取模块获取每台用户设备在最优网络配置下发射功率的最优值，第三确定模块根据最优网络配置 下发射功率的最优值确定最优系统网络效用值，并根据最优系统网络效用值确定是否更换用户设备的当前子信道。由此，该系统能够使每台用户设备在大部分时间都会使用最佳子信道进行传输，从而使电力线通信系统接近最佳效用，减少系统的信令开销，并增强系统的可扩展性。
对应上述实施例，本发明还提出了一种电力线通信系统。
图6为根据本发明实施例的电力线通信系统的方框示意图。
如图6所示，本发明实施例的电力线通信系统600，包括存储器610、处理器620及存储在存储器610上并可在处理器620上运行的多用户室内电力线通信的资源优化程序，处理器620执行多用户室内电力线通信的资源优化程序时，实现上述的多用户室内电力线通信的资源优化方法。
根据本发明实施例的电力线通信系统，通过执行上述的多用户室内电力线通信的资源优化方法，能够使每台用户设备在大部分时间都会使用最佳子信道进行传输，从而使电力线通信系统接近最佳效用，减少系统的信令开销，并增强系统的可扩展性。
需要说明的是，在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的定序列表，可以具体实现在任何计算机可读介质中，以供指令执行系统、装置或设备(如基于计算机的系统、包括处理器的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用，或结合这些指令执行系统、装置或设备而使用。就本说明书而言，"计算机可读介质"可以是任何可以包含、存储、通信、传播或传输程序以供指令执行系统、装置或设备或结合这些指令执行系统、装置或设备而使用的装置。计算机可读介质的更具体的示例(非穷尽性列表)包括以下：具有一个或多个布线的电连接部(电子装置)，便携式计算机盘盒(磁装置)，随机存取存储器(RAM)，只读存储器(ROM)，可擦除可编辑只读存储器(EPROM或闪速存储器)，光纤装置，以及便携式光盘只读存储器(CDROM)。另外，计算机可读介质甚至可以是可在其上打印所述程序的纸或其他合适的介质，因为可以例如通过对纸或其他介质进行光学扫描，接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序，然后将其存储在计算机存储器中。
应当理解，本发明的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，多个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或固件来实现。例如，如果用硬件来实现，和在另一实施方式中一样，可用本领域公知的下列技术中的任一项或他们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离散逻辑电路，具有合适的组合逻辑门电路的专用集成电路，可编程门阵列(PGA)，现场可编程门阵列(FPGA)等。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实 施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。在本发明的描述中，“多个”的含义是至少两个，例如两个，三个等，除非另有明确具体的限定。
在本发明中，除非另有明确的规定和限定，术语“安装”、“相连”、“连接”、“固定”等术语应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或成一体；可以是机械连接，也可以是电连接；可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通或两个元件的相互作用关系，除非另有明确的限定。对于本领域的普通技术人员而言，可以根据具体情况理解上述术语在本发明中的具体含义。
尽管上面已经示出和描述了本发明的实施例，可以理解的是，上述实施例是示例性的，不能理解为对本发明的限制，本领域的普通技术人员在本发明的范围内可以对上述实施例进行变化、修改、替换和变型。

Claims (12)

