5g power iiot resource coordination optimization method and system

Abstract

This invention relates to a 5G power Internet of Things (IoT) resource collaborative optimization method and system. The method includes: obtaining the number of times a channel is selected; determining whether the channel receives a data packet at a preset time, and generating a determination result; calculating a reward confidence term and an empirical average estimate obtained before a target time based on the determination result and the number of selections; generating a confidence interval based on the reward confidence term and the empirical average estimate; and adding the confidence interval to the channel's average value to generate an optimal channel. By calculating the reward confidence term to obtain the confidence interval, the UCB method achieves a significant improvement in the probability of successful transmission, thereby improving the access and autonomy of power IoT devices to the network.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a 5G power Internet of Things resource collaborative optimization method and system. Background Technology

[0002] In recent years, the successful application of next-generation information technologies, such as the Internet of Things (IoT), 5G, and artificial intelligence, has led to a closer integration of IoT technology with power services. Traditional power information communication methods, primarily based on 4G private networks, are ill-suited for the information collection and transmission of massive, dispersed resources. Simultaneously, power systems are placing higher demands on the quality of service in power communication, and the bandwidth and transmission rate of 4G communication are insufficient to meet the scheduling needs of massive, dispersed resources. With the gradual increase in the energy consumption and number of 5G base stations, their importance on the demand side is becoming increasingly prominent. Therefore, 5G power IoT is the focus of this invention.

[0003] To accommodate the increasing number of energy-constrained terminal devices in 5G power IoT networks, the need necessitates the development of contention-based protocols better suited for low-power wide-area (LPWAN) technologies. Therefore, new access mechanisms to avoid collisions are required to prevent degraded network performance in these unlicensed frequency bands. Indeed, the number of packet collisions increases as more uncoordinated devices share the same frequency band. Thus, a crucial issue in media access design is reducing packet loss rates due to interference caused by collisions between devices. The Multi-Armed Robber (MAB) algorithm has been widely studied as a potential solution for channel access in 5G power IoT networks. However, these studies have neglected the impact of retransmissions on the performance of the MAB learning algorithm.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the fact that the prior art does not consider the impact of retransmission on network performance.

[0006] To address the aforementioned technical problems, a first aspect of the present invention provides a 5G power Internet of Things resource collaborative optimization method, the method comprising:

[0007] Get the number of times the channel has been selected;

[0008] Determine whether the channel receives a data packet at a preset time, and generate a determination result;

[0009] Based on the judgment result, the reward confidence term and the empirical average estimate obtained before the target time are calculated using the number of times selected.

[0010] Generate a confidence interval based on the reward confidence term and the empirical average estimate;

[0011] The confidence interval is added to the average value of the channel to generate the optimal channel.

[0012] In one embodiment of the present invention, the step prior to obtaining the number of times the channel has been selected further includes:

[0013] The device transmits data packets to the channel;

[0014] Determine whether the channel has received the data packet;

[0015] If so, then generate the number of times the channel has been selected;

[0016] If not, then change the channel.

[0017] In one embodiment of the present invention, the step of determining whether the channel receives a data packet at a preset time and generating a determination result includes:

[0018] If the channel receives the data packet at a preset time, the determination result is that the transmission was successful;

[0019] If the channel does not receive the data packet within a preset time, the judgment result is a transmission failure, and the channel is switched.

[0020] In one embodiment of the present invention, the formula for the number of times selected is as follows:

[0021]

[0022] Where t and τ are time, Let be the indicator function, k be the channel, and C(τ) be the channel selected when τ.

[0023] In one embodiment of the present invention, the formula for the empirical average estimator is as follows:

[0024]

[0025] Where, N k (t) represents the number of times the element is selected, where t and τ are time intervals. Let be the indicator function, k be the channel, C(τ) be the channel selected at time τ, and r be the channel selected at time τ. k (τ) represents the reward obtained after transmission in channel k at time τ.

[0026] In one embodiment of the present invention, the formula for the reward confidence term is as follows:

[0027]

[0028] Where, Nk (t) represents the number of times the sample is selected, and α represents the exploration coefficient.

[0029] In one embodiment of the present invention, the formula for the confidence interval is as follows;

[0030]

[0031] Among them, B k (t) represents the reward confidence term. It is an empirical average estimator.

[0032] A second aspect of the present invention provides a 5G power Internet of Things resource collaborative optimization system, applied to a method proposed in any one of the first aspects above, the system comprising:

[0033] The module consists of a judgment module, a calculation module, and an optimization module.

[0034] The judgment module is configured to: obtain the number of times the channel has been selected; determine whether the channel has received a data packet at a preset time, and generate a judgment result;

[0035] The calculation module is configured to: calculate the reward confidence term and the empirical average estimate obtained before the target time based on the judgment result and the number of times selected; and generate a confidence interval based on the reward confidence term and the empirical average estimate.

