CN111526530A - Optimization method of random access congestion control algorithm for NB-IoT - Google Patents

Abstract

The invention discloses an optimization method of a random access congestion control algorithm facing NB-IoT, which comprises the following steps: a plurality of MTC terminals in a cell randomly access a current NB-IoT base station; predicting the current network load condition based on a Markov chain model, estimating the number of terminals initiating an access request of the current frame and judging whether the number of the terminals is less than the number of lead codes; if so, a plurality of terminals compete for access; if not, calculating an access limiting factor p_iA value of (d); according to calculated p_iThe MTC terminal randomly accesses according to the current priority of the MTC terminal and generates conflict; after the conflict occurs, recording the time delay back-off times phi of each terminal_iAnd calculate eachThe terminal dynamically adjusts the self time delay according to the self latest priority factor η for accessing until normal access or the maximum time delay back-off times are reached.

Description

An optimization method for random access congestion control algorithm for NB-IoT

technical field

The present invention relates to the field of communication technologies, in particular to an optimization method for an NB-IoT-oriented random access congestion control algorithm.

Background technique

With the in-depth integration and in-depth development of information technology and the physical world, the demand for low power consumption, wide coverage, long distance, and low bandwidth of the Internet of Things has become prominent, represented by Narrow Band Internet of Things (NB-IoT). Low Power Wide Area Networks (LPWAN) are widely used in field environment monitoring, power equipment monitoring, and agricultural applications. The characteristics of NB-IoT's low data transmission rate, low power consumption, and low bandwidth determine that its typical application scenario is for Machine Type Communications (MTC) services. Time burst access to massive requests and other characteristics, so when a large number of MTC devices initiate access requests at the same time, it will cause the preamble to collide and block, resulting in a sharp drop in network performance. Therefore, how to optimize and coordinate the access of MTC equipment terminals and reduce the delay has become an important problem to be solved urgently in the current NB-IoT system research.

SUMMARY OF THE INVENTION

The present invention provides an optimization method for NB-IoT-oriented random access congestion control algorithm, which can effectively improve the success rate of MTC terminal equipment access in the NB-IoT system, reduce the access delay of MTC terminal equipment, and alleviate the network congestion.

The present invention provides an optimization method for an NB-IoT-oriented random access congestion control algorithm, comprising the following steps:

Several MTC terminal devices in the cell randomly access the current NB-IoT base station eNB;

Predict the current network load based on the Markov chain model, and estimate the number of MTC terminal devices that initiate access requests in the current frame;

If the number of MTC terminal devices that initiate an access request in the current frame is less than the number of preambles, then the access restriction factor p _i is set to the maximum value, and several MTC terminal devices compete for access;

计算接入限制因子p_i的值；其中K为前导码数量，A_i为当前帧发起接入请求的MTC终端设备个数；If the number of MTC terminal devices that initiate an access request in the current frame is greater than the number of preambles, then according to the calculation formula of the optimal access restriction factor

Calculate the value of the access restriction factor p _i ; wherein K is the number of preambles, and A _i is the number of MTC terminal devices that initiate an access request in the current frame;

According to the value of the access restriction factor p _i , several MTC terminal devices randomly access according to their own current priorities, and several MTC terminal devices collide;

When several MTC terminal equipment collides, record the time delay backoff times φ _i of each MTC terminal equipment, and calculate the latest priority factor n of each MTC terminal equipment;

Several MTC terminal devices dynamically adjust their own time delay to access according to their latest priority factor η, until normal access or the maximum number of time delay backoffs is reached.

其中，m为系统竞争接入MTC终端数，π_m为系统竞争接入终端数为m时的稳定状态概率。Preferably, based on the Markov chain model, it is estimated that the number of MTC terminal devices that initiate an access request in a frame is:

Among them, m is the number of terminals competing to access the MTC in the system, and π _m is the steady state probability when the number of terminals competing for access to the system is m.

则接入限制因子p_i的最大值为1。Preferably, the optimal access restriction factor function is

Then the maximum value of the access restriction factor p _i is 1.

Preferably, the priority factor of the MTC terminal equipment is defined as η=αgi + βφ _i , where _gi is the number of services corresponding to each priority _{, and φ i is} the number of times of delay backoff of each MTC terminal.

Preferably, the MTC terminal device will be prohibited for a random period of time when a conflict occurs: T _baring =(θ+α×rand)×T, where rand is a random number, the value range is [0, 1], and T is a Constant parameters, let θ=0.7 and α=0.6.

