CN112738839B - NB-IoT network capacity evaluation method, device, equipment and storage medium - Google Patents
NB-IoT network capacity evaluation method, device, equipment and storage medium Download PDFInfo
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- CN112738839B CN112738839B CN201910974257.XA CN201910974257A CN112738839B CN 112738839 B CN112738839 B CN 112738839B CN 201910974257 A CN201910974257 A CN 201910974257A CN 112738839 B CN112738839 B CN 112738839B
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Abstract
The embodiment of the invention provides an assessment method, a device, equipment and a storage medium of NB-IoT network capacity. The method comprises the following steps: acquiring load information of a narrowband Internet of things NB-IoT cell; determining the number of times of initiating access requests of a plurality of terminals for accessing an NB-IoT cell according to the service reporting parameters of the terminals; determining a hidden Markov chain model for evaluating the network capacity of the NB-IoT cell during multi-service access according to the load information and the access initiation times; the network capacity of the NB-IoT cell is determined according to a hidden markov chain model of the network capacity estimation. The embodiment of the invention can evaluate the network capacity of the NB-IoT cell, thereby fundamentally relieving the problem of insufficient network access capacity.
Description
Technical Field
The present invention relates to the field of wireless communication technologies, and in particular, to a method, an apparatus, a device, and a storage medium for evaluating NB-IoT network capacity.
Background
The Narrow-Band Internet of Things (NB-IoT) is an important branch of the world wide Internet. The NB-IoT is built into the cellular network, consuming only approximately 180kHz of bandwidth, and is thus capable of supporting cellular data connectivity for low power devices over wide area networks.
At present, an NB-IoT network protocol provides that a single cell serves about 5W of internet of things terminals, but the capacity of the single cell 5W internet of things access only exists theoretically, and since the current specification of a terminal reporting mechanism is unclear, once a large number of users access concurrently, network congestion may be caused, and a situation that the terminal cannot normally receive and transmit data occurs. The existing NB-IoT network congestion solving method is mainly to reject an access request initiated by a service terminal by adjusting parameters related to access and admission of base station equipment, thereby achieving the purpose of relieving network access load.
Therefore, the network refusing the access request of the service terminal can cause the service terminal to continuously initiate re-access, so that the network at the service terminal side cannot be stable.
Disclosure of Invention
The embodiment of the invention provides an assessment method, a device, equipment and a storage medium for NB-IoT network capacity, which can solve the problem that a network at a service terminal side cannot be stable due to continuous initiation of re-access of a service terminal.
In a first aspect, a method for evaluating NB-IoT network capacity is provided, where the method includes:
acquiring load information of a narrow-band Internet of things NB-IoT cell;
Determining the number of times of initiating access requests of a plurality of terminals for accessing an NB-IoT cell according to the service reporting parameters of the terminals;
determining a hidden Markov chain model for evaluating the network capacity of the NB-IoT cell during multi-service access according to the load information and the access initiation times;
the network capacity of the NB-IoT cell is determined according to a hidden markov chain model of the network capacity estimation.
In one possible implementation, the method further includes: and adjusting the network access mechanism of the terminal according to the network capacity.
In one possible implementation, optimizing a network access mechanism of a terminal according to network capacity includes:
when the load state in the hidden Markov chain model is a first preset threshold value, adjusting the time interval of the service for re-accessing the network;
when the load state in the hidden Markov chain model is a second preset threshold value, adjusting the time interval of the periodic access network;
and when the load state in the hidden Markov chain model is a third preset threshold value, adjusting the timer.
In a possible implementation manner, determining the number of times of initiating access requests for a plurality of terminals to access an NB-IoT cell according to a service reporting parameter of the terminal includes:
determining the event triggering rate and the failure allowance of each terminal according to the service reporting parameters;
Determining the number of times of initiating the access request of each terminal according to the event triggering rate and the failure allowance of each terminal;
and summing the access request initiating times of each terminal to obtain the access request initiating times of a plurality of terminals accessing the NB-IoT cell.
