CN103220757B - A kind of optimum relay selection method based on two-way auction model - Google Patents

Abstract

The invention discloses an optimal relay selection method based on a two-way auction model. First, the auctioneer notifies the start of the auction, and each node determines its own quotation or asking price according to its own resource conditions, and then defines the energy efficiency between buyers and sellers according to actual conditions. function to establish a complete weight pre-matching bipartite graph; use the maximum weight matching algorithm to obtain the maximum energy efficiency matching relationship between nodes; finally delete the virtual pairing relationship according to the actual relationship to obtain the final successful transaction. The present invention adopts a two-way auction model to realize the optimal relay selection method, and the method is aimed at the optimal relay node selection of edge users, thereby obtaining higher energy efficiency and extending the communication range. Effectively reduce the energy consumption of the system and nodes, and use the maximum weight matching algorithm to assist the system to select the optimal energy efficiency matching combination, and delete the false matching to obtain the final transaction matching relationship. Effectively reduce the energy consumption of individual users and even the entire system, and improve network performance.

Description

An Optimal Relay Selection Method Based on Two-way Auction Model

technical field

The invention relates to a cooperative communication relay node selection method in a wireless communication network, in particular to an optimal relay selection method based on a two-way auction model.

Background technique

Wireless communication is one of the most active research hotspots in the field of communication today, and the fading characteristics of wireless channels are the main reasons that hinder the increase of channel capacity and the improvement of service quality. Space diversity technology is a relatively simple and effective method to suppress channel fading. Multiple-input multiple-output (MIMO: multiple-input multiple-output) technology greatly improves the reliability of transmission and has dual functions of space diversity and space multiplexing. The cooperative communication technology is a virtual MMO technology, which can effectively improve the transmission rate and reliability of the wireless communication network, and expand the transmission distance and coverage of the wireless network.

There are some edge users in the wireless communication network, and the quality of the communication link between the edge users and the base station is poor, and even the lowest communication signal-to-noise ratio cannot be achieved, that is, there is no direct communication link. Cooperative communication technology can solve communication problems for them, but because users as relay nodes are limited by their own resources (such as remaining energy, transmission power, etc.), they are not willing to forward information for other nodes, and even those who need relay The source node also hopes to use the minimum cost to obtain communication. This selfish nature makes the cooperation mechanism not work effectively, which leads to the loss of users, blind cooperation, and the degradation of network performance. Therefore, it is an urgent problem to be solved to select a suitable relay node for edge users and take into account the node selfishness in cooperative communication. In addition, how to obtain higher system capacity and improve overall system performance while minimizing overall system overhead is also particularly important.

At present, the existing research is mainly to provide incentive strategies for nodes with corresponding rewards to solve the problem of selfishness of nodes. The auction model can effectively solve this problem and can select a more suitable relay node for the source node that needs relay service.

In view of the superiority of collaborative communication, both academia and industry are paying great attention to it. In recent years, there have been more and more researches on relay scheduling algorithms for cooperative communication, among which there are strategies based on auction theory. Auction of relay services between a source node and multiple relay nodes (see literature: Beibei Wang, Zhu Han, and K.J.Ray Liu, "Distributed Relay Selection and Power Control for Multiuser Cooperative Communication Networks Using Stackelberg Game," IEEE Transactions on Mobile Computing, Vol.8, No.7, pp.975-990, July 2009) is also a one-way auction model. This mode does not take into account that any node in the cooperative communication network can be the source node and there is competition between the source nodes when selecting relays, so the two-way auction is more suitable for the cooperative communication mechanism. The relay selection strategy based on the two-way auction model is TASC (see literature: Dejun Yang, X.F., and GuoliangXue, "Truthful Auction for Cooperative Communications," ACM MobiHoc2011, Paris, France, May2011). The main idea is to use honest bids and asking prices for relay services between nodes to conduct auctions. Arrange the quotations of source nodes from high to low, and arrange the asking prices of relay nodes from low to high, so far to complete the relay arrangement, but this method needs to sacrifice some nodes to ensure an honest auction mechanism, and cannot obtain a better system energy efficiency.