Compared with the prior art, the beneficial effects of the present invention are as follows:

Through the present invention, firstly, the number of MTC terminal equipment accessed for the first time is deduced through the probability model of the accessed MTC terminal equipment, and then the number of access MTC terminal equipment including the MTC terminal with multi-step backoff is estimated in combination with the Markov chain model. The access priority classification mechanism with delay awareness optimizes the random access algorithm, so that the MTC terminal can identify the delay degree of the device according to the weight of its service priority, dynamically set the optimal access restriction parameters, and optimize the access. It can effectively improve the success rate of MTC terminal device access in the NB-IoT system, reduce the device access delay, and relieve network congestion.

Description of drawings

The accompanying drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the attached picture:

1 is a flowchart of an optimization method for an NB-IoT-oriented random access congestion control algorithm according to an embodiment of the present invention;

2 is a scenario diagram of large-scale MTC terminal equipment access under the single cell coverage situation according to an embodiment of the present invention;

3 is a frame state diagram of an NB-IoT system according to an embodiment of the present invention;

4 is a state transition diagram of an NB-IoT system according to an embodiment of the present invention;

FIG. 5 is a diagram showing a dynamic change relationship between an MTC load and an ACB access factor according to an embodiment of the present invention.

Detailed ways

The technical solutions of the present invention will be described below with reference to the accompanying drawings of the present invention, but the described embodiments are only a part of the embodiments of the present invention. Based on the embodiments of the present invention, those of ordinary skill in the art can make All other examples obtained belong to the protection scope of the present invention.

An embodiment of the present invention provides an optimization method for an NB-IoT-oriented random access congestion control algorithm. FIG. 1 is a flowchart of an optimization method for an NB-IoT-oriented random access congestion control algorithm according to an embodiment of the present invention. Figure, as shown in Figure 1, includes the following steps:

Step S101: Several MTC terminal devices in the cell randomly access the current NB-IoT base station eNB.

In the embodiment of the present invention, NB-IoT is set as a large-scale MTC terminal equipment access scenario under the coverage of a single cell. As shown in Figure 2, there is only one eNB (eNode Base) and a large number of MTC terminal equipment in the cell, wherein The MTCD in the figure represents the user terminal equipment, and the eNB represents the NB-IoT base station.

Step S102: Predict the current network load condition based on the Markov chain model, and estimate the number of MTC terminal devices that initiate an access request in the current frame.

In the embodiment of the present invention, in order to realize the adjustment and optimization of the random access control parameters of the MTC terminal equipment, it is first necessary to reasonably predict and estimate the number of MTC terminals accessed, and the MTC can be derived through the probability model of the accessed MTC terminal equipment. The number of terminal devices accessing for the first time. In the NB-IoT system, when the current frame initiates an access request, the number C of MTC terminals accessed includes: the number of newly randomly generated terminals σ (that is, based on β distribution); The number of blocked terminals ψ; the terminal ζ for which collision backoff occurs when the current frame initiates an access request, then the total number of MTC terminals accessed is C=σ+ψ+ζ.

Further, since the number of MTC terminal requests in the current frame is not only related to the current frame state, but also related to the previous frame, in the random access process, the state of the MTC terminal transitions with a certain probability, so the Markov chain model is used to estimate the current state. The number of MTC terminals that initiate connection requests in the frame, and the frame state diagram of the NB-IoT system is shown in Figure 3.

表示稳定状态概率向量，则

定义p_m,k为马尔科夫链的状态转移概率，表示由状态m转移到状态k的概率。The state of the Markov chain represents the number of MTC terminals that initiate access requests in the current frame. Assuming that the maximum number of MTC terminals that can be processed in a frame is much larger than random access resources, the state transition diagram of the NB-IoT system is shown in Figure 4. Define π _m as the steady state probability when the number of competing access terminals in the system is m,

represents the steady state probability vector, then

Define p _m,k as the state transition probability of the Markov chain, indicating the probability of transitioning from state m to state k.

