CN113242605B - Resource allocation method based on multiple relays in low-delay network - Google Patents

Resource allocation method based on multiple relays in low-delay network Download PDF

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CN113242605B
CN113242605B CN202110506094.XA CN202110506094A CN113242605B CN 113242605 B CN113242605 B CN 113242605B CN 202110506094 A CN202110506094 A CN 202110506094A CN 113242605 B CN113242605 B CN 113242605B
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error probability
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CN113242605A (en
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张华�
王萍超
王俊波
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Southeast University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
    • H04W72/542—Allocation or scheduling criteria for wireless resources based on quality criteria using measured or perceived quality
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
    • H04W72/543—Allocation or scheduling criteria for wireless resources based on quality criteria based on requested quality, e.g. QoS

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Abstract

The invention discloses a resource allocation method based on multiple relays in a low-delay network. Aiming at a multi-data source multi-relay transmission scene, the invention performs resource allocation under the condition of meeting the transmission delay limitation. By jointly optimizing relay matching and block length allocation, the average error probability of system transmission is minimized. Mathematically, the optimization problem is modeled as an integer programming problem. A joint optimization algorithm based on alternate iteration is provided, a joint optimization problem is decomposed into two sub-problems of relay matching and block length distribution, and the two sub-problems are alternately optimized until a target is converged. The algorithm provided by the invention can obviously reduce the average error probability under the time delay limit and meet the requirements of high reliability and low time delay.

Description

Resource allocation method based on multiple relays in low-delay network
Technical Field
The invention belongs to the field of wireless communication, and particularly relates to a resource allocation method based on multiple relays in a low-delay network.
Background
With the advent of the 5G era, low-latency and high-reliability communication has attracted much attention as an emerging application scenario. In many practical scenarios, low latency communication plays a crucial role, such as: smart grid, unmanned, industrial automation, etc. These scenarios require high reliability of transmission within certain transmission delay constraints. In some industrial automation scenarios, transmission intervals of less than 1ms are required while ensuring error probability of transmission around.
To meet latency requirements, the amount of data transmitted in low latency communication networks is often very small, typically around 20bytes, in which case the transmission is often referred to as short packet transmission. In a conventional communication mode, a shannon formula is used to obtain an achievable upper limit of the transmission rate. However, in the short packet transmission scenario, the shannon formula is no longer applicable.
The cooperative communication mode based on the relay can effectively improve the transmission rate of the system and increase the reliability of transmission. For practical low-latency communication networks, a plurality of nodes are generally included, each node can serve as a potential relay to help data transmission of other nodes, and in a multi-node network, system performance can be improved by using the relay. However, at present, no short packet transmission scenario satisfying the low latency requirement is considered in the relay-based transmission scenario. Meanwhile, in a relay-based short packet transmission scenario, how to ensure high reliability of transmission under the condition of satisfying the time delay limit has become a hot point of research in the 5G technology.
Disclosure of Invention
The invention aims to provide a resource allocation method based on multiple relays in a low-delay network, which aims to solve the technical problems that the average error probability is obviously reduced under the time delay limit, and the requirements of high reliability and low time delay are met.