Contents of the invention

In view of this, the technical problem to be solved by the present invention is to provide an optimal relay selection method based on a two-way auction model node incentive game strategy. The cooperation between nodes can effectively reduce the energy consumption of the system and nodes, improve the system performance of the network, and obtain higher system capacity.

The purpose of the present invention is achieved like this:

A kind of optimal relay selection method based on two-way auction model provided by the present invention comprises the following steps:

S1: Establish a timely auction model according to the network topology, and the auctioneer notifies the start of the auction;

Wherein, the timely auction model includes a buyer, a seller and an auctioneer, the buyer is an edge user who needs relay, the seller is a node providing relay service, and the auctioneer is a base station;

S2: The buyer proposes an offer function according to the following formula At the same time, the seller puts forward the asking price function according to the following formula ${\mathrm{ask}}_{R_{\left(\mathrm{no}\right)}\mathrm{D.}};$

$\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; Bid</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 0,1</span>}<span\; class="notranslate">\; )</span>\\ {\mathrm{<span\; class="notranslate">\; ask</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; \&xi;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 0,1</span>}<span\; class="notranslate">\; ]</span>\end{array}\right.$

in, The quotation for the buyer represents the real valuation of the service of the source node m to the relay node n, The seller's asking price represents the real valuation from the relay node n to the destination node, λ _i is the buyer's elevation, ξ _j is the seller's elevation, and the buyer's elevation and the buyer's elevation are random values between [0,1];

S3: Determine whether the quotation function and the asking price function satisfy the following formula:

${\mathrm{Bid}}_{S_{\left(m\right)}{R}_{\left(\mathrm{no}\right)}}\&Greater\; Equal;{\mathrm{ask}}_{R_{\left(\mathrm{no}\right)}\mathrm{D.}},$ If not, return to step S1;

S4: If it is, it means that there is a transaction that has been reached, and at the same time build a complete weight pre-matched bipartite graph in the two-way auction mode, that is, When , there is an adjacent edge between the source node (buyer m) and the relay node (seller n);

S5: According to the complete weight pre-matching bipartite graph, using the maximum weight matching algorithm in graph theory to obtain the maximum energy efficiency matching relationship between nodes;

S6: Determine whether there is a virtual allocation transaction relationship in the maximum energy efficiency matching relationship, if not, go to step S8;

Wherein, the virtual allocation transaction relationship is a transaction relationship when the quotation function is smaller than the asking price function;

S7: If there is, delete the virtual allocation transaction relationship according to the actual relationship;

S8: Obtain the transaction price between the buyer and the seller;

S9: The buyer pays a reward and obtains a communication opportunity, and the seller provides a relay service to obtain income and complete the transaction.

Further, the weight value of the complete weight pre-matched bipartite graph constructed in the two-way auction mode in the step S4 is performed according to the following formula:

${\mathrm{<span\; class="notranslate">\; EE</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; C</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}}<span\; class="notranslate">\; ,</span>$

in, represents the energy efficiency function, is the capacity of a pair of matching relations, Indicates the transmission power of the nth relay node, that is, the seller to the base station; Indicates the transmission power from the source node (buyer m) to the relay node (seller n); SNR means signal-to-noise ratio; σ ² means Gaussian white noise power; Indicates the distance from the nth relay node, that is, the seller to the base station, and k is the path fading factor; Indicates the distance from the source node (buyer m) to the relay node (seller n).

Further, the transaction price between the buyer and the seller in the step S8 is calculated by the following formula:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; Bid</span>}}_{{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; ask</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 2</span>}}$

in, Represents a virtual remuneration paid by the buyer to the seller.

Further, in the step S2, the buyer proposes a quotation function At the same time, the seller puts forward the asking price function All are determined by evaluating the own resources, and the own resources include remaining energy and transmission power.

Further, the maximum weight matching algorithm in the step S5 refers to a matching operation that maximizes the total weight based on the complete weight pre-matched bipartite graph and edge weights, specifically including the following steps:

S51: Construct the weight matrix according to the complete weighted pre-matched bipartite graph to form a fully weighted bipartite graph G(V ₁ ,V ₂ ); wherein, G(V ₁ ,V ₂ ) represents a bipartite graph containing vertex sets V ₁ and V ₂ Partial diagram; V ₁ represents the collection of source nodes, namely buyers; V ₂ represents the collection of relay nodes, namely sellers;

S52: Execute the Hungarian algorithm on the fully weighted bipartite graph G(V ₁ , V ₂ ), and modify the feasible top mark to find the matching with the largest weight value, and finally obtain the matching relationship with the largest weight.

