CN109905864B - Power Internet of things oriented cross-layer resource allocation scheme - Google Patents

Power Internet of things oriented cross-layer resource allocation scheme Download PDF

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CN109905864B
CN109905864B CN201910148634.4A CN201910148634A CN109905864B CN 109905864 B CN109905864 B CN 109905864B CN 201910148634 A CN201910148634 A CN 201910148634A CN 109905864 B CN109905864 B CN 109905864B
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周振宇
陈亚鹏
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North China Electric Power University
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Abstract

The invention mainly relates to a cross-layer resource allocation scheme applied to the power Internet of things, and realizes long-term stability of queues by optimizing data queues transmitted to a base station by multiplexing channels of other user equipment in a cellular network by various machine type communication equipment. Through the research on Lyapunov optimization and a Gell-Solifep matching algorithm, a cross-layer rate control and resource allocation mechanism is provided. The algorithm provided by the invention mainly converts the original long-term optimization problem into a rate control subproblem and a resource allocation subproblem in each time slot, firstly decomposes two convex functions by utilizing the Lyapunov algorithm, can well complete solution by utilizing a convex optimization tool with lower algorithm complexity, and for a subchannel selection problem, firstly establishes a bidirectional preference list of a machine pair and cellular user equipment according to different transmission performances of different subchannels, and then completes final stable matching by utilizing an iterative Galer-Sharpu algorithm. Simulation results show that the method can obviously improve the queue stability and optimize the network performance under the condition of no prior knowledge of data arrival and subchannel statistics.

Description

Power Internet of things oriented cross-layer resource allocation scheme
Technical Field
The invention belongs to the field of wireless communication, and particularly relates to a cross-layer resource allocation scheme applied to Machine-to-Machine (M2M) communication in an electric power Internet of things. Firstly, randomly arriving data queues are optimized through a Lyapunov optimization algorithm, and then suitable sub-channel selection is carried out on the data queues with different priorities in a network adopting Orthogonal Frequency Division Multiplexing (OFDM) by applying a Gell-Sharpu matching theory, so that the resource utilization rate is improved to the maximum extent and the network delay is minimized.
Background art:
with the rapid development of communication technologies and the large-scale access of data acquisition terminals, the era is undergoing a tremendous shift from traditional human-to-human communication to machine-to-machine communication. The M2M communication is one of key support technologies for networking and running of the Internet of things, and is very important for implementation of industrial automation and smart power grids due to excellent self-organizing and self-repairing capabilities. In terms of smart power grids, research and construction of the power internet of things are well-organized, and the existing mobile cellular network also provides a good foundation for popularization, but although the M2M communication technology in the power internet of things has been widely researched and applied, there still exist some problems and challenges that need to be solved urgently, which are summarized as follows:
1) stability of transmission queue: in a real scenario, due to the fact that the arrival of data streams is dynamic and unpredictable, and the influence of time-varying channels, data transmission queues are often not distributed smoothly, which puts a great strain on the operation of a base station. To solve this problem, avoiding channel congestion and reducing packet loss rate, an efficient device access control scheme is very necessary.
2) Optimizing performance facing user experience: the rapid increase in data and traffic will inevitably lead to insufficient radio spectrum resources, which is also an important limiting factor limiting the quality of user experience. Unfortunately, current research typically ignores the user's subjective perception and focuses more on optimizing only network performance. Therefore, how to utilize limited spectrum resources to improve the quality of user experience is currently a significant challenge.
3) Long-term system performance optimization: in the power internet of things, massive real-time data generated by data acquisition terminals with different functions are poured into an existing cellular network, so that a base station is overloaded, even a control system formed by the devices is broken down, and the current mainstream network performance optimization algorithm (such as a queuing theory) can only realize short-term optimization. However, the complex environment always brings many uncertainties and randomness to the data transmission process. Therefore, in consideration of the above dynamic influence factors, it is an urgent problem to design a long-term performance optimization scheme to stabilize a data queue and increase resource utilization rate.