In order to solve the technical problems, the specific technical scheme of the invention is as follows:
a resource allocation method based on multiple relays in a low-delay network comprises the following steps:
step 1, in a low-delay scene, short packet transmission is carried out, and the error probability of a receiving end is as follows:
Figure BDA0003058501770000021
wherein, γ represents the signal-to-noise ratio, and m is the block length transmitted in the channel, i.e. the number of symbols; d is the number of transmitted data bits; the Q function is
Figure BDA0003058501770000022
C(γ)＝log2(1+ γ) represents the shannon capacity; v (gamma) ═ 1- (1+ gamma)-2Representing the channel dispersion;
step 2, establishing a low-delay communication network, wherein the low-delay communication network comprises a data collection central node C, NAA data source node AiAnd NBA relay node BjA low latency communication network of (a); wherein N isB≥NA；
Each data source node Ai(i＝1，2，...，NA) Selecting a matching relay node Bj(j＝1，2，...，NB) Simultaneously sending data to the central node C within the time delay range;
each relay node BjIs transmitted throughOnly one data source node A can be forwarded in the processiThe signal of (a);
step 3, for each data source node AiTransmitting, data source node AiEncoding the Dbits data into mi，1A symbol transmitted to a relay node B matched therewithj(ii) a D represents the size of the transmitted data bit quantity, and bits is a unit; relay node BjReceiving data from a data source node AiAfter the signal(s), the demodulated Dbits data is encoded into mi，2The symbol is forwarded to the central node C to form a slave data source node AiTo the relay node BjAnd then to the central node C, m for satisfying the requirement of time delay limitationi，1+mi，2M is less than or equal to M, wherein M is the total number of transmission symbols;
step 4, the channel in the low-delay network is a quasi-static fading channel and is kept unchanged in the whole transmission process; data source node AiAnd relay node BjSignal to noise ratio therebetween
Figure BDA0003058501770000031
Relay node BjAnd center node C
Figure BDA0003058501770000032
Wherein the content of the first and second substances,
Figure BDA0003058501770000033
is represented by AiAnd BjThe channel fading coefficient in between is determined,
Figure BDA0003058501770000034
is represented by BjAnd the channel fading coefficient between C and C,
Figure BDA0003058501770000035
is represented by AiAnd BjThe average received signal-to-noise ratio of all transmission links in between,
Figure BDA0003058501770000036
is represented by BjAnd between CThere is an average received signal-to-noise ratio of the transmission link;
step 5, obtaining each data source node AiCorresponding slave data source node AiTo the relay node BjThe error probability of the transmission link to the central node C is:
Figure BDA0003058501770000037
wherein
Figure BDA0003058501770000038
Is represented by AiAnd BjThe probability of a transmission error in-between,
Figure BDA0003058501770000039
Figure BDA00030585017700000310
is represented by BjAnd a transmission error probability between C;
step 6, establishing an optimization target of minimizing the average error probability of all transmission links in the whole system, and taking m asi，1，mi，2，
Figure BDA00030585017700000311
To optimize the mathematical model of the variables, the optimization problem is as follows:
Figure BDA00030585017700000312
mi，1+mi，2≤M
Figure BDA00030585017700000313
Figure BDA00030585017700000314
Figure BDA00030585017700000315
Figure BDA00030585017700000316
wherein epsilonaveRepresenting the average error probability of all transmission links in the whole system;
Figure BDA00030585017700000317
represents mi，1，mi，2Is a positive integer;
Figure BDA00030585017700000318
represents a set of positive integers;
Figure BDA00030585017700000319
represents a relay matching state when
Figure BDA00030585017700000320
Is represented by AiSelection BjCompleting signal transmission as a relay, otherwise
Figure BDA00030585017700000321
Figure BDA0003058501770000041
Indicating that each relay can only forward signals of at most one data source node, NBThe number of the relay nodes is;
Figure BDA0003058501770000042
indicating per data source selection and only one relayed signal, NAThe number of the data source nodes is;
step 7, using a contradiction theory to prove that one optimal solution satisfies m in all optimal solutions of the optimization problem in step 6i，1+mi，2＝M；
And 8, based on the step 6 and the step 7, rewriting the optimization problem into:
Figure BDA0003058501770000043
mi，1+mi，2＝M
Figure BDA0003058501770000044
Figure BDA0003058501770000045
Figure BDA0003058501770000046
Figure BDA0003058501770000047
and 9, the optimization problem in the step 8 is an integer optimization problem, in order to solve the problem, a joint optimization algorithm based on alternate iteration is adopted, the optimization problem is divided into two sub-problems of relay matching and block length distribution, and the two sub-problems are alternately iterated until the optimization target is converged.