Further, the weight matrix is a square matrix, and virtual nodes are set in the square matrix, and the virtual nodes are source nodes or relay nodes, and the corresponding weight values of the virtual nodes and other nodes are 0.

Further, the feasible top mark is performed according to the following formula:

$\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; max</span>}}& \mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}\\ \mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\end{array}\right.$

Among them, ω represents the weight value of each edge, l(y) represents the relay node is the top mark of the seller; l(x) represents the source node is the top mark of the buyer; y represents a certain relay node is the seller; x Indicates that a certain source node is the buyer.

Further, the top mark modification method in the step S5 is performed according to the following formula:

in, Indicates the source node is the set of buyers, T indicates the relay node is the set of sellers, and they are defined as T=H∩V ₂ , H is the vertex set of the interleaved subgraph rooted at a certain node in the bipartite graph G(V ₁ ,V ₂ ); l′(u) is the modified topscript; ω(x,y ) represents the weight value between the source node, buyer x, and relay node, seller y; α _l represents the top mark adjustment factor; u represents a certain node, namely the buyer or seller; The home or seller corresponds to the top mark.

The advantage of the present invention is that: the present invention adopts the two-way auction model to realize the optimal relay selection method, and the method is aimed at the optimal relay node selection of edge users, thereby obtaining higher energy efficiency and extending the communication range. It not only considers the node’s valuation according to its own resources, but also considers the node’s own state (residual energy, transmission power) and income (the seller’s income after the auction is over) from a system perspective. The virtual remuneration paid by the buyer, the buyer obtains the relay service) to determine the pricing function, construct the quotation and asking price function, so as to reasonably motivate the cooperation between nodes, and effectively reduce the energy consumption of the system and nodes, and also adopt the maximum weight The matching algorithm assists the system to select the optimal energy-efficiency matching combination, and deletes the false matching combination according to the actual auction situation to obtain the final transaction matching relationship. Effectively reduce the energy consumption of individual users and even the entire system, and improve network performance.

Description of drawings

In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with the accompanying drawings, wherein:

Fig. 1 is a system model diagram for edge users in the present invention;

Fig. 2 is the algorithm flowchart based on two-way auction model among the present invention;

Fig. 3 is the two-way auction mechanism of the present invention;

Fig. 4 is the quotation and asking price function of the present invention;

Fig. 5 is the complete weight pre-matching bipartite graph of two-way auction among the present invention;

Fig. 6 is a schematic diagram of transactions that may exist in virtual allocation or non-existence in the invention.

Detailed ways

The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings; it should be understood that the preferred embodiments are only for illustrating the present invention, rather than limiting the protection scope of the present invention.

Example 1

Fig. 1 is a system model diagram for edge users in the present invention, Fig. 2 is an algorithm flow chart based on a two-way auction model in the present invention, Fig. 3 is a two-way auction mechanism of the present invention, and Fig. 4 is a quotation and asking price function of the present invention , Fig. 5 is a complete weight pre-matching bipartite graph of a two-way auction in the present invention, and Fig. 6 is a schematic diagram of a possible virtual allocation or non-existent transaction in the present invention, as shown in the figure: the present invention provides a An optimal relay selection method for a two-way auction model, including the following steps:

S1: Establish a timely auction model according to the network topology, and the auctioneer notifies the start of the auction; after completing the last auction, the auctioneer starts broadcasting to notify all idle nodes to prepare for this round of auction activities.

Wherein, the timely auction model includes a buyer, a seller and an auctioneer, the buyer is an edge user who needs relay, the seller is a node providing relay service, and the auctioneer is a base station;

As shown in Figure 1, edge users are at the edge of the cell and cannot directly communicate with the base station, but can only obtain communication through other user relays. Node communication resources are limited during communication, such as limiting the minimum signal-to-noise ratio threshold SNR _TH of communication, or the maximum transmission power and so on. So not all nodes can communicate directly.

