WO2019000851A1

WO2019000851A1 - Game theory-based relay selection and power distribution method in smart power grid

Info

Publication number: WO2019000851A1
Application number: PCT/CN2017/116133
Authority: WO
Inventors: 柯峰; 邓子杰
Original assignee: 华南理工大学
Priority date: 2017-06-30
Filing date: 2017-12-14
Publication date: 2019-01-03
Also published as: CN107172674A

Abstract

Disclosed is a game theory-based relay selection and power distribution method in a smart power grid, applicable to a relay network system. The system comprises a source node, a destination node, and a relay node. Under the condition of each relay quotation, the user modeling is that a purchaser takes the maximum utility as the rule to select an optimal relay and optimal purchase power, and the relay modeling is that a seller determines a selling price strategy according to different cost prices provided by the smart power grid to obtain the maximum profit. After continuous game iteration, the user's selection of a relay, the optimal power distribution and the quotation of the relay finally do not change again, i.e., reaching a Nash equilibrium point. The present invention can effectively improve the transmission rate while taking the profits of the user and the relay node into account, is smaller in calculation burden, and is high in convergence rate.

Description

Game theory based relay selection and power allocation method in smart grid

Technical field

The present invention relates to the field of wireless communication technologies, and in particular, to a game theory based relay selection and power allocation method in a smart grid.

Background technique

Future wireless communication networks can achieve full-range signal coverage through relay transmission, reduce the impact of communication dead zones, and improve communication transmission quality. Through the coordinated transmission of the relay, the broadcast characteristics of the wireless network can be effectively utilized to achieve spatial diversity. Relay selection in cooperative relay networks and power allocation on different relay nodes can greatly affect network performance.

In the future cooperative relay network, the relay node may be provided by different service providers or individuals, and has selfish characteristics, and the resource demand has non-homogeneous constraint characteristics, which makes the cooperative transmission of nodes through effective incentive mechanism become inevitable. On the other hand, the smart grid as a next-generation power network is very different from the traditional grid, which will have different impacts on the field of wireless network communication technology. In the future smart grid, renewable energy plays a very important role in the power grid. Because different renewable energy generation conditions are different, the price offered by the grid when transmitting energy is also different, so it is carried out in the communication network. Factors to consider when relaying and power allocation. At present, most of the relay selection and power allocation in the study only consider the optimal power selection and relay selection, ignoring the impact of the energy price difference provided by the grid on the final decision.

Summary of the invention

In order to overcome the shortcomings and deficiencies of the prior art, the present invention provides a game theory based relay selection and power allocation method in a smart grid.

The invention adopts the following technical solutions:

A game-based relay selection and power allocation method in a smart grid is applicable to a half-duplex relay network system, including a source node S, a destination node D, and K relay nodes, including the following steps:

Before the S1 user transmits each data block, the user can select the relay cooperation mode. Specifically, the user selects the most suitable relay from the K relay nodes according to the utility function to establish a cooperative communication link. The quotation of each relay node is η _k (t), and all relays constitute a quotation set of Ψ(t)={η ₁ (t), η ₂ (t),...η _K (t)};

The utility function of the S2 user for each relay node k is U _S,k (η _k (t)), and the user selects each relay node under the condition that each relay offer set Ψ(t) is known. Optimal purchase power p _k to maximize its utility function

The maximum set of utility functions composed by the user for K relay nodes

The user then selects the maximum value from the set Θ(Ψ(t)), and the corresponding relay node is the optimal relay node k ^* =argmax(Θ(Ψ(t)));

S3 optimal relay k ^* profit function

If the selected relay k ^* can pass the price reduction

Sell more power to get more profit, then update the quotation set Ψ(t)={η ₁ (t), η ₂ (t),...η _K (t)}, if not cut, the quotation set Ψ(t) constant;

Other relays that are not selected by the user will be used to attract users by price reduction, ie η(t+1)=max(η ^min , η(t)-Δη), and then get a new set of offers as Ψ(t+1) )={η ₁ (t+1), η ₂ (t+1),...η _K (t+1)}, if Ψ(t)=Ψ(t+1), the relay no longer modifies the quote. The user and relay policies no longer change, that is, the Nash equilibrium point is reached, otherwise it returns to S1.

