CN113556195A - Interrupt probability-based multi-user cooperative wireless transmission network performance prediction method - Google Patents

Interrupt probability-based multi-user cooperative wireless transmission network performance prediction method Download PDF

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CN113556195A
CN113556195A CN202110824389.1A CN202110824389A CN113556195A CN 113556195 A CN113556195 A CN 113556195A CN 202110824389 A CN202110824389 A CN 202110824389A CN 113556195 A CN113556195 A CN 113556195A
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CN113556195B (en
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黄海燕
师玉洁
张学军
李亚红
王春丽
李新颖
张鸿生
李翔
张宁
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Lanzhou Jiaotong University
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
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Abstract

The invention discloses a multi-user cooperative wireless transmission network performance prediction method based on interruption probability, which comprises the following steps: step 1: calculating the signal-to-interference ratio delta from the source node to the relay node according to the channel state informationsrSignal-to-interference ratio delta from source node to destination nodesdAnd the signal-to-interference ratio delta from the relay node to the destination noderd(ii) a Step 2: selecting optimal source node s in multi-source multi-relay cooperative wireless networkbAnd an optimal relay node rb(ii) a And step 3: best source node sbSending information to the best relay node rbAnd a destination node d, an optimal relay node rbA received source node signal ysbrbDecoding and forwarding to a destination node d; and 4, step 4: the destination node d adopts a selective combination method to combine the signals received by the two time slots and calculate the end-to-end signal-to-noise-and-interference ratio gammaend SC(ii) a And 5: for multi-user cooperative radioThe network performs outage probability analysis.

Description

Interrupt probability-based multi-user cooperative wireless transmission network performance prediction method
Technical Field
The invention belongs to the cooperative relay technology, and particularly relates to a multi-user cooperative wireless transmission network performance prediction method based on interruption probability.
Background
With the development of society and the continuous improvement of information technology level, mobile wireless communication has become an indispensable part of people's daily life. In order to meet the demand of people for high quality and high performance of communication services, cooperative communication is proposed as a new idea and a new technology is provided.
Typical cooperative relay transmission includes three-node single-relay two-hop cooperative relay transmission of a source node, a relay node and a destination node. In a single relay transmission system, a communication node and other nodes share resources by utilizing the broadcast transmission characteristics of wireless communication to form a virtual antenna array, so that the spatial gain similar to that of the MIMO technology can be obtained. In order to improve the spectrum utilization rate and the system capacity in the MIMO technology, a plurality of antennas need to be installed at the transmitting and receiving ends of the communication nodes to form a plurality of parallel independent transmission channels. Unlike MIMO technology, cooperative communication provides a new direction of research, namely, a communication node shares antennas and frequencies with other network nodes nearby, and a communication link is no longer point-to-point, but covers the whole network system. The cooperative communication network improves the space diversity gain of the communication system, enhances the reliability of information transmission, improves the capacity of the network and improves the system performance.
With the development of cooperative communication, a relay cooperation model is also continuously changed, and the single-source single-relay cooperation model is developed into a single-source multi-relay cooperation model and then is extended into a multi-source multi-relay cooperation model. And the multi-source multi-relay model is closer to reality in a real wireless network.
Disclosure of Invention
The invention aims to provide a multi-user cooperative wireless transmission network performance prediction method based on interruption probability, which comprises the following steps:
the multi-user wireless transmission network model comprises M source nodes smN relay nodes rnAnd the destination node d consists of Q co-channel interferences at the relay node and T co-channel interferences at the destination node.
1≤m≤M,1≤n≤N，1≤q≤Q,1≤t≤T。
Each node is equipped with a single omni-directional antenna.
Step 1: calculating the signal-to-interference ratio delta from the source node to the relay node according to the obtained channel state informationsrSignal-to-interference ratio delta from source node to destination nodesdAnd the signal-to-interference ratio delta from the relay node to the destination noderd；
Step 2: selecting the best source node sbAnd an optimal relay node rb(ii) a Step 1 and step 2 are prior art and are directly transferred here.
