CN111294775A - Resource allocation method based on H2H dynamic characteristics in large-scale MTC and H2H coexistence scene - Google Patents

Resource allocation method based on H2H dynamic characteristics in large-scale MTC and H2H coexistence scene Download PDF

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CN111294775A
CN111294775A CN202010085231.2A CN202010085231A CN111294775A CN 111294775 A CN111294775 A CN 111294775A CN 202010085231 A CN202010085231 A CN 202010085231A CN 111294775 A CN111294775 A CN 111294775A
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CN111294775B (en
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王熠晨
王弢
王璋楠
肖湘湘
赵书玉
焦晓芬
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Xian Jiaotong University
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
    • H—ELECTRICITY
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/53—Allocation or scheduling criteria for wireless resources based on regulatory allocation policies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W74/00—Wireless channel access
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Abstract

The invention discloses a resource allocation method based on H2H dynamic characteristics in a large-scale MTC and H2H coexistence scene, which is used for establishing a system model of the MTC and H2H coexistence scene; analyzing basic parameters of resource allocation in a scene of coexistence of large-scale MTC and H2H; deducing the average throughput with the identity information of the machine type communication equipment in each random access opportunity; constructing a resource allocation optimization problem; the state of the H2H user in the network is modeled by a Markov chain; analyzing the state transition probability of the H2H user in random access and data transmission; calculating the successful transmission probability of the H2H service, and calculating the arrival rate under stable distribution by using an immobile point iterative algorithm; and calculating the probability of successful transmission completion of any H2H user accessing the network, and returning the maximum MTC throughput and the corresponding resource configuration scheme parameter set to obtain the optimal resource configuration scheme. The invention maximizes the throughput of large-scale MTC service on the premise of ensuring the transmission success probability of H2H service.

Description

Resource allocation method based on H2H dynamic characteristics in large-scale MTC and H2H coexistence scene
Technical Field
The invention belongs to the technical field of resource allocation of cellular Internet of things, and particularly relates to a resource allocation method based on H2H dynamic characteristics in a large-scale MTC and H2H coexistence scene.
Background
The international telecommunications union radio communication office (ITU-R) identified three major application scenarios for 5G in 2015: enhanced Mobile Broadband (eMBB), massive machine communication (mMTC), and ultra-high reliable & Low latency communication (uRLLC). The application scenario that is about to widely deploy 5G networks has not only achieved higher rates and wide area coverage of Human-to-Human (H2H) communication in traditional cellular networks, but also includes large-scale internet of things applications. As a key supporting technology of the internet of things, M2M (Machine to Machine) communication will become the most important component of 5G, and since the M2M communication is supported and realized by using a traditional cellular network, the technology becomes a significant research field of 5G development, and in the future, cellular network design will face a network scenario where large-scale MTC and H2H coexist.
The 3GPP standardization organization defines a communication mode supporting M2M service through a cellular network as Machine Type Communication (MTC), and makes appropriate adjustments to the MTC on the conventional cellular network, especially for large-scale MTC Access which may cause network congestion, introduces an Access Class Blocking (ACB) method, whose basic idea is to redistribute the burst large-scale Access requests uniformly in time, thereby mitigating the instantaneous Access collision. On the other hand, in order to reduce signaling overhead of large-scale MTC access, it is generally accepted by the industry and the academia to design a more simplified random access and data transmission procedure for MTC.
The wireless channel resources of the existing cellular network are very limited, and it is difficult to simultaneously support access and data transmission of H2H and large-scale MTC, so a more reasonable uplink resource allocation scheme needs to be designed for a large-scale MTC and H2H coexistence scenario. The existing research generally considers only the problem of allocation of a Physical Random Access Channel (PRACH) between H2H and the MTC during random access, or considers the problem of allocation of a Physical Uplink Shared Channel (PUSCH) between H2H and the MTC separately. Few researchers jointly consider the whole uplink transmission resource in the scene of coexistence of large-scale MTC and H2H, but these researchers usually adopt a simplified model, assume that the resource is always sufficient when allocating PUSCH, and are only limited to maximizing the transmission rate or minimizing the energy consumption, and do not pay attention to the requirement of H2H and the service quality of MTC traffic, and especially the access of MTC traffic to the conventional cellular network cannot cause obvious influence on the existing H2H traffic in the network. Therefore, it is necessary to analyze the resource allocation problem by using more reasonable mathematical modeling in the scenario where MTC and H2H coexist, and to optimally design the resource allocation on the premise of ensuring successful transmission of H2H service, thereby improving the service quality of MTC.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a resource allocation method based on H2H dynamic characteristics in a scene where large-scale MTC and H2H coexist, so as to solve the above-mentioned deficiencies in the prior art, more accurately model the dynamic characteristics of H2H service compared with the existing research, and maximize the throughput of large-scale MTC services on the premise of ensuring the transmission success probability of H2H service.
The invention adopts the following technical scheme:
the resource allocation method based on H2H dynamic characteristics in a large-scale MTC and H2H coexistence scene comprises the following steps:
s1, establishing a system model of a scene with MTC and H2H coexisting;
s2, analyzing basic parameters of resource allocation in a scene of coexistence of large-scale MTC and H2H;
s3, in each random access opportunity, the base station sets an optimal threshold for access class blocking detection by using load estimation, and deduces the average throughput with machine type communication equipment identity information in each random access opportunity;
s4, constructing a resource allocation optimization problem, and maximizing the throughput of MTC in each random access opportunity on the premise of ensuring the successful transmission probability of H2H service;
s5, modeling the state of the H2H user in the network by adopting a Markov chain, and representing the dynamic characteristic of H2H service transmission;
s6, analyzing the state transition probability of the H2H user in random access and data transmission based on the Markov chain model established in the step S5;
s7, analyzing the stable distribution of H2H users according to the H2H state transition probability characteristic, calculating the successful transmission probability of the H2H service, and calculating the arrival rate under the stable distribution by using an immobile point iterative algorithm;
s8: calculating the probability that any H2H user accesses the network and successfully completes transmission according to the arrival rate obtained in the step S7, substituting the probability into an optimization problem and solving the optimization problem by adopting a three-dimensional search algorithm as follows:
Figure BDA0002381801720000031
s.t.
Figure BDA0002381801720000032
Figure BDA0002381801720000033
Figure BDA0002381801720000034
Figure BDA0002381801720000035
wherein N isRBFor the total number of resource blocks in each RAO,
Figure BDA0002381801720000036
number of resource blocks for the construction of PRACH, NPFor the total number of preambles available on the PRACH,
Figure BDA0002381801720000037
number of preambles allocated to MTC services, NsIs the total number of the possible PUSCHs,
Figure BDA0002381801720000038
for the number of PUSCHs allocated to MTC services, TMFor average throughput of MTC traffic within each RAO, PFThe probability that the H2H user does not acquire PUSCH transmission data in one random access is shown, m is the maximum lead code retransmission times, PRThe retransmission probability of each data transmission of H2H user, r is the maximum data retransmission times, PthA threshold of minimum probability of successful transmission required for H2H traffic;
returning maximum MTC throughput
Figure BDA0002381801720000041
And corresponding resource configuration scheme parameter set
Figure BDA0002381801720000042
And obtaining an optimal resource allocation scheme.