其中，B_m,φ为初始帧中m个MTC终端中φ个被禁止的概率，服从二项分布；F_m,φ,n表示一帧中m-φ个终端发起竞争接入有n个接入失败的概率；Assuming that the probability that the number of new MTC terminals arriving in a frame is k is A _k , the state transition probability of the Markov chain can be expressed as:

Among them, B _m,φ is the probability that φ among the m MTC terminals in the initial frame are forbidden, which obeys the binomial distribution; F _m,φ,n indicates that m-φ terminals in a frame initiate competition access and there are n accesses. the probability of entry failure;

因此，可以估计出系统到达平稳状态后，一帧中发起接入请求的MTCD数为：

It can be seen that this is a non-periodic irreducible homogeneous Markov chain. If P is defined as the transition matrix, there is a unique stable state probability vector:

Therefore, it can be estimated that after the system reaches a steady state, the number of MTCDs that initiate access requests in one frame is:

Step S103: If the number of MTC terminal devices that initiate an access request in the current frame is less than the number of preambles, the access restriction factor p _i is set to a maximum value, and several MTC terminal devices compete for access.

In the embodiment of the present invention, the ACB algorithm is used to control the random access parameters, which is specifically described as follows: in each access time slot i (i=1, 2, ..., L), the eNB will periodically broadcast an ACB restriction factor p (0≤p≤1), the activated MTC terminal equipment generates a random number q (0≤q≤1), if q is less than p, the activated MTC terminal equipment passes the ACB check and accesses the application preamble, otherwise , the MTC terminal device will be banned for a random period of time: T _baring = (θ+α×rand)×T, and the ACB check needs to be repeated in the next time slot, where rand is a random number, the value range is [0,1], T is a constant parameter, let θ=0.7 and α=0.6.

The random access control factor p can effectively manage network congestion, and its calculation process is as follows:

其中p_i是ACB在时隙i的限制因子，假定A_i≥1，有B_i期望：

从总的期望可得：

When the number A _i of MTC terminal devices arrive at time slot i, the probability of passing the competition is:

where pi is the limiting factor of ACB at slot _i , assuming A _i ≥ 1, there is an expectation of B _i :

From the general expectation we get:

因此，如果有G_i个前导码成功传输，则概率为：

则前导码成功传输数目的期望表示为：

则有：

The MTC terminal device arriving at slot i will select from K preambles with equal probability p _i /K, and the probability that a preamble is selected by only one MTC device can be obtained as:

Therefore, if there are G _i preambles successfully transmitted, the probability is:

Then the expectation of the number of successful preamble transmissions is expressed as:

Then there are:

In order to maximize access and optimize network congestion control, the ideal situation is that the number of MTC terminals successfully accessed is equal to the number of successful preamble transmissions, and the number of device collisions is F _i =B _i -G _i , and its expectation can be expressed as:

系统吞吐率与A_i和p_i有关，理想情况下，希望每次接入A_i的数目时，对应的接入限制因子p刚好与之适应。λ对A_i求偏导可以得到：

由此可知，当

时，

在此种情况下，A_i值越大系统的吞吐比率越高，当p_i＝1时A_i取得最大值K。当A_i＞K时，另

能够使得系统取得最大吞吐率，能够得到A_i：

Assuming that the total number of time slots is L, the total number of MTC users is N, and random access is requested, the system throughput can be expressed as:

The system throughput rate is related to A _i and p _i . Ideally, it is hoped that each time the number of A _i is accessed, the corresponding access restriction factor p is just adapted to it. The partial derivative of λ with respect to A _i can be obtained:

From this, it can be seen that when

hour,

In this case, the larger the value of A _i , the higher the throughput ratio of the system, and when _{pi =1, A i obtains} the maximum value K. When A _i > K, another

The system can achieve the maximum throughput rate, and A _i can be obtained:

Therefore, when the number of access devices is greater than the number of preambles, the optimal access restriction factor of the system can be obtained:

The optimal access restriction factor function of the system is:

The dynamic relationship between MTC load and ACB access factor is shown in Figure 5. According to the dynamic relationship between MTC load and ACB access factor, it can be seen that when the number of MTC terminals is less than the number of preambles, do not prohibit access constraints , the access restriction factor can be maximized, taking the maximum value; as the number of MTC terminals increases greater than the number of preambles, the access restriction factor will gradually decrease to limit the number of accesses and avoid congestion caused by excessive network load.

计算接入限制因子p_i的值。Step S104: If the number of MTC terminal devices that initiate the access request in the current frame is greater than the number of preambles, then according to the calculation formula of the optimal access restriction factor

Calculate the value of the access restriction factor _pi .

In the embodiment of the present invention, K is the number of preambles, and A _i is the number of MTC terminal devices that initiate an access request in the current frame.