Step 2.1: at all source nodes smOn the direct link to the destination node d, the source node with the maximum SINR is selected as the optimal source node sb。
Figure BDA0003173072480000021
Step 2.2: selecting an optimal relay node r by a relay selection method of an Opportunistic Relay (OR)b。
Figure BDA0003173072480000022
And step 3: in the first time slot, the best source node sbSending information to the best relay node rbAnd a destination node d;
optimal relay node rbReceivedThe signals are:
Figure BDA0003173072480000023
where P is all source nodes smAnd all relay nodes rnTransmit power of PIAs interference power, xsFor signals transmitted by the source node, xqIn order to interfere with the signal, it is,
Figure BDA0003173072480000024
representing the best source node sbTo the optimal relay node rbThe channel coefficients of (a) are determined,
Figure BDA0003173072480000025
representing interference q to the best relay node rbThe channel coefficients of (a) are determined,
Figure BDA0003173072480000026
representing noise.
rbHas an SINR of
Figure BDA0003173072480000027
Wherein
Figure BDA0003173072480000028
Representing the best source node sbTo the optimal relay node rbChannel gain of, N0Representing the variance of additive white gaussian noise.
The destination node d receives a signal of
Figure BDA0003173072480000029
Wherein xtFor the purpose of the interfering signal at the destination node,
Figure BDA00031730724800000210
to representBest source node sbChannel coefficient to destination node d, htdRepresenting the channel coefficients of the interference t to the best relay node d,
Figure BDA00031730724800000211
representing noise.
SINR at d is
Figure BDA0003173072480000031
Wherein
Figure BDA0003173072480000032
Representing the best source node sbChannel gain, N, to destination node d0Representing the variance of additive white gaussian noise.
In the second time slot, the best relay node rbSource node signal to be received
Figure BDA0003173072480000033
Decoding and forwarding to a destination node d; d the received signal is
Figure BDA0003173072480000034
Wherein
Figure BDA0003173072480000035
Indicating the best relay node rbChannel coefficient to destination node d, htdRepresenting the channel coefficients of the interference t to the destination node d,
Figure BDA0003173072480000036
representing noise, xtFor interfering signals at the destination node, xsA signal sent for a source node.
SINR at destination node d is
Figure BDA0003173072480000037
Wherein
Figure BDA0003173072480000038
Indicating the best relay node rbChannel gain, N, to destination node d0Representing the variance of additive white gaussian noise.
And 4, step 4: the destination node d adopts the selective combination method to the signals received by the two time slots
Figure BDA0003173072480000039
And
Figure BDA00031730724800000310
merging, namely selecting one path with the maximum SINR from the direct link and the relay link, wherein the end-to-end signal-to-noise-and-interference ratio is
Figure BDA00031730724800000311
And 5: the outage probability is the most important performance index of the cooperative wireless network. Interruption probability analysis for multi-user cooperative wireless networks, i.e. gammaend SCGamma below thresholdth，γth＝22R-1. Namely, it is
Pout SC＝Pr(sb|γend SC＜γth) (4-1)
Calculating and selecting the optimal source node s according to the formula (1-1)bHas a probability of
Figure BDA00031730724800000312
Calculating the signal-to-noise-and-interference ratio of the relay link to be less than a threshold value gamma according to a formula (1-2)thProbability of (2)
Figure BDA0003173072480000041
Wherein the probability density function of the interference at the interrupt node is
Figure BDA0003173072480000042
Computing end-to-end SINR gammaend SCLess than threshold value gammathHas a probability of
Figure BDA0003173072480000043
Wherein the probability density function of interference at the destination node is
Figure BDA0003173072480000044
The system outage probability can be calculated as
Figure BDA0003173072480000045
Drawings
FIG. 1 is a model diagram of a method for predicting performance of a multi-user cooperative wireless transmission network based on outage probability in the presence of co-channel interference;
FIG. 2 is a graph of system outage probability as a function of SIR γ for different numbers of source nodes M;
fig. 3 is a curve of the system outage probability with the SIR γ at different numbers of relay nodes M;
fig. 4 is a graph of the outage probability as a function of the system outage probability as a function of the signal-to-noise ratio SIR γ at different interferences Q, T.
Detailed Description
The invention is further described with reference to the following figures and specific embodiments. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. Functional details disclosed herein are merely illustrative of example embodiments of the invention. This invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.
It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments of the invention. When the terms "comprises," "comprising," "includes," and/or "including" are used herein, they specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, numbers, steps, operations, elements, components, and/or groups thereof.
