CN117692961A - Random access congestion control method and device for low-orbit satellite Internet of things - Google Patents
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Abstract
The invention provides a random access congestion control method and device for a low-orbit satellite Internet of things, which relate to the technical field of communication and comprise the following steps: broadcasting a leading sequence pool to all users to be accessed into the low-orbit satellite Internet of things, receiving the leading sequence sent by each user, determining the total number of successfully accessed first leading sequences and the total number of collided second leading sequences, and processing the total number of the first leading sequences and the total number of the second leading sequences by utilizing a target congestion control model to obtain a target access class limiting ACB factor and a target back-off window total number so as to perform access congestion control on the low-orbit satellite Internet of things. The training target of the target congestion control model is that the weighted sum of the access time delay qualification rate and the access energy consumption qualification rate of all users is maximized, so that the method can effectively control the random access congestion problem of the low-orbit satellite Internet of things on the premise that the access time delay and the access energy consumption of all users are minimum.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a random access congestion control method and device for the Internet of things of a low-orbit satellite.
Background
With the rapid development of satellite internet of things service, the amount of machine-to-machine (M2M) devices is also rapidly increasing. Especially, M2M communication has a "tidal effect", i.e. bursts access requests of massive M2M devices, which may cause access congestion, delay increase, packet loss or even interruption of service at that time, so how to solve the access congestion problem in mixed mctc (massive Machine Type of Communication, massive machine type communication) and URLLC (Ultra-Reliable Low-Latency Communications, low latency high Reliable communication) scenarios, to achieve optimization of network performance to a maximized extent and maintain stability of performance, while meeting different latency requirements of devices, is a problem that needs to be solved at present.
Disclosure of Invention
The invention aims to provide a random access congestion control method and device for the low-orbit satellite Internet of things, which can effectively control the random access congestion problem of the low-orbit satellite Internet of things on the premise that the access time delay and the access energy consumption of all users are minimum.
In a first aspect, the present invention provides a random access congestion control method for a low-orbit satellite internet of things, which is applied to a low-orbit satellite, and includes: broadcasting a leading sequence pool to all users to be accessed into the low-orbit satellite Internet of things, so that each user randomly selects one leading sequence to be sent to the low-orbit satellite; wherein the pool of leader sequences is a collection of a plurality of leader sequences; receiving the preamble sequences sent by each user, and determining the total number of the first preamble sequences which are successfully accessed and the total number of the second preamble sequences which collide; processing the total number of the first preamble sequences and the total number of the second preamble sequences by using a target congestion control model to obtain a target access class limit ACB factor and a target back-off window total number; the training target of the target congestion control model is that the weighted sum of the access delay qualification rate and the access energy consumption qualification rate of all users is maximized; and performing access congestion control on the low-orbit satellite Internet of things based on the target access class limit ACB factor and the total number of target back-off windows.
In an alternative embodiment, receiving the preamble sequences sent by each user, and determining the total number of the first preamble sequences successfully accessed and the total number of the second preamble sequences collided, including: counting the number of target leader sequences in the leader sequences sent by all users; wherein the target preamble sequence represents any one of the preamble sequences transmitted by all users; in the case that the number of the target leader sequences is determined to be 1, determining the target leader sequence as a first leader sequence; determining the target leader sequence as a second leader sequence in the case that the number of the target leader sequences is determined to be more than 1; and counting all the first leader sequences and all the second leader sequences to obtain the total number of the first leader sequences and the total number of the second leader sequences.
In an alternative embodiment, the method further comprises: initializing a local network model and a target network model; after initialization, the network parameters of the local network model are the same as those of the target network model; the local network model and the target network model belong to the same agent; for each time slot, the control agent is based on A greedy algorithm selects actions; wherein, under each time slot, the state of the agent includes: the actions of the agent include: the access class limits ACB factors and total number of backoff windows, and rewards acquired after the intelligent agent performs actions are as follows: the weighted sum of the access time delay qualification rate and the access energy consumption qualification rate of all users; storing the state, action and rewards acquired after executing the action of the agent in the current time slot and the state of the next time slot as an experience tuple into an experience pool until a preset number of experience tuples are obtained; batch sampling is carried out from the experience pool so as to train the local network model and the target network model, and a target loss function is obtained; updating network parameters of the local network model based on the target loss function, and controlling the target network model to copy the network parameters of the local network model every other preset period; updating the network parameters of the local network model for a preset number of timesAnd (c) taking the local network model as the target congestion control model.
In an alternative embodiment, calculating rewards acquired by an agent after performing an action includes: acquiring a first moment when preparation of a data packet of a target user is completed and a second moment when transmission of the data packet is completed; wherein the target user represents any one of all users; calculating the access time delay of the target user based on the first time and the second time; acquiring a first duration of maintaining a sending state, a second duration of maintaining a receiving state, sending state unit energy consumption, receiving state unit energy consumption and idle state unit energy consumption of the target user; calculating the idle time length of the target user based on the access time delay of the target user, the first time length and the second time length; calculating access energy consumption of the target user based on the first time length, the second time length, the idle time length, the sending state unit energy consumption, the receiving state unit energy consumption and the idle state unit energy consumption; and calculating access time delay qualification rate and access energy consumption qualification rate of all users based on a preset time delay threshold value, a preset energy consumption threshold value, access time delay and access energy consumption of each user, and taking a weighted sum of the access time delay qualification rate and the access energy consumption qualification rate as the rewards.