Based on the above-mentioned problems and challenges, the present invention mainly proposes a power internet of things oriented cross-layer resource allocation scheme, in which the leiamproff optimization and the guerre-scheimpflug algorithm are jointly applied to M2M communication in a spectrum-shared OFDM cellular network, so as to maximize network performance and meet the demands of dense users.
The invention content is as follows:
the invention firstly simulates the scene of coexistence of a plurality of common cellular users in a cell and machine communication pairs in the power internet of things so as to achieve the aims of stable distribution of data transmission queues and maximization of resource utilization rate, and provides a cross-layer resource allocation scheme facing the power internet of things. According to the scheme, the experience requirements of cellular user equipment and M2M are considered, firstly, the subchannel selection of the multiplexing former of the latter is completed according to the current known information, the acquired data is sent to the base station, the received data queue is optimized at the base station side, the arrival of the next data and the subchannel selection are controlled according to the optimization condition, and the problems of base station overload and user experience quality reduction are solved rapidly. The specific process is as follows:
1) establishment of queue model
Fig. 1 is a cellular network system model based on M2M uplink, which is composed of a base station, N M2M pairs, and K cellular user equipments. The base station is responsible for resource coordination and subchannel allocation in a cell, different cellular user equipment can correspondingly generate K mutually orthogonal and non-interfering subchannels, the M2M pair respectively consists of a transmitter (MT) and a receiver (MR), and in our scenario, only the uplink part of the base station for transmitting data by the transmitter is considered.
In the system, a discrete time model is used, with a total duration T of time, every 1 second as a time slot T,. Within the communication range of the base station, the number of machine communication pairs and the number of cellular user equipments remain the same, but their positions are randomly distributed in different time slots for the sake of randomness. In the time slot t, it is assumed that there are M vehicles and K user equipments, which are respectively denoted as
Figure BDA0001980839970000021
And
Figure BDA0001980839970000022
correspondingly, K orthogonal sub-channels are generated, which are expressed as
Figure BDA0001980839970000023
Assume M within a time slot tnThe data admission rate of is denoted as An(t) corresponding to a transmission rate Rn(t) the current data packing queue is Qn(t) of (d). Data admission rate an(t) is Qn(t) input, Qn(t) is a networkLayer parameter, transmission rate RnAnd (t) is the output, which is the physical layer parameter. Qn(t) changes over time as follows:
Qn(t+1)＝[Qn(t)-Rn(t)]++An(t)
wherein, [ x ]]+Denotes max (x, 0). And when q (t) satisfies the following condition, we consider it to be strongly stable:
Figure BDA0001980839970000031
in order to realize the stabilization of the dynamic queue, the A needs to be controlled respectivelyn(t) and Rn(t), which will be described later.
2) Establishment of MOS (mean Opinion score) evaluation model
An(t) is a parameter that reflects the performance of the network layer, which directly affects the quality of experience of the user. In some cases, the uplink network load is too heavy to meet the quality requirement of all users under bad channel conditions, which requires adjusting the corresponding data rate according to the user experience quality to avoid congestion, and the admission rate adjustment of M2M pair is also called rate control. To mathematically characterize the user experience quality, we build the following MOS evaluation model, as follows:
MOS[An(t)]＝ηnlog2[An(t)]
wherein A isn(t) denotes time MnData admission rate, parameter ηn∈[0，1]Represents a pair MnSet priority parameter, ηnLarger means MnThe higher the delay requirement of the generated data, the more communication resources should be occupied.
3) Transmission channel modeling
Uplink communication resource allocation (e.g., power optimization and subchannel selection) occurs at the beginning of each time slot. In an OFDM system where cellular user equipment and M2M pairs coexist, the bandwidth is divided equally into K sub-channels, each having a bandwidth of B.