It should also be noted that, in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed substantially concurrently, or the figures may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
It should be understood that specific details are provided in the following description to facilitate a thorough understanding of example embodiments. However, it will be understood by those of ordinary skill in the art that the example embodiments may be practiced without these specific details. For example, systems may be shown in block diagrams in order not to obscure the examples in unnecessary detail. In other instances, well-known processes, structures and techniques may be shown without unnecessary detail in order to avoid obscuring example embodiments.
Example 1:
the multi-user cooperative wireless transmission network performance prediction method based on the interruption probability comprises the following steps:
establishing a multi-user wireless transmission network model, which comprises a plurality of source nodes, a plurality of relay nodes and a target node;
step 1, calculating the signal-to-interference ratio delta from a source node to a relay node according to the obtained channel state informationsrSignal-to-interference ratio delta from source node to destination nodesdAnd signal-to-interference ratio of relay node to destination nodeδrd；
Step 2: in a multi-source multi-relay cooperative wireless network, an optimal source node and an optimal relay node are selected.
Step 2.1: at all source nodes smOn the direct link to the destination node d, the source node with the maximum SINR is selected as the optimal source node sb。
Step 2.2: selecting an optimal relay node r by a relay selection method of an Opportunistic Relay (OR)b。
And step 3: in the first time slot, the best source node sbSending information to the best relay node rbAnd a destination node d;
optimal relay node rbThe received signal is
Figure BDA0003173072480000061
Optimal relay node rbHas an SINR of
Figure BDA0003173072480000062
The destination node d receives a signal of
Figure BDA0003173072480000063
SINR at destination node d is
Figure BDA0003173072480000064
And 4, step 4: in the second time slot, the best relay node rbSource node signal to be received
Figure BDA0003173072480000065
Decoding and forwarding to a destination node d;
the signal received by the destination node d is:
Figure BDA0003173072480000066
the SINR at destination node d is:
Figure BDA0003173072480000067
and 5: the destination node d adopts the selective combination method to the signals received by the two time slots
Figure BDA0003173072480000068
And
Figure BDA0003173072480000069
and merging, namely, one path with the selected maximum SINR in the direct link and the relay link.
Step 6: the outage probability is the most important performance index of the cooperative wireless network. And carrying out interruption probability analysis on the multi-user cooperative wireless network.
Example 2:
the multi-user wireless transmission network model comprises M source nodes smN relay nodes rnAnd the destination node d consists of Q co-channel interferences at the relay node and T co-channel interferences at the destination node.
1≤m≤M,1≤n≤N，1≤q≤Q,1≤t≤T。
Each node is equipped with a single omni-directional antenna.
Step 1, calculating the signal-to-interference ratio delta from a source node to a relay node according to the obtained channel state informationsrSignal-to-interference ratio delta from source node to destination nodesdAnd the signal-to-interference ratio delta from the relay node to the destination noderd；
Step 2.1: at all source nodes smOn the direct link to the destination node d, the source node with the maximum SINR is selected as the optimal source node sb。
Figure BDA0003173072480000071
Step 2.2: selecting an optimal relay node r by a relay selection method of an Opportunistic Relay (OR)b。
Figure BDA0003173072480000072
And step 3: in the first time slot of the time slot,best source node sbSending information to the best relay node rbAnd a destination node d;
optimal relay node rbThe received signals are:
Figure BDA0003173072480000073
where P is all source nodes smAnd a relay node rnTransmit power of PIAs interference power, xsFor signals transmitted by the source node, xqIn order to interfere with the signal, it is,
Figure BDA0003173072480000074
representing the best source node sbTo the optimal relay node rbThe channel coefficients of (a) are determined,
Figure BDA0003173072480000075
representing interference q to the best relay node rbThe channel coefficients of (a) are determined,
Figure BDA0003173072480000076
representing noise.
rbHas an SINR of
Figure BDA0003173072480000077
Wherein
Figure BDA0003173072480000078
Figure BDA0003173072480000079
Representing the best source node sbTo the optimal relay node rbChannel gain of, N0Representing the variance of additive white gaussian noise.
The destination node d receives a signal of
Figure BDA00031730724800000710
Wherein xtFor the purpose of the interfering signal at the destination node,
Figure BDA00031730724800000711
representing the best source node sbChannel coefficient to destination node d, htdRepresenting the channel coefficients of the interference t to the best relay node d,
Figure BDA00031730724800000712
representing noise.
SINR at d is
Figure BDA00031730724800000713
Wherein
Figure BDA00031730724800000714
Figure BDA00031730724800000715
Representing the best source node sbChannel gain, N, to destination node d0Representing the variance of additive white gaussian noise.