Specifically, in step S1, the cell base station BS has NHH2H user terminal equipment and NMThe service arrival of each terminal device is independent and is lambda in strength1The total H2H traffic in the cell network reaches obedience strength λH＝NHλIIn the poisson process, after each terminal device service arrives, the corresponding sending data volume of each arrival is a random variable uniformly distributed from one time to another
Figure BDA0002381801720000043
An arrival model of the communication equipment facing the Internet of things obeys beta distribution, and generally has a fixed data packet format, and the data volume sent after the arrival of the identity information of each communication equipment with machines is a constant
Figure BDA0002381801720000044
Different random access and data transmission are adopted in large-scale MTC and H2H coexistence sceneThe transmission protocol serves two services, the H2H service still adopts the original LTE protocol in the traditional cellular network to complete random access and data transmission, and the MTC service adopts a simplified signaling interaction mode to complete random access and data transmission.
Further, the MTC service specifically includes:
the method comprises the steps that an access blocking method in a cellular network is adopted for checking, machine type communication equipment checked through the access blocking method sends a lead code with machine type communication equipment identity information to a base station, the base station identifies the equipment ID of the machine type communication equipment without lead code collision through decoding and replies a random access response message, in the random access response message, the base station distributes a physical uplink shared channel for the machine type communication equipment, the machine type communication equipment then sends scheduling information and small data packets on the specified physical uplink shared channel, and the base station returns collision solving and receiving feedback information after the decoding is successful.
Specifically, in step S2, within each random access opportunity, there is
Figure BDA0002381801720000045
One resource block is used to construct a physical random access channel,
Figure BDA0002381801720000046
the resource blocks are used for constructing a physical uplink shared channel and distributing H2H service
Figure BDA0002381801720000047
A preamble and
Figure BDA0002381801720000048
a physical uplink shared channel (PUCCH), the rest
Figure BDA0002381801720000049
A preamble and
Figure BDA00023818017200000410
allocating a physical uplink shared channel to a large-scale MTC service, and configuring a parameter set for resources
Figure BDA0002381801720000051
According to the optimum
Figure BDA0002381801720000052
And
Figure BDA0002381801720000053
and (4) taking values to determine a resource allocation scheme.
Specifically, in step S3, the optimal threshold for the access class blocking method is checked
Figure BDA0002381801720000054
Average throughput T of machine type communication devices within each random access opportunityMComprises the following steps:
Figure BDA0002381801720000055
wherein the content of the first and second substances,
Figure BDA0002381801720000056
the average number of machine type communication devices with no collision of the preamble codes,
Figure BDA0002381801720000057
a preamble assigned to a large-scale MTC within each RAO,
Figure BDA00023818017200000516
number of PUSCHs, T, assigned to large-scale MTC within each RAOMPUSCH consumed for MTCD random access and data transmission,
Figure BDA0002381801720000058
to employ load estimation techniques to estimate the number of active devices within the current RAO that will initiate an access request.
Specifically, in step S4, the resource allocation optimization problem specifically includes:
Figure BDA0002381801720000059
s.t.psuccess≥Pth
Figure BDA00023818017200000510
Figure BDA00023818017200000511
Figure BDA00023818017200000512
wherein, TMFor the average throughput of the machine type communication equipment in each random access opportunity, the optimization variable comprises the number of resource blocks used for constructing the PRACH for each random access opportunity
Figure BDA00023818017200000513
Preamble assigned to massive MTC per random access opportunity
Figure BDA00023818017200000514
The number of physical uplink shared channels allocated to large-scale MTC in each random access opportunity
Figure BDA00023818017200000515
The first constraint condition represents the requirement of the successful transmission probability of the H2H service and represents the probability p of any H2H user accessing the network and successfully completing the transmissionsuccessCannot be less than a given threshold value Pth(ii) a The latter three constraint conditions respectively indicate that the number of the resource blocks, the lead codes and the physical uplink shared channels should be greater than 0 and not exceed the requirement of the total resource number.
Specifically, in step S5, a H2H user will experience the following states in the network:
an idle state: when an H2H user has no data to send, the user is in an idle state;
random accessThe state is as follows: when an H2H user has data to send, the user first enters a random access state from that assigned to H2H
Figure BDA0002381801720000061
Randomly selecting one lead code from the lead codes and sending the lead code to the base station, and after receiving the lead code, the base station returns a response message, namely a random access response message, wherein m +1 random access states exist in one H2H user;
a backoff state: H2H users failing in the processes of random access and data transmission request carry out random backoff and enter a backoff state, H2H users carry out backoff at most m times, each backoff randomly selects one backoff time l in a time window W to be 0, … and W-1, and m multiplied by W backoff states are total;
connection request state: H2H users with collision-free lead codes in the random access process initiate connection establishment requests to the base station, apply subsequent PUSCH resources to the base station for sending service data, and the H2H users have m +1 connection request states;
the first transmission state: if the H2H user successfully applies for PUSCH resource for data transmission within m times of maximum lead code retransmission times, performing a first transmission state and starting data transmission; in the data transmission process, the H2H user retransmits after the base station decodes errors, and the retransmission probability is pRThe maximum allowed data retransmission times of each H2H user is r, if the r-th retransmission of the base station still makes a decoding error, the H2H user discards the data and does not transmit any more;
and (4) retransmission state: the H2H user resends data, and r retransmission states exist;
success status: if the H2H user successfully applies for PUSCH resource for data transmission within m times of maximum lead code retransmission times and successfully completes data transmission within r times of maximum allowed data retransmission times, the H2H user enters a success state;
a discarding state: if the H2H user does not successfully complete data transmission within m times of maximum preamble retransmission times and r times of maximum allowed data retransmission times, the H2H user enters a discard state, does not initiate random access and discards the data, and after discarding the data, the H2H user returns to the idle state again.
Specifically, in step S6, after the H2H user initiates random access, the probability p of preamble collision is sentCComprises the following steps:
Figure BDA0002381801720000071
wherein λ isNRandom access strength of arrival is initiated for each H2H user within the RAO,
Figure BDA0002381801720000072
the number of lead codes allocated to MTC service, e is a natural constant in mathematics, and the jth random access state RAjTransition to the backoff BOj，lHas a probability of pCW, j 1, …, m, L0, …, W-1; first transmission state FT and k-th retransmission state RTkThe probability of transferring to the working state is 1-p when k is 1, …R，pRThe retransmission probability for each data transmission of H2H user.
Specifically, in step S7, the probability p that any H2H user accesses the network and successfully completes transmissionsuccessExpressed as:
Figure BDA0002381801720000073
wherein, pisuccessAnd pidropProbability of H2H user being in success state and discarding state under smooth distribution, pF＝1-(1-pC)(1-pL) The probability that no PUSCH transmission data is acquired in H2H random access is shown, and the probability that at least j +1 times of random access requests are required to be initiated in total after any H2H initiates the first access is obtained
Figure BDA0002381801720000074
Then a probability of exactly one H2H originating j random access requests is
Figure BDA0002381801720000075
Figure BDA0002381801720000076
To distribute the probability that H2H user is in the jth random access state smoothly, j is 1, …, m + 1.