The transmission mode of the relay in the S1 adopts a time division multiplexing mode, in which the user sends data to the base station in the first time slot, and each relay node receives the broadcast data block, and performs relay selection in the second time slot. power distribution, selected relay power k ^* p _k to transmit data to the base station.

The user's own utility function for the relay node in S2

among them

Interrupt capacity available to the user.

The profit function of the relay node itself in S3 is: U _k (p _k , η _k )=(η _k -c _k )p _k , where c _k is the cost price of the relay, ie the relay is from the smart grid The price of the purchase.

η ^{min in} the S3 is the lowest price that the relay node can drop, that is,

The beneficial effects of the invention:

The invention introduces an economic game theory method under the relay selection and power allocation method, and models the user as the buyer to select the optimal relay and the best purchasing power based on the maximum utility, and models the relay as a seller. The smart grid provides different cost prices to determine the selling price strategy to obtain the maximum profit. The invention can effectively increase the transmission rate while taking into account the interests of the user and the relay node, and the calculation amount is small and the convergence speed is fast.

DRAWINGS

Figure 1 is a flow chart of the operation of the present invention;

2 is a block diagram of a relay selection and power allocation method of the present invention;

Figure 3 is a graph showing the user utility in the user and the relay game during the game;

Figure 4 is a graph showing the variation of the relay price during the user-relay game in this example;

Figure 5 is a diagram showing changes in the selected optimal relay with user position change in the present example;

Figure 6 is a plot of the selected optimal relay price as a function of user position change in this example.

Detailed ways

The present invention will be further described in detail below with reference to the embodiments and drawings, but the embodiments of the present invention are not limited thereto.

Example

A game theory based relay selection and power allocation method for a relay network system, the relay network system under the smart grid includes a source node S, a destination node D, and K relay nodes R, the core step is that the user chooses the optimal relay and the best purchasing power as the buyer with the maximum utility as the criterion. The relay acts as the seller to provide the maximum profit by the smart grid to provide different cost price to determine the selling price strategy. The relay competes in the market, and finally the game is balanced. The user and the relay node both reach the Nash equilibrium point according to the maximization of their own utility and no longer change the decision.

As shown in FIG. 1 , a game-based relay selection and power allocation method in a smart grid is applicable to a half-duplex relay network system, and includes the following steps:

In the relay network system model, the user has two transmission modes: a direct transmission mode and a relay cooperation mode, and the user selects which working mode is determined by the value of the utility function value.

The maximum set of utility functions composed by the user for K relay nodes

S3 optimal relay k ^* profit function

If the selected relay k ^* can pass the price reduction

The S3 is specifically that the relay node takes action, and if the relay is selected, if the price is reduced to sell more power, the profit function is increased, the price is lowered, and if the profit cannot be increased, the price is not lowered; the selected relay does not reduce the price and then obtains a new one. The quotation set Ψ(t+1), if Ψ(t)=Ψ(t+1) then no iteration, otherwise iteratively.

After the end of the game iteration, the end user's selection of the relay, the optimal power allocation, and the quotation of the relay no longer change, and then the user and the relay node can be considered to no longer change the decision to reach the Nash equilibrium point. A block diagram of the relay selection and power allocation method of the embodiment of the present invention is shown in FIG. 2.

The basic scenarios adopted in this embodiment are as follows:

In the cooperative communication network, there is a destination node D located at (0m, 0m), and there are four relay nodes Ω={1, 2, 3, 4}, located at (-150m, 0m), (-150m, 50m), (-100m, 0m), (100m, 0m), there is a source node S moving from (-300m, 0m) to (+300m, 0m). Noise power

10 ^-8 W, channel gain is Rayleigh fading

Where d _i,j is the distance between the nodes, the channel fading factor α is 2, the outage probability ε is 0.001, and the prices of the four relay nodes purchased from the smart grid are respectively {3, 1.5, 2, 2} The amplitude of the price reduction Δη is 0.2.

