CN115348565A - Dynamic access and backoff method and system based on load perception in large-scale MTC scene - Google Patents
Dynamic access and backoff method and system based on load perception in large-scale MTC scene Download PDFInfo
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Abstract
The invention discloses a dynamic access and backoff method and a system based on load perception, which predict the number of devices trying to initiate a random access process (called as activated device number for short) in the current RAO and the future RAO on the premise of knowing a device activation model. Based on the load prediction result of the current RAO, the base station dynamically adjusts the ACB factor, controls the probability of initiating the random access process by the active equipment in the current RAO, so that the number of the equipment initiating the random access process in the current RAO does not exceed the number of the equipment maximizing the network throughput, based on the load prediction result of the future RAO, the scheme dynamically adjusts the distribution of the backoff equipment, controls the number of the active equipment of each RAO in the backoff window to be as close to an ideal load as possible, ensures the adaptability of the scheme in a time-varying large-scale access scene, improves the probability of the active equipment passing the ACB test, reduces the times of monitoring the ACB factor required by the equipment to access the network, and reduces the energy consumption of the equipment to access the network on the premise of not influencing the throughput.
Description
Technical Field
The invention belongs to the technical field of access and transmission of cellular Internet of things, and particularly relates to a dynamic access and backoff method and system based on load sensing in a large-scale MTC scene.
Background
Machine Type Communication (MTC) is a main Communication mode of a Machine Type device in the internet of things. Large-scale machine type communication (MTC ) is determined by the international telecommunication union radio communication bureau (ITU-R) as one of three typical application scenarios of 5G in 2015 as a key supporting technology of the internet of things. The mass devices in the scene can realize mutual communication or remote server communication without manual intervention. The core of the mtc scenario is to provide large-scale connection, and technologies such as Wi-Fi and ZigBee can be used to support connection, but the mobile cellular network is widely considered as a core support network for machine type communication due to its coverage, flexibility and high communication reliability. Currently, narrow-Band Internet of Things (NB-IoT) technology and enhanced machine type communication (eMTC) technology embedded in cellular systems are proposed in the industry to support future MTC applications.
The access scheme in a conventional cellular network is a contention-based random access scheme designed for Human to Human (H2H) communication habits. In the scheme, a base station randomly allocates limited channel resources for equipment initiating an access process, and the equipment randomly selects the channel resources and performs competitive access. Because the mMTC scene has the characteristic of massive connection, massive equipment simultaneously initiates an access request to the base station to cause a large amount of collision among the equipment, the base station cannot successfully decode the equipment information, the successful probability of equipment communication is greatly reduced, the network communication capacity is obviously reduced, and finally serious system overload and congestion are caused. Therefore, research on an access and transmission scheme oriented to an mtc scenario solves the problem of network access congestion in the mtc scenario, realizes network load control, optimizes network performance to the maximum extent, and maintains performance stability, and is a major issue in the research on mtc.
In order to solve the problem that the large-scale MTC Access may cause network congestion, research is actively carried out by domestic and foreign researchers, and a series of solutions are proposed mainly from the aspects of Access Class Blocking (ACB), group transmission (GP), resource allocation and the like. The 3GPP standardization organization introduced ACB checking mechanism for access control of devices. On one hand, an ACB mechanism controls the probability of initiating Access by an active device in a current Random Access Opportunity (RAO) by using an ACB factor; on the other hand, the ACB mechanism controls equipment which does not pass the ACB check to carry out backoff, and distributes the burst large-scale access requests uniformly in time, thereby alleviating instant access collision. The basic ACB mechanism can alleviate access collision to a certain extent, but cannot effectively control the number of devices initiated by each RAO, and has a limited degree of optimizing network throughput performance. The existing research further proposes a dynamic ACB scheme, where a base station dynamically adjusts an ACB factor based on a load estimation value of a current Random Access Opportunity (RAO), and controls a probability that an activation device initiates a Random Access Procedure (RA Procedure). Meanwhile, the base station controls the active device which does not pass the ACB check to access again with the active device which has access collision after passing the ACB check at the next RAO. The dynamic ACB scheme can achieve approximately optimal throughput in a large-scale access scenario, but requires the device to frequently listen to the ACB factor, and cannot initiate a practical random access process through ACB check, and the energy efficiency of the device access process is extremely low. The basic idea of GP is that mtc devices are grouped, and devices to devices (D2D) link connections are used in the group to initiate an access request to a base station as a whole, and the size of the random access request is reduced to reduce access collisions. The basic idea of GP is that mtc devices are grouped, and devices to devices (D2D) link connections are used in the group to initiate an access request to a base station as a whole, and the size of the random access request is reduced to reduce access collisions. However, considering that the machine type device may have a certain mobility and needs to be directly connected to the base station for communication, the GP mode has a certain limitation. The basic idea of resource allocation for solving network congestion is to limit the resource blocks that various devices can initiate access, and to control the number of devices initiating access of each resource block. However, the resource allocation scheme has insufficient adaptability to the device activation model, the activation time of the device has high randomness, the number of devices actually initiating access in each resource block is affected, and the network performance is reduced.