Specifically, in step S7, the arrival rate λ under the smooth distributionTThe method specifically comprises the following steps:
s701: setting the iteration number i to 1, lambdaTThe initial value is set as lambdaT(0) The termination condition corresponds to a parameter epsilon;
s702: i ═ i +1, according to a given λT(i-1) calculating p corresponding to the ith iteration according to the Markov state transition modelFIs denoted by pF(i) Updating λ according to the following iterative formulaTThe value of (c):
Figure BDA0002381801720000081
s703: let the difference of two iterations be equal to lambdaT(i)-λT(i-1)|
S704: if delta is less than epsilon, terminating iteration and returning to the latest state; otherwise, steps S702 and S703 are repeated.
Compared with the prior art, the invention has at least the following beneficial effects:
in the resource allocation method based on the H2H dynamic characteristics in the large-scale MTC and H2H coexistence scene, in order to ensure that the MTC service accessed to the traditional cellular network cannot cause obvious influence on the existing H2H service in the network, aiming at the characteristics of the H2H and the MTC service, two services are served by adopting different random access and data transmission protocols, namely the H2H service still adopts the original LTE protocol in the traditional cellular network to complete random access and data transmission, the MTC service adopts a simplified signaling interaction mode to complete random access and data transmission, and the resource allocation is optimized and designed on the premise of ensuring the successful transmission probability of the H2H service, so that the throughput of MTC in each RAO is maximized. The states of H2H users in the network are modeled by adopting a Markov chain, the dynamic characteristics of the H2H users are analyzed by utilizing the transition between the states, the expression of the successful transmission probability of the H2H users is pushed, and finally, the optimal resource allocation scheme is solved by utilizing a fixed point iteration algorithm and a three-dimensional search algorithm, so that the maximum throughput of MTC in each RAO is realized on the premise of ensuring the successful transmission probability of the H2H service, the resource utilization efficiency is improved, and the system performance is maximized.
Further, step S1 establishes a system model of a large-scale MTC and H2H coexistence scenario, and different arrival models, data transmission models, and signaling interaction modes are used to model two services with different characteristics, so that the scenario characteristics of the MTC and H2H coexistence network can be represented more reasonably, and thus, resource allocation is optimized according to the service quality requirements of different services more specifically.
Further, step S2 analyzes the basic parameters of resource allocation based on the system model established in step S1, and corresponds the resource allocation problem team to the actual time-frequency resource block configuration of the cellular network, and it is explicitly indicated that three variables that the resource allocation scheme requires but ing are: number of physical random access channels used for construction
Figure BDA0002381801720000082
Number of preambles allocated to large-scale MTC services
Figure BDA0002381801720000091
And the number of physical uplink shared channels allocated to the large-scale MTC service
Figure BDA0002381801720000092
Therefore, the optimization variables of the resource allocation are determined, and a theoretical analysis basis is provided for the establishment of the subsequent optimization problem.
Further, step S3 provides an optimal threshold for detecting the optimal blocking of the access class, and deduces the average throughput with the identity information of the machine type communication device in each random access opportunity, which reflects the relationship between the MTC service index and the resource allocation scheme, and provides an optimization objective function for the establishment of the subsequent optimization problem.
Further, step S4 constructs an optimization problem of maximizing the throughput of MTC under the premise of ensuring the successful transmission probability of the H2H service on the basis of steps S2 and S3, defines constraint conditions and optimization targets that the optimal resource allocation scheme needs to satisfy, is the core of the whole resource allocation optimization design, and provides a solution for ensuring different service qualities of services and improving the overall performance of the system in a large-scale MTC and H2H coexistence scenario.
Further, in order to analyze and represent the successful transmission probability of the H2H service in the optimization problem constructed in step S4, it is necessary to analyze the state transition rule of the H2H service in the random access and data transmission processes. Step S5 introduces a markov chain model, which can clearly reflect the state change of the H2H user along with the signaling interaction flow, and provides a tool for further analyzing the H2H service state transition rule.
Further, step S6 derives the state transition probability of the H2H user in random access and data transmission based on the markov chain model analysis of step S5, comprehensively reflects the dynamic characteristics of the transition between the states of H2H, and provides a theoretical basis for further analysis and representation of the successful transmission probability of the H2H service. Further, in step S7, based on the state transition probability analysis in step S6, the successful transmission probability of the H2H service under the stable distribution is derived, and the optimization problem is substituted into step S4, so that the optimization problem can be completely expressed by using a mathematical formula, and the subsequent optimization problem solution can be performed.
In summary, the invention is suitable for large-scale MTC and H2H coexistence scenarios, adopts more reasonable random access and data transmission flows for different characteristics of two services, studies resource allocation problems through mathematical modeling, proposes to optimize resource allocation on the premise of ensuring successful transmission of H2H service, analyzes dynamic characteristics in the H2H service random access and data transmission flows by using a markov chain, and provides an expression of H2H service successful transmission probability, constructs an optimization problem of maximizing MTC throughput, and improves system resource utilization and comprehensive performance on the premise of ensuring H2H service quality.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a diagram of a system model with large-scale MTC coexisting with H2H;
fig. 2 is a signaling interaction flow chart adopted by the MTC service;
FIG. 3 is a Markov chain state transition diagram dynamically constructed from the H2H user in accordance with the present invention;
FIG. 4 is a graph of MTC throughput as a function of H2H user traffic arrival rate;
fig. 5 is a graph of MTC throughput as a function of the number of H2H terminal devices.
Detailed Description
Referring to fig. 1, in a large-scale MTC and H2H coexistence scenario, a resource allocation method based on H2H dynamic characteristics according to the present invention considers the following system models:
in a cellular network where large-scale Machine Type Communication (MTC) coexists with conventional human-to-human (H2H) communication, the coverage of one cell base station BS is considered, where there is NHH2H User terminal Equipment (HUE, H2H User Equipment), and NMMachine Type Communication Device (MTCD), the traffic of each HUE arrives independently and all with strength of lambda1The total H2H traffic in the cell network reaches obedience strength λH＝NHλIIn the poisson process, after each HUE service arrives, different data sending requests are usually available, and the sending data volume corresponding to each arrival is a random variable distributed uniformly
Figure BDA0002381801720000111
The unit is bytes (bytes). The MTCD arrival model facing the Internet of things is subject to beta distribution and generally has a fixed data packet format, and the data volume sent after each MTCD arrival is a constant
Figure BDA0002381801720000112
The unit is bytes (bytes). Considering that MTCD each transmission has a small data packet size, MTCD can be usedThe random access and data transmission are completed in a simplified signaling interaction mode. Therefore, for the characteristics of H2H and MTC services, in a large-scale MTC and H2H coexistence scenario, two services, namely, random access and data transmission, are served by using different random access and data transmission protocols, i.e., the H2H service still uses the original LTE protocol in the conventional cellular network to complete random access and data transmission, while the MTC service uses a simplified signaling interaction manner to complete random access and data transmission, and the basic signaling interaction process is as shown in fig. 2.
Referring to fig. 2, in a large-scale MTC and H2H coexistence scenario, a resource allocation method based on H2H dynamic characteristics according to the present invention includes the following specific steps of MTC service signaling interaction: the MTCD to be accessed to the base station is firstly checked by using an Access Class Blocking (ACB) method in a cellular network, the MTCD checked by the ACB sends a preamble with machine communication device identity information (MTCD ID, hereinafter abbreviated as ID) to the base station, the base station identifies the MTCD device ID without collision by decoding and replies a random Access Response message (RAR), in the RAR message, the base station allocates a Physical Uplink Shared Channel (PUSCH) for the MTCD, the MTCD then sends scheduling information and small data packets on a designated PUSCH, and the base station returns collision resolution and reception feedback information after successful decoding.