The simulation results of this example were obtained using the simulation software Matlab.

Figure 3 and Figure 4 show the game between the user and the relay when the user is at (+130m, 0m). At the beginning, because the user has the most utility in selecting the relay 1, the optimal relay is selected as the relay 1. Other unselected trunks began to cut prices to attract users. As the price dropped, users found that when they selected trunk 3, their utility was relatively large, so they began to choose trunk 3, while

relays

1, 2, and 4 continued to cut prices. After about 75 iterations, the price of each relay no longer changes, and then the Nash equilibrium point is reached to select the optimal relay. At this time, the optimal purchase power is also iterated.

Figures 5 and 6 reflect the case where the user source node S moves from (-300m, 0m) to (+300m, 0m) when the relay is selected and the price changes. In the process of continuous movement, the user selects the direct transmission mode and the relay cooperation mode, and also selects the optimal relay and the optimal purchase power at different position moments according to different situations, wherein the price is also due to competition between the relays and Smart grids offer different cost prices and vary.

The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the embodiments, and any other changes, modifications, substitutions, and combinations may be made without departing from the spirit and scope of the present invention. And simplifications, all of which are equivalent replacement means, are included in the scope of protection of the present invention.

Claims

A game-based relay selection and power allocation method in a smart grid is applicable to a half-duplex relay network system, including a source node S, a destination node D, and K relay nodes, and is characterized by including the following step:

Before the S1 user transmits each data block, the user can select the relay cooperation mode. Specifically, the user selects the most suitable relay from the K relay nodes according to the utility function to establish a cooperative communication link. The quotation of each relay node is η k (t), and all relays constitute a quotation set of Ψ(t)={η 1 (t), η 2 (t),...η K (t)};

The utility function of the S2 user for each relay node k is U S,k (η k (t)), and the user selects each relay node under the condition that each relay offer set Ψ(t) is known. Optimal purchase power p k to maximize its utility function
The maximum set of utility functions composed by the user for K relay nodes
The user then selects the maximum value from the set Θ(Ψ(t)), and the corresponding relay node is the optimal relay node k * =argmax(Θ(Ψ(t)));

S3 optimal relay k * profit function
If the selected relay k * can pass the price reduction
Sell more power to get more profit, then update the quotation set Ψ(t)={η 1 (t), η 2 (t),...η K (t)}, if not cut, the quotation set Ψ(t) constant;

Other relays that are not selected by the user will be used to attract users by price reduction, ie η(t+1)=max(η min , η(t)-Δη), and then get a new set of offers as Ψ(t+1) )={η 1 (t+1), η 2 (t+1),...η K (t+1)}, if Ψ(t)=Ψ(t+1), the relay no longer modifies the quote. The user and relay policies no longer change, that is, the Nash equilibrium point is reached, otherwise it returns to S1.
The relay selection and power allocation method according to claim 1, wherein the transmission mode of the relay in the S1 adopts a time division multiplexing manner, in which the user transmits data to the base station in the first time slot, and each relay node receives the broadcast data block, relay selection and power allocation for the second time slot, k * selected relay station to transmit data to the power p k.
The relay selection and power allocation method according to claim 1, wherein the user's own utility function for the relay node in S2
among them
Interrupt capacity available to the user.
The relay selection and power allocation method according to claim 1, wherein the profit function of the relay node itself in the S3 is: U k (p k , η k )=(η k -c k )p k , where c k is the cost price of the relay, ie the price that the relay purchases from the smart grid.
The relay selection and power allocation method according to claim 4, wherein η min in the S3 is a lowest price that the relay node can drop, that is,