In an alternative embodiment, calculating access delay qualification rates and access energy consumption qualification rates of all users based on a preset delay threshold, a preset energy consumption threshold, access delay and access energy consumption of each user includes: judging whether the access time delay of the target user is larger than the preset time delay threshold value or not; if yes, determining that the access time delay of the target user is unqualified; if not, determining that the access time delay of the target user is qualified; judging whether the access energy consumption of the target user is larger than the preset energy consumption threshold; if yes, determining that the access energy consumption of the target user is unqualified; if not, determining that the access energy consumption of the target user is qualified; counting the total number of first users with qualified access time delay in all users, and counting the total number of second users with qualified access energy consumption in all users; and calculating access time delay qualification rates of all users based on the total number of the first users and the total number of the users to be accessed to the low-orbit satellite Internet of things, and calculating access energy consumption qualification rates of all users based on the total number of the second users and the total number of the users to be accessed to the low-orbit satellite Internet of things.
In an alternative embodiment, the formula for the reward is expressed as: The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Representing the access delay qualification rate, < >>Representing the access energy consumption qualification rate, < >>An adjustment factor representing balancing said access delay qualification rate and said access energy consumption qualification rate, +.>。
In an alternative embodiment, the objective loss function is expressed as:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Indicating the number of experience tuples for batch sampling, +.>，/>Representing rewards in the j-th experience tuple in the batch sample,/and>representing discount factors->，/>Model parameters representing the model in said local network +.>Under the condition that the intelligent body is in the state->Take action down->Action value of time, ->Representing the status of agents in the j+1th experience tuple,/th experience tuple>Representing actions of agents in the j-th experience tuple,/->Model parameters representing the target network model, < >>Representing the action value of the output of the target network model,/->Model parameters representing the model in said local network +.>Under the condition that the intelligent body is in the state->Take action down->Action value in time.
In a second aspect, the present invention provides a low-orbit satellite internet of things random access congestion control device, applied to a low-orbit satellite, comprising: the broadcasting module is used for broadcasting the leading sequence pool to all users to be accessed into the low-orbit satellite Internet of things, so that each user randomly selects one leading sequence to be sent to the low-orbit satellite; wherein the pool of leader sequences is a collection of a plurality of leader sequences; the receiving and determining module is used for receiving the preamble sequences sent by each user and determining the total number of the first preamble sequences which are successfully accessed and the total number of the second preamble sequences which collide; the processing module is used for processing the total number of the first preamble sequences and the total number of the second preamble sequences by utilizing a target congestion control model to obtain a target access class limit ACB factor and a target back-off window total number; the training target of the target congestion control model is that the weighted sum of the access delay qualification rate and the access energy consumption qualification rate of all users is maximized; and the congestion control module is used for controlling the access congestion of the low-orbit satellite Internet of things based on the target access class limiting ACB factor and the total number of target back-off windows.
In a third aspect, the present invention provides an electronic device, including a memory, and a processor, where the memory stores a computer program that can be executed on the processor, and when the processor executes the computer program, implement the steps of the random access congestion control method for the low-orbit satellite internet of things in any one of the foregoing embodiments.
In a fourth aspect, the present invention provides a computer readable storage medium storing computer instructions that, when executed by a processor, implement a low orbit satellite internet of things random access congestion control method according to any of the preceding embodiments.
According to the random access congestion control method for the low-orbit satellite Internet of things, a target congestion control model with a training target being a weighted sum of access delay qualification rates and access energy consumption qualification rates of all users is utilized to process the total number of successfully accessed first preamble sequences and the total number of collided second preamble sequences, and the target access class limiting ACB factor and the target backoff window total number are obtained so as to control access congestion of the low-orbit satellite Internet of things. The method can simultaneously determine ACB factors and total number of the backoff windows, and effectively control the random access congestion problem of the low-orbit satellite Internet of things on the premise that the access time delay and the access energy consumption of all users are minimum by combining an ACB mechanism and a backoff mechanism.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a random access congestion control method for a low-orbit satellite internet of things according to an embodiment of the present invention;
fig. 2 is a flowchart of determining a total number of first preamble sequences successfully accessed and a total number of second preamble sequences that collide according to an embodiment of the present invention;
fig. 3 is a functional block diagram of a random access congestion control device for the internet of things of low-orbit satellites according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Some embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Example 1
The embodiment of the invention provides a random access congestion control method of a low-orbit satellite Internet of things, which is applied to a low-orbit satellite, and fig. 1 is a flow chart of the random access congestion control method of the low-orbit satellite Internet of things, and specifically comprises the following steps:
step S102, broadcasting a leading sequence pool to all users to be accessed into the Internet of things of the low-orbit satellite, so that each user randomly selects one leading sequence to be sent to the low-orbit satellite.
Specifically, to send a data packet to a low-orbit satellite, a user must use a preamble sequence specified by the satellite, so before the user initiates an access request to the low-orbit satellite, the low-orbit satellite firstly broadcasts its preamble sequence pool to all users to be accessed to the internet of things of the low-orbit satellite, where the users include: mctc (massive Machine Type of Communication, mass machine type communication) devices and URLLC (Ultra-Reliable Low-Latency Communications) devices, the pool of preamble sequences being a collection of multiple preamble sequences. After each user receives the preamble sequence pool, one preamble sequence is randomly selected and is uplink transmitted to the satellite through the PRACH channel.