And 4, step 4:
in the second time slot, the best relay node rbSource node signal to be received
Figure BDA0003173072480000081
Decoding and forwarding to a destination node d; d the received signal is
Figure BDA0003173072480000082
Wherein
Figure BDA0003173072480000083
Indicating the best relay node rbChannel coefficient to destination node d, hqdRepresenting the interference q toThe channel coefficient of the destination node d,
Figure BDA0003173072480000084
representing noise.
SINR at destination node d is
Figure BDA0003173072480000085
Wherein
Figure BDA0003173072480000086
Figure BDA0003173072480000087
Indicating the best relay node rbChannel gain, N, to destination node d0Representing the variance of additive white gaussian noise.
And 5: the destination node d adopts the selective combination method to the signals received by the two time slots
Figure BDA0003173072480000088
And
Figure BDA0003173072480000089
merging, namely selecting one path with the maximum SINR from the direct link and the relay link, wherein the end-to-end signal-to-noise-and-interference ratio is
Figure BDA00031730724800000810
Step 6: the outage probability is the most important performance index of the cooperative wireless network. Interruption probability analysis for multi-user cooperative wireless networks, i.e. gammaend SCGamma below thresholdth，γth＝22R-1. Namely, it is
Pout SC＝Pr(sb|γend SC＜γth) (5-1)
Calculating and selecting the optimal source node s according to the formula (1-1)bHas a probability of
Figure BDA00031730724800000811
Calculating the signal-to-noise-and-interference ratio of the relay link to be less than a threshold value gamma according to a formula (1-2)thProbability of (2)
Figure BDA00031730724800000812
Wherein the probability density function of the interference at the interrupt node is
Figure BDA0003173072480000091
Computing end-to-end SINR gammaend SCLess than threshold value gammathHas a probability of
Figure BDA0003173072480000092
Wherein the probability density function of interference at the destination node is
Figure BDA0003173072480000093
The system outage probability can be calculated as
Figure BDA0003173072480000094
Example 3:
simulation conditions
Figure BDA0003173072480000095
σ2 td＝σ2 d
1≤m≤M,1≤n≤N，1≤q≤Q,1≤t≤T
Wherein sigma2 srRepresenting a source node smTo the relay node rnThe variance, σ, of the Rayleigh channel fading coefficients therebetween2 rdIndicates a relay node rnVariance, σ, of Rayleigh channel fading coefficients to destination node d2 rIndicates a relay node rnThe Rayleigh fading coefficient variance, σ, of the interfering link2 dRepresenting the rayleigh fading coefficient variance of the interfering link at the destination node d.
Simulation 1: fig. 2 depicts the effect on the system outage probability when the signal-to-noise ratio γ changes and the outage probability versus the number of source nodes for the same number of relay nodes and co-channel interference. It can be clearly seen that the analysis results are consistent with the simulation results, verifying the validity of the derivation. Taking sigma under the condition that M is 1,4 and 8sr 2＝8dB，σrd 2＝8dB,σsd 2＝8dB,σr 2＝1dB,σd 2When N is 4, Q is 4, and T is 4, the outage probability of the system decreases with increasing SNR γ, i.e., the larger the SNR is, the better the system performance is, and when a system outage threshold is given, the transmission power can be increased appropriately. Meanwhile, the influence on the system performance when the number of the source nodes is changed is given, namely, when the number M of the source nodes is increased, the interruption probability of the system is reduced.
Example 4:
fig. 3 depicts the outage probability versus the number of relay nodes for the same number of source nodes and co-channel interference. Taking sigma under the condition that the number N of the relay nodes takes 1,4 and 8sr 2＝8dB，σrd 2＝8dB,σsd 2＝8dB,σr 2＝1dB,σd 21dB, M5, Q4, and T4, it can be seen that the outage probability of the system decreases with increasing SNR γ, i.e., the larger the signal-to-noise ratio, the better the system performance. Meanwhile, when the number of the relay nodes N increases, the outage probability of the system decreases. By combining fig. 2 and fig. 3, the system performance can be improved by increasing the number of source nodes or the number of relay nodes.