Step S105: According to the value of the access restriction factor p _i , several MTC terminal devices access randomly according to their own current priorities, and several MTC terminal devices collide.

Step S106: After several MTC terminal devices collide, record the number of times of delay backoff φ _i of each MTC terminal device, and calculate the latest priority factor η of each MTC terminal device.

In the embodiment of the present invention, the specific implementation of the priority level division of MTC terminal equipment is as follows: assuming that there are N MTC terminal equipment waiting to be accessed in the current network, the set of MTC terminal equipment to be accessed is denoted as U={U ₁ ,U ₂ ,...,U _n }, U _i represents the i-th device to be connected. Considering that different types of MTC devices have different sensitivity to delay, and different types of services have different requirements for delay, it is proposed to dynamically adjust the access priority level according to the current delay status and service characteristics of MTC devices. .

则各个优先级对应业务数量为g_i，共计数量为

其次，时延与设定的最大退避次数有关，最大退避次数为φ，每个MTC终端设备退避次数表示为φ_i。Let MTC terminal equipment set the priority level according to the service as

Then the number of services corresponding to each priority is g _i , and the total number is

Secondly, the time delay is related to the set maximum number of backoffs, the maximum number of backoffs is φ, and the number of backoffs for each MTC terminal device is represented as φ _i .

Then the priority of each MTC terminal device is defined as: η=αg _i +βφ _i .

The priority classification rules comprehensively consider the characteristics of delay fairness and service priority attributes, which ensure the uniformity of network traffic fairness and efficiency.

Step S107: Several MTC terminal devices dynamically adjust their own time delays to access according to their latest priority factors η until normal access or the maximum number of time delay backoffs is reached.

To sum up the above, through the above embodiments, firstly, the number of MTC terminal equipment accessed for the first time is deduced through the probability model of the MTC terminal equipment accessed, and then the number of access MTC terminals including the MTC terminal with multi-step backoff is estimated in combination with the Markov chain model. The number of devices, the access priority classification mechanism with delay awareness is designed, and the random access algorithm is optimized, so that the MTC terminal can identify the delay degree of the device according to the weight of its service priority, and dynamically set the optimal access restriction parameters. , optimize the access performance, effectively improve the success rate of MTC terminal device access in the NB-IoT system, reduce the device access delay, and relieve network congestion.

Claims (5)

1. an optimization method for the random access congestion control algorithm for NB-IoT, is characterized in that, comprises the following steps: Several MTC terminal devices in the cell randomly access the current NB-IoT base station eNB; Predict the current network load based on the Markov chain model, and estimate the number of MTC terminal devices that initiate access requests in the current frame; If the number of MTC terminal devices that initiate an access request in the current frame is less than the number of preambles, then the access restriction factor p _i is set to the maximum value, and several MTC terminal devices compete for access; If the number of MTC terminal devices that initiate an access request in the current frame is greater than the number of preambles, then according to the calculation formula of the optimal access restriction factor

Calculate the value of the access restriction factor p _i ; wherein K is the number of preambles, and A _i is the number of MTC terminal devices that initiate an access request in the current frame; According to the value of the access restriction factor p _i , several MTC terminal devices randomly access according to their own current priorities, and several MTC terminal devices collide; When several MTC terminal equipment collides, record the time delay backoff times φ _i of each MTC terminal equipment, and calculate the latest priority factor n of each MTC terminal equipment; Several MTC terminal devices dynamically adjust their own time delay to access according to their latest priority factor η, until normal access or the maximum number of time delay backoffs is reached. 2. method according to claim 1, is characterized in that, the number of MTC terminal equipments that initiates access request in one frame is estimated based on Markov chain model:

Among them, m is the number of terminals competing to access the MTC in the system, and π _m is the steady state probability when the number of terminals competing for access to the system is m. 3. The method according to claim 1, wherein the optimal access restriction factor function is

Then the maximum value of the access restriction factor p _i is 1. 4. The method according to claim 1, wherein the priority factor of the MTC terminal equipment is defined as η= _αgi + _βφi , wherein, _gi is the number of services corresponding to each priority, and _φi is each MTC Delay backoff times of the terminal. 5. The method according to claim 1, wherein when the MTC terminal equipment collides, it will be prohibited for a random period of time: T _baring =(θ+α×rand)×T, wherein, rand is a random number, The value range is [0,1], T is a constant parameter, let θ=0.7, α=0.6.

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