S2: The buyer proposes an offer function according to the following formula At the same time, the seller puts forward the asking price function according to the following formula ${\mathrm{ask}}_{R_{\left(\mathrm{no}\right)}\mathrm{D.}};$

$\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; Bid</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 0,1</span>}<span\; class="notranslate">\; )</span>\\ {\mathrm{<span\; class="notranslate">\; ask</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; \&xi;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 0,1</span>}<span\; class="notranslate">\; ]</span>\end{array}\right.$

in, The quotation for the buyer represents the real valuation of the service of the source node m to the relay node n, The seller's asking price represents the real valuation from the relay node n to the destination node, λ _i is the buyer's elevation, and ξ _j is the seller's elevation. The real estimate is the evaluation price of the buyer and the seller for their own resources, such as the estimated price of the buyer and the seller for their own remaining energy, the estimated price for their own transmission power, etc. Taking the evaluation of transmission power as an example: in order to optimize energy efficiency, nodes use the lowest Send power to send data, and use this as a true estimate. Since both the buyer and the seller are power-limited mobile devices, the minimum sending power satisfies the constraint condition: P _min ≤ P ≤ P _max . A node takes the minimum transmission power that can complete communication as its real evaluation, and sets the minimum signal-to-noise ratio threshold that it can communicate as SNR _TH , then the real evaluation can be expressed as Where σ ² is Gaussian white noise power, G is the path loss expressed as G=d ^-k , d is the distance between two nodes, and k is the path fading factor. Due to the randomness of the topology and the difference in the distance between nodes, the real valuation may be greater than P _max , which is defined as P=P _max at this time, which still satisfies the constraint condition. Due to the non-negativity of the buyer's quotation, the buyer's mark is a random value between [0,1), and the seller's mark is a random value between [0,1] to satisfy the selfish psychology of both buyers and sellers;

In the step S2, the buyer proposes a quotation function At the same time, the seller puts forward the asking price function All are determined by evaluating the own resources, and the own resources include remaining energy and transmission power.

S3: Determine whether the quotation function and the asking price function satisfy the following formula:

${\mathrm{Bid}}_{S_{\left(m\right)}{R}_{\left(\mathrm{no}\right)}}\&Greater\; Equal;{\mathrm{ask}}_{R_{\left(\mathrm{no}\right)}\mathrm{D.}},$ If not, return to step S1;

S4: If it is, it means that there is a transaction that has been reached, and at the same time build a complete weight pre-matched bipartite graph in the two-way auction mode, that is, , there is an adjacent edge between the source node, i.e. buyer m, and the relay node, i.e. seller n; the weight value of the complete weight pre-matched bipartite graph constructed in the two-way auction mode in the step S4 is carried out according to the following formula:

${\mathrm{<span\; class="notranslate">\; EE</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; C</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}}<span\; class="notranslate">\; ,</span>$

in, represents the energy efficiency function, is the capacity of a pair of matching relationships, and ω represents the weight value of each edge; Indicates the transmission power of the nth relay node, that is, the seller to the base station; Indicates the transmission power from the source node (buyer m) to the relay node (seller n); SNR means signal-to-noise ratio; σ ² means Gaussian white noise power; Indicates the distance from the nth relay node, that is, the seller to the base station, and k is the path fading factor; Indicates the distance from the source node (buyer m) to the relay node (seller n).

S5: According to the complete weight pre-matching bipartite graph, use the maximum weight matching algorithm in graph theory to obtain the maximum energy efficiency matching relationship between nodes; the maximum weight matching algorithm refers to the complete weight pre-matching bipartite graph based on S4 and Its edge weights are subjected to a matching operation that maximizes the total weights. The edge weight is the weight value on each adjacent edge, which is defined as the corresponding energy efficiency function