Further, the joint optimization algorithm based on alternate iteration in step 9 includes the following steps:
step 9.1, initialize the block length distribution matrix
Figure BDA0003058501770000048
Step 9.2, obtaining an error probability matrix by using the initial block length distribution matrix
Figure BDA0003058501770000049
Step 9.3, fix the error probability matrix
Figure BDA00030585017700000410
Carrying out relay matching to obtain a relay matching matrix
Figure BDA00030585017700000411
Step 9.4, fix the relay matching matrix
Figure BDA00030585017700000412
Block length assignment is performed for each AiBlock length (m) on the corresponding transmission linki，1，mi，2) Is optimized according to (m)i，1，mi，2) Obtaining the error probability epsilon of the transmission linkijThen updating the error probability matrix
Figure BDA00030585017700000413
Step 9.5, based on ε and
Figure BDA00030585017700000414
calculating epsilonave；
Step 9.6, loop step 3 through step 5 until εaveAnd (6) converging.
Further, step 9.3 comprises the steps of:
step 9.3.1, fix the error probability matrix
Figure BDA0003058501770000051
And performing relay matching, wherein the relay matching problem is expressed as follows:
Figure BDA0003058501770000052
Figure BDA0003058501770000053
Figure BDA0003058501770000054
Figure BDA0003058501770000055
9.3.2, modeling the relay matching network as a weighted bipartite graph, abstracting the network nodes as vertexes of the bipartite graph, dividing the data source nodes into a set A, dividing the relay nodes into a set B, wherein each node in A and B is connected in pairs, and each edge A is connected with each otheriBjIs weighted from the data source node AiTo the relay node BjUplink transmission error probability epsilon of transmission link to central node Cij；
Step 9.3.3 for NB≥NAAnd carrying out relay matching by utilizing a two-dimensional matching algorithm JVC algorithm to obtain a relay matching matrix
Figure BDA0003058501770000056
Further, step 9.4 comprises the steps of:
step 9.4.1 blue
Figure BDA0003058501770000057
When, AiIs not in the slave data source node AiTo the relay node BjThen, the signal is transmitted to the transmission link of the central node C, and the transmission block length (m) on the link is at the momenti，1，mi，2) The value of (d) remains unchanged;
step 9.4.2, blue
Figure BDA0003058501770000058
When, AiMatched with corresponding relay node BjAt the corresponding slave data source node AiTo the relay node BjThen, the transmission link to the central node C carries out short packet transmission, and the transmission block length (m) on the link is longi，1，mi，2) Optimizing to make the transmission error probability epsilon of the linkijAt a minimum, the block length assignment problem is expressed as follows:
Figure BDA0003058501770000061
step 9.4.3, rewriting the optimization target of step 2 into
Figure BDA0003058501770000062
Step 9.4.4, pair
Figure BDA0003058501770000063
At point (m)i，1，mi，2) The convexity of the part is verified;
step 9.4.5, the block length allocation problem is optimized by using the convex optimization tool to obtain the block length (m) allocated on the linki，1，mi，2) According to (m)i，1，mi，2) Obtaining the error probability epsilon of the transmission linkij。
The resource allocation method based on multiple relays in the low-delay network has the following advantages that:
the invention provides an optimization algorithm based on alternate iteration, minimizes the average error probability of system transmission, can obviously reduce the average error probability under the time delay limitation, and simultaneously meets the requirements of high reliability and low time delay.
Drawings
FIG. 1 is a schematic diagram of a system model of the present invention;
FIG. 2 is a schematic diagram of relay matching according to the present invention;
FIG. 3 is a comparison graph of the results of the alternating iteration-based optimization algorithm of the present invention with an bisection algorithm and a random matching algorithm.
Detailed Description
In order to better understand the purpose, structure and function of the present invention, the following describes the resource allocation method based on multiple relays in a low latency network in detail with reference to the accompanying drawings.