In one possible implementation manner, a hidden markov chain model for determining network capacity evaluation of an NB-IoT cell during multi-service access is determined according to load information and access initiation times, and includes:
determining a load probability matrix according to the load information;
determining a multi-service access probability matrix according to the access initiation times;
and obtaining a hidden Markov chain model for evaluating the network capacity of the NB-IoT cell during multi-service access according to the load probability matrix and the multi-service access probability matrix.
In a second aspect, an apparatus for evaluating NB-IoT network capacity is provided, the apparatus comprising:
the acquisition module is used for acquiring the load information of the NB-IoT cell of the narrowband Internet of things;
the number determining module is used for determining the number of times of initiating access requests of a plurality of terminals for accessing the NB-IoT cell according to the service reporting parameters of the terminals;
the model determining module is used for determining a hidden Markov chain model for evaluating the network capacity of the NB-IoT cell during multi-service access according to the load information and the access initiating times;
And the capacity determining module is used for determining the network capacity of the NB-IoT cell according to the hidden Markov chain model of the network capacity evaluation.
In a possible implementation manner, the apparatus further includes an adjusting module, configured to adjust a network access mechanism of the terminal according to the network capacity.
In one possible implementation, the adjusting module is configured to:
when the load state in the hidden Markov chain model is a first preset threshold value, adjusting the time interval of the service for re-accessing the network;
when the load state in the hidden Markov chain model is a second preset threshold value, adjusting the time interval of the periodic access network;
and when the load state in the hidden Markov chain model is a third preset threshold value, adjusting the timer.
In one possible implementation, the number determining module is configured to:
determining the event triggering rate and the failure allowance of each terminal according to the service reporting parameters;
determining the number of times of initiating the access request of each terminal according to the event triggering rate and the failure allowance of each terminal;
and summing the access request initiating times of each terminal to obtain the access request initiating times of a plurality of terminals accessing the NB-IoT cell.
In one possible implementation, the model determination module is configured to:
Determining a load probability matrix according to the load information;
determining a multi-service access probability matrix according to the access initiation times;
and obtaining a hidden Markov chain model for evaluating the network capacity of the NB-IoT cell during multi-service access according to the load probability matrix and the multi-service access probability matrix.
In a third aspect, an embodiment of the present invention provides an apparatus, including: at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement the method of the first aspect or any of the possible implementations of the first aspect as described in the embodiments above.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which computer program instructions are stored, which, when executed by a processor, implement the first aspect or the method of any possible implementation manner of the first aspect.
Based on the provided assessment method, device, equipment and storage medium of NB-IoT network capacity, the load information of an NB-IoT cell of the narrowband IoT is obtained; determining the number of times of initiating access requests of a plurality of terminals for accessing an NB-IoT cell according to the service reporting parameters of the terminals; determining a hidden Markov chain model for evaluating the network capacity of the NB-IoT cell during multi-service access according to the load information and the access initiation times; the network capacity of the NB-IoT cell is determined according to a hidden markov chain model of the network capacity estimation. The embodiment of the invention can evaluate the network capacity of the NB-IoT cell, thereby fundamentally relieving the problem of insufficient network access capacity.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 illustrates a flow diagram of a method for evaluating NB-IoT network capacity provided in accordance with some embodiments of the present invention;
fig. 2 is a flowchart illustrating a service reporting mechanism for an intelligent parking event according to some embodiments of the present invention;
fig. 3 illustrates a block diagram of an NB-IoT network capacity assessment apparatus provided in accordance with some embodiments of the present invention;
fig. 4 illustrates a schematic diagram of an apparatus provided according to some embodiments of the invention.
Detailed Description
Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention.
It is to be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
And the NB-IoT adopts an innovative air interface technology, and is more suitable for the service of the Internet of things. The ultra-narrow band design (200kHz system bandwidth, 3.75kHz/15kHz channel bandwidth) and long-time transmission (such as repeated transmission) are adopted to realize coverage enhancement; and designing narrower resource granularity bandwidth to realize large connection.