Therefore, there is a need to develop a load control scheme for effectively controlling access loads of multiple RAOs in a time-varying large-scale random access scenario, and reducing energy consumption of devices accessing a network while maximizing network throughput.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a dynamic access and backoff method and system based on load sensing in a large-scale MTC scene. Compared with the existing research, the method and the device have the advantages that the access load of each RAO is effectively controlled in the random access process, and the energy consumption of the device for accessing the network is reduced while the network throughput is maximized under the condition of limited retransmission times.
In order to achieve the purpose, the invention adopts the technical scheme that: a dynamic access and backoff method based on load sensing in a large-scale MTC scene comprises the following steps:
s1, establishing a dynamic back-off system model based on load sensing in a large-scale MTC scene;
s2, maximizing the number of access devices required by each RAO expected throughput based on the system model, namely ideal load;
s3, predicting current RAO and future T based on equipment activation model m The access load of each RAO obtains a current RAO load predicted value and a load predicted value of a future RAO;
s4, generating an ACB factor based on the load predicted value of the current RAO, and estimating the number of newly generated backoff devices after the random access is finished;
s5, adjusting a backoff window and a backoff probability vector based on the new activated load and the backoff load of the RAO, minimizing the mean square error between the backoff device number of each RAO in the backoff window and the ideal backoff device number, and constructing an optimization problem as follows:
constraint (1) denotes a backoff window
Possible range of (1), T m Is the upper bound of the length of the backoff window, T m For manual setting, the constraint conditions (2) and (3) guarantee that the load prediction capability of the base station and the maximum allowable access delay of the equipment are influenced
Forming a probability space;
s6, solving the optimization problem by adopting a hierarchical optimization method; obtaining an optimal backoff probability vector
And optimal backoff window size
And the quantity of the backoff devices of each RAO in the backoff window is effectively controlled.
In S1, consider an mtc cell with N MTCDs, and assume that N devices are activated in an obeta distribution within T seconds, and the probability density function of the activation of a device at time τ is:
in the formula, B (α, β) represents a beta function with shape parameters of α and β, when each random access opportunity starts, the activation device needs to perform ACB check first, and the device passing the ACB check initiates a random access request to the base station, otherwise, the device enters a backoff process.
For the activation device of the t-th RAO, the base station broadcasts the ACB factor to the device
To limit the probability of the activation device initiating a random access request to the base station, the activation device will generate a random number p between 0 and 1 and an ACB factor
In comparison, p is not more than
The corresponding active device passes the ACB check, otherwise the device accesses the backoff procedure.
In S2, it is assumed that the reason why the device has not successfully completed the random access procedure is only preamble collision, the access load of any RAO is m, the number of available preambles is R, and in the RAO, the successful access probability P of a certain device is calculated s|m Comprises the following steps:
when S is the number of successfully accessed devices, and the access load is m, the expected value E (S | m) of the number of successfully accessed devices in the RAO is:
m is a dynamically convertible integer, then
When m = R or m = R-1, the RAO throughput is expected to be E (S | m) maximum, i.e. the ideal load L Ideal = R or L Ideal = R-1, and further provided with L Ideal ＝R。
In S3, before the current RAOt starts, the future T is carried out m Predicting the number of activated devices of each RAO; the active devices in each RAO are divided into new active devices and backoff devices, wherein the number of the new active devices is determined by an active model, the number of the backoff devices is determined by backoff devices generated by the past RAO and backoff probability vectors, and the base station is based on the device active model, the number of the backoff devices generated by the past RAO and the backoff probability vectors
Predicting current and future T m New activation load of RAO
Escape load
The method specifically comprises the following steps:
setting the current RAO as the tth RAO, and the base station activates the load predicted value for the t + i-th RAO
Is composed of
In the formula, T RAO Representing the interval of two adjacent RAOs, p (τ) representing the probability density of activation of a device at time τ, N representing the total number of devices in the cell,<·>representing a rounding operation;
based on the backoff load predicted value and backoff probability vector generated by the base station in the (t-1) th RAO and the estimated value of the number of backoff devices newly generated by the (t-1) th RAO
And
the base station returns to the avoidance load prediction value in the (t + i) th RAO in the t th RAOBackoff load prediction value of over-previous RAO to (t + i) th RAO
Adding expected value of newly-switched-in backoff device number
The iterative update is carried out and the data is updated,
new activation load prediction value based on (t + i) th RAO
And backoff load prediction value
Obtaining the predicted value L of the access load of the RAO t (i) The following were used:
s4, after the base station finishes load prediction, accessing a load prediction value L based on the current RAO t (0) Dynamic regulation of ACB factors
Estimating the number of backoff devices generated by the current RAO based on the lead code collision condition in the current RAO;
factor ACB
Controlling the probability of the access initiated by the activating equipment of the current RAO to ensure that the expected value of the number of the activating equipment actually initiating the random access process is close to the ideal load R to the maximum extent,