The invention comprises the following steps:
step one, in a low-delay scene, short packet transmission is carried out, and the error probability of a receiving end is as follows:
Figure BDA0003058501770000071
wherein, γ represents the signal-to-noise ratio, and m is the block length transmitted in the channel, i.e. the number of symbols; d is the number of transmitted data bits; the Q function is
Figure BDA0003058501770000072
C(γ)＝log2(1+ γ) represents the shannon capacity; v (gamma) ═ 1- (1+ gamma)-2Representing the channel dispersion.
Step two, establishing a low-delay communication network, wherein the low-delay communication network comprises a data collection center node C, NAA data source node AiAnd NBA relay node BjTo a low latency communication network. Let NA＝4，NB＝14；
Each data source node Ai(i 1, 2.., 4) selecting a matching relay node Bj(j 1, 2.., 14), and simultaneously transmitting data to the central node C within a time delay range;
each relay node BjOnly one data source node A can be forwarded in the transmission processiThe signal of (a);
step 3, for each data source node AiTransmitting, data source node AiCoding D-100 bits data into mi，1A symbol transmitted to a relay node B matched therewithj(ii) a D represents the size of the transmitted data bit quantity, and bits is a unit; relay node BjReceiving data from a data source node AiAfter the signal is demodulated, the demodulated D-100 bits data is coded into mi，2The symbol is forwarded to the central node C to form a slave data source node AiTo the relay node BjAnd then to the central node C, m for satisfying the requirement of time delay limitationi，1+mi，2And less than or equal to M, wherein M is the total number of transmission symbols.
Step 4, the channel in the low-delay network isA quasi-static fading channel, which remains unchanged during the whole transmission process; data source node AiAnd relay node BjSignal to noise ratio therebetween
Figure BDA0003058501770000073
Relay node BjAnd center node C
Figure BDA0003058501770000074
Wherein the content of the first and second substances,
Figure BDA0003058501770000075
is represented by AiAnd BjThe channel fading coefficient in between is determined,
Figure BDA0003058501770000076
is represented by BjAnd the channel fading coefficient between C and C,
Figure BDA0003058501770000077
is represented by AiAnd BjThe average received signal-to-noise ratio of all transmission links in between,
Figure BDA0003058501770000078
is represented by BjAnd C, average received signal-to-noise ratio of all transmission links; is provided with
Figure BDA0003058501770000079
Step 5, obtaining each data source node AiCorresponding slave data source node AiTo the relay node BjThe error probability of the transmission link to the central node C is:
Figure BDA0003058501770000081
wherein
Figure BDA0003058501770000082
Is represented by AiAnd BjTransmission error probability therebetween，
Figure BDA0003058501770000083
Figure BDA0003058501770000084
Is represented by BjAnd transmission error probability between C
Step 6, establishing an optimization target of minimizing the average error probability of all transmission links in the whole system, and taking m asi，1，mi，2，
Figure BDA0003058501770000085
To optimize the mathematical model of the variables, the optimization problem is as follows:
Figure BDA0003058501770000086
mi，1+mi，2≤M
Figure BDA0003058501770000087
Figure BDA0003058501770000088
Figure BDA0003058501770000089
Figure BDA00030585017700000810
wherein epsilonaveRepresenting the average error probability of all transmission links in the whole system;
Figure BDA00030585017700000811
represents mi，1，mi，2Is a positive integer;
Figure BDA00030585017700000812
represents a set of positive integers;
Figure BDA00030585017700000813
represents a relay matching state when
Figure BDA00030585017700000814
Is represented by AiSelection BjCompleting signal transmission as a relay, otherwise
Figure BDA00030585017700000815
Figure BDA00030585017700000816
Figure BDA00030585017700000817
Indicating that each relay can only forward signals of at most one data source node, NBThe number of the relay nodes is;
Figure BDA00030585017700000818
indicating per data source selection and only one relayed signal, NAThe number of the data source nodes is;
step 7, using a contradiction theory to prove that one optimal solution satisfies m in all optimal solutions of the optimization problem in step 6i，1+mi，2＝M
And 8, based on the step 6 and the step 7, rewriting the optimization problem into:
Figure BDA0003058501770000091
Figure BDA0003058501770000092
Figure BDA0003058501770000093
Figure BDA0003058501770000094
Figure BDA0003058501770000095
Figure BDA0003058501770000096
and 9, the optimization problem in the step 8 is an integer optimization problem, in order to solve the problem, a joint optimization algorithm based on alternate iteration is adopted, the optimization problem is divided into two sub-problems of relay matching and block length distribution, and the two sub-problems are alternately iterated until the optimization target is converged.