In a cellular network, wireless channel resources are allocated in the form of Resource Blocks (RBs) in two dimensions of time and frequency, the time domain width of each Resource Block is 1ms, the width of each Resource Block in the frequency domain is 12 continuous subcarriers, each subcarrier occupies 15kHz in the frequency domain, and the Resource Block is the minimum physical Resource for Resource allocation in the cellular network. In the Uplink access and data transmission process, an Uplink resource block is used for constructing two types of wireless channels, namely a Physical Random Access Channel (PRACH) and a Physical Uplink Shared Channel (PUSCH). The PRACH occurs periodically in time, each cycle representing a Random Access Opportunity (RAO), with a period interval TRAOWithin each RAO, there is a total of NRBA resource block of
Figure BDA0002381801720000121
A number of resource blocks are used to construct the PRACH,
Figure BDA0002381801720000122
if each resource block is used for constructing the PUSCH, the following steps are performed:
Figure BDA0002381801720000123
the PRACH configuration commonly used in the cellular network usually occupies 1ms in the time domain and 6 × 12 subcarriers in the frequency domain, so that one PRACH needs to consume 12 resource blocks, and each PRACH can be used for HUE and MTCD to transmit 54 orthogonal preambles in the random access procedure, so that constructing one preamble average needs to be performed
Figure BDA0002381801720000124
A resource block, so the number of available orthogonal preambles per RAO is
Figure BDA0002381801720000125
Where the function | x | represents taking the largest integer not exceeding x. Similarly, n is consumed to construct a PUSCHsFor each resource block, the number of PUSCHs available in each RAO is
Figure BDA0002381801720000126
In order to ensure that the access of MTC service to the traditional cellular network cannot cause obvious influence on the existing H2H service in the network, in each RAO, a base station firstly distributes H2H service
Figure BDA0002381801720000127
A preamble and
Figure BDA0002381801720000128
one PUSCH, the rest
Figure BDA0002381801720000129
A preamble and
Figure BDA00023818017200001210
and allocating the PUSCH to the large-scale MTC service. Then there are:
Figure BDA00023818017200001211
Figure BDA00023818017200001212
as can be seen from the above analysis, the resource allocation scheme is actually determined
Figure BDA00023818017200001213
And
Figure BDA00023818017200001214
the values of the six variables are actually determined only in scheme design due to the existence of the equation constraint of mutual coupling between partial variables
Figure BDA00023818017200001215
And
Figure BDA00023818017200001216
these three variables constitute a resource configuration parameter set
Figure BDA00023818017200001217
Once these three variables are determined, the scheme of resource allocation can be uniquely determined, and therefore, the problem of resource allocation in random access and data transmission in the large-scale MTC and H2H coexistence scenario is to determine the optimal resource allocation
Figure BDA00023818017200001218
And
Figure BDA00023818017200001219
and value taking is carried out, so that the performance of the system is improved to the maximum extent.
In each RAO, the base station obtains the number N of active devices which will initiate access requests in the current RAO by utilizing the load estimation technology of the cellular networkaThe best threshold (i.e., ACB factor) for ACB checking before MTCD initiates access is
Figure BDA0002381801720000131
Wherein the content of the first and second substances,
Figure BDA0002381801720000132
for the number of preambles allocated to the large-scale MTC traffic,
Figure BDA0002381801720000133
to use an estimate of the number of active devices within the current RAO that will initiate an access request, NaFor activating the true value of the number of devices, irrespective of the estimation error
Figure BDA0002381801720000134
Then each RAO has on average
Figure BDA0002381801720000135
The MTCDs may be verified by the ACB and initiate a random access request in which they are requested from
Figure BDA0002381801720000136
One of the preambles is randomly selected and attached with the device ID to be sent to the base station, the MTCD (multiple Access Response, RAR) with no collision of the preambles (namely the preamble selected by the MTCD is different from other MTCDs) receives a Random Access Response (RAR) replied by the base station, and the scheduling information (with the size of being equal to that of the RAR) is sent on a specified PUSCH according to the instruction of the RAR
Figure BDA0002381801720000137
Bytes) and small data packets (size of
Figure BDA0002381801720000138
Byte), maximum supportable per PUSCH transmission BSByte, one MTCD will consume
Figure BDA0002381801720000139
A PUSCH, wherein
Figure BDA00023818017200001310
Meaning taking the smallest integer greater than x.
Derived according to a probability theory formula, the average number of MTCDs without lead code collision is
Figure BDA00023818017200001311
Since the PUSCH allocated to MTCD in each RAO is only
Figure BDA00023818017200001312
And thus each RAO is on average maximally supportable
Figure BDA00023818017200001313
If the MTCDs complete data transmission, the maximum supportable MTCD number per RAO is averaged, i.e. the average throughput of MTCDs in each RAO is:
Figure BDA00023818017200001314
in order to ensure that the access of the MTC service to the conventional cellular network cannot significantly affect the existing H2H service in the network, the resource allocation needs to be optimally designed. Because the H2H service has higher requirements on delay and transmission rate, and large-scale MTC services generally emphasize that throughput is increased and relatively tolerant to delay, the method optimizes resource allocation on the premise of ensuring the successful transmission probability of the H2H service, and maximizes the throughput of MTC within each RAO (which is defined as the MTCD number of successfully transmitted data on average within each RAO), so that the following optimization problem can be constructed:
Figure BDA0002381801720000141
s.t.psiccess≥Pth
Figure BDA0002381801720000142
Figure BDA0002381801720000143
Figure BDA0002381801720000144
wherein, TMFor the mean throughput of MTCDs within each RAO, the optimization variables include the number of resource blocks used by each RAO to construct the PRACH
Figure BDA0002381801720000145
Preambles assigned to large-scale MTC within each RAO
Figure BDA0002381801720000146
Number, and number of PUSCHs allocated to large-scale MTC within each RAO
Figure BDA0002381801720000147
The first constraint condition represents the requirement of the successful transmission probability of the H2H service and represents the probability p of any H2H user accessing the network and successfully completing the transmissionsuccessCannot be less than a given threshold value Pth. The latter three constraints indicate that the allocated number of resource blocks, preambles and PUSCHs should be greater than 0 but cannot exceed the total resource number requirement, respectively. By solving the optimization problem, the optimal resource allocation scheme corresponding to the maximum MTC throughput can be obtained for resource allocation on the premise of ensuring the successful transmission probability of the H2H service, namely, the optimal resource allocation scheme is obtained
Figure BDA0002381801720000148
And
Figure BDA0002381801720000149
the three variables form a resource configuration parameter set
Figure BDA00023818017200001410
To solve the above optimization problem, it is first necessary to show any H2H user access networkProbability p of successfully completing a transmissionsuccessTherefore, it is necessary to model and analyze the business characteristics of H2H users. In a cellular network, H2H users complete random access and data transmission by adopting an LTE specified protocol, states of H2H users in the network can be modeled by adopting a Markov chain, and the dynamic characteristics of the H2H users in the random access and data transmission are analyzed by using transition between the states. According to the dynamic process of random access and data transmission, an H2H user will experience the following states in the network:
1. idle (idle) state: when a H2H user has no data to send, the user is in an idle state, which is denoted by idle;