Step S104, receiving the preamble sequences sent by each user, and determining the total number of the first preamble sequences which are successfully accessed and the total number of the second preamble sequences which collide.
If multiple users send different preamble sequences at the same time, collision does not occur, the satellite can be successfully decoded, and multiple users can be successfully connected to the Internet of things of the low-orbit satellite, however, the users selecting the same preamble sequence collide, and at the moment, the satellite cannot decode the user ID where collision occurs. The collision causes access congestion problems, so in order to determine the congestion level of the current network, after receiving the preamble sequences sent by each user, the low-orbit satellite needs to determine the total number of successfully accessed first preamble sequences and the total number of collided second preamble sequences. After the two data are obtained, the data such as the number of the users in the congestion state, the duty ratio of the congested users and the like can be determined by combining the total number of the preamble sequences received by the satellite.
And S106, processing the total number of the first preamble sequences and the total number of the second preamble sequences by using a target congestion control model to obtain a target access class limit ACB factor and a target back-off window total number.
In the embodiment of the invention, the optimization strategy for the access congestion of the low-orbit satellite Internet of things comprises the following steps: ACB (Access Class Barring, access class limitation) mechanism and backoff mechanism. The ACB mechanism is that the satellite broadcast access class limits the ACB factor to the users with the confliction of the preamble sequence, meanwhile, the users with the confliction of the preamble sequence need to generate a random number between 0 and 1 to be compared with the ACB factor of the satellite broadcast, and the users can initiate access again only when the random number generated by the users is smaller than the ACB factor of the satellite broadcast, and the access flow is the same as the above; if the random number generated by the user is greater than or equal to the ACB factor broadcast by the satellite, the user cannot immediately re-initiate access, i.e., the user enters a back-off state and initiates access according to the requirements of the back-off mechanism.
In the back-off mechanism, the satellite broadcasts the total number of back-off windows to users with the collision of the preamble sequences, one back-off window corresponds to one back-off state, one back-off state corresponds to one back-off time slot, the minimum back-off time slot is 0, the maximum back-off time slot is the total number of back-off windows minus 1, and the time slot interval of the adjacent back-off windows is 1. That is, if the total number of backoff windows is 4, the 4 backoff windows correspond to: the backoff slot is 0 slot, the backoff slot is 1 slot, the backoff slot is 2 slot, and the backoff slot is 3 slot.
The crashed users are uniformly scattered on the W back-off windows according to the total number W of the back-off windows broadcasted by the satellite, and each user backs off to one of the back-off states, namely, the user needs to wait for the corresponding back-off time slot and then re-initiate access.
In the prior art, how to dynamically adjust the ACB factor and the total number of backoff windows according to the network congestion degree is an important challenge facing the current research, and if the ACB factor is determined first and then the total number of backoff windows is determined based on the ACB factor, the problem of error accumulation exists, so that the tuning effect of access congestion is affected. In view of this, the embodiment of the present invention provides a congestion control method based on a deep reinforcement learning joint access control and backoff mechanism, where a target congestion control model can determine a target ACB factor and a target backoff window total simultaneously according to a first preamble sequence total and a second preamble sequence total, where a training target of the target congestion control model is that a weighted sum of access delay qualification rates and access energy consumption qualification rates of all users is maximized.
And step S108, performing access congestion control on the low-orbit satellite Internet of things based on the target access class limit ACB factor and the total number of target back-off windows.
The ACB factor and the total number of the target back-off windows are determined simultaneously, so that the problem of error accumulation can be avoided, and the tuning effect of access congestion is ensured. In addition, a large number of experimental results show that the method can achieve lower access time delay and lower access power consumption, and effectively improves the system stability.
According to the random access congestion control method for the low-orbit satellite Internet of things, a target congestion control model with a training target being a weighted sum of access delay qualification rates and access energy consumption qualification rates of all users is utilized to process the total number of successfully accessed first preamble sequences and the total number of collided second preamble sequences, and the target access class limiting ACB factor and the target backoff window total number are obtained so as to control access congestion of the low-orbit satellite Internet of things. The method can simultaneously determine ACB factors and total number of the backoff windows, and effectively control the random access congestion problem of the low-orbit satellite Internet of things on the premise that the access time delay and the access energy consumption of all users are minimum by combining an ACB mechanism and a backoff mechanism.
In an alternative embodiment, as shown in fig. 2, step S104, the step of receiving the preamble sequences sent by each user, and determining the total number of the first preamble sequences successfully accessed and the total number of the second preamble sequences that collide specifically includes the following steps:
In step S1041, the number of target preamble sequences in the preamble sequences sent by all users is counted.
Wherein the target preamble sequence represents any one of the preambles transmitted by all users.
In step S1042, in the case where the number of target preamble sequences is determined to be 1, the target preamble sequence is determined to be the first preamble sequence.
In step S1043, in the case where the number of target preamble sequences is determined to be greater than 1, the target preamble sequence is determined to be the second preamble sequence.
Step S1044, counting all the first leader sequences and all the second leader sequences to obtain the total number of the first leader sequences and the total number of the second leader sequences.
