CN109041128B - Resource allocation method for multi-source multi-relay cooperative network - Google Patents

Resource allocation method for multi-source multi-relay cooperative network Download PDF

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CN109041128B
CN109041128B CN201811202574.1A CN201811202574A CN109041128B CN 109041128 B CN109041128 B CN 109041128B CN 201811202574 A CN201811202574 A CN 201811202574A CN 109041128 B CN109041128 B CN 109041128B
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张兆维
师晓晔
吴尘
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Nanjing University of Posts and Telecommunications
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Abstract

The invention discloses a resource allocation method for a multi-source multi-relay cooperative network, which comprises the steps that a source node sets a profit allocation coefficient for relay nodes participating in cooperation, and the relay nodes participating in cooperation distribute profits on average; the relay node selects a source node based on the evolutionary game, and the source node adjusts the income distribution coefficient based on the Steinberg game until the source node and the relay node achieve a balanced solution at the same time. According to the invention, an evolutionary game is adopted among the relay nodes, a Steinberg game is adopted among the source nodes and the relay nodes, and multi-source multi-relay cooperative network resources are distributed through a double-layer game, so that the utilization efficiency of the network resources is effectively improved.

Description

Resource allocation method for multi-source multi-relay cooperative network
Technical Field
The invention relates to a resource allocation method for a multi-source multi-relay cooperative network, and belongs to the field of communication networks.
Background
A large number of user nodes (including a source node and a relay node) exist in the cooperative relay network, and the resource utilization efficiency can be improved by realizing the optimal allocation of network resources among a plurality of nodes. In the cooperative relay network, the signal transmission of the cooperative source node of the relay node can improve the transmission rate and the service quality of the source node, and the source node distributes part of the income of the source node to the relay nodes participating in the cooperation, thereby achieving the purpose of mutual win. In this process, how to allocate network resources becomes a key factor affecting the system performance.
Network resource allocation methods are mainly divided into two categories: centralized and distributed. The centralized distribution method requires that a network has a central node and is responsible for the whole calculation amount of network optimization, and then the optimal distribution result is distributed to all nodes of the network. The method has high requirements on the central node, cannot reflect the change of the network environment in real time, and is not suitable for the network with fast network topology change. The distributed distribution method disperses the network optimization goal to the sub-goals of each node, and each node is only responsible for optimizing the respective sub-goal, thereby realizing the distribution of network distribution calculation. In the distributed distribution method, nodes in the network are used as participants in the distribution method based on the game theory, and each participant optimizes the income function of the participant to achieve network equilibrium solution, so that the distribution method obtains wide attention. According to a specific network environment, a plurality of classical models of game theory are applied respectively. Wang B, Han Z and Liu K J R, etc. consider the case of a source node and a plurality of relay nodes in "Distributed relay selection and power control for multi-user cooperative communication network using Stackelberg gate" (IEEE Transactions on Mobile Computing, vol.8, No.7, pp.975-990,2009), and take the source node and the relay nodes as a buyer and a seller, respectively. And respectively optimizing the self revenue function based on the Steenberg model source node and the target node to achieve the purpose of relay selection. For the situation of a large number of Relay nodes and a plurality of source nodes in a Cooperative network, Zhang Z and Zhang h and the like construct a competition relationship between the Relay nodes by using an Evolutionary Game Model (source node is a policy, and Relay nodes are participants) in "a Variable-position evolution Game Model for Resource Allocation in Cooperative Game Networks" (IEEE Communications Letters, vol.17, No.2, pp.361-364,2013). Under the condition that the parameters of the source nodes are fixed, the relay nodes adjust the selection of the respective source nodes in real time according to the strategy revenue function, and finally reach the equilibrium solution. However, in the multi-source multi-relay cooperative network, most of the resource allocation methods based on the game theory only consider the revenue function of one of the source node and the relay node, and use the revenue function as a participant, so that the cooperation enthusiasm of the other party cannot be mobilized. Therefore, for a multi-source multi-relay cooperative network, how to design a game model to enable both a source node and a relay node to be used as participants so as to fully mobilize the enthusiasm of both the source node and the relay node becomes a problem to be solved urgently at present.