The specific implementation process is as follows:
step 9.1, initialization: transmission symbol interval limit M, all data source node allocated block length
Figure BDA0003058501770000097
Figure BDA0003058501770000098
All relay nodes allocate block length
Figure BDA0003058501770000099
Step 9.2, utilize m1And m2Obtaining an initial error probability matrix
Figure BDA00030585017700000910
Step 9.3, fix the error probability matrix
Figure BDA00030585017700000911
Modeling a relay matching network as a weighted bipartite graph, the weight epsilon of each edgeij. Using JVC (Jonker-Volgenant) which is a two-dimensional matching algorithm in graph theory
Figure BDA00030585017700000912
The relay matching is carried out by the algorithm to obtain a relay matching matrix
Figure BDA00030585017700000913
Step 9.4, fix the relay matching matrix
Figure BDA00030585017700000914
The block length distribution is carried out, and the method specifically comprises the following steps:
(1) blue
Figure BDA00030585017700000915
In time, block length allocation is not performed, corresponding to link Ai→BjAllocated block length (m) on → Ci，1，mi，2) The value of (c) remains unchanged.
(2) Blue
Figure BDA00030585017700000916
For transmission link Ai→BjAllocated block length on → C. The block length assignment problem at this time is expressed as:
Figure BDA00030585017700000917
mi，1+mi，2＝M
Figure BDA00030585017700000918
(3) rewriting the optimization target of step (2) into
Figure BDA0003058501770000101
(4) To pair
Figure BDA0003058501770000102
At point (m)i，1，mi，2) The convexity of the part is verified
(5) Optimizing the block length distribution problem by using a convex optimization tool to obtain the block length (m) distributed on the linki，1，mi，2) According to (m)i，1，mi，2) And obtaining the error probability of the transmission link, and updating the error probability matrix.
Step 9.5, according to the error probability matrix
Figure BDA0003058501770000103
Positive relay matching matrix
Figure BDA0003058501770000104
Calculating epsilonave。
Step 9.6, circulating step 9.3-9.5 until epsilonaveAnd (6) converging.
To demonstrate the superiority of the proposed algorithm, two comparative algorithms were proposed:
a) an equal division algorithm: fixing the block length of each data source and relay allocation to be
Figure BDA0003058501770000105
And then carrying out relay matching by using a relay matching algorithm based on a JVC algorithm.
b) A random matching algorithm: and randomly giving a weight coefficient matrix in a two-dimensional matching algorithm, and after obtaining a corresponding matching matrix by using a JVC algorithm, distributing the transmission block length of each node by using a block length distribution algorithm. The result shows that the performance of the proposed algorithm is superior to that of the bisection algorithm and the random matching algorithm. The algorithm alternately optimizes the relay matching and the block length distribution, and can obviously reduce the error probability of the system.
It is to be understood that the present invention has been described with reference to certain embodiments, and that various changes in the features and embodiments, or equivalent substitutions may be made therein by those skilled in the art without departing from the spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.