Specifically, in order to determine the total number of the first preamble sequences and the total number of the second preamble sequences, the satellite needs to count the number of target preamble sequences, if the number is 1, it is indicated that only one user transmits the preamble sequences, and no collision occurs, and the target preamble sequences are the first preamble sequences which can be successfully accessed into the satellite; if the number of target preambles is greater than 1, it is stated that more than one user selects the preamble and thus a collision will be initiated, i.e. the target preamble is the second preamble to collide. And carrying out statistics and judgment on all the leader sequences received by the satellite according to the method, so as to obtain the total number of the first leader sequences and the total number of the second leader sequences.
In an alternative embodiment, the method of the present invention further comprises the steps of:
step S201, initializing a local network model and a target network model.
After initialization, network parameters of the local network model are the same as those of the target network model; the local network model and the target network model belong to the same agent.
The traditional reinforcement learning method has the problem of poor stability in the process of training the network model,mainly reflected in the difficulty in convergence caused by the continuous change of the target Q value. In order to overcome the above problems, the embodiment of the present invention introduces a target network model in addition to a local network model to alleviate instability in the training process, where the local network model and the target network model belong to the same agent, and in an initial state, the local network model and the target network model share the same set of network parameters, and in the initial state, the exploration rate is the same as that of the target network model=1。
Step S202, for each time slot, the control agent is based onA greedy algorithm selects actions.
Wherein, under each time slot, the state of the agent includes: the actions of the agent include: the access class limits ACB factors and total number of backoff windows, and rewards acquired after the intelligent agent performs actions are as follows: and (3) the weighted sum of the access time delay qualification rate and the access energy consumption qualification rate of all users.
Step S203, the state of the agent, the action, the rewards acquired after executing the action and the state of the next time slot in the current time slot are stored as an experience tuple in the experience pool until a preset number of experience tuples are obtained.
In order to train a local network model and a target network model, the embodiment of the invention firstly controls an intelligent agent based on network parameters of a current intelligent agent under each time slot t, and is based on the network parameters of the current intelligent agentGreedy algorithm based on agent state +.>Make action->That is, the agent is 1-/-ed>To select the action with the largest Q value (i.e. action value) to +.>Is selected randomly. And calculates the corresponding rewards +.>Finally the state, action, rewards and the state of the next time slot of the current time slot are +.>As an experience tuple->And storing into an experience pool. With reference to the above method, the experience tuples corresponding to the preset number of time slots are stored in the experience pool.
Step S204, batch sampling is carried out from the experience pool so as to train the local network model and the target network model, and a target loss function is obtained.
Step S205, updating the network parameters of the local network model based on the target loss function, and controlling the target network model to copy the network parameters of the local network model every preset period.
In order to eliminate the correlation between samples, in the training process of the intelligent agent, a specified number of experience tuples are sampled in batches from an experience pool to train a local network model and a target network model, and network parameters of the local network model are updated based on a target loss function, wherein the local network model is used for selecting actions and evaluating the Q value of the current state, and the network parameters of the network model are continuously updated in the training process, and meanwhile, the exploration rate is reduced. The target network model is used for calculating the target Q value, and the network parameters of the target network model are not updated in each iteration like the local network model, but are copied every preset period, i.e. periodicallyUpdating. In view of the relatively slow updating of network parameters of the target network model, the calculation of the target Q value is relatively more stable, so that the problems of unstable training and poor convergence, which may occur in reinforcement learning, are solved.
In an alternative embodiment, the objective loss function is expressed as:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Indicating the number of experience tuples for batch sampling, +.>，/>Representing rewards in the j-th experience tuple in the batch sample,/and >Representing discount factors->，/>Model parameters representing the model in the local network +.>Under the condition that the intelligent body is in the state->Take action down->Action value of time, ->Representing the status of agents in the j+1th experience tuple,/th experience tuple>Represents the jthAction of agent in experience tuple, +.>Model parameters representing the target network model, +.>Representing the action value (i.e., target Q value) of the target network model output,/and (ii)>Model parameters representing the model in the local network +.>Under the condition that the intelligent body is in the state->Take action down->Action value (i.e., Q value of current state).
Step S206, in the case of determining that the network parameters of the local network model are updated for a preset number of times, the local network model is taken as a target congestion control model.
Under the condition that the network parameters of the local network model are updated for preset times, the network model is determined to reach a convergence state, so that after the converged local network model is used as a target congestion control model to be deployed on a satellite, the satellite can be used as an intelligent agent to dynamically adjust access configuration parameters according to the congestion degree: ACB factor and total number of backoff windows.
In an alternative embodiment, the method for calculating the rewards acquired after the intelligent agent performs the action specifically comprises the following steps:
Step S301, a first time when the preparation of the data packet of the target user is completed and a second time when the transmission of the data packet is completed are obtained.
Wherein the target user represents any one of all users.
Step S302, based on the first time and the second time, the access time delay of the target user is calculated.