Example 5:
fig. 4 describes the relationship between the outage probability and the co-channel interference under the same number of source nodes and relay nodes, and studies the magnitude relationship between the impact of the relay node interference Q and the impact of the destination node interference T on the system outage probability. The relay node interference Q and the destination node interference T are respectively Q-10 and T-10; q is 2, T is 10; q is 10, T is 2; q is 2, and T is 2sr 2＝8dB，σrd 2＝8dB,σsd 2＝8dB，σr 2＝1dB，σd 2The system outage probability is reduced as the SNR γ increases, and when the interference decreases, the system outage probability decreases, i.e., the larger the interference, the worse the system performance, and the magnitude of the outage probability decrease when the destination node interference T decreases is larger than that when the relay node interference Q decreases, i.e., the interference at the destination node has a larger effect on the system performance than the interference at the relay node has on the system performance.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and they may alternatively be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, or fabricated separately as individual integrated circuit modules, or fabricated as a single integrated circuit module from multiple modules or steps. Thus, the present invention is not limited to any specific combination of hardware and software.
The embodiments described above are merely illustrative, and may or may not be physically separate, if referring to units illustrated as separate components; if a component displayed as a unit is referred to, it may or may not be a physical unit, and may be located in one place or distributed over a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment. Can be understood and implemented by those skilled in the art without inventive effort.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: modifications of the technical solutions described in the embodiments or equivalent replacements of some technical features may still be made. And such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
The present invention is not limited to the above-described alternative embodiments, and various other forms of products can be obtained by anyone in light of the present invention. The above detailed description should not be taken as limiting the scope of the invention, which is defined in the claims, and which the description is intended to be interpreted accordingly.

Claims (10)

1. A multi-user cooperative wireless transmission network performance prediction method based on interruption probability is used for establishing a multi-user wireless transmission network model, and the multi-user wireless transmission network model comprises a plurality of source nodes, a plurality of relay nodes and a target node, wherein each node is provided with a single omnidirectional antenna, and the method is characterized by comprising the following steps:
step 1: calculating the signal-to-interference ratio delta from the source node to the relay node according to the obtained channel state informationsrSignal-to-interference ratio delta from source node to destination nodesdAnd the signal-to-interference ratio delta from the relay node to the destination noderd；
Step 2: selecting the best source node sbAnd an optimal relay node rb；
And step 3: in the first time slot, the best source node sbSending information to the best relay node rbAnd destination node d, optimal relay node sbThe received signal is
Figure FDA0003173072470000011
The best relay node s at this timebHas a signal-to-noise-and-interference ratio SINR of
Figure FDA0003173072470000012
The destination node d receives a signal of
Figure FDA0003173072470000013
The signal-to-noise-and-interference ratio SINR of the destination node d is
Figure FDA0003173072470000014
In the second time slot, the best relay node rbSource node signal to be received
Figure FDA0003173072470000015
Decoded and forwarded to a destination node d, which receives a signal of
Figure FDA0003173072470000016
The signal-to-noise-and-interference ratio SINR of the destination node d is
Figure FDA0003173072470000017
And 4, step 4: the destination node d adopts a selective combination method to combine the signals received by the two time slots and calculate the end-to-end signal-to-noise-and-interference ratio gammaend SC；
And 5: the method for analyzing the interruption probability of the multi-user cooperative wireless network specifically comprises the following steps:
step 5.1: calculating the probability of selecting the optimal source node;
step 5.2: calculating the probability that the signal-to-noise-and-interference ratio of the interrupted link is smaller than a threshold value;
step 5.3: computing end-to-end SINR gammaend SCLess than threshold value gammathThe probability of (d);
step 5.4: and calculating the system outage probability.