When NB-IoT network protocol is made, i.e., considering large capacity, according to the third Generation Partnership Project (3rd Generation Partnership Project, 3GPP), a single cell serves about 5W ten thousand terminals of an internet of things. However, the 5 ten thousand connections are theoretical values based on the extremely low activation ratio of the terminal, the discrete access time and the user distribution, and the standard packet size, so that, based on the current network reporting mechanism, once a large number of users access concurrently, the network is blocked, and the data transmission and reception cannot be interrupted.
Therefore, the method, the device, the equipment and the storage medium for evaluating the NB-IoT network capacity provided by the embodiment of the invention can evaluate the network capacity of the NB-IoT cell, and further can fundamentally relieve the problem of insufficient network access capacity.
To facilitate understanding of the present embodiment, a detailed description is first given of an NB-IoT network capacity evaluation method disclosed in the present embodiment.
Referring to fig. 1, an embodiment of the present invention provides an NB-IoT network capacity evaluation method, including:
s101, acquiring load information of the NB-IoT cell.
In one embodiment of the present invention, the load information refers to information characterizing a load change state that may occur in the NB-IoT cell, wherein the load change state is a randomly generated random sequence of unobservable states, for example, the load information of the NB-IoT cell is (10%, 20%, 30% … … 100%), wherein 10% represents that the load state of the NB-IoT cell at the current time is changed by 1% to 10% from the last time.
S102: and determining the access request initiation times of a plurality of terminals accessing the NB-IoT cell according to the service reporting parameters of the terminals.
In an embodiment of the present invention, the service reporting parameter refers to a parameter for reporting a service event by the terminal, for example, the number of times the terminal retransmits the service event, the reporting period, and the like. The multi-service reporting mechanisms of the terminal of the Internet of things are different, and the embodiment of the invention takes intelligent parking as an example, and elaborates the service reporting process of the intelligent parking event in detail. Fig. 2 is a flowchart of a service reporting mechanism of an intelligent parking event according to an embodiment of the present invention. The geomagnetic terminal detects that the magnetic field disturbance of the parking space exceeds a calibration reference value recorded in the absence of the vehicle, the state change is transmitted to the service platform through the NB-IoT gateway in a data packet mode, the service platform judges the vehicle driving-in and driving-out state in combination with the background big data analysis system after data receiving is completed, the message is sent to a manager, and finally the geomagnetic terminal is replied with an ACK confirmation message through the NB-IoT network.
As can be seen from fig. 2, in the service event of intelligent parking, the service reporting mechanism flow of the terminal is as follows:
the geomagnetic terminal detects the change of the parking space state, the parking space state enters a connection state from an idle state, the service time of the change of the parking space state is reported, the intelligent parking system detects that the magnetic field disturbance of the parking space exceeds a calibration reference value recorded when the vehicle is not parked, the service event of the change of the parking space state is transmitted to a service platform through an NB-IoT gateway in a data packet mode, the service platform judges the driving-in and driving-out state of the vehicle by combining a background big data analysis system after data receiving is finished, the message is sent to a manager, and finally the geomagnetic terminal is replied with an ACK confirmation message through the intelligent parking system.
And (3) retransmission mechanism: and normally and continuously monitoring the parking space change state after 3 seconds, waiting for ACK, retransmitting the parking space change state for 8 times if not received in 30 seconds, and retransmitting the parking space change state after resetting the module after about 60 minutes.
The peak staggering mechanism is as follows: under normal conditions, the terminal datagram of the internet of things is not stored, the data cannot be lost, and the terminal datagram of the internet of things can be continuously reported when the terminal datagram of the internet of things can be reported.
The retransmission and peak staggering mechanism of the internet of things is as shown in the table one:
watch 1
Therefore, according to the service reporting parameters of the terminal, the number of times of initiating access requests for accessing the plurality of terminals to the NB-IoT cell needs to be obtained.