[0036] The optimization module is configured to add the confidence interval to the average value of the channel to generate the optimal channel.

[0037] A third aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method described in the first aspect or any possible implementation thereof.

[0038] A fourth aspect of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0039] The technical solution of the present invention has the following advantages compared with the prior art:

[0040] The present invention discloses a 5G power Internet of Things (IoT) resource collaborative optimization method and system. By calculating the reward confidence term to obtain the confidence interval, the UCB method is significantly improved in terms of successful transmission probability, thereby improving the access and autonomy of power IoT devices to the network. Attached Figure Description

[0041] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0042] Figure 1 This is a flowchart of a 5G power Internet of Things resource collaborative optimization method and system provided by the present invention;

[0043] Figure 2 This invention provides a 5G power Internet of Things resource collaborative optimization method and a line graph showing the collision probability in the system;

[0044] Figure 3 This is a line graph showing the successful transmission probability in a 5G power Internet of Things resource collaborative optimization method and system provided by the present invention.

[0045] Figure 4 The present invention provides a system architecture diagram of a 5G power Internet of Things resource collaborative optimization method and system. Detailed Implementation

[0046] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0047] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0048] Reference Figure 1 As shown, the present invention provides a 5G power Internet of Things resource collaborative optimization method, the method comprising:

[0049] S100: Obtain the number of times the channel has been selected;

[0050] In step S100, the steps prior to obtaining the number of times the channel has been selected further include: the device transmitting a data packet to the channel; determining whether the channel has received the data packet; if yes, generating the number of times the channel has been selected; if no, changing the channel. The formula for the number of times the channel has been selected is as follows:

[0051]

[0052] Where t and τ are time, Let be the indicator function, k be the channel, and C(τ) be the channel selected when τ.

[0053] In practical applications, a power IoT network consists of a large number of power terminal devices that periodically send short data packets, with K channels (K>1) available for transmitting their packets. Assume this network comprises N power IoT devices, each capable of selecting any of the K available channels to perform a transmission. Regardless of device type, each power IoT device follows a slotted ALOHA protocol, with a probability p>0 of transmitting a data packet within a slot. Assuming transmission succeeds if a channel is available, and otherwise a radio collision occurs, these devices will attempt to transmit their data packets up to M times. Note that each retransmission occurs after a random backoff time uniformly distributed in [0, m-1], where m>0 is the length of the backoff interval.

[0054] The above contention process can be described using a Markov chain model. The device containing the transmission packet transitions from an idle state to a transmission state, and during each M-1 backoff phase, considering the collision probability {p}... c ,p c1 ,…,p cM-2 Different retransmissions occur. In each time slot, if a data packet needs to be transmitted, a transition from an idle state to a transmitting state occurs, while the waiting state corresponds to a backoff interval of m.

[0055] In detail, for a device, let N k (t) represents the number of times channel k (for k∈[1,K]) is selected before t-1. For any t, when t≥0, the number of times the channel is selected is as shown in equation (1). If the IoT device selects the channel, its τth transmission is 1, otherwise it is 0.

[0056] S200: Determine whether the channel has received a data packet at a preset time, and generate a determination result;

[0057] In step S200, the step of determining whether the channel has received a data packet at a preset time and generating a determination result includes: if the channel receives the data packet at the preset time, the determination result is that the transmission is successful; if the channel does not receive the data packet at the preset time, the determination result is that the transmission has failed, and the channel is changed.

[0058] In practical applications, the device aims to select the channel with the highest probability of successful transmission, and reinforcement learning is employed for this purpose. It is formulated as the MAB problem, where each channel (also called an arm) is considered a robber, and each robber receives a reward. Then, in each attempt, the device selects the channel that maximizes the total collected rewards. These rewards are acknowledgments received after sending a data packet to the gateway. Thus, when an acknowledgment is received, the transmission is considered successful, and the optimal channel is selected using a learning method.

[0059] When a collision occurs, the device enters a backoff state and retransmits the same data packet on the channel. If all devices continue to retransmit on the same channel, it can lead to consecutive collisions of data packets from the same devices that previously collided. Therefore, the possibility of devices retransmitting on different channels should be considered in the decision-making strategy.

[0060] Given this possibility, the device will learn more, thus the learning time is expected to be longer, but the final performance gain, i.e., the successful transmission rate, may also increase. To this end, the collision probability of the Markov process is investigated, the impact of the robber strategy on mitigation is anticipated, and guidelines are set for the design of heuristic methods.

[0061] Collisions occurring during access can be overcome through retransmission (requiring multiple retransmission attempts). The collision probability p at the second transmission slot will be obtained. c1 The mathematical approximation of the first collision probability p c The function.

