CN111526530A - Optimization method of random access congestion control algorithm for NB-IoT - Google Patents
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- H04W74/08—Non-scheduled access, e.g. ALOHA
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/0215—Traffic management, e.g. flow control or congestion control based on user or device properties, e.g. MTC-capable devices
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
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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 piA value of (d); according to calculated piThe 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 terminaliAnd 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
Technical Field
The invention relates to the technical field of communication, in particular to an optimization method of an NB-IoT-oriented random access congestion control algorithm.
Background
With the deep integration and deep development of information technology and the physical world, the demand of Low Power consumption, Wide coverage, long distance and Low bandwidth for Internet of Things is prominent, and Low Power Wide Area Networks (LPWANs) represented by Narrow-Band Internet of Things (NB-IoT) are widely applied in the fields of field environment monitoring, Power equipment monitoring, agricultural application and the like. The characteristics of low data transmission rate, low power consumption and low bandwidth of NB-IoT determine that a typical application scenario is Machine Type Communication (MTC) -oriented traffic, which has the characteristics of large data volume, small data packet, short burst access to massive requests and the like, and therefore when a large number of MTC devices initiate access requests simultaneously, preamble collision and blocking may be caused, which may result in a drastic decrease in network performance. Therefore, how to optimize and coordinate the MTC device terminal access and reduce the delay becomes an important issue to be solved urgently in the current NB-IoT system research.
Disclosure of Invention
The invention provides an optimization method of a random access congestion control algorithm facing NB-IoT, which can effectively improve the success rate of MTC terminal equipment access in an NB-IoT system, reduce the access delay of the MTC terminal equipment and relieve network congestion.
The invention provides an optimization method of a random access congestion control algorithm facing NB-IoT, which comprises the following steps:
a plurality of MTC terminal devices in a cell randomly access to a current NB-IoT base station eNB;
predicting the current network load condition based on a Markov chain model, and estimating the number of MTC terminal devices initiating an access request at the current frame;
if the number of the MTC terminal devices initiating the access request in the current frame is less than the number of the lead codes, the access limiting factor p is madeiAs the maximum value, a plurality of MTC terminal devices compete for access;
if the number of the MTC terminal equipment initiating the access request at the current frame is larger than the number of the lead codes, calculating according to a calculation formula of the optimal access limiting factor
Calculating an access restriction factor piA value of (d); where K is the number of preambles, AiThe number of MTC terminal devices initiating an access request for a current frame;
according to the access limiting factor piThe value of (1) is that a plurality of MTC terminal devices are randomly accessed according to the current priority of the MTC terminal devices, and a plurality of MTC terminal devices conflict;
when a plurality of MTC terminal devices conflict, recording the time delay back-off times phi of each MTC terminal deviceiCalculating the latest priority factor η of each MTC terminal device;
and the MTC terminal equipment dynamically adjusts the self time delay according to the latest priority factor eta of the MTC terminal equipment for access until normal access or the maximum time delay backoff times are reached.
Preferably, the number of MTC terminal devices initiating an access request in a frame is estimated based on a markov chain model as follows:
wherein m is the number of MTC terminals accessed by system competition, pimAnd competing the stable state probability when the number of the access terminals is m for the system.
Preferably, the optimal access restriction factor function is
Then the access restriction factor piIs 1.
Preferably, the priority factor of the MTC terminal device is defined as η ═ α gi+βφiWherein g isiFor the number of services, phi, corresponding to each priorityiAnd the time delay back-off times of each MTC terminal are obtained.