Specifically, determining the number of times of initiating access requests of a plurality of terminals to access an NB-IoT cell according to the service reporting parameters of the terminals includes: determining the event triggering rate and the failure allowance of each terminal according to the service reporting parameters; determining the number of times of initiating the access request of each terminal according to the event triggering rate and the failure allowance of each terminal; and summing the access request initiating times of each terminal to obtain the access request initiating times of a plurality of terminals accessing the NB-IoT cell.
In one embodiment of the invention, the access request initiation times T1 of one terminal Number of accesses Satisfies the following formula:
T1 number of accesses ＝Q1 Terminal device ×K×(1+f)
Wherein, Q1 Terminal device Indicating the number of terminals accessing the NB-IoT cell, K indicating the event trigger rate, and f indicating the failure margin.
The failure margin f satisfies the following formula:
f＝(S CEL＝1 +S CEL＝2 )×K
wherein S is CEL＝1 Indicates the number of retransmissions in network coverage level 1, S CEL＝2 Indicating the number of retransmissions at network coverage level 2.
The access request initiating times Tn of multiple terminals can be obtained from the access request initiating times of one terminal Number of accesses Satisfies the following formula:
wherein, Qi Terminal device The number of access terminals accessing an NB-IoT cell corresponding to the ith terminal is represented, K represents an event trigger rate, and f represents a failure margin.
In addition, the NB-IoT network configuration and Narrowband Physical Random Access Channel (NPRACH) capacity also needs to be determined before the number of access request originations is determined. The narrowband physical random access channel uses a 3.75KHz carrier, and uses different MCSs (different rates, different spreading factors, and repetition times) under different coverage levels, and the different coverage levels have a large influence on the capacity, as shown in table two.
Watch 2
The calculation formula of the preamble collision probability P of the narrowband physical random access channel is as follows:
P＝1-e -r/L
wherein, L is the number of terminals per second for random access, L is frequency domain resource number × 1000ms/T, r is random access density, r is random access capacity × total number of random accesses per unit time of single user, and T is resource period.
From this, it can be derived that the random access capacity satisfies the following formula:
random access capacity less than or equal to-L x ln (1-P)/total random access number per unit time of single user
Wherein, the total random access number per unit time of a single user is (average traffic/day/terminal)/24/3600
When the NPRACH capacity is calculated, the collision probability P takes a value of 0.05.
Then NPRACH capacity, the number of random access terminals per second configured at different resources and periods is shown in table three:
watch III
After the access times of a plurality of terminals are determined, a hidden Markov chain model for evaluating the network capacity of the NB-IoT cell during multi-service access can be determined by combining load information, and before the hidden Markov chain model for evaluating the network capacity of the NB-IoT cell during multi-service access is introduced, the principle of the hidden Markov chain model is firstly introduced in detail.
The hidden Markov chain model is a probability model related to time sequence, and describes a process of generating a random sequence of non-observable states by a hidden Markov chain randomly and generating an observation random sequence by each state, wherein the sequence of the states generated by the hidden Markov chain randomly is called a state sequence; each state generates an observation, and the resulting random sequence of observations is called an observation sequence. Each position of the sequence can in turn be regarded as a time instant. The hidden markov model is determined from an initial probability distribution, a state transition probability distribution, and an observation probability distribution. The form of the hidden markov model is defined as follows:
let Q be the set of all possible states and V be the set of all possible observations.
Q＝{q 1 ,q 2 ,…,q N }，V＝{v 1 ,v 2 ,…,v M }
Where N is the number of possible states and M is the number of possible observations.
I＝{i 1 ,i 2 ,…,i T }，O＝{o 1 ,o 2 ,…,o T }
I is the state sequence of length T and O is the corresponding observation sequence.
Then, the state transition probability matrix a satisfies the following equation:
A＝[a ij ]N×N
wherein, a ij ＝P(i t+1 ＝q j |i t ＝q i )，i＝1,2,…,N；j＝1,2,…,N，P(i t+1 ＝q j |i t ＝q i ) Is shown in state q at time t i Is transferred to the state q at the time t +1 j The probability of (c).