2. random Access (RA) state: when an H2H user has data to send, the user first enters a random access state from that assigned to H2H
Figure BDA0002381801720000152
One of the lead codes is randomly selected to be sent to the base station, and the base station returns a Response message, namely a Random Access Response (RAR) message after receiving the lead code. In the RAR, a corresponding PUSCH is allocated for each received preamble for sending next signaling data (referred to as Message 3 in 3GPP LTE standard, abbreviated as Msg3), since there is a case that different H2H users select the same preamble, these user devices will generate a collision when sending Msg3, and the Message that the base station cannot recognize the collision will cause access failure of these H2H users; on the other hand, the equipment without collision will receive the feedback information of the base station, and initiate a request for establishing connection to the base station, and apply for subsequent PUSCH resources to the base station for sending service data, because the base station allocates PUSCH number for H2H users in each RAO as
Figure BDA0002381801720000151
This indicates that within a certain time, the PUSCH resources that the base station can provide for H2H are limited, and when the PUSCH resources required by the H2H user exceed the limit, there must be a case where the H2H user cannot acquire the PUSCH within a certain waiting time, resulting in a data transmission failure. At random accessAnd H2H users failing in the process of the data transmission request carry out random back-off, namely randomly select a period of time in a back-off window, no longer initiate random access in the period of time, but reinitiate random access in a new RAO after the back-off is finished, and simultaneously stipulate the maximum number of times of reinitiating the random access request of each H2H user, namely the maximum lead code retransmission number is m, if the lead code of the H2H user still fails in the process of random access or data transmission request after being retransmitted for m times, the data is discarded, and the data is not transmitted any more. Thus, there are m +1 random access states (first random access and m re-initiated random accesses) for one H2H user, denoted RAj，j＝1，…，m+1。
3. Backoff (backoff) state: H2H users failing in the process of random access and data transmission request will carry out random backoff and enter a backoff state, because H2H users can reinitiate random access at most m times, H2H users can carry out backoff at most m times, and each backoff randomly selects a backoff time i in a time window W to be 0, … and W-1, so that m × W backoff states are represented as BOj，l，j＝1，…，m，l＝0，…，W-1。
4. Connection Request (CR) status: in the random access process, an H2H user with a collision-free preamble initiates a connection establishment request to the base station, applies for subsequent PUSCH resources to the base station for sending service data, and because the H2H user can reinitiate random access for m times at most, the H2H user has m +1 connection request states, which are denoted as CRj，j＝1，…，m+1。
5. First Transmission (FT) state: if the H2H user successfully applies for PUSCH resources for data transmission within m maximum preamble retransmissions, the first transmission state is performed and data transmission is started, denoted as FT. In the data transmission process, if errors can occur in the base station decoding, the H2H user is required to carry out retransmission with the retransmission probability pRAnd simultaneously, the maximum allowed data retransmission number of each H2H user is defined as r, and if the r-th retransmission of the base station still has decoding errors, the H2H user discards the data and does not transmit any more.
6. Retransmission (retransmission) state: in the state where the H2H user retransmits data, r retransmission states, denoted as RT, exist because the total maximum allowed number of data retransmissions is rk，k＝1，…，r。
7. Success (success) status: if the H2H user successfully applies for data transmission to the PUSCH resource within m maximum preamble retransmission times and successfully completes data transmission within r maximum allowed data retransmission times, the H2H user enters a success state, which is denoted by success.
8. Discard (drop) state: if the H2H user does not successfully complete data transmission within m times of maximum preamble retransmission and r times of maximum allowed data retransmission, the H2H user enters a discard state, and does not initiate random access and discard the data, where the state is represented by drop. After discarding the data, the H2H user returns to the idle state.
The following is an analysis of the state transition probability of H2H users in random access and data transmission. Since the traffic arrival of each H2H user is of strength λ1So that within one RAO the H2H user transitions from idle state idle to random access state RA1Has a transition probability of
Figure BDA0002381801720000171
In each RAO, the newly added H2H user initiating random access and the user previously retreated to the RAO due to failure will initiate random access simultaneously, and respectively use lambdaHAnd λRRepresenting the Poisson arrival rates of the two types of users, whereH＝NHλIRepresenting the strength of the newly arrived H2H user, the total arrival rate of the two types of users is lambdaT＝λH+λRNumber of users N initiating random access in each RAOTThe arrival will obey an intensity of λN＝λTTRAOThe probability distribution of (a) is expressed as:
Figure BDA0002381801720000172
in NTUnder the condition of n, m is 0, 1, …, the conditional probability that the H2H user collides when selecting a preamble at the time of random access can be obtained as:
Figure BDA0002381801720000173
combining with the total probability formula, after the H2H user initiates random access, the probability of sending the preamble collision is
Figure BDA0002381801720000174
Taylor expansion using exponential function
Figure BDA0002381801720000175
X [ ∞, the above formula can be further simplified as:
Figure BDA0002381801720000176
for the collided H2H user, a backoff time l ∈ 0, …, W-1 is randomly selected from the backoff window W to enter a backoff state, and therefore, the jth random access state RAjTransition to the backoff BOj，lHas a probability of pC/W，j＝1，…，m，L＝0，…，W-1。
After the H2H user sends the lead codes, the base station allocates PUSCH resources for each sent lead code for the sending of the following signaling Msg3, and the number of the lead codes selected by the H2H user in unit time, namely the arrival rate of the lead codes is lambdaAAnd a part of preambles are collision preambles, that is, the same preamble is selected by multiple H2H users, which will cause the collision of Msg3, and only the user corresponding to the collision-free preamble can receive the feedback from the base station and further request the PUSCH for transmitting data. Let the collision-free preamble arrival rate be λsThen λAAnd λSThis can be approximated by the following equation:
Figure BDA0002381801720000181
Figure BDA0002381801720000182
where X is the number of preambles selected by H2H user in each RAO, and its probability distribution can be expressed as poisson distribution
Figure BDA0002381801720000183
Wherein the content of the first and second substances,
Figure BDA0002381801720000184
indicating the number of times a preamble is selected by the H2H user per unit time. Then there is
Figure BDA0002381801720000185
The number of PUSCH resource demands by H2H users in a unit time is:
Figure BDA0002381801720000186
wherein the function
Figure BDA0002381801720000187
Meaning taking the smallest integer greater than x,
Figure BDA0002381801720000188
the data size, in bytes, of Msg3 sent for H2H users,
Figure BDA0002381801720000189
sending data length for H2H user
Figure BDA00023818017200001810
Is due to average value of
Figure BDA00023818017200001811
Then
Figure BDA00023818017200001812
And the total number of PUSCHs allocated to H2H by the base station in unit time is
Figure BDA00023818017200001813