the calculation method is as follows:
wherein L is t (0) An access load predicted value of the base station for the current RAO is obtained;
activating the device to
The probability is tested by ACB, the random access process is initiated to the base station by the activation equipment tested by ACB, the access load is estimated by the base station side according to the lead code use condition, and the access load is estimated
Comprises the following steps:
wherein, the first and the second end of the pipe are connected with each other,
in the formula, S t Number of preambles successfully accessed for the tth RAO, E t For the number of preambles that the t-th RAO is not used by the user, the number of backoff devices generated by the current RAO is further estimated based on the access load estimation result
In S5, the number C of ideal backoff devices for the (t + i) th RAO t (i) To minimize the number of active devices L t (i) Number of backoff devices, i =1,2, \ 8230;, T, different from the ideal load R m ，
For any one
And
estimation value of number of backoff devices in (t + i) th RAO after backoff is completed
Comprises the following steps:
solving the optimization problem by adopting a hierarchical optimization method specifically comprises the following steps:
s601, initializing input parameters
And
setting an initial backoff window
S602, solving a Lagrange multiplier lambda by adopting a water injection algorithm:
s603, solving
The optimal number of backoff devices for each RAO at a given time
S604, solving
The number of backoff devices for each RAO is always
The value of the objective function of the problem is optimized,
S605，
when in use
If yes, return to step S602;
s606, comparison
And outputs the value corresponding to the minimum objective function
And with
S607, according to
And solving the optimal backoff probability vector
Meanwhile, a dynamic backoff system based on load sensing in a large-scale MTC scene is provided, and comprises a model building module, an ideal load calculation module, a load prediction module, an estimation module, a problem building module and a resolving module;
the model building module is used for building a system model of dynamic backoff based on load sensing in a large-scale MTC scene;
the ideal load calculation module maximizes the number of access devices required by each RAO expected throughput based on the system model, namely ideal load;
load prediction module predicts current RAO and future T based on device activation model m The access load of each RAO obtains a current RAO load predicted value and a load predicted value of a future RAO;
the estimation module generates an ACB factor based on a load predicted value of the current RAO and estimates the number of newly generated backoff devices after the random access is finished;
the problem construction module adjusts a backoff window and a backoff probability vector based on a new activation load and a backoff load of the RAO, minimizes the mean square error between the backoff device number of each RAO in the backoff window and the ideal backoff device number, and constructs an optimization problem as follows:
constraint (1) denotes a backoff window
Possible range of (A), T m Is the upper bound of the length of the backoff window, T m For manual setting, the constraint conditions (2) and (3) guarantee that the load prediction capability of the base station and the maximum allowable access delay of the equipment are influenced
Forming a probability space;
the solving module adopts a layered optimization method to solve the optimization problem; obtaining an optimal backoff probability vector
And optimal backoff window size
And the quantity of the backoff devices of each RAO in the backoff window is effectively controlled.
The invention also provides a large-scale MTC scene communication system which comprises the base station and a plurality of user terminals, wherein the base station can execute the dynamic access and backoff method based on load perception in the large-scale MTC scene.
Compared with the prior art, the invention at least has the following beneficial effects:
based on the calculation of the network throughput and the energy consumption of the equipment access network, the number of the activated equipment for ACB inspection in each RAO is controlled to be equal to the number of the available preamble codes in the RAO, so that the energy consumption required by the equipment access network can be reduced while the network throughput is maximized. In order to realize effective control of activated devices in each RAO, the method and the device predict the number of newly activated devices and the number of retreated devices in the current RAO and the future RAO respectively based on a device activation model, the number of retreated devices generated by the past RAO and the retreating probability vector. Based on the load prediction result of the current RAO, the invention dynamically adjusts the ACB factor and controls the probability of the random access process initiated by the activated equipment in the current RAO, so that the number of the equipment initiating the random access process in the current RAO does not exceed the number of the equipment maximizing the network throughput, namely the ideal load. Based on the load prediction result of the future RAO, the invention takes the mean square error of the number of the backoff devices of each RAO in the minimum backoff window and the ideal backoff load as an optimization target, dynamically adjusts the large and small ports of the backoff window and the backoff probability vector, and controls the probability of backoff devices generated in the current RAO to each RAO in the backoff window. The invention dynamically adjusts the access and retreat processes of the equipment based on the load prediction result, and realizes the effective control of the number of the RAO activated equipment, thereby realizing the reduction of the access energy consumption of the equipment while maximizing the network throughput.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
Fig. 1 is a system model diagram of a large-scale MTC network.
Fig. 2 is a schematic diagram illustrating a principle of a dynamic access and dynamic backoff method based on load sensing.
Fig. 3 is a graph of throughput of a large-scale MTC network as a function of total number of devices.