Disclosure of Invention
The invention provides a resource allocation method for a multi-source multi-relay cooperative network, which solves the problem that the traditional method only considers the income of one of a source node and a relay node and is difficult to mobilize the enthusiasm of the two parties.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a resource allocation method for a multi-source multi-relay cooperative network comprises the following steps,
the source node sets a profit distribution coefficient for the relay nodes participating in the cooperation, and the relay nodes participating in the cooperation distribute profits on average;
the relay node selects a source node based on the evolutionary game, and the source node adjusts the income distribution coefficient based on the Steinberg game until the source node and the relay node achieve a balanced solution at the same time.
The revenue function of the source node is,
Figure BDA0001830364090000031
wherein u iss(t) benefit of source node s at time t, AsA unit gain parameter, alpha, for the signal transmission rate of the source node ssAllocating coefficient, gamma, to the profit of source node s to relay nodes participating in cooperations(t) direct path signal-to-noise ratio, n, of source node s at time ts(t) is the number of relay nodes participating in the cooperation of the source node s at the moment t,
Figure BDA0001830364090000032
average signal-to-noise ratio increment for relay node participating in source node s cooperation
The revenue function of the relay nodes participating in the cooperation is,
Figure BDA0001830364090000033
wherein u isr,s(t) profit of relay node r participating in source node s cooperation at time t, AsA unit gain parameter, alpha, for the signal transmission rate of the source node ssAllocating coefficient, gamma, to the profit of source node s to relay nodes participating in cooperations(t) direct path signal-to-noise ratio, n, of source node s at time ts(t) is the number of relay nodes participating in the cooperation of the source node s at the moment t,
Figure BDA0001830364090000034
participating in source node for relay nodeMean signal-to-noise ratio increment for point s cooperation
In the evolutionary game, the dynamic formula of group replication is as follows,
Figure BDA0001830364090000035
wherein the content of the first and second substances,
Figure BDA0001830364090000036
is xs(t) derivative of time t, xs(t)＝ns(t)/N is the relay node proportion of the source node s, N is the total number of the relay nodes, Ns(t) the number of relay nodes participating in the cooperation of the source node s at the moment t, pis(t) is the policy return, equal to the relay node return,
Figure BDA0001830364090000037
in order to average out the benefits of the strategy,
Figure BDA0001830364090000038
and M is the total number of the source nodes.
In the Stainberg game, the source node is a main participant, the relay node is a secondary participant, and the profit distribution coefficient is adjusted to maximize the profit of the source node.
If the following formula is satisfied, judging that the source node and the relay node reach equilibrium solution simultaneously;
the concrete formula is that,
Figure BDA0001830364090000041
wherein the content of the first and second substances,
Figure BDA0001830364090000042
is xs(t) derivative of time t, xs(t)＝ns(t)/N is the relay node ratio of the source node s, us(t) the benefit of the source node s at time t, αsAnd distributing coefficients for the benefit of the source node s to the relay nodes participating in the cooperation.
The invention achieves the following beneficial effects: according to the invention, an evolutionary game is adopted among the relay nodes, a Steinberg game is adopted among the source nodes and the relay nodes, and multi-source multi-relay cooperative network resources are distributed through a double-layer game, so that the utilization efficiency of the network resources is effectively improved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of a multi-source multi-relay cooperative network;
FIG. 3 is a diagram of a revenue sharing factor adjustment process for a source node;
fig. 4 is an evolution process diagram of the number of relay nodes of different source nodes.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, a resource allocation method for a multi-source multi-relay cooperative network includes the following steps:
step 1, a source node sets a profit distribution coefficient for a relay node participating in cooperation, the relay node participating in cooperation distributes profits averagely, and the relay mode is amplification-forwarding.