Specifically, for the random access process of users, the embodiment of the invention establishes a dual queue model for each user, each user includes a request queue and a data queue, the data queue stores the data packets arrived by each device, the data queue has an infinite buffer area size, and when the data queue is not empty and no new data is added to the queue (i.e. the data packets are ready to be completed), the user generates an access request, i.e. the request queue is not empty. It should be noted that the request queue can only have at most one access request in progress, so the request queue has a buffer of one unit size, and if the request queue of the device is not empty, it will continue to try to perform the random access procedure. When random access is successful, all data packets in the data queue will be transmitted to the satellite, and the data queue and the request queue will be emptied simultaneously. Therefore, when the preparation of the data packet of the target user is completed, the random access flow is started, and the starting time is the first time. And the time when all the data packets in the data queue of the target user are successfully transmitted to the satellite (i.e. the data packet transmission is completed) is the second time. Second moment of time Is +.>The time difference between them is the access delay of the target user, i.e. < ->。
Step S303, obtaining a first duration of maintaining the sending state, a second duration of maintaining the receiving state, sending state unit energy consumption, receiving state unit energy consumption and idle state unit energy consumption of the target user.
Step S304, calculating the idle time length of the target user based on the access time delay, the first time length and the second time length of the target user.
Step S305 calculates access energy consumption of the target user based on the first time period, the second time period, the idle time period, the transmission state unit energy consumption, the reception state unit energy consumption, and the idle state unit energy consumption.
In the access process of the target user, when the target user is in a receiving state, the preamble sequence pool for receiving satellite broadcasting is used for receiving the preamble sequence pool, when the target user is in a transmitting state, the preamble sequence pool and the data packet are transmitted to the satellite, the rest states are idle states, and the time length of the target user for maintaining the transmitting state and the receiving state can be accurately determined according to the working states, so that after the access time delay is obtained, the first time length and the second time length are subtracted by the access time delay, and the obtained calculation result is the idle time length of the target user. Then, the access energy consumption E of the target user can be obtained by multiply and then accumulating the time length and the corresponding energy consumption, and the access energy consumption E is expressed as: The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Indicates the idle time length +.>A first time period is indicated and a second time period is indicated,representing a second duration, +.>Representing idle state unit energy consumption->Representing transmission state unit energy consumption,/-), for>Representing the received state unit energy consumption.
Step S306, based on a preset time delay threshold, a preset energy consumption threshold, the access time delay and the access energy consumption of each user, the access time delay qualification rate and the access energy consumption qualification rate of all users are calculated, and the weighted sum of the access time delay qualification rate and the access energy consumption qualification rate is used as rewards.
According to the method, the access time delay and the access energy consumption of each user can be calculated, in the process that the user accesses the low-orbit satellite Internet of things, the access time delay and the access energy consumption are smaller and better, so that the access time delay and the access energy consumption of each user are respectively compared with the preset time delay threshold value and the preset energy consumption threshold value, the number of users qualified in the access time delay and the number of users qualified in the access energy consumption can be determined, the access time delay qualification rate and the access energy consumption qualification rate are calculated, and finally the weighted sum of the access time delay and the access energy consumption qualification rate is used as rewards.
In an optional embodiment, in the step S306, based on the preset time delay threshold, the preset energy consumption threshold, the access time delay and the access energy consumption of each user, the access time delay qualification rate and the access energy consumption qualification rate of all users are calculated, which specifically includes the following steps:
Step S3061, judging whether the access time delay of the target user is larger than a preset time delay threshold.
If yes, the following step S3062 is executed; if not, the following step S3063 is performed.
Step S3062, determining that the access time delay of the target user is unqualified.
Step S3063, determining that the access delay of the target user is qualified.
Step S3064, judging whether the access energy consumption of the target user is larger than a preset energy consumption threshold.
If yes, execute the following step S3065; if not, the following step S3066 is performed.
Step S3065, determining that the access energy consumption of the target user is unqualified.
Step S3066, determining that the access energy consumption of the target user is qualified.
Step S3067, the total number of the first users with qualified access time delay in all the users is counted, and the total number of the second users with qualified access energy consumption in all the users is counted.
Step S3068, calculating access time delay qualification rates of all users based on the total number of the first users and the total number of the users to be accessed to the low-orbit satellite Internet of things, and calculating access energy consumption qualification rates of all users based on the total number of the second users and the total number of the users to be accessed to the low-orbit satellite Internet of things.
The formula of the access time delay qualification rate is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>First user total number which indicates access delay qualification, < - >Indicating the total number of users to be connected to the low orbit satellite Internet of things, < >>Indicating the access delay qualification rate of all users.
The formula of the access energy consumption qualification rate is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Representing the total number of second users eligible for access energy consumption,and representing the access energy consumption qualification rate of all users.
In an alternative embodiment, the formula for the reward is expressed as:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Representing access delay qualification rate, +.>Representing access energy consumption qualification rate, < >>Adjusting factors for balancing access delay qualification rate and access energy consumption qualification rate>。
In summary, the method provided by the embodiment of the invention is essentially a congestion control method based on deep reinforcement learning and combining the advantages of an ACB mechanism and a backoff mechanism on congestion control, and is directed to the characteristic that the congestion degree in a large-scale access scene is continuously changed so as to dynamically adjust access configuration parameters in real time, and is introduced into the method of deep reinforcement learning, wherein a satellite is regarded as an intelligent body, acquires experience in continuous interaction with the environment and takes action according to the environment state, and meanwhile, the total number of ACB factors and backoff windows is determined, so that large-scale access collision is avoided, access congestion is effectively relieved, and access delay and access power consumption of users are reduced.