Fig. 4 is a graph of energy consumption of devices accessing a network in a large-scale MTC network as a function of the total number of devices.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be understood that the terms "comprises" and/or "comprising" indicate the presence of the stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The invention provides a dynamic access and backoff method based on load perception, and provides a load prediction mechanism, which predicts the number of devices trying to initiate a random access process (called as activated device number for short) in the current RAO and the future RAO on the premise of knowing a device activation model. Based on the load prediction result of the current RAO, the base station dynamically adjusts the ACB factor and controls the probability of initiating the random access process by the active equipment in the current RAO, so that the number of the equipment initiating the random access process in the current RAO does not exceed the number of the equipment maximizing the network throughput, namely the ideal load. Based on the load prediction result of the future RAO, the scheme dynamically adjusts the distribution of the backoff devices, and controls the number of the active devices of each RAO in the backoff window to be as close to the ideal load as possible. The load prediction mechanism ensures the adaptability of the scheme in a time-varying large-scale access scene, the dynamic access mechanism based on load perception ensures that the throughput of the network is as close to the theoretical maximum value as possible, meanwhile, the dynamic backoff mechanism based on load perception distributes backoff devices as dispersedly as possible, and the probability that the activation devices pass the ACB test is improved, so that the frequency of monitoring the ACB factors required by the devices for accessing the network is reduced, and the energy consumption of the devices for accessing the network is reduced on the premise of not influencing the throughput.
Referring to fig. 1, the present invention provides a dynamic access and backoff method based on load sensing in a large-scale MTC scenario, considering the following system models:
the invention considers a large-scale cellular access network with N devices, wherein N machine type communication terminals are activated in time T according to beta distribution, namely the activation probability of the devices in time T is as follows:
in the formula, B (α, β) represents a beta function having shape parameters α and β, and the active model base station is known.
In an mtc network, there is a special time-frequency resource block to transmit Random access requests of devices, and this type of resource block is called Random Access Opportunity (RAO) and is denoted by T RAO Is a periodic occurrence. Devices within the RAO that attempt to initiate a random access request are referred to as active devices. The activation device first performs an ACB check. The activation device passing the ACB check may initiate a random access procedure, while the activation device not passing the ACB check enters a backoff procedure, and after a period of random backoff, selects the nearest RAO to initiate a random access request again. For the equipment initiating the random access process, if the transmission lead code does not collide with other equipment, the equipment can successfully access the network and carry out subsequent data packet transmission; if the collision lead code is transmitted, the equipment declares that the random access process fails, enters a backoff process, and selects the nearest RAO to initiate the random access request again after a period of random backoff.
Specifically, at the t-th RAO, the Base Station (BS) broadcasts the ACB factor to the device
To limit the probability of the device initiating a random access request to the base station. For active devices in the t-th RAO, the ACB factor is sensed
Thereafter, a random number p between 0 and 1 will be generated and compared to the ACB factor. When p is not more than
If so, the activation equipment passes the ACB check, otherwise, the activation equipment enters the back-off process; and after the ACB check is passed, the activation equipment initiates a random access process based on competition and sends an access request to the base station. The contention-based random access procedure is mainly divided into four steps, and the specific procedure is as shown in fig. 1, where UE represents a user equipment and eNB represents a base station. The four-way handshake of the contention-based random access procedure is as follows.
Step1: the user equipment UE randomly selects an available preamble (preamble) to initiate access. The contention-based random access procedure may use a front derivative of R. The equipment randomly selects an available lead code as Random Access request information (Msg 1) and sends the Random Access request information to the base station through a Physical Random Access Channel (PRACH);
step2: the base station accepts and detects messages in the PRACH channel and generates a Random Access Response (RAR) message for the decoded information. The RAR comprises a lead code serial number, an uplink transmission Timing Advance (TA), an uplink channel resource corresponding to the lead code, a cell radio network temporary identifier (C-RNTI) and a back-off indication parameter which are detected by a base station. The base station sends Msg2 to each cell device through a Physical Downlink Shared Channel (PDSCH). And after the preamble transmission is finished, activating the equipment to listen to the RAR message carrying the equipment transmission preamble sequence number in the PDSCH channel. And when the activated equipment senses the corresponding RAR message in the RAR window, executing step3. Otherwise, the device declares the random access failure.
Step3: and the UE performs scheduling transmission according to the guidance of the Msg 2. And if the Msg2 comprises the preamble information sent by the UE, transmitting the Msg3 in an uplink channel designated by the Msg2, and if the Msg2 does not comprise the preamble information, performing backoff according to the Msg2 instruction. The Msg3 comprises an equipment identity identifier and is used for the eNB to register a message source;
step4: and (4) resolving the competition. If no collision occurs, the base station successfully decodes the Msg3 to obtain the equipment identity information, and transmits an access success message (Msg 4) through a downlink control channel (PDCCH).