Preferably, the random time period for which the MTC terminal devices are prohibited from colliding is: t isbaring(θ + α× rand) × T, where rand is a random number and ranges from [0,1 ]]T is a constant parameter, where θ is 0.7 and α is 0.6.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, the number of the MTC terminal devices accessed for the first time is deduced through the accessed MTC terminal device probability model, the number of the accessed MTC terminal devices including the multi-step backoff MTC terminal is estimated by combining the Markov chain model, an access priority classification mechanism with delay consciousness is designed, and a random access algorithm is optimized, so that the MTC terminal identifies the delay degree of the device according to the weighting of the service priority of the MTC terminal device, dynamically sets an optimal access limiting parameter, optimizes the access performance, effectively improves the success rate of the MTC terminal device access in an NB-IoT system, reduces the device access delay, and relieves network congestion.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flowchart of an optimization method of NB-IoT-oriented random access congestion control algorithm according to an embodiment of the present invention;
fig. 2 is a view of a large-scale MTC terminal device access scenario under a single cell coverage condition according to an embodiment of the present invention;
fig. 3 is an NB-IoT system frame state diagram of an embodiment of the present invention;
fig. 4 is a state transition diagram of an NB-IoT system of an embodiment of the present invention;
fig. 5 is a diagram illustrating a dynamic relationship between MTC load and ACB access factor according to an embodiment of the present invention.
Detailed Description
The technical solution of the present invention will be described below with reference to the accompanying drawings, but the described embodiments are only a part of the embodiments of the present invention, and all other embodiments obtained by those skilled in the art without any inventive work belong to the scope of the present invention.
An embodiment of the present invention provides an NB-IoT-oriented random access congestion control algorithm optimization method, and fig. 1 is a flowchart of an NB-IoT-oriented random access congestion control algorithm optimization method according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps:
step S101: several MTC terminal devices within a cell randomly access the current NB-IoT base station eNB.
In the embodiment of the present invention, a large-scale MTC terminal device access scenario is set under the condition that NB-IoT is covered by a single cell, as shown in fig. 2, there are only one eNB (enode base) and a large number of MTC terminal devices in a cell, where MTCD in the figure represents a user terminal device and eNB represents an NB-IoT base station.
Step S102: and predicting the current network load condition based on a Markov chain model, and estimating the number of MTC terminal devices initiating access requests at the current frame.
In the embodiment of the present invention, in order to implement adjustment and optimization of random access control parameters of MTC terminal devices, firstly, a reasonable prediction and estimation of the number of MTC terminals to be accessed is required, and the number of MTC terminals to be accessed for the first time may be derived through an accessed MTC terminal device probability model, where in an NB-IoT system, when an access request is initiated at a current frame, the number C of MTC terminals to be accessed includes: a new randomly generated terminal number σ (i.e., based on a β distribution); the number psi of terminals blocked during access contention; when the current frame initiates an access request, the terminal ζ that has collided and backed off is generated, and the total number of the accessed MTC terminals is C ═ σ + ψ + ζ.
Further, since the number of MTC terminal requests of the current frame is not only related to the state of the current frame, but also related to the previous frame, and the state of the MTC terminal is shifted with a certain probability during the random access process, a markov chain model is used to estimate the number of MTC terminals initiating connection requests of the current frame, and an NB-IoT system frame state diagram is shown in fig. 3.
The state of the markov chain represents the number of MTC terminals initiating an access request at a current frame, and assuming that the maximum number of MTC terminals that can be processed in a frame is much greater than the random access resource, the state transition diagram of the NB-IoT system is shown in fig. 4, and pi is definedmTo compete for the steady state probability when the number of access terminals is m for the system,
representing a steady state probability vector, then
Definition of pm,kThe state transition probability of the markov chain represents the probability of transition from state m to state k.
Assuming that the probability of the arrival number of new MTC terminals in a frame being k is AkThe state transition probability of a Markov chain can be expressed as:
wherein, Bm,φObeying two-term distribution for the forbidden probability of phi in m MTC terminals in the initial frame; fm,φ,nThe probability that n access failures exist when m-phi terminals initiate competition access in one frame is represented;
it can be seen that this is a non-periodic irreducible homogeneous markov chain, and defining P as a transition matrix, there is a unique steady-state probability vector:
therefore, after the system reaches a steady state, the MTCD number of the access request initiated in one frame can be estimated as:
step S103: if the number of the MTC terminal devices initiating the access request in the current frame is less than the number of the lead codes, the access limiting factor p is madeiAnd as the maximum value, a plurality of MTC terminal devices compete for access.