Claims (4)

1. A resource allocation method based on multiple relays in a low-delay network is characterized by comprising the following steps:
step 1, in a low-delay scene, short packet transmission is carried out, and the error probability of a receiving end is as follows:
Figure FDA0003058501760000011
wherein, γ represents the signal-to-noise ratio, and m is the block length transmitted in the channel, i.e. the number of symbols; d is the number of transmitted data bits; the Q function is
Figure FDA0003058501760000012
C(γ)＝log2(1+ γ) represents the shannon capacity; v (gamma) ═ 1- (1+ gamma)-2Representing the channel dispersion;
step 2, establishing a low-delay communication network, wherein the low-delay communication network comprises a data collection central node C, NAA data source node AiAnd NBA relay node BjA low latency communication network of (a); wherein N isB≥NA；
Each data source node Ai(i＝1,2,...,NA) Selecting a matching relay node Bj(j＝1,2,...,NB) Simultaneously sending data to the central node C within the time delay range;
each relay node BjOnly one data source node A can be forwarded in the transmission processiThe signal of (a);
step 3, for each data source node AiTransmitting, data source node AiEncoding the Dbits data into mi,1A symbol transmitted to a relay node B matched therewithj(ii) a Generation DThe size of data bit quantity transmitted by the table, and bits is a unit; relay node BjReceiving data from a data source node AiAfter the signal(s), the demodulated Dbits data is encoded into mi,2The symbol is forwarded to the central node C to form a slave data source node AiTo the relay node BjAnd then to the central node C, m for satisfying the requirement of time delay limitationi,1+mi,2M is less than or equal to M, wherein M is the total number of transmission symbols;
step 4, the channel in the low-delay network is a quasi-static fading channel and is kept unchanged in the whole transmission process; data source node AiAnd relay node BjSignal to noise ratio therebetween
Figure FDA0003058501760000013
Relay node BjAnd center node C
Figure FDA0003058501760000021
Wherein the content of the first and second substances,
Figure FDA0003058501760000022
is represented by AiAnd BjThe channel fading coefficient in between is determined,
Figure FDA0003058501760000023
is represented by BjAnd the channel fading coefficient between C and C,
Figure FDA0003058501760000024
is represented by AiAnd BjThe average received signal-to-noise ratio of all transmission links in between,
Figure FDA0003058501760000025
is represented by BjAnd C, average received signal-to-noise ratio of all transmission links;
step 5, obtaining each data source node AiCorresponding slave data source node AiTo the relay node BjTransmission link to central node CThe error probability of (2) is:
Figure FDA0003058501760000026
wherein
Figure FDA0003058501760000027
Is represented by AiAnd BjThe probability of a transmission error in-between,
Figure FDA0003058501760000028
Figure FDA0003058501760000029
is represented by BjAnd a transmission error probability between C;
step 6, establishing an optimization target of minimizing the average error probability of all transmission links in the whole system, and taking m asi,1，mi,2，
Figure FDA00030585017600000210
To optimize the mathematical model of the variables, the optimization problem is as follows:
Figure FDA00030585017600000211
mi,1+mi,2≤M
Figure FDA00030585017600000212
Figure FDA00030585017600000213
Figure FDA00030585017600000214
Figure FDA00030585017600000215
wherein epsilonaveRepresenting the average error probability of all transmission links in the whole system;
Figure FDA00030585017600000216
represents mi,1,mi,2Is a positive integer;
Figure FDA00030585017600000217
represents a set of positive integers;
Figure FDA00030585017600000218
represents a relay matching state when
Figure FDA00030585017600000219
Is represented by AiSelection BjCompleting signal transmission as a relay, otherwise
Figure FDA00030585017600000220
Figure FDA00030585017600000221
Indicating that each relay can only forward signals of at most one data source node, NBThe number of the relay nodes is;
Figure FDA0003058501760000031
indicating per data source selection and only one relayed signal, NAThe number of the data source nodes is;
step 7, using a contradiction theory to prove that one optimal solution satisfies m in all optimal solutions of the optimization problem in step 6i,1+mi,2＝M；
And 8, based on the step 6 and the step 7, rewriting the optimization problem into:
Figure FDA0003058501760000032
mi,1+mi,2＝M
Figure FDA0003058501760000033
Figure FDA0003058501760000034
Figure FDA0003058501760000035
Figure FDA0003058501760000036
and 9, the optimization problem in the step 8 is an integer optimization problem, in order to solve the problem, a joint optimization algorithm based on alternate iteration is adopted, the optimization problem is divided into two sub-problems of relay matching and block length distribution, and the two sub-problems are alternately iterated until the optimization target is converged.