Only random access procedure failures due to preamble collisions are considered. Specifically, when multiple devices transmit the same preamble at the same time, the base station cannot correctly decode the preamble. Therefore, the device cannot sense the corresponding RAR message in the RAR window, declares that the random access process fails, enters the backoff process, and performs the ACB test again after randomly backoff for a period of time. For the active devices which are not successfully accessed, including the devices which do not pass the ACB test and the active devices which pass the ACB test but have access collision, according to the backoff probability vector
And giving probability distribution, randomly selecting each RAO in the backoff window to perform ACB (access barring) test again, and initiating a random access request.
And setting the throughput of each RAO as the number of the devices successfully accessed by the RAO. Assuming that the reason for the device not successfully completing the random access procedure is only preamble collision, the access load of any RAO is m, and the number of available preambles is R, when the preamble selected by a certain device is different from the preambles selected by the remaining m-1 devices, the device successfully accesses. Therefore, in the RAO, the probability P of successful access of a certain device s|m Is composed of
When S is the number of successfully accessed devices, the expected value E (S | m) of the number of successfully accessed devices in the RAO is m
Since m is a dynamically transformable integer, then
Thus, when m = R or m = R-1, the RAO throughput is expected to be E (S | m) maximum, i.e. the ideal load L Ideal = R or L Ideal And (c) = R-1. To simplify the analysis, L is set directly Ideal ＝R。
The maximization of the network throughput can be achieved when the number of devices initiating the random access procedure controlling each RAO as close as possible to the ideal load. Furthermore, each RAO access load, namely the number of the activated devices, can be controlled to be as close to an ideal load as possible, the probability that the activated devices pass the ACB test is improved, the frequency of the devices for intercepting the ACB factors required by the network access is further reduced, and the energy consumption of the device access process is reduced while the network throughput is maximized.
Referring to fig. 2, the method for dynamic access and backoff based on load sensing in a large-scale MTC scenario includes three steps of load prediction, generation and broadcast of ACB factor and backoff parameter, and random access and dynamic backoff.
Step11: and (4) load sensing. Before the current RAOt starts, the base station first performs load prediction. Base station predicts current and future T by using device activation model information, backoff parameters and device access conditions m New activation load of RAO
Back-off load
(it isIn
Respectively representing the new activation load and the backoff load of the (t + i) th RAO, and providing an information basis for the next step of generating the equipment access adjustment parameters by the base station;
step12: and generating and broadcasting the ACB factor and the backoff parameter. In one aspect, the base station generates and broadcasts ACB factors based on load predictions for the current RAO
Controlling the probability of initiating a random access process by the activation equipment, and ensuring the throughput of the current RAO; on the other hand, based on the load prediction result of the future RAO, the base station generates and broadcasts a backoff probability vector
Controlling a backoff device generated by the t-th RAO to select a backoff window
And carrying out access retransmission probability distribution on each RAO in the backoff window to realize the minimization of the difference between the access load and the ideal load of each RAO in the backoff window. Wherein, the backoff equipment comprises the equipment that the current RAO does not pass the ACB check and the equipment that the access collision occurs after the current RAO passes the ACB check,
representing the probability of the backoff device generated by the t-th RAO to the (t + i) -th RAO.
Step13: random access and dynamic backoff of the device. And for the activation equipment of the t-th RAO, initiating access and finishing backoff according to the ACB factor and backoff parameters broadcasted by the base station. The activation equipment randomly generates a number p between 0 and 1 and ACB factor broadcasted by the base station
By comparison, if p is not greater than
The device performs a random access procedure, otherwise at the device it follows
The backoff RAO is randomly selected given a probability distribution. For an active device passing the ACB check, if the preamble transmitted by the active device does not collide with other devices, the device successfully accesses the network, otherwise, the device follows the method
The backoff RAO is randomly selected given a probability distribution.
Next, taking the t-th RAO as an example, we specifically analyze and design the dynamic access and backoff method based on load prediction.