In the embodiment of the invention, an ACB algorithm is adopted to control the random access parameters, and the specific description is as follows: in each access time slot i (i ═ 1, 2.. gtorel), the eNB broadcasts an ACB limiting factor p (p is greater than or equal to 0 and less than or equal to 1) periodically, the activated MTC terminal device generates a random number q (q is greater than or equal to 0 and less than or equal to 1), if q is less than p, the MTC terminal device is activated to pass the ACB check and the preamble access is applied, otherwise, the MTC terminal device is prohibited for a random time: t isbaring(θ + α× rand) × T, and the ACB check needs to be repeated in the next slot, where rand is a random number, with a range of values [0,1 [ ]]T is a constant parameter, where θ is 0.7 and α is 0.6.
The random access control factor p can effectively manage network congestion, and the calculation process is as follows:
when the amount A isiWhen the MTC terminal equipment reaches the time slot i, the probability of contention passing is as follows:
wherein p isiIs the limiting factor of ACB in time slot i, assuming AiNot less than 1, with BiIt is desired that:
from the overall expectation it follows:
MTC terminal equipment arriving at time slot i will have equal probability pithe/K is selected from K preambles, and the probability that one preamble is selected by only one MTC device can be obtained as follows:
therefore, if there is GiIf the preamble is successfully transmitted, the probability is:
the expected number of successful transmissions of the preamble is then expressed as:
then there are:
in order to achieve access maximization and network congestion control optimization, the number of successful accesses of the MTC terminal is equal to the number of successful transmissions of the lead code, and the number of collisions of the equipment is Fi＝Bi-GiThe expectation may be expressed as:
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 rate may be represented as:
system throughput rate and AiAnd piIn this regard, ideally, it is desirable to access A each timeiThe corresponding access limiting factor p is adapted to it exactly. Lambda to AiThe partial derivative can be obtained as follows:
thus, when it is known that
When the temperature of the water is higher than the set temperature,
in this case, AiThe larger the value, the higher the throughput ratio of the system, when piWhen 1 is equal to AiThe maximum value K is obtained. When A isiWhen > K, in addition
The system can obtain the maximum throughput rate, and can obtain Ai：
Therefore, it can be determined that the number of access devices is greater than the number of preamblesSystem optimal access restriction factor:
the system optimal access restriction factor function is then:
the dynamic change relationship between the MTC load and the ACB access factor is shown in fig. 5, and it can be seen from the dynamic change relationship between the MTC load and the ACB access factor that when the number of MTC terminals is less than the number of preambles, no access restriction is prohibited, the access restriction factor can be maximized, and the maximum value is taken; when the number of the MTC terminals is increased to be larger than the number of the lead codes, the access limiting factor is gradually reduced, the access number is limited, and congestion caused by excessive network load is avoided.
Step S104: if the number of the MTC terminal equipment initiating the access request at the current frame is larger than the number of the lead codes, calculating according to a calculation formula of the optimal access limiting factor
Calculating an access restriction factor piThe value of (c).
In the embodiment of the invention, K is the number of lead codes, AiAnd the number of the MTC terminal devices initiating the access request for the current frame.
Step S105: according to an access restriction factor piThe plurality of MTC terminal devices are randomly accessed according to the current priority of the MTC terminal devices, and the plurality of MTC terminal devices conflict.
Step S106: when a plurality of MTC terminal devices conflict, recording the time delay back-off times phi of each MTC terminal deviceiAnd calculates η the latest priority factor for each MTC terminal device.