First, construct a weight matrix based on a complete weight pre-matched bipartite graph. The weight matrix is a square matrix, and a virtual node is introduced to form a square matrix. The virtual node can be a source node (buyer) or a relay node (Seller), the corresponding weight value of the virtual node and other nodes is 0, which can form a fully weighted bipartite graph G(V ₁ ,V ₂ ). Execute the Hungarian algorithm on the fully weighted bipartite graph, and continuously modify the feasible top mark to find the match with the largest weight value, and finally obtain the perfect matching relationship with the maximum weight; the feasible top mark is performed according to the following formula:

$\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; max</span>}}& \mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}\\ \mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\end{array}\right.$

Wherein, ω represents the weight value of each edge, and the weight matrix is a square matrix, and virtual nodes are set in the square matrix, and the virtual nodes are source nodes or relay nodes, and the virtual nodes correspond to weights of other nodes The value is 0.

The method for modifying the top mark can be carried out according to the following formula:

in T=H∩V ₂ , where H is the vertex set of the interleaved subgraph rooted at a certain node in the bipartite graph G(V ₁ , V ₂ ). l'(u) is the modified top mark; Indicates the source node is the set of buyers, T indicates the relay node is the set of sellers, and they are defined as T=H∩V ₂ , H is the vertex set of the interleaved subgraph rooted at a certain node in the bipartite graph G(V ₁ ,V ₂ ); l′(u) is the modified topscript; ω(x,y ) represents the weight value between the source node, buyer x, and relay node, seller y; α _l represents the top mark adjustment factor; u represents a certain node, namely the buyer or seller; The home or seller corresponds to the top mark.

S6: Determine whether there is a virtual allocation transaction relationship in the maximum energy efficiency matching relationship, if not, go to step S8;

Wherein, the virtual allocation transaction relationship is a transaction relationship when the weight of the matched relationship edge is 0;

S7: If there is, delete the virtual allocation transaction relationship according to the actual relationship;

S8: Obtain the transaction price between the buyer and the seller;

The transaction price between the buyer and the seller in the step S8 is calculated by the following formula:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; Bid</span>}}_{{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; ask</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 2</span>}}$

in, Represents a virtual remuneration paid by the buyer to the seller.

S9: The buyer pays a reward and obtains a communication opportunity, and the seller provides a relay service to obtain income and complete the transaction.

Example 2

This embodiment 2 specifically describes the principle and implementation process of the optimal relay selection method based on the two-way auction model:

As shown in Figure 2, it is an algorithm flow chart based on the two-way auction model in the present invention, and the specific implementation steps are as follows:

1) After completing the last auction, the auctioneer starts broadcasting to notify all idle nodes to prepare for this round of auction. As shown in Figure 3. There are m source nodes and n relay nodes. The source nodes are buyers and the relay nodes are sellers. After receiving the notification, they submit their quotations and asking prices to the auctioneer at the same time in this relay auction. Generate your own offer or ask price via the offer or ask function, respectively. The auctioneer starts the auction after receiving all bids and asking prices.

2) As shown in Figure 4, the quotation and asking price functions of buyers and sellers in a two-way auction. Both buyers and sellers determine that they can provide a price by evaluating their own resources (such as: remaining energy, transmission power, etc.). We define the bid and ask prices of buyers and sellers as and Respectively represent the real valuation of the service from the source node m to the relay node n and the real valuation from the relay node n to the destination node. Since each node has its own selfishness, each node wants to use the minimum cost to obtain the maximum benefit, then the source node as a buyer will make a certain price reduction on the basis of the real valuation, similarly, as The seller's relay node will also raise the price based on the real valuation, so I introduce the concept of elevation, and we define the quotation and asking price of the buyer and the seller as and λ _i and ξ _j are elevations, and the elevations are randomly selected between [0,1], then we can get

$\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; Bid</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 0,1</span>}<span\; class="notranslate">\; ]</span>\\ {\mathrm{<span\; class="notranslate">\; ask</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; \&xi;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 0,1</span>}<span\; class="notranslate">\; ]</span>\end{array}\right.$

3) After comprehensively considering the quotations and asking prices of all buyers and sellers, when there is a relationship At this time, all possible transactions can be obtained. At this time, a buyer can most likely conclude a transaction with multiple sellers, so only the seller with the lowest asking price can be selected. However, from the perspective of the entire system, We expect the most transactions to be reached, so our next step is to build a complete weighted pre-matched bipartite graph in the two-way auction mode.