  1. 一种多用户室内电力线通信的资源优化方法，其特征在于，包括：
    为每台用户设备随机分配子信道，并确定每个子信道的网络配置；
    计算所述每台用户设备在其对应的当前网络配置下发射功率的最优值，并根据当前网络配置下发射功率的最优值寻找最优网络配置；
    获取所述每台用户设备在所述最优网络配置下发射功率的最优值；
    根据所述最优网络配置下发射功率的最优值确定最优系统网络效用值，并根据所述最优系统网络效用值确定是否更新用户设备的子信道集合。
  2. 根据权利要求1所述的多用户室内电力线通信的资源优化方法，其特征在于，计算所述每台用户设备在其对应的当前网络配置下发射功率的最优值，包括：
    基于网络效用函数建立混合整数非线性规划模型；
    根据所述混合整数非线性规划模型确定功率分配模型；
    保持当前网络配置不变，采用拉格朗日函数将所述功率分配模型转换为无约束优化问题，并确定拉格朗日乘子的最优值；
    根据所述拉格朗日乘子的最优值采用Karush-Kuhn-Tucker条件确定当前网络配置下发射功率的最优值。
  3. 根据权利要求1所述的多用户室内电力线通信的资源优化方法，其特征在于，根据当前网络配置下发射功率的最优值确定最优网络配置，包括：
    保持当前网络配置下发射功率的最优值不变，将当前网络配置下的所述最优系统网络效用值增加加权熵项，以获得网络配置模型；
    采用Karush-Kuhn-Tucker条件对所述网络配置模型进行求解，确定所述最优网络配置。
  4. 根据权利要求2所述的多用户室内电力线通信的资源优化方法，其特征在于，所述混合整数非线性规划模型如下：
    Figure PCTCN2022122547-appb-100001
    Figure PCTCN2022122547-appb-100002
    Figure PCTCN2022122547-appb-100003
    Figure PCTCN2022122547-appb-100004
    其中，NU(·)表示系统网络效用值；α表示权值，0≤α≤1；R n,k表示设备n在子信道k上的传输速率；x n,k表示一个二元变量，当设备n选择子信道k时，x n,k＝1，否则x n,k＝0；p n,k表示分配的发射功率；
    Figure PCTCN2022122547-appb-100005
    表 示电路消耗功率；
    Figure PCTCN2022122547-appb-100006
    表示设备n的服务质量需求；
    Figure PCTCN2022122547-appb-100007
    表示设备n发送功率的最大值。
  5. 根据权利要求2所述的多用户室内电力线通信的资源优化方法，其特征在于，通过以下公式将所述功率分配模型转换为无约束优化问题：
    Figure PCTCN2022122547-appb-100008
    其中，λ n和μ n表示拉格朗日乘子，α表示权值，0≤α≤1，R n,k表示设备n在子信道k上的传输速率；p n,k表示分配的发射功率；
    Figure PCTCN2022122547-appb-100009
    表示电路消耗功率；
    Figure PCTCN2022122547-appb-100010
    表示设备n的服务质量需求；
    Figure PCTCN2022122547-appb-100011
    表示设备n发送功率的最大值。
  6. 根据权利要求3所述的多用户室内电力线通信的资源优化方法，其特征在于，通过以下公式获得所述网络配置模型：
    Figure PCTCN2022122547-appb-100012
    Figure PCTCN2022122547-appb-100013
    其中，NU(c)表示网络配置c的权重，
    Figure PCTCN2022122547-appb-100014
    表示加权熵项，β表示熵项的权值，pr c表示系统处于网络配置c的时间百分比，C表示网络配置集合。
  7. 根据权利要求1所述的多用户室内电力线通信的资源优化方法，其特征在于，根据所述最优系统网络效用值确定是否更新用户设备的子信道集合，包括：
    根据所述最优系统网络效用值和当前用户设备选择的子信道集合生成指数分布计时器；
    在当前用户设备的所述指数分布计时器计时结束时，确定当前用户设备选择的子信道集合中子信道的释放概率；
    在子信道的释放概率大于预设阈值时，将该子信道从子信道集合中释放，并更新当前用户设备的子信道集合，以及从空闲的子信道中为当前用户设备随机分配一条新的子信道，并将新的子信道加入当前用户设备选择的子信道集合。
  8. 根据权利要求7所述的多用户室内电力线通信的资源优化方法，其特征在于，通过以下公式生成所述指数分布计时器：
    Figure PCTCN2022122547-appb-100015
    其中，timer表示所述指数分布计时器，
    Figure PCTCN2022122547-appb-100016
    表示当前用户设备的当前子信道的最优系统网络效用值；σ表示常数，β表示熵项的权值，k i表示当前用户设备选择的子信道的集合中的任一条，K n表示用户设备n选择的子信道的集合，i为小于等于K的正整数，K表示可用子信道的数目。
  9. 根据权利要求8所述的多用户室内电力线通信的资源优化方法，其特征在于，通过以下公式确定当前用户设备选择的子信道集合中子信道的释放概率：
    Figure PCTCN2022122547-appb-100017
    其中，pr n,k表示用户设备n选择的子信道集合中子信道k的释放概率。
  10. 一种计算机可读存储介质，其特征在于，其上存储有多用户室内电力线通信的资源优化程序，该多用户室内电力线通信的资源优化程序被处理器执行时实现根据权利要求1-9中任一项所述的多用户室内电力线通信的资源优化方法。
  11. 一种多用户室内电力线通信的资源优化系统，其特征在于，
    第一确定模块，用于为每台用户设备随机分配子信道，并确定每个子信道的网络配置；
    计算模块，用于计算所述每台用户设备在其对应的当前网络配置下发射功率的最优值；
    第二确定模块，用于根据当前网络配置下发射功率的最优值寻找最优网络配置；
    获取模块，用于获取所述每台用户设备在所述最优网络配置下发射功率的最优值；
    第三确定模块，用于根据所述最优网络配置下发射功率的最优值确定最优系统网络效用值，并根据所述最优系统网络效用值确定是否更新用户设备的子信道集合。
  12. 一种电力线通信系统，其特征在于，包括存储器、处理器及存储在存储器上并可在处理器上运行的多用户室内电力线通信的资源优化程序，所述处理器执行所述多用户室内电力线通信的资源优化程序时，实现根据权利要求1-9中任一项所述的多用户室内电力线通信的资源优化方法。
PCT/CN2022/122547 2022-05-17 2022-09-29 多用户室内电力线通信的资源优化方法、系统和存储介质 WO2023221365A1 (zh)

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