Example two
The embodiment of the invention also provides a low-orbit satellite internet of things random access congestion control device, which is applied to the low-orbit satellite and is mainly used for executing the low-orbit satellite internet of things random access congestion control method provided by the embodiment one.
Fig. 3 is a functional block diagram of a low-orbit satellite internet of things random access congestion control device according to an embodiment of the present invention, where, as shown in fig. 3, the device mainly includes: a broadcasting module 10, a receiving and determining module 20, a processing module 30, a congestion control module 40, wherein:
the broadcasting module 10 is configured to broadcast the preamble sequence pool to all users to be connected to the low-orbit satellite internet of things, so that each user randomly selects one preamble sequence to send to the low-orbit satellite; wherein the pool of leader sequences is a collection of a plurality of leader sequences.
The receiving and determining module 20 is configured to receive the preamble sequences sent by each user, and determine the total number of first preamble sequences that are successfully accessed and the total number of second preamble sequences that collide.
A processing module 30, configured to process the total number of the first preamble sequences and the total number of the second preamble sequences by using a target congestion control model, so as to obtain a target access class limit ACB factor and a target backoff window total number; the training target of the target congestion control model is that the weighted sum of the access delay qualification rate and the access energy consumption qualification rate of all users is maximized.
And the congestion control module 40 is configured to perform access congestion control on the low-orbit satellite internet of things based on the target access class limit ACB factor and the target total number of backoff windows.
According to the random access congestion control device for the low-orbit satellite Internet of things, provided by the embodiment of the invention, the total number of successfully accessed first preamble sequences and the total number of collided second preamble sequences are processed by utilizing the target congestion control model with the training target of maximizing the weighted sum of the access delay qualification rate and the access energy consumption qualification rate of all users, so that the target access class limiting ACB factor and the target backoff window total number are obtained, and the access congestion control is performed on the low-orbit satellite Internet of things. The device can simultaneously determine ACB factors and total number of the backoff windows, and effectively control the random access congestion problem of the low-orbit satellite Internet of things on the premise that the access time delay and the access energy consumption of all users are minimum by combining an ACB mechanism and a backoff mechanism.
Optionally, the receiving and determining module 20 is specifically configured to:
counting the number of target leader sequences in the leader sequences sent by all users; wherein the target preamble sequence represents any one of the preambles transmitted by all users.
In the case where the number of target leader sequences is determined to be 1, the target leader sequence is determined to be the first leader sequence.
In the case where the number of target leader sequences is determined to be greater than 1, the target leader sequence is determined to be the second leader sequence.
Counting all the first leader sequences and all the second leader sequences to obtain the total number of the first leader sequences and the total number of the second leader sequences.
Optionally, the apparatus further comprises:
the initialization module is used for initializing the local network model and the target network model; after initialization, network parameters of the local network model are the same as those of the target network model; the local network model and the target network model belong to the same agent.
A control module for controlling the agent based on each time slotA greedy algorithm selects actions; wherein, under each time slot, the state of the agent includes: the actions of the agent include: the access class limits ACB factors and total number of backoff windows, and rewards acquired after the intelligent agent performs actions are as follows: and (3) the weighted sum of the access time delay qualification rate and the access energy consumption qualification rate of all users.
And the storage module is used for storing the state, the action of the agent in the current time slot, the rewards acquired after the action is executed and the state of the next time slot as an experience tuple into the experience pool until a preset number of experience tuples are obtained.
And the sampling and training module is used for carrying out batch sampling from the experience pool so as to train the local network model and the target network model to obtain the target loss function.
And the updating module is used for updating the network parameters of the local network model based on the target loss function and controlling the target network model to copy the network parameters of the local network model every other preset period.
And the determining module is used for taking the local network model as a target congestion control model under the condition of determining the network parameter updating preset times of the local network model.
Optionally, the device is further configured to: calculating rewards obtained after the intelligent agent executes the actions, specifically comprising:
the first acquisition module is used for acquiring a first moment when the preparation of the data packet of the target user is completed and a second moment when the transmission of the data packet is completed; wherein the target user represents any one of all users.
The first calculating module is used for calculating the access time delay of the target user based on the first time and the second time.
The second acquisition module is used for acquiring the first duration of maintaining the sending state, the second duration of maintaining the receiving state, the sending state unit energy consumption, the receiving state unit energy consumption and the idle state unit energy consumption of the target user.
And the second calculation module is used for calculating the idle time length of the target user based on the access time delay, the first time length and the second time length of the target user.
The third calculation module is used for calculating the access energy consumption of the target user based on the first time length, the second time length, the idle time length, the sending state unit energy consumption, the receiving state unit energy consumption and the idle state unit energy consumption.
And the fourth calculation module is used for calculating the access delay qualification rate and the access energy consumption qualification rate of all the users based on a preset delay threshold, a preset energy consumption threshold, the access delay and the access energy consumption of each user, so that the weighted sum of the access delay qualification rate and the access energy consumption qualification rate is used as rewards.
Optionally, the fourth computing module is specifically configured to:
and judging whether the access time delay of the target user is larger than a preset time delay threshold value.
If yes, determining that the access time delay of the target user is unqualified.
If not, determining that the access time delay of the target user is qualified.
And judging whether the access energy consumption of the target user is larger than a preset energy consumption threshold.