2. The outage probability-based multi-user cooperative wireless transmission network performance prediction method according to claim 1, characterized in that: the step 2 specifically comprises the following steps:
step 2.1: at all source nodes smOn the direct link to the destination node d, the source node with the maximum SINR is selected as the optimal source node sb；
Figure FDA0003173072470000018
Step 2.2: selecting an optimal relay node r by a relay selection method of an Opportunistic Relay (OR)b。
Figure FDA0003173072470000019
3. The outage probability-based multi-user cooperative wireless transmission network performance prediction method according to claim 2, characterized in that: the step 3: in the first time slot, the best relay node rbThe received signals are:
Figure FDA0003173072470000021
where P is all source nodes smAnd all relay nodes rnTransmit power of PIAs interference power, xsFor signals transmitted by the source node, xqIn order to interfere with the signal, it is,
Figure FDA0003173072470000022
representing the best source node sbTo the optimal relay node rbThe channel coefficients of (a) are determined,
Figure FDA0003173072470000023
representing interference q to the best relay node rbThe channel coefficients of (a) are determined,
Figure FDA0003173072470000024
representing noise;
rbthe SINR of (1) is:
Figure FDA0003173072470000025
wherein
Figure FDA0003173072470000026
Figure FDA0003173072470000027
Representing the best source node sbTo the optimal relay node rbThe channel gain of (a) is determined,
Figure FDA0003173072470000028
representing q to the best relay node rbChannel gain of, N0A variance representing additive white gaussian noise;
the signal received by the destination node d is:
Figure FDA0003173072470000029
wherein xtFor the purpose of the interfering signal at the destination node,
Figure FDA00031730724700000210
representing the best source node sbChannel coefficient to destination node d, htdRepresenting the channel coefficients of the interference t to the best relay node d,
Figure FDA00031730724700000211
representing noise;
the SINR at destination node d is:
Figure FDA00031730724700000212
wherein
Figure FDA00031730724700000213
Figure FDA00031730724700000214
Representing the best source node sbChannel gain, N, to destination node d0Variance, g, representing additive white Gaussian noisetdRepresenting the channel gain of the interference t to the best destination node d.
4. The outage probability-based multi-user cooperative wireless transmission network performance prediction method according to claim 3, characterized in that: in the second time slot, the best relay node rbSource node signal to be received
Figure FDA00031730724700000215
Decoding and forwarding to a destination node d;
the signal received by the destination node d is:
Figure FDA0003173072470000031
wherein
Figure FDA0003173072470000032
Indicating the best relay node rbChannel coefficient to destination node d, htdRepresenting the interference t to d channel coefficients,
Figure FDA0003173072470000033
representing noise;
the SINR at destination node d is:
Figure FDA0003173072470000034
wherein
Figure FDA0003173072470000035
Figure FDA0003173072470000036
Indicating the best relay node rbChannel gain, N, to destination node d0Representing the variance of additive white gaussian noise.
5. The outage probability-based multi-user cooperative wireless transmission network performance prediction method according to claim 4, characterized in that: the step 4 specifically comprises the following steps: the destination node d adopts the selective combination method to the signals received by the two time slots
Figure FDA0003173072470000037
And
Figure FDA0003173072470000038
merging, namely, one path with the maximum SINR is selected from the direct link and the relay link, and the end-to-end signal-to-noise-and-interference ratio is as follows:
Figure FDA0003173072470000039
6. the outage probability-based multi-user cooperative wireless transmission network performance prediction method according to claim 5, characterized in that: in said step 5,. gamma.end SCGamma below thresholdth，γth＝22R-1；
Namely: pout SC＝Pr(sb|γend SC＜γth) (5-1)
Step 5.1: calculating and selecting the optimal source node s according to the formula (1-1)bThe probability of (c) is:
Figure FDA00031730724700000310
7. the outage probability-based multi-user cooperative wireless transmission network performance prediction method according to claim 5, characterized in that: the step 5.2 is specifically as follows: calculating the signal-to-noise-and-interference ratio of the broken link to be less than a threshold value gamma according to a formula (1-2)thThe probability of (c) is:
Figure FDA0003173072470000041
wherein the probability density function of the interference at the interrupt node is:
Figure FDA0003173072470000042
8. the outage probability-based multi-user cooperative wireless transmission network performance prediction method according to claim 7, characterized in that: in said step 5.3, the end-to-end SINR γ is calculatedend SCLess than threshold value gammathThe probability of (d) specifically includes:
Figure FDA0003173072470000043
wherein the probability density function of interference at the destination node is:
Figure FDA0003173072470000044
9. the outage probability-based multi-user cooperative wireless transmission network performance prediction method according to claim 8, characterized in that: in step 5.4, the system outage probability is calculated as:
Figure FDA0003173072470000045
10. the outage probability-based multi-user cooperative wireless transmission network performance prediction method according to claim 1, characterized in that: the multi-user wireless transmission network model comprises M source nodes smN relay nodes rnAnd a destination node d, wherein Q co-channel interferences at the relay node and T co-channel interferences at the destination node are formed, wherein: m is more than or equal to 1 and less than or equal to M, N is more than or equal to 1 and less than or equal to N, Q is more than or equal to 1 and less than or equal to Q, and T is more than or equal to 1 and less than or equal to T.
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