And MnC of shared sub-channelkThe channel snr (Signal to Interference plus noise ratio) of (SINR) can be expressed as:
Figure BDA0001980839970000041
wherein p isk(t) represents CkTransmission power in time slot t, gk(t) represents CkThe gain in the transmit power in the time slot t,
Figure BDA0001980839970000042
is represented by CkDistance from base station, αCRepresenting the path loss parameters of all cellular user equipments in the current scenario. N is a radical of0Representing the magnitude of the additive white gaussian noise of the environment. Corresponding to, pn(t)、gnk(t)、
Figure BDA0001980839970000043
And alphaMRespectively representing MT in time slot tnTransmit power, transmit power gain, MTnDistance from the base station and path loss parameters.
Similarly, the SINR backhaul channel signal-to-noise ratio from the base station to the M2M pair receiver is:
Figure BDA0001980839970000044
wherein the content of the first and second substances,
Figure BDA0001980839970000045
represents MTnAnd MRnThe distance between the two or more of the two or more,
Figure BDA0001980839970000046
is represented by CkAnd MRnThe distance between them. Then in time slot t, MnMultiplexing channel S between MT and MRkThe resulting transmission rates are:
Figure BDA0001980839970000047
wherein the content of the first and second substances,
Figure BDA0001980839970000048
indicates a determination as to whether to switch C in time slot tkOccupied SkIs allocated to MnThe binary decision variable of (4).
Figure BDA00019808399700000411
Means MnMultiplexing channel Sk。
Each sub-channel
Figure BDA0001980839970000049
Can only be reused by at most one M2M pair per time slot t to avoid pair CkAnd excessive interference of the existing uplink between the BSs. Therefore, we have
Figure BDA00019808399700000410
4) Long term data admission rate and delay constraints
Since there are many delay sensitive devices, often requiring an upper delay bound and a lower rate transmission rate bound, we impose a time-averaged rate constraint and a delay constraint on each pair of M2M.
Specifically, the data admission rate averaged over time is constrained as follows:
Figure BDA0001980839970000051
wherein, OnRepresents MnThe minimum long-term data admission rate.
Queuing delay is generally defined as the length of time a packet waits in a queue until it can be transmitted. It is noted that the transmission delay is small compared to the queuing delay in networks with high load and can therefore be neglected. The average delay over time constraint equation is defined as follows:
Figure BDA0001980839970000052
where ρ isnDenotes the average delay with an upper bound of Dn。
5) Modeling of maximum MOS optimization problem
Optimization of the weighted MOS of all M2M pairs requires solving joint rate control, power optimization and subchannel selection problems and involves two-dimensional matching between the M2M pairs and the subchannels. Thus, in time slot t, a two-dimensional matrix of size NxK is set
Figure BDA0001980839970000053
For expressing subchannel selection strategy, P ═ PnIs used to represent a power optimization strategy, R ═ RnAnd is used for expressing the data admission rate control strategy. The optimization problem is modeled as follows:
Figure BDA0001980839970000054
Figure BDA0001980839970000055
Figure BDA0001980839970000056
Figure BDA0001980839970000057
Figure BDA0001980839970000058
Figure BDA0001980839970000061
Figure BDA0001980839970000062
Figure BDA0001980839970000063
C6: queue Qn(t) is strongly stable and,
Figure BDA0001980839970000064
Figure BDA0001980839970000065
Figure BDA0001980839970000066
wherein constraints C1 and C2 are to ensure that each subchannel can be reused by at most one M2M pair per slot, and vice versa. C3 specifies a transmit power constraint for the M2M pair. C4 is the SINR threshold constraint for the cellular user equipment and M2M pair. C5 is the maximum tolerable data queue rate for the base station. C6 is a stability constraint for the M2M pair. C7 and C8 ensure that the rate requirement and time-averaged delay of each subchannel of the M2M pair are guaranteed simultaneously.