If yes, determining that the access energy consumption of the target user is unqualified.
If not, determining that the access energy consumption of the target user is qualified.
And counting the total number of the first users with qualified access time delay in all the users, and counting the total number of the second users with qualified access energy consumption in all the users.
And calculating access time delay qualification rates of all users based on the first user total number and the user total number of the low-orbit satellite Internet of things to be accessed, and calculating access energy consumption qualification rates of all users based on the second user total number and the user total number of the low-orbit satellite Internet of things to be accessed.
Alternatively, the formula of the reward is expressed as:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Representing access delay qualification rate, +.>Representing access energy consumption qualification rate, < >>Represents an adjustment factor that balances access delay yield and access energy consumption yield,。
alternatively, the target loss function is expressed as:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Indicating the number of experience tuples for batch sampling, +.>，/>Representing rewards in the j-th experience tuple in the batch sample,/and>representing discount factors->，/>Model parameters representing the model in the local network +.>Under the condition that the intelligent body is in the state->Take action down->Action value of time, ->Representing the status of agents in the j+1th experience tuple,/th experience tuple>Representing actions of agents in the j-th experience tuple,/->Model parameters representing the target network model, +.>Representing the action value of the output of the target network model, +.>Model parameters representing the model in the local network +.>Under the condition that the intelligent body is in the state->Take action down->Action value in time.
Example III
Referring to fig. 4, an embodiment of the present invention provides an electronic device, including: a processor 60, a memory 61, a bus 62 and a communication interface 63, the processor 60, the communication interface 63 and the memory 61 being connected by the bus 62; the processor 60 is arranged to execute executable modules, such as computer programs, stored in the memory 61.
The memory 61 may include a high-speed random access memory (RAM, random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. The communication connection between the system network element and at least one other network element is achieved via at least one communication interface 63 (which may be wired or wireless), and may use the internet, a wide area network, a local network, a metropolitan area network, etc.
Bus 62 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 4, but not only one bus or type of bus.
The memory 61 is configured to store a program, and the processor 60 executes the program after receiving an execution instruction, and the method executed by the apparatus for defining a process disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 60 or implemented by the processor 60.
The processor 60 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware or instructions in software in the processor 60. The processor 60 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but may also be a digital signal processor (Digital Signal Processing, DSP for short), application specific integrated circuit (Application Specific Integrated Circuit, ASIC for short), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA for short), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 61 and the processor 60 reads the information in the memory 61 and in combination with its hardware performs the steps of the method described above.
The embodiment of the invention provides a computer program product of a random access congestion control method and device for low-orbit satellite internet of things, which comprises a computer readable storage medium storing a non-volatile program code executable by a processor, wherein the program code comprises instructions for executing the method described in the previous method embodiment, and specific implementation can be seen in the method embodiment and will not be repeated here.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the present invention, it should be noted that, directions or positional relationships indicated by terms such as "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., are directions or positional relationships based on those shown in the drawings, or are directions or positional relationships conventionally put in use of the inventive product, are merely for convenience of describing the present invention and simplifying the description, and are not indicative or implying that the apparatus or element to be referred to must have a specific direction, be constructed and operated in a specific direction, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Furthermore, the terms "horizontal," "vertical," "overhang," and the like do not denote a requirement that the component be absolutely horizontal or overhang, but rather may be slightly inclined. As "horizontal" merely means that its direction is more horizontal than "vertical", and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the present invention, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the 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 scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (10)
1. The random access congestion control method for the Internet of things of the low-orbit satellite is characterized by being applied to the low-orbit satellite and comprising the following steps of:
Broadcasting a leading sequence pool to all users to be accessed into the low-orbit satellite Internet of things, so that each user randomly selects one leading sequence to be sent to the low-orbit satellite; wherein the pool of leader sequences is a collection of a plurality of leader sequences;
receiving the preamble sequences sent by each user, and determining the total number of the first preamble sequences which are successfully accessed and the total number of the second preamble sequences which collide;
processing the total number of the first preamble sequences and the total number of the second preamble sequences by using a target congestion control model to obtain a target access class limit ACB factor and a target back-off window total number; the training target of the target congestion control model is that the weighted sum of the access delay qualification rate and the access energy consumption qualification rate of all users is maximized;
and performing access congestion control on the low-orbit satellite Internet of things based on the target access class limit ACB factor and the total number of target back-off windows.
2. The method for controlling random access congestion of the internet of things of low orbit satellite according to claim 1, wherein receiving the preamble sequences transmitted by each user and determining the total number of successfully accessed first preamble sequences and the total number of collided second preamble sequences comprises:
Counting the number of target leader sequences in the leader sequences sent by all users; wherein the target preamble sequence represents any one of the preamble sequences transmitted by all users;
in the case that the number of the target leader sequences is determined to be 1, determining the target leader sequence as a first leader sequence;
determining the target leader sequence as a second leader sequence in the case that the number of the target leader sequences is determined to be more than 1;
and counting all the first leader sequences and all the second leader sequences to obtain the total number of the first leader sequences and the total number of the second leader sequences.