When the number of the H2H users' demands for the PUSCH resource exceeds the number of the resources allocated by the base station, a part of H2H users cannot acquire the corresponding resource for transmitting data in a limited time, thereby resulting in data transmission failure. In practical systems, H2H users usually have a certain delay requirement, that is, after the traffic data arrives, transmission must be started within a given waiting time T, and if the H2H users still obtain enough PUSCH transmission data beyond the waiting time T, the data will be discarded, and the system enters an idle state to wait for the next data transmission. The process of H2H user requesting PUSCH can be modeled as a impatient customer queue on a first come first serve basis, i.e. the user only waits for a time T after entering the queue, leaves after T is exceeded and has not yet started serving, and becomes a lost customer. Combining the analysis, the arrival rate of the queue is lambda, the service rate is mu, and combining the queuing theory, the probability of customer loss obtained by adopting an M/M/1 model is as follows:
Figure BDA0002381801720000191
where ρ ═ λ/μ is the load of the queue, and Ω ═ e-μ(1-ρ)(T1/μ). In the case that the customer loss is corresponding to the situation that the H2H user cannot acquire enough PUSCH and data transmission fails, the H2H user failing in the data transmission request process performs random backoff, randomly selects a backoff time L ∈ 0, …, W-1 from the backoff window W, and enters the backoff state, so that the jth connection request state CR enters the backoff statejTransition to the backoff BOj，lHas a probability of pL/W，j＝1，…，m，L＝0，…，W-1。
If the H2H user in the connection request state obtains the PUSCH allocated by the base station within the waiting time T, the system will switch to the first transmission state for the first dataTransmission, transition probability of state 1-pLAfter the first data transmission is finished, the base station may request the H2H user to perform data retransmission due to decoding errors, the maximum retransmission time is set to r, if the base station does not decode successfully for the retransmission of r times, the H2H user discards the data packet and enters the discard state drop, so the first transmission state FT is transferred to the first retransmission state RT1Kth retransmission state RTkTransition to the k +1 st retransmission state RTk+1(k-1, …, r-1), and an r-th retransmission state RTrThe transition probabilities of transitioning to the drop state are all retransmission probabilities pR. If the H2H user successfully completes data transmission within r times of retransmission, the success state will be entered, so the first transmission state FT and the k-th retransmission state RTkK is 1, …, and the probability of r transitioning to success state success is all 1-pR。
Referring to fig. 3, the resource allocation method based on the H2H dynamic characteristics in the large-scale MTC and H2H coexistence scenario according to the present invention combines the above H2H state transition characteristics to analyze the smooth distribution of H2H users. By using a piidle，
Figure BDA0002381801720000192
πFT，
Figure BDA0002381801720000193
πsuccessRepresents the smooth distribution of H2H, and respectively represents that H2H is in idle state idle and j-th random access state RAjJ-th connection request state CRjBackoff state BO with jth backoff duration of lj，l(j-1, …, m +1, L-0, …, W-1), first transmission state FT, kth retransmission state RTk(k-1, …, r), success status success, and drop status drop. According to the state transition probability of the Markov chain, the relationship between the stationary distribution probabilities can be obtained:
Figure BDA0002381801720000201
Figure BDA0002381801720000202
Figure BDA0002381801720000203
Figure BDA0002381801720000204
Figure BDA0002381801720000205
Figure BDA0002381801720000206
Figure BDA0002381801720000207
Figure BDA0002381801720000208
Figure BDA0002381801720000209
after sorting and simplification, the probability p that any H2H user accesses the network and successfully completes transmission can be obtainedsuccessExpressed as:
Figure BDA00023818017200002010
wherein, pisuccessAnd pidropProbability of H2H user being in success state and discarding state under smooth distribution, pF＝1-(1-pC)(1-pL) Indicates the probability that no PUSCH transmission data is acquired in H2H one-time random access, and combines the state transition analysis result of the Markov chain to obtain the lambda under the condition of smooth distributionTShould equal new arrivalAchieved H2H user intensity lambdaHAnd each H2H initiates random access average times NTXProduct of (i.e.. lambda.)T＝λHNTXAccording to the transition probability ratio, the probability that at least j +1 times of random access requests are required to be initiated in total after any H2H initiates the first access can be obtained as
Figure BDA00023818017200002011
The probability that one H2H originates exactly j random access requests is
Figure BDA0002381801720000211
Figure BDA0002381801720000212
To distribute the probability that H2H user is in the jth random access state smoothly, j is 1, …, m + 1.
The probability that one H2H initiates m +1 random access requests exactly is due to the maximum preamble retransmission limit in the presence of random access requests
Figure BDA0002381801720000213
Therefore, each H2H initiates random access on average number of times NTXCan be expressed as:
Figure BDA0002381801720000214
for a given resource allocation scheme (which, as previously mentioned, consists of a set of
Figure BDA0002381801720000215
And
Figure BDA0002381801720000216
uniquely determined) under the condition of smooth distribution, pFIs only with respect to the request arrival rate λTIs thus NTXIs also lambdaTDue to a smoothly distributed lambdaT＝λHNkXThus λTSatisfy the following immobilityPoint equation:
Figure BDA0002381801720000217
therefore, the stationary point iterative algorithm can be adopted to solve the lambda under the stable distributionTThe method comprises the following specific steps:
the first step is as follows: setting the iteration number i to 1, lambdaTThe initial value is set as lambdaT(0) The termination condition corresponds to a parameter epsilon;
the second step is that: i ═ i +1, according to a given λT(i-1) calculating p corresponding to the ith iteration according to the Markov state transition modelFIs denoted by pF(i) And updating λ according to the following iterative formulaTThe value of (c):
Figure BDA0002381801720000218
the third step: let the difference Δ ═ λ between two iterations be calculatedT(i)-λT(i-1)|
The fourth step: if delta is less than epsilon, terminating iteration and returning to the latest state; otherwise, repeating the second step and the third step.
Lambda under stable distribution solved according to fixed point iterative algorithmTCan obtain p under smooth distributionFAnd further obtains the probability that any H2H user accesses the network and successfully completes the transmission
Figure BDA0002381801720000219
Therefore, the optimization problem that resource allocation is optimally designed on the premise of ensuring the successful transmission probability of the H2H service to maximize the throughput of the MTC in each RAO, which is proposed by the method, is finally expressed as:
Figure BDA0002381801720000221
s.t.
Figure BDA0002381801720000222
Figure BDA0002381801720000223
Figure BDA0002381801720000224
Figure BDA0002381801720000225
the optimization problem can be solved by a three-dimensional search algorithm, and is expressed by pseudo codes as follows:
initialization: is provided with
Figure BDA0002381801720000226
Figure BDA0002381801720000227
The corresponding resource configuration scheme parameter set S ═ {0, 0, 0 };
for
Figure BDA0002381801720000228
Figure BDA0002381801720000229
for
Figure BDA00023818017200002210
for
Figure BDA00023818017200002211
Figure BDA00023818017200002212
method for solving lambda under stable distribution by using stationary point iterative algorithmT
According to λTComputing p according to a Markov state transition modelF；
Figure BDA00023818017200002213
Figure BDA0002381801720000231
Finally return to
Figure BDA0002381801720000232
And corresponding resource configuration scheme parameter set
Figure BDA0002381801720000233
Obtained according to the algorithm
Figure BDA0002381801720000234
That is, the maximum throughput of MTC in each RAO is ensured on the premise of ensuring the successful transmission probability of H2H service, and the corresponding resource configuration scheme parameter set
Figure BDA0002381801720000235
I.e. an optimal resource allocation scheme.
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.