6) Problem solution based on Lyapunov optimization algorithm
To model the average delay and rate constraints, we introduce the concept of virtual queues. The virtual queue associated with the average rate constraint, y (t), varies over time as follows:
Yn(t+1)＝[Yn(t)-An(t)]++On(t)
if the virtual power queue Y (t) is average rate stable, it satisfies the average power constraint C7。
The virtual queue z (t) associated with the delay constraint varies over time as follows:
Zn(t+1)＝[Zn(t)-DnRn(t)]++Qn(t)
from the above analysis, if the data queue and two virtual queues (Y, Z) are stable for all M2M pairs, we consider the entire network to be stable and the long-term data admission rate constraints and delay constraints are satisfied.
Therefore, we can be based on the queue stability constraint C1、C2、C3、C4And C5The original optimization problem of step 6) is converted into a problem that maximizes the weighted MOS values of all M2M pairs. The problem after transformation is expressed as follows:
Figure BDA0001980839970000071
s.t.C1、C2、C3、C4and C5
C6: queue Q (t), Yn(t) and Zn(t) is strongly stable and,
Figure BDA0001980839970000072
make Q ═ Qn(t)}，Y＝{Yn(t) } and Z ═ Zn(t) } denotes the backlog of the three queues, respectively, such that G (t) ═ Q (t), Y (t), Z (t)]Representing the joint queue backlog of the M2M pair, then the leiapunov equation can be defined as follows:
Figure BDA0001980839970000073
the instantaneous lyapunov drift amount Δ (g (t)) from one time slot to the next is defined as:
Figure BDA0001980839970000074
by subtracting the average expectation of the weighted MOS values from it, we can get the following negative return on drift term:
Figure BDA0001980839970000076
where V is a non-negative adjustable parameter. According to the design principle of Lyapunov optimization, an appropriate rate control and resource allocation decision should be selected to minimize the upper limit of the negative return term of drift per time slot t, namely:
Figure BDA0001980839970000075
Figure BDA0001980839970000081
where X is a non-negative constant that satisfies the following inequality in all time slots t:
Figure BDA0001980839970000082
we transform the original optimization problem into an upper bound value that minimizes the negative return on drift, which is again subject to resource allocation constraints C, again at each time slot t1、C2、C3And C4And rate control constraint C5The influence of (c). Therefore, the original random network long-term optimization problem is transformed into a series of continuous transient static optimization sub-problems, which can be specifically divided into a data admission rate control sub-problem and a resource allocation sub-problem.
Data admission rate control: the admission rate control strategy means that the algorithm adjusts the admission rate associated with the MOS based on the demand of M2M pair and the current data queue backlog. For example, in the presence of backlog of data queues, the M2M pair will reject newly arriving data with higher priority and larger amounts with a very high probability to avoid more severe channel congestion. Furthermore, for the case of a certain fixed subchannel, assuming that the non-negative adjustable parameter V is larger, the M2M pair may employ a more relaxed admission rate control strategy to allow accepting more data. Therefore, the corresponding MOS value of the M2M pair can reach a higher level.
The second term of negative return on drift relates only to the admission rate control related parameter an(t), so minimization of this term can be considered a first sub-problem,the method comprises the following specific steps:
Figure BDA0001980839970000083
s.t.C5
wherein the content of the first and second substances,
Figure BDA0001980839970000084
because of MOS [ An(t)]Is about An(t) a convex function, we can directly apply convex function optimization tool to optimize the above formula.
Resource allocation sub-problem: the third term of negative reward due to drift relates only to the resource allocation related parameter Rn(t), i.e. power allocation result pn(t) and subchannel selection results
Figure BDA0001980839970000091
Minimization of this term can therefore be considered a second sub-problem, specifically as follows:
Figure BDA0001980839970000092
s.t.C1，C2，C3and C4
Wherein the content of the first and second substances,
Figure BDA0001980839970000093
the resource allocation problem is a more complex combinatorial problem in which variables
Figure BDA0001980839970000094
Is discrete, but the variable pn(t) is continuous. In practical application, due to high complexity, detailed search of an optimal value is difficult to realize, but by applying a successive super-relaxation method, the original integer programming problem can be relaxed to a convex optimization problem, the method is low in complexity, and an optimal solution meeting constraint conditions is easy to find.