The revenue function for the source node is:
Figure BDA0001830364090000051
wherein u iss(t) benefit of source node s at time t, AsA unit gain parameter, alpha, for the signal transmission rate of the source node ssAllocating coefficient to the profit of the relay node participating in the cooperation for the source node s, wherein alpha is more than or equal to 0s≤1，γs(t) direct path signal-to-noise ratio, n, of source node s at time ts(t) is the number of relay nodes participating in the cooperation of the source node s at the moment t,
Figure BDA0001830364090000052
participating in a source node for a relay nodes-coordinated mean signal-to-noise ratio delta
The revenue function of the relay nodes participating in the cooperation is as follows:
Figure BDA0001830364090000053
wherein u isr,sAnd (t) the profit of the relay node r participating in the cooperation of the source node s at the time t.
Step 2, the relay node selects a source node based on an evolutionary game; namely, based on the evolutionary game, each relay node selects one node from M source nodes as a cooperation strategy, wherein M is the total number of the source nodes.
In the evolutionary game, for the relay node selecting the source node s, the strategy benefit is equal to the benefit function of the relay node, that is:
Figure BDA0001830364090000054
wherein, pis(t) is the policy revenue.
The average policy yield is then:
Figure BDA0001830364090000055
wherein the content of the first and second substances,
Figure BDA0001830364090000056
for average policy revenue, xs(t)＝nsAnd (t)/N is the relay node proportion of the source node s, and N is the total number of the relay nodes.
At this time, the population replication dynamic formula is:
Figure BDA0001830364090000061
wherein the content of the first and second substances,
Figure BDA0001830364090000062
is xs(t) derivative of time t, representing xs(t) tendency of change.
And 3, the source node adjusts the income distribution coefficient based on the Stainberg game so as to maximize the income of the source node.
In the stainberg game, the source node is a master participant (upper stage), the relay nodes are slave participants (lower stages), and for the source node, the source node participates in the cooperation and is influenced by benefit distribution coefficients (as shown in step two), that is, different benefit distribution coefficients correspond to different numbers of relay nodes. To obtain maximum revenue, the source node adjusts its revenue distribution coefficient, αs＝argmaxus(t)。
Step 4, judging whether the direct source node and the relay node reach the equilibrium solution at the same time, if so, ending, otherwise, turning to the step 2; wherein when it is satisfied
Figure BDA0001830364090000063
And judging that the source node and the relay node reach equilibrium solution simultaneously.
To further illustrate the above process, the following examples are given.
Taking a cooperative network composed of 2 source nodes and 50 relay nodes as an example (i.e., M is 2 and N is 50), the unit benefit parameters of the signal transmission rates of the source nodes 1 and 2 are a125 and A2At 25, the direct path snr of the source nodes 1 and 2 is γ, respectively1(t) 1.0 and γ2(t) 0.5, the average SNR increment of the relay node participating in the source node
Figure BDA0001830364090000064
The simulation results were averaged over 1000 independent channel tests.
As can be seen from fig. 3, when the game start t is equal to 0, the profit sharing coefficients of the source nodes 1 and 2 are respectively α10.9 and α20.1. The source node 1 has little profit and gradually decreases the profit sharing factor due to the profit sharing factor being too large, whereas the source node 2 gradually increases the profit sharing factor. Over time, source nodes 1 andthe revenue sharing factor of 2 gradually settles (t 13) and remains at 0.42 and 0.63, respectively. The reason why the allocation of revenue coefficient of the source node 1 is smaller than that of the source node 2 is that the direct signal-to-noise ratio of the source node 1 is larger than that of the source node 2.
As shown in fig. 4, when the game start t is equal to 0, the numbers of the relay nodes of the selection source nodes 1 and 2 are n respectively1(0) 46 and n2(0) 4. At this time, the policy benefit of the source node 1 is less than that of the relay node 2, which is a disadvantageous policy. According to the evolutionary game, at the next moment, the number of the relay nodes of the selection source node 1 is reduced, and meanwhile, the number of the relay nodes of the selection source node 2 is increased. With the progress of evolution, the number of relay nodes for selecting the source nodes 1 and 2 gradually becomes stable. When the time t is 13, the number of relay nodes selecting the source nodes 1 and 2 is stabilized at 16 and 34. Based on the above analysis, the final equilibrium solution of the double-layer game is: alpha is alpha1＝0.42，α2＝0.63，n1(t)＝16，n2(t) 34. Once the equilibrium solution is reached, the source node and the relay node will not readjust their selection and remain stable.