Numerical analysis based on large-scale MTC and H2H coexistence sceneThe resource allocation method of H2H dynamics was evaluated. If not, the parameters of the simulation experiment are set as: number N of H2H user terminal equipments (HUE)H1000, number of Machine Type Communication Devices (MTCD) NM10000, traffic arrival for each HUE is independent and all with intensity λ1The poisson process of 0.01, the traffic of each HUE reaches the corresponding sending data volume
Figure BDA0002381801720000236
I.e. obey uniform distribution
Figure BDA0002381801720000237
Wherein
Figure BDA0002381801720000238
H2H user send Msg3 data size
Figure BDA0002381801720000239
The MTCD arrival model follows beta distribution, and the scheduling information and the traffic data volume of each MTCD arrival transmission are respectively
Figure BDA00023818017200002310
And
Figure BDA00023818017200002311
periodic interval of random access opportunity, RAO, of TRAOTotal N in each RAO for 5msRBResource block according to different system bandwidth NRBThe values are as follows (if not explicitly stated in the simulation, the system bandwidth is 3 MHz):
Figure BDA0002381801720000241
building a preamble averaging requirement
Figure BDA0002381801720000242
N is consumed for constructing one PUSCH in each resource blockSMaximum supportable transmission B of each PUSCH (1 resource block)S36 bvtes. At random accessIn-process backoff window W is 20ms, and H2H successfully transmits probability threshold Pth95%, the maximum number of preamble retransmissions is m 9, the waiting time T for H2H user to request PUSCH is 40ms, the maximum allowed number of data retransmissions is r 5, and the probability of data retransmission p isR＝0.02。
FIG. 4 is maximum throughput of MTC within each RAO
Figure BDA0002381801720000243
With H2H user traffic arrival rate λ1When the H2H user traffic arrival rate is small and the H2H traffic data packet is small, the excess resources can be used for MTCD data transmission, and as the H2H user traffic arrival rate increases, the system preferentially allocates resources to the data transmission of the H2H user in order to ensure the successful transmission probability of the H2H user, the supportable MTC maximum throughput will be reduced, and especially when H2H has a large traffic data volume, the MTC throughput is reduced significantly.
FIG. 5 is maximum throughput of MTC within each RAO
Figure BDA0002381801720000244
Number N of terminal devices according to H2HHWhen the traffic data volume of the H2H user is small, the supportable MTC throughput change is not obvious as the number of H2H terminal devices increases, and when the traffic data volume of the H2H user is large, the system preferentially allocates resources to the data transmission of the H2H user in order to ensure the successful transmission probability of the H2H user, and then the MTC throughput is obviously reduced under the given arrival rate condition. Fig. 5 also compares the MTC throughput with the number of H2H terminals in different bandwidths, and it can be seen that when the bandwidth is smaller, as the number of H2H terminals increases, the MTC throughput drops to 0, in which case the system will not support MTC, so that when a large-scale MTC and H2H coexisting system is designed, a larger bandwidth configuration should be selected.
In summary, the invention is suitable for large-scale MTC and H2H coexistence scenarios, adopts more reasonable random access and data transmission flows for different characteristics of two services, studies resource allocation problems through mathematical modeling, proposes to optimize resource allocation on the premise of ensuring successful transmission of H2H service, analyzes dynamic characteristics in the H2H service random access and data transmission flows by using a markov chain, and provides an expression of H2H service successful transmission probability, constructs an optimization problem of maximizing MTC throughput, and improves system resource utilization and comprehensive performance on the premise of ensuring H2H service quality.
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 the like) 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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. The resource allocation method based on H2H dynamic characteristics in a large-scale MTC and H2H coexistence scene is characterized by comprising the following steps of:
s1, establishing a system model of a scene with MTC and H2H coexisting;
s2, analyzing basic parameters of resource allocation in a scene of coexistence of large-scale MTC and H2H;
s3, in each random access opportunity, the base station sets an optimal threshold for access class blocking detection by using load estimation, and deduces the average throughput with machine type communication equipment identity information in each random access opportunity;
s4, constructing a resource allocation optimization problem, and maximizing the throughput of MTC in each random access opportunity on the premise of ensuring the successful transmission probability of H2H service;
s5, modeling the state of the H2H user in the network by adopting a Markov chain, and representing the dynamic characteristic of H2H service transmission;
s6, analyzing the state transition probability of the H2H user in random access and data transmission based on the Markov chain model established in the step S5;
s7, analyzing the stable distribution of H2H users according to the H2H state transition probability characteristic, calculating the successful transmission probability of the H2H service, and calculating the arrival rate under the stable distribution by using an immobile point iterative algorithm;
s8: calculating the probability that any H2H user accesses the network and successfully completes transmission according to the arrival rate obtained in the step S7, substituting the probability into an optimization problem and solving the optimization problem by adopting a three-dimensional search algorithm as follows:
Figure FDA0002381801710000011
Figure FDA0002381801710000012
Figure FDA0002381801710000013
Figure FDA0002381801710000014
Figure FDA0002381801710000015
wherein N isRBFor the total number of resource blocks in each RAO,
Figure FDA0002381801710000016
number of resource blocks for the construction of PRACH, NPFor the total number of preambles available on the PRACH,
Figure FDA0002381801710000017
number of preambles allocated to MTC services, NSIs the total number of the possible PUSCHs,
Figure FDA0002381801710000021
for the number of PUSCHs allocated to MTC services, TMFor average throughput of MTC traffic within each RAO, PFThe probability that the H2H user does not acquire PUSCH transmission data in one random access is shown, m is the maximum lead code retransmission times, PRFor each data transmission of H2H userR is the maximum number of data retransmissions, PthA threshold of minimum probability of successful transmission required for H2H traffic;
returning maximum MTC throughput
Figure FDA0002381801710000022
And corresponding resource configuration scheme parameter set
Figure FDA0002381801710000023
And obtaining an optimal resource allocation scheme.
2. The method for resource allocation based on H2H dynamic characteristics in the massive MTC and H2H coexistence scenario according to claim 1, wherein in step S1, the cell BS has NHH2H user terminal equipment and NMThe service arrival of each terminal device is independent and is lambda in strength1The total H2H traffic in the cell network reaches obedience strength λH＝NHλIIn the poisson process, after each terminal device service arrives, the corresponding sending data volume of each arrival is a random variable uniformly distributed from one time to another
Figure FDA0002381801710000024
An arrival model of the communication equipment facing the Internet of things obeys beta distribution, and generally has a fixed data packet format, and the data volume sent after the arrival of the identity information of each communication equipment with machines is a constant
Figure FDA0002381801710000025
In a large-scale MTC and H2H coexistence scene, two services, namely, different random access and data transmission protocols, are adopted, the H2H service still adopts the original LTE protocol in the traditional cellular network to complete random access and data transmission, and the MTC service adopts a simplified signaling interaction mode to complete random access and data transmission.
3. The method for resource allocation based on H2H dynamic characteristics in the large-scale MTC and H2H coexistence scenario according to claim 2, wherein the MTC service specifically comprises:
the method comprises the steps that an access blocking method in a cellular network is adopted for checking, machine type communication equipment checked through the access blocking method sends a lead code with machine type communication equipment identity information to a base station, the base station identifies the equipment ID of the machine type communication equipment without lead code collision through decoding and replies a random access response message, in the random access response message, the base station distributes a physical uplink shared channel for the machine type communication equipment, the machine type communication equipment then sends scheduling information and small data packets on the specified physical uplink shared channel, and the base station returns collision solving and receiving feedback information after the decoding is successful.