In addition, we solve the sub-channel selection problem by applying the Galer-Serlipp matching algorithm,i.e., a two-dimensional matching problem involving N M2M pairs and K subchannels. First we give the following definitions: matching
Figure BDA0001980839970000099
Representing a slave set
Figure BDA0001980839970000095
One-to-one mapping to itself, phi (M)n)＝SkRepresents MnAnd sub-channel SkIs matched at this time
Figure BDA0001980839970000096
Otherwise it is 0.
When phi (M)n)＝SkIn other words, when
Figure BDA0001980839970000097
The optimum value of (c) can be found by the following relation:
Figure BDA00019808399700000910
s.t.C1，C2，C3and C4
The same is a convex optimization problem, and the optimal value can be directly solved through a convex optimization tool
Figure BDA0001980839970000098
6) Problem solution based on Gal-Sharpu matching algorithm
To accomplish sub-channel selection, we first need to establish a bi-directional preference list of M2M pairs and cellular user equipment. We define the preference of each M2M for different sub-channels by its transmission rate
Figure BDA0001980839970000101
Meaning that by briefly connecting each M2M pair to each subchannel to obtain a transmission rate corresponding to each subchannel, the higher the rate, the higher the priority. For each cellWhether the user equipment is willing to provide the sub-channel to the M2M pair is determined by the size of the SINR value, and the interference caused to the user itself is larger, i.e. the smaller the SINR value is, the less willing it is. And finally realizing a stable matching phi through an iterative matching algorithm. The basic flow is as follows:
step 1: each MnS ranked first in its preference list and not expressing rejection for itselfkA matching application is proposed;
step 2: if it corresponds to SkIf the matching is not selected, the matching between the applicant and the original matcher is successful, if the matching is selected, the sequencing of the applicant and the original matcher in the subchannel is compared, the former M2M pair is selected, and the other bit is rejected;
and step 3: repeating the steps 1 and 2 until each MnOne of the sub-channels is selected and rejected by all remaining sub-channels.
Description of the drawings:
fig. 1 is a cellular network system model based on the M2M uplink.
FIG. 2 is a simulation parameter of the present invention during simulation.
Fig. 3 is a result of queue control proposed by the present invention.
Fig. 4 shows the result of resource allocation proposed by the present invention.
Fig. 5 is a comparison of the performance of the proposed system of the present invention with the stability of the results of the random matching algorithm.
Detailed Description
The implementation mode of the invention is divided into two steps, wherein the first step is the establishment of a model, and the second step is the implementation of an algorithm. The model is shown in fig. 1, which corresponds to the introduction of the cellular network system model based on M2M uplink in the summary of the invention.
1) For a system model, with the wide construction of the power internet of things, a large amount of data floods the existing mobile cellular network, but because the dynamic and unpredictable arrival of data streams bring great pressure to the operation of a base station, a scheme which is labourious to perform long-term stable optimization on a large amount of data under the condition that global information is unknown is urgently needed to be designed. As shown in fig. 1, M2M in the power internet of things transmits data by multiplexing channels of appropriate cellular user equipment, and controls data arriving at a base station by using an optimization method of a random network, so as to realize long-term stability of a transmission queue and greatly improve network performance and user experience quality.
2) In order to solve the optimization problem, firstly, a data admission rate control scheme based on Lyapunov is designed, and a cover-Sharpu matching algorithm is combined to realize reasonable distribution of communication resources. Finally, the design scheme is decomposed into two convex function optimization subproblems of data admission rate control and resource allocation, a convex optimization tool box with low complexity can be well applied to solve, and a Gal-Sharpu matching algorithm is used for realizing subchannel selection.
For the present invention, we have performed a number of simulations. The specific parameters in the simulation are shown in table 2, where 4M 2M pairs and 5 cellular user equipments are randomly distributed in a cellular network with radius R-200M, and the results are illustrated in terms of both data queue rate control and power delay.