2. The method according to claim 1, wherein the joint optimization algorithm based on alternate iteration in step 9 comprises the following steps:
step 9.1, initialize the block length distribution matrix
Figure FDA0003058501760000037
Step 9.2, obtaining an error probability matrix by using the initial block length distribution matrix
Figure FDA0003058501760000038
Step 9.3, fix the error probability matrix
Figure FDA0003058501760000039
Carrying out relay matching to obtain a relay matching matrix
Figure FDA00030585017600000310
Step 9.4, fix the relay matching matrix
Figure FDA00030585017600000311
Block length assignment is performed for each AiBlock length (m) on the corresponding transmission linki,1,mi,2) Is optimized according to (m)i,1,mi,2) Obtaining the error probability epsilon of the transmission linkijThen updating the error probability matrix
Figure FDA00030585017600000312
Step 9.5, based on ε and
Figure FDA00030585017600000313
calculating epsilonave；
Step 9.6, loop step 3 through step 5 until εaveAnd (6) converging.
3. The method according to claim 2, wherein the step 9.3 comprises the following steps:
step 9.3.1, fix the error probability matrix
Figure FDA0003058501760000041
And performing relay matching, wherein the relay matching problem is expressed as follows:
Figure FDA0003058501760000042
Figure FDA0003058501760000043
Figure FDA0003058501760000044
Figure FDA0003058501760000045
9.3.2, modeling the relay matching network as a weighted bipartite graph, abstracting the network nodes as vertexes of the bipartite graph, dividing the data source nodes into a set A, dividing the relay nodes into a set B, wherein each node in A and B is connected in pairs, and each edge A is connected with each otheriBjIs weighted from the data source node AiTo the relay node BjUplink transmission error probability epsilon of transmission link to central node Cij；
Step 9.3.3 for NB≥NAAnd carrying out relay matching by utilizing a two-dimensional matching algorithm JVC algorithm to obtain a relay matching matrix
Figure FDA0003058501760000046
4. The method according to claim 2, wherein the step 9.4 comprises the following steps:
step 9.4.1 when
Figure FDA0003058501760000047
When, AiIs not in the slave data source node AiTo the relay node BjThen, the signal is transmitted to the transmission link of the central node C, and the link is transmittedTransport block length (m) on the roadi,1,mi,2) The value of (d) remains unchanged;
step 9.4.2, when
Figure FDA0003058501760000048
When, AiMatched with corresponding relay node BjAt the corresponding slave data source node AiTo the relay node BjThen, the transmission link to the central node C carries out short packet transmission, and the transmission block length (m) on the link is longi,1,mi,2) Optimizing to make the transmission error probability epsilon of the linkijAt a minimum, the block length assignment problem is expressed as follows:
Figure FDA0003058501760000051
step 9.4.3, rewriting the optimization target of step 2 into
Figure FDA0003058501760000052
Step 9.4.4, pair
Figure FDA0003058501760000053
At point (m)i,1,mi,2) The convexity of the part is verified;
step 9.4.5, the block length allocation problem is optimized by using the convex optimization tool to obtain the block length (m) allocated on the linki,1,mi,2) According to (m)i,1,mi,2) Obtaining the error probability epsilon of the transmission linkij。
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