First, before the start of the tth RAO, the base station will target the current RAO and the future T m The number of active devices within each RAO is predicted. The active devices in each RAO can be divided into two types, namely new active devices and backoff devices, wherein the new active devices are devices which have not been subjected to ACB check, and the backoff devices are devices which select the RAO to initiate the random access request again after backoff is completed. Thus, the base station predicts the current RAO and the future T separately m The number of newly activated devices and the number of backoff devices in each RAO. Assuming that the base station knows the activation model of the device, the N machine type communication terminals are activated in time T according to the beta distribution, that is, the activation probability of the device at time tau is
In the formula, B (α, β) represents a beta function having shape parameters of α and β. Estimate of the number of newly activated devices in the (t + i) th RAO
Is composed of
In addition, the backoff load in the (t + i) -th RAO is iteratively influenced by the backoff device number and the backoff probability vector generated by the past RAO. Therefore, the backoff load prediction value in the t-th RAO to the (t + i) -th RAO can be iteratively updated by adding the newly transferred backoff device number to the backoff load prediction value of the (t + i) -th RAO by the previous RAO. Specifically, it is known that the predicted value of the backoff load of the (t + i) th RAO generated at the (t-1) th RAO is
The generated backoff load is
The backoff probability vector generated by the base station is
Therefore, the backoff load prediction value of the (t + i) th RAO slot generated at the t-th RAO
Comprises the following steps:
new activation load prediction based on (t + i) th RAO
And a backoff load prediction value
The predicted value L of the access load of the RAO can be obtained t (i) The following:
in the t-th RAO, after the base station finishes load prediction, accessing a load prediction value L based on the current RAO t (0) Dynamic regulation of ACB factors
And controlling the probability of the access initiated by the current RAO activation equipment, so that the expected value of the number of the activation equipment actually initiating the random access process is close to the ideal load R to the maximum extent. Therefore, the temperature of the molten metal is controlled,
the calculation method is as follows:
after the equipment completes the random access process, the base station detects that S exists in the t-th RAO t Preamble of a successful access, E t The number of preambles unused by the user. When the number of devices initiating the random access process is M and the available front derivative is R, the numbers of the successful access preamble codes and the idle preamble codes are S respectively t And E t The probability of (c) is as follows:
therefore, the maximum likelihood estimation method is adopted to estimate the number of devices actually initiating random access in the t-th RAO
Namely:
based on the access load estimation result, the base station further estimates the number of backoff devices generated by the current RAO
After the estimation of the number of the backoff devices is completed, the base station constructs an optimization problem, adjusts a backoff window and a backoff probability vector, and minimizes the square error between the number of the backoff devices of each RAO in the backoff window and the ideal number of the backoff devices. Specifically, each active device in the RAO may be divided into a new active device and a backoff device, where the number of the new active devices is determined by the device activation model and is not controlled by the base station. Therefore, the present invention achieves minimization of the difference between the number of active devices of each RAO and the ideal load by controlling the number of backoff devices in each RAO. Define the ideal backoff device number C of (t + i) th RAO t (i)(i＝1，2，…，T m ) To minimize the number of active devices L t (i) The number of the retreating devices different from the ideal load R is
Designing backoff probability vectors
And controlling the probability of reinitiating the random access request by each RAO generated by the t-th RAO in the backoff window. For any purpose
And
expected value of the number of backoff devices in (t + i) -th RAO after backoff is completed
Is composed of
Based on the number of ideal backoff devices C t (i) Expected value of number of equipment to be retreated in each RAO after adjustment of retreat parameters
An optimization problem is proposed to minimize the ideal backoff load C for each RAO within the backoff window t (i) And an estimated number of backoff devices
The sum of squared errors of the two is used as an optimization target, and the size of a backoff window is dynamically adjusted
And back-off probability vector
The specific optimization problem is as follows:
wherein the constraint condition (1) represents a backoff window
Possible range of (1), T m Is the upper bound of the length of the backoff window, T m The setting is manual, and is influenced by the load prediction capability of the base station and the maximum allowable access time delay of the equipment. Constraint conditions (2) and (3) guarantee
A probability space is formed.
Due to the fact that
At a given time, the Optimization Problem (OP) is
A convex function of (a) and
and
one to one correspondence, so the Optimization Problem (OP) is first equivalently transformed into the optimization problem as shown below:
the present invention treats this optimization problem as a two-layer optimization. First, give
Time is solved based on Lagrange multiplier method
Of (2) an optimal solution
The following:
wherein λ is the solution of
Subsequently, one-dimensional search changes are applied
Finding an optimal backoff window size
And corresponding back-off probability vector
Based on optimization
And
the optimal backoff probability vector can be found as follows:
obtained by the above process
The optimal backoff probability vector is the optimal backoff probability vector which can reduce the energy consumption of the equipment for accessing the network while maximizing the network throughput.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, 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 present invention, 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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
And carrying out throughput and energy consumption performance evaluation on the dynamic access and backoff method based on load perception in a large-scale scene by adopting numerical analysis. If not, the parameters of the simulation experiment are set as: intra-cell MTCDs device activation model α =3, β =4, device activation time T A =10s; activating device transmission energy consumption P Tx =460mW, intercept energy consumption P Rx =80mW, idle energy consumption P Idle =3WW; RAO in the network lasts 1ms and occurs every 5ms, i.e. T RAO =5ms; in the random access process, the RAR window length is W RAR =5ms, the length of the CR window being W CR =48ms, maximum number of retransmissions J =9; load prediction window size T m And (5) =1000. To illustrate the superiority of the proposed scheme, we compare RACH throughput and RACH energy consumption in the following scheme: dynamic ACB interfacing and uniform backoff method (abbreviated as D-ACB scheme), in which a backoff window W is used B Set to 1, 128, 512 or 1024, respectively, and a dynamic access and backoff method based on load sensing (abbreviated as LAB scheme). Fig. 3 and 4 show the average throughput and average access energy consumption of the D-ACB of the LAB scheme versus the number N of MTCD devices in the network, respectively. As can be seen from fig. 3 and 4, the D-ACB scheme throughput for the backoff window WB =1 is the largest, while the access energy consumption is the highest, because the backoff MTCD needs to constantly listen to the updated ACB factor and perform ACB check in each RAO without performing the actual random access procedure. At the same time, W B The larger D-ACB scheme realizes the reduction of the access energy consumption of the equipment by sacrificing the throughput performance. More importantly, the dynamic access and backoff method based on load sensing provided by the invention can realize the lowest RACH energy consumption, and simultaneously keep the almost highest RACH throughput.