[0062] Consider two hypotheses, H1 and H2:

[0063] H1: Probability p c1 It consists of the sum of two probabilities: 1) the probability of two consecutive collisions, i.e., the probability that a device that collides in a given time slot will collide again when retransmitting its data packet; 2) the probability of a collision occurring between devices that did not collide in the previous collision. Furthermore, it is assumed that the number of devices involved in a collision is small compared to the total number of devices.

[0064] H2: The total number of backoff stages in time slot t is constant. Assuming it is large enough, it can be assumed that after M consecutive failed retransmissions, no device will be in a final fault state.

[0065] For a device and a channel, let Let be the probability of transmitting a data packet in the (i+1)th time slot t (where i∈[0,M-1]). Given the probability of transmitting data packets, N devices follow the same strategy.

[0066] In the Markov chain model, it is assumed that the system is in a stable state, so the probability no longer depends on the time slot t (i.e., ...). x t =x). Therefore, the probability of a collision occurring during the first transmission is p. c The expression is as follows:

[0067]

[0068] Furthermore, the probability p of a collision occurring among the n data packets sent by each IoT device (for any 1≤n≤N-1) in the first transmission time slot is defined. cp (n) is defined by the following formula:

[0069]

[0070] As mentioned above, if the device encounters a collision during the initial transmission, it will retransmit its data packets after a random backoff interval. (The last part, "p," appears to be an unrelated instruction and is left untranslated.) ca This represents the probability of colliding with a data packet involved in a previous collision. Under the H1 assumption, the number of data packets involved in the same previous collision is still very small compared to the total number of devices that may transmit during this period. In other words, this collision probability does not depend on previous retransmissions and is equal to p. c Therefore, the probability that data packets from the same device will collide again in the second time slot is:

[0071] p c1 =p ca +(1-p ca )p c (4);

[0072] If the device experiences a collision on its first attempt, it is considered p bp (n) is the probability that it collides with exactly n data packets (for any 1≤n≤N-1), and that at least one of the n devices involved in the first collision chooses the same backoff interval.

[0073]

[0074] p ca This is the conditional probability of a data packet colliding with a data packet sent by a device that participated in the previous collision, given that a collision occurred during the first transmission of the data packet. Therefore, under assumption H2, p can be calculated using Bayes' theorem and the law of total probability. ca With p bp If (n) are considered together, then the different probabilities of devices colliding in the first time slot and having the same retransmission backoff interval are:

[0075]

[0076] Therefore, p ca The expression is:

[0077]

[0078] Under H1, assuming the number of devices involved in the first collision is less than N-1, the first N0 << N-1 term in the above equation dominates. We can obtain:

[0079]

[0080] Furthermore, for these terms, n is smaller than N-1, so N-1-n can be approximated as N-1. Therefore,

[0081]

[0082] Assume H1 equals x << 1. Therefore, the sum in the above equation can be supplemented by a negligible term.

[0083]

[0084] Calculate the sum in the above expression using the binomial theorem, and then... ca The expression is rewritten as:

[0085]

[0086] Finally, it can be done by p c1 Substituting into the above equation, we get p c1 Approximate to .

[0087] In this application, if the channel receives a data packet sent by the device, it proves that the channel has a relatively high transmission success rate. Therefore, the reward confidence term and the empirical average estimate obtained before the target time are further calculated for the channel.

[0088] S300: Based on the judgment result and the number of times selected, calculate the reward confidence term and the empirical average estimate obtained before the target time;

[0089] In step S300, the formula for the empirical average estimate is as follows:

[0090]

[0091] Where, N k (t) represents the number of times the element is selected, where t and τ are time intervals. Let be the indicator function, k be the channel, C(τ) be the channel selected at time τ, and r be the channel selected at time τ. k (τ) represents the reward obtained after transmission through channel k at time τ. The formula for the reward confidence term is as follows:

[0092]

[0093] Where, N k (t) represents the number of times the sample is selected, and α represents the exploration coefficient.

[0094] In practical applications, if the judgment result is successful transmission, it proves that the transmission success rate of the channel is relatively high. Therefore, the number of times the channel was selected obtained above is substituted into equation (12) to obtain the empirical average estimate of the channel before t-1, and the number of times the channel was selected is substituted into equation (12).

[0095] (13) Calculate the reward confidence term for the channel. If the judgment result is transmission failure, it proves that the transmission success rate of the channel is relatively low, so there is no need to continue to calculate it. The channel will be reselected and the calculation will start from the number of times it has been selected.

[0096] S400: Generate a confidence interval based on the reward confidence term and the empirical average estimate;

[0097] In step S400, the formula for the confidence interval is as follows;

[0098]

[0099] Among them, B k (t) represents the reward confidence term. It is an empirical average estimator.

[0100] In practical applications, after calculating the reward confidence term and the empirical average estimate of the channel, the reward confidence term and the empirical average estimate are added together to obtain the confidence interval of the channel.