4) As shown in Figure 5, it represents the complete weight pre-matched bipartite graph in the two-way auction mode in the present invention. The solid line in the figure represents the pre-pairing that can be traded for any node, that is, The dotted line in the figure indicates that the transaction pair does not exist, that is, Taking source node 2 as an example, the adjacent edge of source node 2 in Figure 5, the solid line indicates that it satisfies side, the dotted line indicates side. Therefore, based on the transaction pre-matching relationship between all source nodes and relay nodes, we define the weight value of each edge, and we use ω to represent the weight value. In order to have the highest energy efficiency while minimizing the overhead of the entire system, we define the energy efficiency function

${\mathrm{<span\; class="notranslate">\; EE</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; C</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}}$

in, is the capacity of a certain pair of possible matching relations. Here, we define the weight value of each edge with transaction possibility as its corresponding energy efficiency function, and we define the edge weight value as 0 for impossible transactions, as shown by the dotted line in the figure, it is an impossible transaction. Taking source node 2 as an example, the solid line indicates that the edge weight is non-zero, and the dashed line indicates that the edge weight is 0.

5) After the complete weight pre-matching bipartite graph is established, we use the maximum weight matching algorithm in graph theory to complete the final matching relationship determination. After executing the algorithm, a perfect matching relationship can be obtained. A deal to be reached, that is, a relay pre-arrangement with optimal energy efficiency can be obtained.

As shown in Figure 6, since there may be transactions with a matching relationship with a weight value of 0, that is, transactions that cannot exist, the final transaction that is the relay pairing relationship can be obtained by executing the algorithm to delete the non-existent transactions. Complete the auction process and define the transaction price as:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; Bid</span>}}_{{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; ask</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 2</span>}}$

Finally, the buyer pays the corresponding remuneration to the corresponding seller.

Aiming at the problem of difficult communication for edge users, the present invention proposes an optimal relay selection strategy in combination with a two-way auction model, considers the selfishness of nodes, reasonably defines the quotation and asking price functions of buyers and sellers, and gives an incentive strategy of virtual rewards to encourage nodes to actively Participate in collaboration. At the same time, the energy efficiency of the whole system is taken into account, the energy consumption in the network is reduced, and the capacity and communication range are improved.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and equivalent technologies thereof, the present invention also intends to include these modifications and variations.

Claims (5)