4. The method for resource allocation based on H2H dynamic characteristics in the scenario of coexistence of massive MTC and H2H according to claim 1, wherein in step S2, there is a random access opportunity
Figure FDA0002381801710000032
One resource block is used to construct a physical random access channel,
Figure FDA0002381801710000035
the resource blocks are used for constructing a physical uplink shared channel and distributing H2H service
Figure FDA0002381801710000038
A preamble and
Figure FDA0002381801710000036
a physical uplink shared channel (PUCCH), the rest
Figure FDA0002381801710000033
A preamble and
Figure FDA0002381801710000034
individual articleAllocating physical uplink shared channel to large-scale MTC service, and configuring parameter set of resources
Figure FDA0002381801710000037
According to the optimum
Figure FDA0002381801710000039
And
Figure FDA00023818017100000310
and (4) taking values to determine a resource allocation scheme.
5. The method for resource allocation based on H2H dynamic characteristics in the scenario of coexistence of massive MTC and H2H as claimed in claim 1, wherein in step S3, the optimal threshold for access class blocking method check is
Figure FDA00023818017100000311
Average throughput T of machine type communication devices within each random access opportunityMComprises the following steps:
Figure FDA0002381801710000031
wherein the content of the first and second substances,
Figure FDA00023818017100000312
the average number of machine type communication devices with no collision of the preamble codes,
Figure FDA00023818017100000314
a preamble assigned to a large-scale MTC within each RAO,
Figure FDA00023818017100000313
number of PUSCHs, L, assigned to Large-Scale MTC within Each RAOMPUSCH consumed for MTCD random access and data transmission,
Figure FDA00023818017100000315
to employ load estimation techniques to estimate the number of active devices within the current RAO that will initiate an access request.
6. The method for resource allocation based on H2H dynamic characteristics in the scenario of coexistence of massive MTC and H2H according to claim 1, wherein in step S4, the resource allocation optimization problem is specifically:
Figure FDA0002381801710000041
Figure FDA0002381801710000042
Figure FDA0002381801710000043
Figure FDA0002381801710000044
Figure FDA0002381801710000045
wherein, TMFor the average throughput of the machine type communication equipment in each random access opportunity, the optimization variable comprises the number of resource blocks used for constructing the PRACH for each random access opportunity
Figure FDA0002381801710000046
Preamble assigned to massive MTC per random access opportunity
Figure FDA0002381801710000047
The number of physical uplink shared channels allocated to large-scale MTC in each random access opportunity
Figure FDA0002381801710000048
The first constraint condition represents the requirement of the successful transmission probability of the H2H service and represents the probability p of any H2H user accessing the network and successfully completing the transmissionsuccessCannot be less than a given threshold value Pth(ii) a The latter three constraint conditions respectively indicate that the number of the resource blocks, the lead codes and the physical uplink shared channels should be greater than 0 and not exceed the requirement of the total resource number.
7. The resource allocation method based on H2H dynamic characteristics in the large-scale MTC and H2H coexistence scenario according to claim 1, wherein in step S5, one H2H user will experience the following states in the network:
an idle state: when an H2H user has no data to send, the user is in an idle state;
a random access state: when an H2H user has data to send, the user first enters a random access state from that assigned to H2H
Figure FDA0002381801710000049
Randomly selecting one lead code from the lead codes and sending the lead code to the base station, and after receiving the lead code, the base station returns a response message, namely a random access response message, wherein m +1 random access states exist in one H2H user;
a backoff state: H2H users failing in the processes of random access and data transmission request carry out random backoff and enter a backoff state, H2H users carry out backoff at most m times, each backoff randomly selects one backoff time l in a time window W to be 0, … and W-1, and m multiplied by W backoff states are total;
connection request state: H2H users with collision-free lead codes in the random access process initiate connection establishment requests to the base station, apply subsequent PUSCH resources to the base station for sending service data, and the H2H users have m +1 connection request states;
the first transmission state: if the H2H user successfully applies for PUSCH resource for data transmission within m maximum lead code retransmission times, the first transmission is carried outState, starting to transmit data; in the data transmission process, the H2H user retransmits after the base station decodes errors, and the retransmission probability is pRThe maximum allowed data retransmission times of each H2H user is r, if the r-th retransmission of the base station still makes a decoding error, the H2H user discards the data and does not transmit any more;
and (4) retransmission state: the H2H user resends data, and r retransmission states exist;
success status: if the H2H user successfully applies for PUSCH resource for data transmission within m times of maximum lead code retransmission times and successfully completes data transmission within r times of maximum allowed data retransmission times, the H2H user enters a success state;
a discarding state: if the H2H user does not successfully complete data transmission within m times of maximum preamble retransmission times and r times of maximum allowed data retransmission times, the H2H user enters a discard state, does not initiate random access and discards the data, and after discarding the data, the H2H user returns to the idle state again.
8. The method for resource allocation based on H2H dynamic characteristics in the scenario of coexistence of massive MTC and H2H as claimed in claim 1, wherein in step S6, H2H user sends probability p of preamble collision after initiating random accessCComprises the following steps:
Figure FDA0002381801710000051
wherein λ isNRandom access strength of arrival is initiated for each H2H user within the RAO,
Figure FDA0002381801710000052
the number of lead codes allocated to MTC service, e is a natural constant in mathematics, and the jth random access state RAjTransition to the backoff BOj，lHas a probability of pCW, j 1, …, m, l 0, …, W-1; first transmission state FT and k-th retransmission state RTkThe probability of k being 1, …, r being transferred to the power state is 1-pR，pRThe retransmission probability for each data transmission of H2H user.
9. The method for resource allocation based on H2H dynamic characteristics in the scenario of coexistence of massive MTC and H2H according to claim 1, wherein in step S7, the probability p that any H2H user accesses the network and successfully completes transmissionsuccessExpressed as:
Figure FDA0002381801710000061
wherein, pisuccessAnd pidropProbability of H2H user being in success state and discarding state under smooth distribution, pF＝1-(1-pC)(1-pL) The probability that no PUSCH transmission data is acquired in H2H random access is shown, and the probability that at least j +1 times of random access requests are required to be initiated in total after any H2H initiates the first access is obtained
Figure FDA0002381801710000063
The probability that one H2H originates exactly j random access requests is
Figure FDA0002381801710000064
Figure FDA0002381801710000065
To distribute the probability that H2H user is in the jth random access state smoothly, j is 1, …, m + 1.
10. The method for resource allocation based on H2H dynamic characteristics in the scenario of coexistence of massive MTC and H2H as claimed in claim 1, wherein in step S7, the arrival rate λ under smooth distributionTThe method specifically comprises the following steps:
s701: setting the iteration number i to 1, lambdaTThe initial value is set as lambdaT(0) The termination condition corresponds to a parameter epsilon;
s702: i ═ i +1, according to a given λT(i-1) calculating p corresponding to the ith iteration according to the Markov state transition modelFIs denoted by pF(i) Updating λ according to the following iterative formulaTThe value of (c):
Figure FDA0002381801710000062
s703: let the difference Δ ═ λ between two iterations be calculatedT(i)-λT(i-1)|
S704: if delta is less than epsilon, terminating iteration and returning to the latest state; otherwise, steps S702 and S703 are repeated.
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