In conclusion, the invention is suitable for large-scale MTC scenes, and designs a dynamic access and backoff method based on load perception. Based on the theoretical analysis of the maximized throughput, when the scheme controls the number of the activated devices in each RAO to be equal to the available lead codes in the RAO, the energy consumption of the devices accessing the network can be reduced while the maximized network throughput is realized. The scheme is specifically divided into three steps of load sensing, random access and dynamic backoff. Base station predicts current and future T based on device activation model, number of backoff devices generated by past RAO and backoff probability vector m The number of active devices of each RAO provides an information base for generating an ACB factor and a backoff probability vector for a base stationA foundation; based on the load prediction result of the current RAO, the base station generates and broadcasts ACB factors
Controlling the probability of initiating a random access process by the activation equipment, and ensuring the throughput of the current RAO; based on future RAO load prediction results, the base station generates and broadcasts backoff probability vectors
Controlling a backoff device generated by the t-th RAO to select a backoff window
And carrying out access retransmission probability distribution on each RAO in the backoff window to realize the minimization of the difference between the access load and the ideal load of each RAO in the backoff window. The dynamic access and backoff method based on load perception can realize that the energy consumption of equipment accessing the network is reduced while the network throughput is maximized.
The invention also provides a large-scale MTC scene communication system which comprises the base station and a plurality of user terminals, wherein the base station can execute the dynamic access and backoff method based on load perception in the large-scale MTC scene.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may 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, and so forth) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may 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, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (10)
1. A dynamic access and backoff method based on load sensing in a large-scale MTC scene is characterized by comprising the following steps:
s1, establishing a dynamic back-off system model based on load sensing in a large-scale MTC scene;
s2, maximizing the number of access devices required by each RAO expected throughput based on the system model, namely ideal load;
s3, predicting the current RAO and the current none based on the equipment activation modelFor T m Accessing loads of the RAO to obtain a current RAO load predicted value and a load predicted value of a future RAO;
s4, generating an ACB factor based on a load predicted value of the current RAO, and estimating the number of newly generated backoff devices after random access is finished;
s5, adjusting a backoff window and a backoff probability vector based on the new activated load and the backoff load of the RAO, minimizing the mean square error between the backoff device number of each RAO in the backoff window and the ideal backoff device number, and constructing an optimization problem as follows:
constraint (1) denotes a backoff window
Possible range of (A), T m Is the upper bound of the length of the backoff window, T m The constraint conditions (2) and (3) guarantee that the load prediction capability of the base station and the maximum allowable access time delay of the equipment are given manually
Forming a probability space;
s6, solving the optimization problem by adopting a hierarchical optimization method; obtaining an optimal backoff probability vector
And optimal backoff window size
And the number of backoff devices of each RAO in the backoff window is effectively controlled.
2. The method of claim 1, wherein in S1, considering an MTC cell with N MTCDs, assuming that N devices are activated in an obeta distribution within T seconds, the probability density function of the activation of a device at time τ is:
in the formula, B (α, β) represents a beta function with shape parameters of α and β, when each random access opportunity starts, the activation device needs to perform ACB check first, and the device passing the ACB check initiates a random access request to the base station, otherwise, the device enters a backoff process.
3. The dynamic access and backoff method based on load sensing in large-scale MTC scene of claim 2, wherein for the t-th RAO active device, the base station broadcasts ACB factor to the device
To limit the probability of the activation device initiating a random access request to the base station, the activation device will generate a random number p between 0 and 1 and an ACB factor
By comparison, p is not greater than
The corresponding active device passes the ACB check, otherwise the device accesses the backoff procedure.