[0101] S500: Add the confidence interval to the average value of the channel to generate the optimal channel.

[0102] In step S500, the transmission channel at time step t is such that the UCB exponent U k The channel (t) is maximized because it is considered the best channel at the current time step t. The UCB algorithm does not rely on empirical average rewards, but rather on the unknown average μ of each arm. k Using confidence intervals can be viewed as adding a "reward" to the empirical average during exploration. The optimal channel is:

[0103]

[0104] This application considers a maximum retransmission count M = 10, a maximum return interval m = 10, and a transmission probability p = 10. -3 The ALOHA protocol. Number of channels K = 4, T = 20 × 10⁻⁶. 4 .

[0105] Reference Figure 2 As shown, Figure 2 p was displayed c and p c1 Collision probabilities for different numbers of power IoT devices N (from N=50 to N=350). The collision probability increases with the number of devices. (Refer to...) Figure 3 As shown, p can be verified. c1 The approximation for p c1 ≤30% is very precise. Furthermore, p can be observed. c1 and p c The significant difference between them indicates that the MAB algorithm can be used for channel selection in both the first transmission and the next retransmission. Figure 3 This shows the change in the successful transmission rate over time. It can be seen that as the time slot changes, the successful transmission rate first increases rapidly, then tends to stabilize. Figure 3 It can be seen that the successful transmission rate of the method in this application is higher than that of other algorithms. This is because the method in this application takes into account the first transmission and retransmission, which results in better performance and demonstrates the effectiveness of the method in this application.

[0106] Secondly, referring to Figure 4 As shown, this application provides a 5G power Internet of Things resource collaborative optimization system, the system comprising: a judgment module 100, a calculation module 200 and an optimization module 300;

[0107] The judgment module 100 is configured to: obtain the number of times the channel has been selected; determine whether the channel has received a data packet at a preset time, and generate a judgment result;

[0108] The calculation module 200 is configured to: calculate the reward confidence term and the empirical average estimate obtained before the target time based on the judgment result and the number of times selected; and generate a confidence interval based on the reward confidence term and the empirical average estimate.

[0109] The optimization module 300 is configured to add the confidence interval to the average value of the channel to generate the optimal channel.

[0110] The effects of applying the aforementioned method in the above system can be found in the description of the aforementioned method embodiments, and will not be repeated here.

[0111] A third aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method described in the first aspect or any possible implementation thereof.

[0112] A fourth aspect of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0113] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0114] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0115] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0116] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0117] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A 5G power internet of things resource coordination optimization method, characterized in that, The method comprises: acquiring a selected number of channels; judging whether the channel receives a data packet at a preset time to generate a judgment result; calculating a reward confidence item and an experience average estimate obtained before a target time according to the judgment result and the selected number; the formula of the experience average estimate is as follows: where N k (t) is the selected number of times, t and τ are times, is an indicator function, k is a channel, C(τ) is the channel selected at τ, r k (τ) is the reward obtained after transmitting on channel k at τ; generating a confidence interval according to the reward confidence item and the experience average estimate; the formula of the reward confidence item is as follows: where N k (t) is the selected number of times, and a represents the exploration coefficient. the formula of the confidence interval is as follows; where B k (t) is a reward confidence item, is an empirical average estimator; adding the confidence interval to an average value of a channel to generate an optimal channel.

2. The 5G power internet of things resource coordination optimization method of claim 1, wherein, The step before acquiring the selected number of channels further comprises: the device end transmits a data packet to the channel; judging whether the channel receives the data packet; if yes, generating the selected number of the channel; if no, replacing the channel. 3.The 5G power internet of things resource coordination optimization method of claim 1, wherein, The step of judging whether the channel receives a data packet at a preset time to generate a judgment result comprises: if the channel receives the data packet at the preset time, the judgment result is transmission success; if the channel does not receive the data packet at the preset time, the judgment result is transmission failure, and the channel is replaced.

4. The 5G power internet of things resource coordination optimization method of claim 1, wherein, the formula of the selected number is as follows: where t and τ are time, is an indicator function, k is the channel, and C(τ) is the channel selected at time τ. 5.A 5G power internet of things resource coordination optimization system, characterized in that, The system comprises a judgment module, a calculation module and an optimization module. The judgment module is configured to acquire a selected number of channels; judge whether the channel receives a data packet at a preset time to generate a judgment result. The calculation module is configured to calculate a reward confidence item and an experience average estimate obtained before a target time according to the judgment result and the selected number; generate a confidence interval according to the reward confidence item and the experience average estimate. The optimization module is configured to add the confidence interval to an average value of a channel to generate an optimal channel.

6. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the steps of the 5G power internet of things resource collaborative optimization method according to any one of claims 1 to 4 when executing the program.

7. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program implements the steps of the 5G power internet of things resource collaborative optimization method according to any one of claims 1 to 4 when executed by the processor.

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