3. The low-orbit satellite internet of things random access congestion control method according to claim 1, further comprising:
initializing a local network model and a target network model; after initialization, the network parameters of the local network model are the same as those of the target network model; the local network model and the target network model belong to the same agent;
for each time slot, the control agent is based onA greedy algorithm selects actions; wherein, under each time slot, the state of the agent includes: the actions of the agent include: the access class limits ACB factors and total number of backoff windows, and rewards acquired after the intelligent agent performs actions are as follows: the weighted sum of the access time delay qualification rate and the access energy consumption qualification rate of all users;
Storing the state, action and rewards acquired after executing the action of the agent in the current time slot and the state of the next time slot as an experience tuple into an experience pool until a preset number of experience tuples are obtained;
batch sampling is carried out from the experience pool so as to train the local network model and the target network model, and a target loss function is obtained;
updating network parameters of the local network model based on the target loss function, and controlling the target network model to copy the network parameters of the local network model every other preset period;
and under the condition that the network parameter of the local network model is updated for a preset number of times, taking the local network model as the target congestion control model.
4. The method for random access congestion control of the low-orbit satellite internet of things according to claim 3, wherein calculating rewards acquired after the agent performs the action comprises:
acquiring a first moment when preparation of a data packet of a target user is completed and a second moment when transmission of the data packet is completed; wherein the target user represents any one of all users;
calculating the access time delay of the target user based on the first time and the second time;
Acquiring a first duration of maintaining a sending state, a second duration of maintaining a receiving state, sending state unit energy consumption, receiving state unit energy consumption and idle state unit energy consumption of the target user;
calculating the idle time length of the target user based on the access time delay of the target user, the first time length and the second time length;
calculating access energy consumption of the target user based on the first time length, the second time length, the idle time length, the sending state unit energy consumption, the receiving state unit energy consumption and the idle state unit energy consumption;
and calculating access time delay qualification rate and access energy consumption qualification rate of all users based on a preset time delay threshold value, a preset energy consumption threshold value, access time delay and access energy consumption of each user, and taking a weighted sum of the access time delay qualification rate and the access energy consumption qualification rate as the rewards.
5. The method for controlling random access congestion of the low-orbit satellite internet of things according to claim 4, wherein calculating access delay qualification rates and access energy consumption qualification rates of all users based on a preset delay threshold, a preset energy consumption threshold, access delay and access energy consumption of each user comprises:
Judging whether the access time delay of the target user is larger than the preset time delay threshold value or not;
if yes, determining that the access time delay of the target user is unqualified;
if not, determining that the access time delay of the target user is qualified;
judging whether the access energy consumption of the target user is larger than the preset energy consumption threshold;
if yes, determining that the access energy consumption of the target user is unqualified;
if not, determining that the access energy consumption of the target user is qualified;
counting the total number of first users with qualified access time delay in all users, and counting the total number of second users with qualified access energy consumption in all users;
and calculating access time delay qualification rates of all users based on the total number of the first users and the total number of the users to be accessed to the low-orbit satellite Internet of things, and calculating access energy consumption qualification rates of all users based on the total number of the second users and the total number of the users to be accessed to the low-orbit satellite Internet of things.
6. The method for random access congestion control of the internet of things of low orbit satellite according to claim 4, wherein the equation for rewards is expressed as:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Representing the access delay qualification rate, < >>Representing the access energy consumption qualification rate, < > >Representing an adjustment factor balancing the access delay qualification rate and the access energy consumption qualification rate,。
7. the low-orbit satellite internet of things random access congestion control method according to claim 3, wherein the objective loss function is expressed as:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Indicating the number of experience tuples for batch sampling, +.>，/>Representing rewards in the j-th experience tuple in the batch sample,/and>representing discount factors->，/>Model parameters representing the model in said local network +.>Under the condition that the intelligent body is in the state->Take action down->Action value of time, ->Representing the status of the agent in the j +1 experience tuple,representing actions of agents in the j-th experience tuple,/->Model parameters representing the target network model, < >>Representing the action value of the output of the target network model,/->Model parameters representing the model in said local network +.>Under the condition that the intelligent body is in the state->Take action down->Action value in time.
8. The utility model provides a low orbit satellite thing networking random access jam controlling means which characterized in that is applied to low orbit satellite, includes:
the broadcasting module is used for broadcasting the leading sequence pool to all users to be accessed into the low-orbit satellite Internet of things, so that each user randomly selects one leading sequence to be sent to the low-orbit satellite; wherein the pool of leader sequences is a collection of a plurality of leader sequences;
The receiving and determining module is used for receiving the preamble sequences sent by each user and determining the total number of the first preamble sequences which are successfully accessed and the total number of the second preamble sequences which collide;
the processing module is used for processing the total number of the first preamble sequences and the total number of the second preamble sequences by utilizing a target congestion control model to obtain a target access class limit ACB factor and a target back-off window total number; the training target of the target congestion control model is that the weighted sum of the access delay qualification rate and the access energy consumption qualification rate of all users is maximized;
and the congestion control module is used for controlling the access congestion of the low-orbit satellite Internet of things based on the target access class limiting ACB factor and the total number of target back-off windows.
9. An electronic device comprising a memory, a processor, the memory having stored thereon a computer program executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the random access congestion control method for low earth orbit satellite internet of things of any one of claims 1 to 7.
10. A computer readable storage medium storing computer instructions which, when executed by a processor, implement the low orbit satellite internet of things random access congestion control method of any one of claims 1 to 7.
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