4. The dynamic access and backoff method based on load sensing in large-scale MTC scenario according to claim 1, wherein in S2, it is assumed that the reason that the device has not successfully completed the random access procedure is only preamble collision, the access load of any RAO is m, the number of available preambles is R, and in the RAO, the successful access probability P of a certain device is calculated s|m Comprises the following steps:
when S is the number of successfully accessed devices, and the access load is m, the expected value E (S | m) of the number of successfully accessed devices in the RAO is:
m is a dynamically convertible integer, then
When m = R or m = R-1, the RAO throughput is expected to be E (S | m) maximum, i.e. the ideal load L Ideal = R or L Ideal = R-1, and further provided with L Ideal ＝R。
5. The dynamic access and backoff method based on load sensing in large-scale MTC scene of claim 1, wherein in S3, before current RAO T starts, future T is performed m Predicting the number of activated equipment of each RAO; the active devices in each RAO are divided into new active devices and backoff devices, wherein the number of the new active devices is determined by an active model, and the number of the backoff devices is determined by the backoff devices generated by the past RAO and backoff probability vectorsBase station generates backoff device number and backoff probability vector based on device activation model, past RAO
Predicting current and future T m New activation load of RAO
Back-off load
The method specifically comprises the following steps:
setting the current RAO as the tth RAO, and the base station activates the load predicted value for the t + i-th RAO
Is composed of
In the formula, T RAO Representing the interval of two adjacent RAOs, p (τ) representing the probability density of activation of a device at time τ, N representing the total number of devices in the cell,<·>representing a rounding operation;
based on the backoff load predicted value and backoff probability vector generated by the base station in the (t-1) th RAO and the estimated value of the number of backoff devices newly generated by the (t-1) th RAO
And
the base station processes the backoff load prediction value of the previous RAO to the (t + i) th RAO in the t (th) RAO to the (t + i) th RAO
Plus newly transferred inExpected number of backoff devices
The iterative update is carried out and the data is updated,
new activation load prediction based on (t + i) th RAO
And backoff load prediction value
Obtaining the predicted value L of the access load of the RAO t (i) The following were used:
6. the dynamic access and backoff method based on load sensing in large-scale MTC scenario as claimed in claim 1, wherein in S4, after the base station completes load prediction, it accesses load prediction value L based on current RAO t (0) Dynamic modulation of ACB factor
Estimating the number of backoff devices generated by the current RAO based on the lead code collision condition in the current RAO;
factor ACB
Controlling the probability of the access initiated by the active equipment of the current RAO to ensure that the expected value of the number of the active equipment actually initiating the random access process is close to the ideal load R to the maximum extent,
the calculation method is as follows:
wherein L is t (0) An access load predicted value of the base station to the current RAO is obtained;
activating the device to
The probability is tested by the ACB, the activation equipment tested by the ACB initiates a random access process to the base station, the base station side estimates the access load according to the lead code use condition, and estimates the access load
Is composed of
Wherein the content of the first and second substances,
in the formula, S t Number of preambles successfully accessed for the tth RAO, E t For the number of preambles that the t-th RAO is not used by the user, the number of backoff devices generated by the current RAO is further estimated based on the access load estimation result
7. The dynamic access and backoff method based on load sensing in large-scale MTC scenario according to claim 1, wherein in S5, the number of ideal backoff devices C of the (t + i) th RAO t (i) To minimize the number of active devices L t (i) Number of backoff devices, i =1,2, \ 8230;, T, different from the ideal load R m ，
For any one
And
estimation value of number of backoff devices in (t + i) th RAO after backoff is completed
Comprises the following steps:
8. the dynamic access and backoff method based on load sensing in the large-scale MTC scenario according to claim 1, wherein solving the optimization problem by using a hierarchical optimization method specifically comprises the following steps:
s601, initializing input parameter T m ，C t (i)
And
setting an initial backoff window
S602, solving a Lagrange multiplier lambda by adopting a water injection algorithm:
s603, solving
At a certain time, the optimum number of backoff devices for each RAO
S604, solving
The number of backoff devices for each RAO is constant
The value of the objective function of the problem is optimized,
S605，
when in use
If yes, return to step S602;
s606, comparison
And outputs the value corresponding to the minimum objective function
And
s607, according to
And solving the optimal backoff probability vector
9. A dynamic backoff system based on load sensing in a large-scale MTC scene is characterized by comprising a model building module, an ideal load calculation module, a load prediction module, an estimation module, a problem building module and a resolving module;
the model building module is used for building a system model of dynamic backoff based on load sensing in a large-scale MTC scene;
the ideal load calculation module maximizes the number of access devices required by each RAO expected throughput based on the system model, namely ideal load;
load prediction module predicts current RAO and future T based on device activation model m The access load of each RAO obtains a current RAO load predicted value and a load predicted value of a future RAO;
the estimation module generates an ACB factor based on a load predicted value of the current RAO and estimates the number of newly generated backoff devices after the random access is finished;
the problem construction module adjusts a backoff window and a backoff probability vector based on a new activation load and a backoff load of the RAO, minimizes the mean square error between the backoff device number of each RAO in the backoff window and the ideal backoff device number, and constructs an optimization problem as follows:
constraint (1) denotes a backoff window
Possible range of (A), T m Is the upper bound of the length of the backoff window, T m For manual setting, the constraint conditions (2) and (3) guarantee that the load prediction capability of the base station and the maximum allowable access delay of the equipment are influenced
Forming a probability space;
the solving module adopts a layered optimization method to solve the optimization problem; obtaining an optimal backoff probability vector
And optimal backoff window size
And the number of backoff devices of each RAO in the backoff window is effectively controlled.
10. A large-scale MTC scene communication system is characterized by comprising a base station and a plurality of user terminals, wherein the base station can execute the dynamic access and backoff method based on load perception in the large-scale MTC scene according to any one of claims 1 to 8 during operation.
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