The observation probability matrix B satisfies the following formula:
B＝[b j (k)]N×M
Wherein, b j (k)＝P(o t ＝v k |i t ＝q j )，b j (k)＝P(o t ＝v k |i t ＝q j )，j＝1,2,…,N，P(o t ＝v k |i t ＝q j ) Indicating being in state q at time t j Under conditions to generate an observation v k The probability of (c).
The initial state probability vector pi satisfies the following formula:
π＝(π i )
wherein, pi i ＝P(i 1 ＝q i )，i＝1,2,…,N，π i ＝P(i 1 ＝q i ) Indicates that the state q is present at the time t equal to 1 i The probability of (c).
The hidden Markov model is determined by an initial state probability vector pi, a state transition probability matrix A and an observation probability matrix B, wherein pi and A determine a state sequence and B determines an observation sequence. Thus, a hidden markov model can be represented by a ternary notation, namely:
λ＝(A,B,π)
a, B, π are called the three elements of the hidden Markov model.
The state transition probability matrix a and the initial state probability vector pi define a hidden markov chain, generating an unobservable state sequence. The observation probability matrix B determines how observations are generated from the states and, in combination with the state sequences, determines how the observation sequences are generated.
By definition, hidden markov models make two basic assumptions:
1) the homogeneous Markov assumption, i.e., the assumption that the state of a hidden Markov chain at any time t depends only on the state at the previous time, and is independent of the states and observations at other times, and also independent of time t.
2) The observation independence assumption, that is, the assumption that an observation at any time depends only on the state of the Markov chain at that time, is independent of other observations and states.
And based on the principle of the hidden Markov chain model, the NB-IoT cell carries out access capacity evaluation operation.
S103: and determining a hidden Markov chain model for evaluating the network capacity of the NB-IoT cell during multi-service access according to the load information and the access initiation times.
Specifically, a load probability matrix is determined according to the load information;
determining a multi-service access probability matrix according to the access initiation times;
and obtaining a hidden Markov chain model for evaluating the network capacity of the NB-IoT cell during multi-service access according to the load probability matrix and the multi-service access probability matrix.
In one embodiment of the invention, the state set Q of the NB-IoT cell is the load information of the NB-IoT cell, the observable set V is the access initiation times, and then the load probability matrix A of the NB-IoT cell can be known according to the hidden Markov model Load(s) Satisfies the following formula:
A load(s) ＝[a ij ]N×N
Multi-service access probability matrix B Multi-service Satisfies the following formula:
B multi-service ＝[b j (k)]N×M
If the load information is (10%, 20%, 30% … … 100%), then the state sequence of the state set Q is I ═ 10%, 20%, …, 100% }, a ij ＝P(i t+1 ＝q j |i t ＝q i ) I 1,2, …, N, j 1,2, …, N, the NB-IoT cell load probability matrix a can be obtained Load(s) 。
Thereby, a hidden Markov chain model lambda of the NB-IoT cell for network capacity evaluation in multi-service access is obtained, wherein lambda is (A) Load(s) ,B Multi-service ,π)。
S104: the network capacity of the NB-IoT cell is determined according to a hidden markov chain model of the network capacity estimation.
In one embodiment of the invention, the hidden Markov chain model for network capacity evaluation comprises an NB-IoT cell congestion probability matrix, a multi-service access probability matrix and an initial state probability vector, and the network capacity of the NB-IoT cell can be obtained according to the three elements.
After determining the network capacity of the NB-IoT cell, an embodiment of the present invention further provides a method for adjusting a network access mechanism of a terminal according to the network capacity, where the method includes:
and when the load state in the hidden Markov chain model is a first preset threshold value, adjusting the time interval of the service for re-accessing the network.
And when the load state in the hidden Markov chain model is a second preset threshold value, adjusting the time interval of the periodic access network.