1. A kind of optimal relay selection method based on two-way auction model, it is characterized in that: comprise the following steps: S1: Establish a timely auction model according to the network topology, and the auctioneer notifies the start of the auction; Wherein, the timely auction model includes a buyer, a seller and an auctioneer, the buyer is an edge user who needs relay, the seller is a node providing relay service, and the auctioneer is a base station; S2: The buyer proposes an offer function according to the following formula At the same time, the seller puts forward the asking price function according to the following formula $\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; Bid</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 0,1</span>}<span\; class="notranslate">\; )</span>\\ {\mathrm{<span\; class="notranslate">\; ask</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; \&xi;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 0,1</span>}<span\; class="notranslate">\; ]</span>\end{array}\right.<span\; class="notranslate">\; ;</span>$ in, The quotation for the buyer represents the real valuation of the service of the source node m to the relay node n, The seller's asking price represents the real valuation from the relay node n to the destination node, λ _i is the buyer's elevation, and ξ _j is the seller's elevation; S3: Determine whether the quotation function and the asking price function satisfy the following formula: If not, return to step S1; S4: If it is, it means that there is a transaction that has been reached, and at the same time build a complete weight pre-matched bipartite graph in the two-way auction mode, that is, When , there is an adjacent edge between the source node (buyer m) and the relay node (seller n); S5: According to the complete weight pre-matching bipartite graph, using the maximum weight matching algorithm in graph theory to obtain the maximum energy efficiency matching relationship between nodes; S6: Determine whether there is a virtual allocation transaction relationship in the maximum energy efficiency matching relationship, if not, go to step S8; Wherein, the virtual allocation transaction relationship is a transaction relationship when the weight of the matched relationship edge is 0; S7: If there is, delete the virtual allocation transaction relationship according to the actual relationship; S8: Obtain the transaction price between the buyer and the seller; S9: The buyer pays a reward and obtains a communication opportunity, and the seller provides a relay service to obtain income and complete the transaction; The weight value of the complete weight pre-matching bipartite graph constructed in the two-way auction mode in the step S4 is carried out according to the following formula: ${\mathrm{<span\; class="notranslate">\; EE</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; C</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}}<span\; class="notranslate">\; ,</span>$ in, represents the energy efficiency function, is the capacity of a pair of matching relations, $P_{S_{\left(m\right)}{R}_{\left(\mathrm{no}\right)}}=\frac{\mathrm{SNR}{\mathrm{\&sigma;}}^2}{d_{S_{\left(m\right)}{R}_{\left(\mathrm{no}\right)}}^{-k}},P_{R_{\left(\mathrm{no}\right)}\mathrm{D.}}=\frac{\mathrm{SNR}{\mathrm{\&sigma;}}^2}{d_{R_{\left(\mathrm{no}\right)}\mathrm{D.}}^{-k}};$ Indicates the transmission power of the nth relay node, that is, the seller to the base station; Indicates the transmission power from the source node (buyer m) to the relay node (seller n); SNR means signal-to-noise ratio; σ ² means Gaussian white noise power; Indicates the distance from the nth relay node, that is, the seller to the base station, and k is the path fading factor; Indicates the distance from the source node (buyer m) to the relay node (seller n); The transaction price between the buyer and the seller in the step S8 is calculated by the following formula: ${\mathrm{<span\; class="notranslate">\; f</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; Bid</span>}}_{{\mathrm{<span\; class="notranslate">\; S</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; ask</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span>$ in, Indicates that the buyer pays the seller a fictitious remuneration; In the step S2, the buyer proposes a quotation function At the same time, the seller puts forward the asking price function All are determined by evaluating the own resources, and the own resources include remaining energy and transmission power. 2. The optimal relay selection method based on the two-way auction model according to claim 1, characterized in that: the maximum weight matching algorithm in the step S5 refers to pre-matching bipartite graphs and edge weights based on complete weights The matching operation for maximizing the total weight includes the following steps: S51: Construct the weight matrix according to the complete weighted pre-matched bipartite graph to form a fully weighted bipartite graph G(V ₁ ,V ₂ ); wherein, G(V ₁ ,V ₂ ) represents a bipartite graph containing vertex sets V ₁ and V ₂ Partial diagram; V ₁ represents the collection of source nodes, namely buyers; V ₂ represents the collection of relay nodes, namely sellers; S52: Execute the Hungarian algorithm on the fully weighted bipartite graph G(V ₁ , V ₂ ), and modify the feasible top mark to find the matching with the largest weight value, and finally obtain the matching relationship with the largest weight. 3. the optimal relay selection method based on the two-way auction model according to claim 2, characterized in that: the weight matrix is a square matrix, the square matrix is provided with virtual nodes, and the virtual nodes are source nodes Or a relay node, the weight value corresponding to the virtual node and other nodes is 0. 4. the optimal relay selection method based on the two-way auction model according to claim 2, characterized in that: the feasible top bid is carried out according to the following formula: $\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; max</span>}}& \mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}\\ \mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\end{array}\right.$ ω represents the weight value of each edge, l(y) represents the relay node, which is the top mark of the seller; l(x) represents the source node, which is the top mark of the buyer; y represents a certain relay node, which is the seller; x represents a certain A source node is the buyer. 5. The optimal relay selection method based on the two-way auction model according to claim 2, characterized in that: the modification method of the top bid in the step S5 is carried out according to the following formula: in, Indicates the source node is the set of buyers, T indicates the relay node is the set of sellers, and they are defined as T=H∩V ₂ , H is the vertex set of the interleaved subgraph rooted at a certain node in the bipartite graph G(V ₁ , V ₂ ); l'(u) is the modified topscript; ω(x,y ) represents the weight value between the source node, buyer x, and relay node, seller y; α _l represents the top mark adjustment factor; u represents a certain node, namely the buyer or seller; The corresponding top mark of the home or seller.