In the embodiment of the present invention, the specific implementation manner of the priority level division of the MTC terminal device is as follows: suppose that N MTC terminal devices wait to access in the current network, and the set of MTC terminal devices waiting to access is recorded as U ═ U1,U2,…,Un}，UiRepresenting the ith device to be accessed. Considering that the sensitivity degrees of different types of MTC equipment to time delay are different, and the requirements of different types of services to time delay are different, the access priority level is dynamically adjusted according to the current time delay state and service characteristics of the MTC equipment.
Setting MTC terminal equipment to commonly set priority level as
The corresponding service quantity of each priority is giIn total amount of
Secondly, the time delay is related to the set maximum backoff times, the maximum backoff times are phi, and the backoff times of each MTC terminal device are expressed as phii。
The priority of each MTC terminal device is defined as η - α gi+βφi。
The priority level division rule comprehensively considers the characteristics of time delay fairness and service priority attributes, and guarantees that the fairness and the efficiency of network flow are unified.
Step S107: and the MTC terminal equipment dynamically adjusts the self time delay according to the latest priority factor eta of the MTC terminal equipment for access until normal access or the maximum time delay backoff times are reached.
In summary, according to the embodiments, firstly, the number of MTC terminals accessed for the first time is derived through the accessed MTC terminal probability model, and then the number of the accessed MTC terminals including the multi-step backoff MTC terminals is estimated by combining the markov chain model, an access priority classification mechanism with delay awareness is designed, and a random access algorithm is optimized, so that the MTC terminals identify the delay degree of the devices according to the weighting of the service priority of the MTC terminals, dynamically set the optimal access limiting 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 alleviate network congestion.
Claims (5)
1. An optimization method of NB-IoT-oriented random access congestion control algorithm is characterized by comprising the following steps:
a plurality of MTC terminal devices in a cell randomly access to a current NB-IoT base station eNB;
predicting the current network load condition based on a Markov chain model, and estimating the number of MTC terminal devices initiating an access request at the current frame;
if the number of the MTC terminal devices initiating the access request in the current frame is less than the number of the lead codes, the access limiting factor p is madeiAs the maximum value, a plurality of MTC terminal devices compete for access;
if the number of the MTC terminal equipment initiating the access request at the current frame is larger than the number of the lead codes, calculating according to a calculation formula of the optimal access limiting factor
Calculating an access restriction factor piA value of (d); where K is the number of preambles, AiThe number of MTC terminal devices initiating an access request for a current frame;
according to the access limiting factor piThe value of (1) is that a plurality of MTC terminal devices are randomly accessed according to the current priority of the MTC terminal devices, and a plurality of MTC terminal devices conflict;
when a plurality of MTC terminal devices conflict, recording the time delay back-off times phi of each MTC terminal deviceiCalculating the latest priority factor η of each MTC terminal device;
and the MTC terminal equipment dynamically adjusts the self time delay according to the latest priority factor eta of the MTC terminal equipment for access until normal access or the maximum time delay backoff times are reached.
2. The method of claim 1, wherein estimating the number of MTC terminal devices initiating access requests in a frame based on a Markov chain model comprises:
wherein m is the number of MTC terminals which are accessed by the system in competition，πmAnd competing the stable state probability when the number of the access terminals is m for the system.
3. The method of claim 1, wherein the optimal access restriction factor function is
Then the access restriction factor piIs 1.
4. The method of claim 1, wherein a priority factor of an MTC terminal device is defined as η ═ α gi+βφiWherein g isiFor the number of services, phi, corresponding to each priorityiAnd the time delay back-off times of each MTC terminal are obtained.
5. The method according to claim 1, wherein the MTC terminal devices are prohibited from colliding for a random period of time: t isbaring(θ + α× rand) × T, where rand is a random number and ranges from [0,1 ]]T is a constant parameter, where θ is 0.7 and α is 0.6.
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| CN113473680A (en) * | 2021-08-03 | 2021-10-01 | 福州物联网开放实验室有限公司 | Discrete access method and structure of NB-IoT intelligent street lamp terminal |
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