And when the load state in the hidden Markov chain model is a third preset threshold value, adjusting the timer.
In one embodiment of the present invention, for example, when the predicted load state in the hidden Markov chain model λ is 30% or more, the traffic retry interval is set to 5s + X (X is a random value between 0 and 5); when the predicted load state in the hidden Markov chain model lambda is more than 50%, the periodic access interval time is lengthened by 1S, and collision is reduced. When the predicted load state in the hidden markov chain model lambda is more than 70%, the inactivity timer is modified to the maximum value of 3600 seconds, so as to reduce the access frequency of Radio Resource Control (RRC) as much as possible. The maximum transmission times of the preamble is modified from 10 to 4, so that the retry is reduced, and the congestion is relieved.
The assessment method of NB-IoT network capacity provided by the embodiment of the invention considers not only the optimization adjustment of the network side, but also the optimization adjustment of the service side, simultaneously considers the multi-service scene, utilizes the multi-service access data modeling estimation based on the hidden Markov chain to restore the problem of the multi-service scene, and simultaneously implements mechanism optimization and configuration optimization on the network side and the service side, thereby fundamentally relieving the problem of insufficient NB-IoT network access capacity.
Referring to fig. 3, an embodiment of the present invention provides an NB-IoT network capacity evaluation apparatus, including:
an obtaining module 301, configured to obtain load information of a narrowband internet of things NB-IoT cell;
a frequency determining module 302, configured to determine, according to the service reporting parameter of the terminal, the number of times of initiating access requests for accessing multiple terminals to an NB-IoT cell;
the model determining module 303 is configured to determine, according to the load information and the access initiation times, a hidden markov chain model for network capacity evaluation in multi-service access of the NB-IoT cell;
a capacity determination module 304, configured to determine a network capacity of the NB-IoT cell according to the hidden markov chain model of the network capacity estimation.
Optionally, the apparatus further includes an adjusting module, configured to adjust a network access mechanism of the terminal according to the network capacity.
Optionally, the adjusting module is configured to:
when the load state in the hidden Markov chain model is a first preset threshold value, adjusting the time interval of the service for re-accessing the network;
when the load state in the hidden Markov chain model is a second preset threshold value, adjusting the time interval of the periodic access network;
and when the load state in the hidden Markov chain model is a third preset threshold value, adjusting the timer.
Optionally, the number determining module is configured to:
determining the event triggering rate and the failure allowance of each terminal according to the service reporting parameters;
determining the number of times of initiating the access request of each terminal according to the event triggering rate and the failure allowance of each terminal;
and summing the access request initiating times of each terminal to obtain the access request initiating times of a plurality of terminals accessing the NB-IoT cell.
Optionally, the model determining module is configured to:
determining a load probability matrix according to the load information;
determining a multi-service access probability matrix according to the access initiation times;
and obtaining a hidden Markov chain model for evaluating the network capacity of the NB-IoT cell during multi-service access according to the load probability matrix and the multi-service access probability matrix.
In addition, the method of the embodiment of the present invention may be implemented by a device. Fig. 4 shows a schematic hardware structure diagram of a device provided in an embodiment of the present invention.
The device may include a processor 401 and a memory 402 in which computer program instructions are stored.
Specifically, the processor 401 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured as one or more Integrated circuits implementing embodiments of the present invention.
The processor 401 reads and executes the computer program instructions stored in the memory 402 to implement any of the network access methods in the above embodiments.
In one example, the device may also include a communication interface 403 and a bus 410. As shown in fig. 4, the processor 401, the memory 402, and the communication interface 403 are connected via a bus 410 to complete communication therebetween.
The communication interface 403 is mainly used for implementing communication between modules, devices, and units in the embodiment of the present invention.
In addition, in combination with the network access method in the foregoing embodiments, the embodiments of the present invention may provide a computer-readable storage medium to implement. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the network access methods in the above embodiments.
It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions or change the order between the steps after comprehending the spirit of the present invention.