In the method, the relay nodes participate in cooperative communication of the source nodes to improve the transmission rate of the source nodes, the source nodes are selected through an evolutionary game among the relay nodes to obtain the maximum income, and the income distribution coefficient of the source nodes is adjusted through a Steinber game among the source nodes to achieve the maximum income, so that the utilization efficiency of the whole network resource is improved.
According to the method, an evolutionary game is adopted among the relay nodes, a Steinberg game is adopted among the source nodes and the relay nodes, multi-source multi-relay cooperative network resources are distributed through a double-layer game, and the utilization efficiency of the network resources is effectively improved.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (5)

1. A resource allocation method for a multi-source multi-relay cooperative network is characterized in that: comprises the following steps of (a) carrying out,
the source node sets a profit distribution coefficient for the relay nodes participating in the cooperation, and the relay nodes participating in the cooperation distribute profits on average;
the revenue function of the source node is,
Figure FDA0003103179690000011
wherein u iss(t) benefit of source node s at time t, AsA unit gain parameter, alpha, for the signal transmission rate of the source node ssAllocating coefficient, gamma, to the profit of source node s to relay nodes participating in cooperations(t) direct path signal-to-noise ratio, n, of source node s at time ts(t) is the number of relay nodes participating in the cooperation of the source node s at the moment t,
Figure FDA0003103179690000012
the average signal-to-noise ratio increment of the relay node participating in the source node s cooperation;
the relay node selects a source node based on the evolutionary game, and the source node adjusts the income distribution coefficient based on the Steinberg game until the source node and the relay node achieve a balanced solution at the same time.
2. The resource allocation method for the multi-source multi-relay cooperative network according to claim 1, wherein: the revenue function of the relay nodes participating in the cooperation is,
Figure FDA0003103179690000013
wherein u isr,s(t) profit of relay node r participating in source node s cooperation at time t, AsA unit gain parameter, alpha, for the signal transmission rate of the source node ssAllocation of revenue for source node s to relay nodes participating in cooperationCoefficient, gammas(t) direct path signal-to-noise ratio, n, of source node s at time ts(t) is the number of relay nodes participating in the cooperation of the source node s at the moment t,
Figure FDA0003103179690000014
and (4) adding the average signal-to-noise ratio increment for the relay node participating in the source node s cooperation.
3. The resource allocation method for the multi-source multi-relay cooperative network according to claim 1, wherein: in the evolutionary game, the dynamic formula of group replication is as follows,
Figure FDA0003103179690000021
wherein the content of the first and second substances,
Figure FDA0003103179690000022
is xs(t) derivative of time t, xs(t)＝ns(t)/N is the relay node proportion of the source node s, N is the total number of the relay nodes, Ns(t) the number of relay nodes participating in the cooperation of the source node s at the moment t, pis(t) is the policy return, equal to the relay node return,
Figure FDA0003103179690000023
in order to average out the benefits of the strategy,
Figure FDA0003103179690000024
and M is the total number of the source nodes.
4. The resource allocation method for the multi-source multi-relay cooperative network according to claim 1, wherein: in the Stainberg game, the source node is a main participant, the relay node is a secondary participant, and the profit distribution coefficient is adjusted to maximize the profit of the source node.
5. The resource allocation method for the multi-source multi-relay cooperative network according to claim 1, wherein: if the following formula is satisfied, judging that the source node and the relay node reach equilibrium solution simultaneously;
the concrete formula is that,
Figure FDA0003103179690000025
wherein the content of the first and second substances,
Figure FDA0003103179690000026
is xs(t) derivative of time t, xs(t)＝ns(t)/N is the relay node ratio of the source node s, us(t) the benefit of the source node s at time t, αsAnd distributing coefficients for the benefit of the source node s to the relay nodes participating in the cooperation.
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