Figure 3 shows the backlog variation for different queues and slots. We can observe that each queue tends to settle around the corresponding value after only a few slots when a random initial backlog is given. The numerical results prove that the method can well realize the rate control by processing the backlog queues which are continuously generated. It is worth mentioning that the size of backlog is positively correlated with priority, since M2M with higher priority has more data collection and more frequent data transmission resulting in more queue backlog.
Fig. 4 shows a joint power optimization and subchannel selection scheme for different time slots. In particular, a similar stable result of the virtual queue Z is given in fig. 3- (a), where this value represents the total power P to be allocatedmax. Fig. 3- (b) shows the power optimization results, where the four gradients differ for different priorities of the M2M pairs. After allocation, the transmission rate of the subchannel is as shown in fig. 3- (c). The results show that the combined algorithm of Lyapunov optimization and Gal-Sharpu matching can not only maintain the systemAnd adverse effects caused by time-varying channels can be avoided as much as possible.
Figure 5 compares the overall system stability of our proposed algorithm and the algorithm combining lyapunov optimization with random matching from the queue backlog and power allocation perspective, where two boxplots are shown to show the scatter-over of a set of data. As can be seen from the figure, the overall distribution of the proposed scheme, whether queue backlog or power allocation, is more concentrated than the overall distribution of random subchannel selection, since random matching has the possibility to match a poor performing subchannel with a high queue with a high priority.
Although specific implementations of the invention and the accompanying drawings are disclosed for illustrative purposes and to aid in understanding the contents of the invention and the implementation thereof, those skilled in the art will appreciate that: various substitutions, changes and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the present invention should not be limited to the disclosure of the preferred embodiments and the drawings, but the scope of the invention is defined by the appended claims.

Claims (1)

1. A cross-layer resource allocation method for an electric power Internet of things is characterized by comprising the following steps:
1) considering that under the condition that relevant data queues and channel statistical information are unknown, optimizing data queues transmitted to a honeycomb by using a Lyapunov optimization method for various machine type communication equipment M2M in the power Internet of things, and converting a long-term optimization problem into a series of rate control sub-problems and resource allocation sub-problems in a time slot t to finally realize a long-term stable state and optimize network performance; the method specifically comprises the following steps:
(1) first, a queue model is established, M2M for MNThe corresponding queue backlog varies with time slot t as follows:
Qn(t+1)＝[Qn(t)-Rn(t)]++An(t)
[x]+denotes max (x, 0), Rn(t) denotes the transmission rate, An(t) data standardThe rate of entry is determined by the rate of entry,
(2) setting the MOS evaluation model as follows:
MOS[An(t)]＝ηnlog2[An(t)]
parameter etan∈[0，1]Represents a pair MnThe set priority parameter is set to a priority level,
(3) in an OFDM system where cellular user equipment and M2M pairs coexist, the bandwidth is divided into K subchannels, each subchannel having a bandwidth of B, and the transmission channel is modeled as follows:
Figure FDA0002668970130000011
Figure FDA0002668970130000012
indicates a criterion as to whether or not to switch the cellular user equipment C to the time slot tkOccupied sub-channel SkIs allocated to MnThe two-value decision variable of (a),
Figure FDA0002668970130000013
representing the SINR backhaul channel signal-to-noise ratio from the base station to the M2M pair receiver at this time, while the previous uplink transmission signal-to-noise ratio SINR value is represented as
Figure FDA0002668970130000014
The correspondence is respectively determined by the following formula:
Figure FDA0002668970130000015
Figure FDA0002668970130000016
pk(t) represents CkTransmission power in time slot t, gk(t) represents CkThe gain in the transmit power in the time slot t,
Figure FDA0002668970130000017