The functional blocks shown in the above structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments noted in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed at the same time.
As will be apparent to those skilled in the art, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present invention, and these modifications or substitutions should be covered within the scope of the present invention.
Claims (8)
1. A method of assessment of NB-IoT network capacity, the method comprising:
acquiring load information of a narrow-band Internet of things NB-IoT cell; determining the number of times of initiating access requests of a plurality of terminals accessing the NB-IoT cell according to the service reporting parameters of the terminals;
Determining a hidden Markov chain model for evaluating the network capacity of the NB-IoT cell during multi-service access according to the load information and the initiation times of the access request;
determining a network capacity of the NB-IoT cell according to a hidden Markov chain model of the network capacity evaluation;
determining, according to the service reporting parameter of the terminal, the number of times of initiating access requests of a plurality of terminals to access the NB-IoT cell, including:
determining the event triggering rate and the failure allowance of each terminal according to the service reporting parameters;
determining the number of times of initiating the access request of each terminal according to the event triggering rate and the failure allowance of each terminal;
summing the access request initiation times of each terminal to obtain the access request initiation times of a plurality of terminals accessing the NB-IoT cell;
the hidden Markov chain model for determining the network capacity evaluation of the NB-IoT cell during multi-service access according to the load information and the initiation times of the access request comprises the following steps:
determining a load probability matrix according to the load information;
determining a multi-service access probability matrix according to the number of times of initiating the access request;
and obtaining a hidden Markov chain model for evaluating the network capacity of the NB-IoT cell during multi-service access according to the load probability matrix and the multi-service access probability matrix.
2. The method of claim 1, further comprising: and adjusting a network access mechanism of the terminal according to the network capacity.
3. The method of claim 2, wherein the optimizing the network access mechanism of the terminal according to the network capacity comprises:
when the load state in the hidden Markov chain model is a first preset threshold value, adjusting the time interval of the service for re-accessing the network;
when the load state in the hidden Markov chain model is a second preset threshold value, adjusting the time interval of a periodic access network;
and when the load state in the hidden Markov chain model is a third preset threshold value, adjusting a timer.
4. An apparatus for assessment of NB-IoT network capacity, the apparatus comprising:
the acquisition module is used for acquiring the load information of the NB-IoT cell of the narrowband Internet of things;
the number determining module is used for determining the number of times of initiating access requests of a plurality of terminals accessing the NB-IoT cell according to the service reporting parameters of the terminals;
a model determining module, configured to determine, according to the load information and the initiation frequency of the access request, a hidden markov chain model for network capacity evaluation of the NB-IoT cell during multi-service access;
A capacity determination module for determining a network capacity of the NB-IoT cell according to a hidden Markov chain model of the network capacity assessment;
the number of times determination module is to:
determining the event triggering rate and the failure allowance of each terminal according to the service reporting parameters;
determining the number of times of initiating the access request of each terminal according to the event triggering rate and the failure allowance of each terminal;
summing the access request initiation times of each terminal to obtain the access request initiation times of a plurality of terminals accessing the NB-IoT cell;
the model determination module is to:
determining a load probability matrix according to the load information;
determining a multi-service access probability matrix according to the number of times of initiating the access request;
and obtaining a hidden Markov chain model for evaluating the network capacity of the NB-IoT cell during multi-service access according to the load probability matrix and the multi-service access probability matrix.
5. The apparatus of claim 4, further comprising an adjusting module configured to adjust a network access mechanism of the terminal according to the network capacity.
6. The apparatus of claim 5, wherein the adjustment module is configured to:
When the load state in the hidden Markov chain model is a first preset threshold value, adjusting the time interval of the service for re-accessing the network;
when the load state in the hidden Markov chain model is a second preset threshold value, adjusting the time interval of a periodic access network;
and when the load state in the hidden Markov chain model is a third preset threshold value, adjusting a timer.
7. An apparatus, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory that, when executed by the processor, implement the method of any of claims 1-3.
8. A computer-readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1-3.
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