is represented by CkDistance from base station, αcRepresents the path loss parameter, N, for all cellular user equipments in the current scenario0The magnitude of additive white Gaussian noise representing the environment, correspondingly, pn(t)、gnk(t)、
Figure FDA0002668970130000018
And alphaMRespectively representing MT in time slot tnTransmit power, transmit power gain, MTnDistance to base station, MTnAnd MRnDistance between, CkAnd MRnThe distance between and the path loss parameter,
(4) the long-term data admission rate constraint and the delay constraint are specified as follows:
Figure FDA0002668970130000021
Figure FDA0002668970130000022
(5) finally, the maximum weighted MOS optimization problem is established as follows:
Figure FDA0002668970130000023
s.t.C1：
Figure FDA0002668970130000024
C2：
Figure FDA0002668970130000025
Figure FDA0002668970130000026
C3：
Figure FDA0002668970130000027
C4：
Figure FDA0002668970130000028
Figure FDA0002668970130000029
C5：
Figure FDA00026689701300000210
C6: queue Qn(t) is strongly stable and,
Figure FDA00026689701300000211
C7：
Figure FDA00026689701300000212
C8：
Figure FDA00026689701300000213
Figure FDA00026689701300000214
for expressing subchannel selection strategy, P ═ PnIs used to represent a power optimization strategy, R ═ RnUsed to represent data admission rate control strategies, constraints C1 and C2 are to ensure that each subchannel can be reused by at most one M2M pair per timeslot and vice versa, C3 specifies a transmission power constraint for the M2M pair, C4 is an SINR threshold constraint for the cellular user equipment and M2M pair, C5 is a maximum tolerable data queue rate for the base station, C6 is a stability constraint for the M2M pair, C7 and C8 ensure that both the rate requirement and time-averaged delay for each subchannel of the M2M pair are guaranteed,
(6) to solve the above optimization problem, virtual queues are introduced that are related to average rate and power delay:
Yn(t+1)＝[Yn(t)-An(t)]++On(t)
Zn(t+1)＝[Zn(t)-DnRn(t)]++Qn(t)
the above problem is translated into:
Figure FDA0002668970130000031
s.t.C1、C2、C3、C4and C5
C6: queue Q (t), Yn(t) and Zn(t) is strongly stable and,
Figure FDA0002668970130000032
the joint queue backlog for the three queues of the M2M pair is:
Figure FDA0002668970130000033
the instantaneous lyapunov drift amount Δ (g (t)) is:
Figure FDA0002668970130000034
x is a non-negative constant value,
Figure FDA0002668970130000035
instantaneously from one time slot to the next;
the rate control subproblem is represented as:
Figure FDA0002668970130000036
s.t.C5
wherein the content of the first and second substances,
Figure FDA0002668970130000041
a low complexity convex optimization tool may be applied to solve,
Figure FDA0002668970130000042
s.t.C1，C2，C3and C4
Wherein the content of the first and second substances,
Figure FDA0002668970130000043
in that
Figure FDA0002668970130000044
Under the determined condition, a convex optimization tool can be applied to solve;
2) in the optimization process, a guerre-selip matching algorithm is applied to complete sub-channel selection, that is, M2M completes data transmission on channels of original users in the multiplexing cellular network, and the method specifically comprises the following steps:
first, it is necessary to establish a list of bi-directional preferences of M2M pairs and cellular user equipment, each M2M preference for different sub-channels by its transmission rate
Figure FDA0002668970130000045
Indicating that the transmission rate corresponding to each subchannel is obtained by connecting each M2M pair to each subchannel momentarily; whether each cellular user equipment is willing to provide sub-channels to the M2M pair is determined by the size of the SINR value, and the larger the interference caused to the user itself, i.e. the smaller the SINR value, the less willing it is;
then finally realizing a stable matching phi through an iterative matching algorithm; the specific process comprises the following steps:
step 1: each MnS ranked first in its preference list and not expressing rejection for itselfkA matching application is proposed;
step 2: if it corresponds to SkIf the matching is not selected, the matching between the applicant and the original matcher is successful, if the matching is selected, the sequencing of the applicant and the original matcher in the sub-channel is compared, the former M2M pair is selected, and the other bit is rejected;
and step 3: repeating the steps 1 and 2 until each MnOne of the sub-channels is selected and rejected by all remaining sub-channels.
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