File Name: game theory in wireless and communication networks .zip
Game theory has been used as a tool for modeling and studying interactions between cognitive radios envisioned to operate in future communications systems.
To add more greens into such kind of complicated and time-varying mobile network, we specifically investigate the throughput and transmission delay performances for real-time and delay sensitive services through a repeated game theoretic solution. This paper has employed Nash Equilibrium in the noncooperative game model and analyzes its efficiency. Simulation results have shown an obvious improvement on power efficiency through such efforts.
With the exponential growth of the energy consumption in wireless communications, Green communication has been drawing more and more attention in recent years [ 1 — 3 ]. Transmission power, especially, has crucial impacts on enhancing the throughput of wireless communication.
However, the unlimited extremely high transmission power is unable to improve throughput in many cases since it merely contributes to a terrible Signal-to-Interference-and-Noise Ratio SINR environment for each node.
On the other hand, the energy consumption is also inefficient in a low SINR environment [ 5 — 8 ]. Power efficiency is becoming a major issue in vehicular ad hoc networks VANETs , because of the consciousness of environment-friendly society [ 9 , 10 ]. Unfortunately, the nature of VANETs is high mobility, and self-organized, leading to the power efficiency being ignored commonly.
The remaining six service channels SCH allow each node to choose the power level and modulation type to disseminate packets; it is possible to produce unpredictable interference.
Moreover, due to the limitation of DSRC coverage range, it is difficult to extend the end-to-end service in a large and dynamic scale. Many experiments are performed based on the condition that each node is mandated to participate in the cooperation [ 10 , 12 ].
As shown in Figure 1 , the distance between the sender and receiver is outside of the coverage range of DSRC. To establish the link for and , the mandatory cooperation will certainly improve the receiving gain in VANETs. However, some nodes may not be suitable for such cooperation since they are suffering from severe communication environments. In Figure 1 , some relay nodes i. In this paper, we studied the basic composition of cooperation which is composed of packet forwarding [ 13 ].
We used packet forwarding strategy on each node as the entry point to discover the power efficiency in VANETs. A game theory model is proposed to analyze the behavior of each player node and player are interchangeable in this paper where each player has the objective to maximize its utility function.
In the proposed noncooperative game, each node adjusts the power level and transmission rate through the packet forwarding strategy, achieving a balance between energy consumption and throughput based on current environment. Aforementioned characteristics are summarized as follows: 1 players are rational; 2 the player chooses their strategies independently; 3 complete information is shared. In the following system model section, we will discuss the Nash Equilibrium NE in the noncooperative game model and analyze its efficiency.
In this proposed scheme, we creatively deployed the VANETs by considering both node density and node mobility influences. The rest of the paper is structured as follows: Section 2 discusses the related work.
Section 3 introduces system model and formulates the power efficiency game. Finally, The conclusion of this paper is presented in Section 5. Due to the development of ICT, the energy consumption and emission are becoming a major issue in academic and industry [ 2 , 9 , 10 , 15 ].
In [ 16 ], the authors presented a directional routing and scheduling scheme, considering congestion, buffer, and delay for green VANETs. The target of this urban rescue operation is to reduce the energy consumption for green communication. Parameters to measure the green VANETs communication are throughput, time index in terms of travel time, and buffer time.
On the other hand, to improve the bandwidth efficiency, [ 18 , 19 ] introduced several channel coordination schemes for IEEE It showed that the proposed scheme enhanced saturation throughput based on IEEE The papers [ 20 , 21 ] proposed a basic framework based on game theory including some essential assumptions i.
Moreover, to describe the behavior of each node in a wireless ad hoc network, [ 20 ] proposed a noncooperative game model to analyze the effect of power level in the near-far model NFE. The paper [ 21 ] also investigated the effect of power level based on game theory.
The difference is [ 21 ] separate the system as several layers; then authors studied how an independent decision affects the performance of the network in the corresponding layer. The papers [ 13 , 22 ] applied game theory to analyze the cooperation and packet forwarding mechanism in static wireless ad hoc networks. Especially, in [ 13 , 23 ], authors pointed out that the interactions of nodes are different with different topology.
They also determined which condition the cooperation and packet forwarding can exist without incentives. In [ 24 , 25 ], authors studied a belief-based packet forwarding scheme. The paper [ 24 ] assumed a probability distribution function PDF to measure the belief on nodes.
Then, nodes take action based on the result of PDF, and the belief function is updated by taking advantage of Bayes rule. Similar to [ 24 ], authors in [ 25 ] also proposed a belief-based scheme where the proposed method is to circumvent the complexity of calculating the belief function. The paper [ 26 ] designed the power control mechanism for the reputation-based scheme. The strategy set consists of the level of transmission power and the forwarding probability.
In each round, nodes choose the energy consumption to send traffic; then nodes will update the forwarding probability based on the beliefs. In [ 27 ], authors studied an uplink power control environment where players are selfish to maximize its throughput under the power constraint scenario. They take both noncooperative and cooperative model under the power constraint scenario and provide the best response policy for the noncooperative game model. The paper [ 5 ] proposed an algorithm, combining the power consumption and transmission rate with the different SINR condition.
The cooperation between nodes can be reached although the proposed algorithm is based on the noncooperative game model so that throughput will be improved in the network. Many previous works have been done with the cooperation and packet forwarding strategies in wireless ad hoc networks.
However, some previous works have to be modified due to the dynamic topology and mobility of nodes in VANETs. A Poisson arrival process with the rate function is reputed as an appropriate traffic generation model [ 28 — 31 ] in [ 32 ]. Following the assumption of Poisson arrival process, the average node density on the road is given by where represented a random velocity of the node which follows the Gaussian distribution.
Therefore, the average node number can be easily calculated by where represented the maximum communication distance. Following the Poisson arrival assumption, the distance between two adjacent nodes can be expressed in a pdf as follows: Correspondingly, the distance between two nonadjacent nodes will be the sum of 3 , and it is expressed in the following pdf: where represented the number of nodes between the specified nonadjacent node and represented the node density. The interference between two nodes is reflected in the SINR [ 33 ].
The channel fading between two nodes is supposed to follow the Rayleigh fading. The experimental vehicular channel measurement is used to estimate the path loss. A linear relationship between the path loss and the distance between sender and receiver is discussed by [ 34 — 36 ]. Therefore, this path loss model is simplified but very accurate; it is expressed as follows: represented a reference distance, normally choosing 1 meter. Moreover, the throughput of a node in the game is expressed as follows: represented the data length.
It can be calculated by the data length divided by the data rate. Moreover, the probability of each transmission situation is also required to calculate the in 9. In this part, we applied the aforementioned parameters in a noncooperative game model.
The core idea of the game theory is to predict other players strategies and choose the best strategy depending on their utility. Nash Equilibrium as the most popular approach to predict which strategy to choose is wildly used. The mathematical definition of Nash Equilibrium is defined as follows. Definition 1 Nash Equilibrium. Where the is denoted as player set, represented th player. In the other words, they can not enhance their utility except when other players change the strategy.
The different payoff function and cost function will contribute to the different Nash Equilibrium or even no Nash Equilibrium. Enumerating all the possibilities without practical assumptions will be confusing and inconclusive and therefore the detailed analysis will be presented in the simulation part combined with the realistic assumption in VANETs.
If Kakutani fixed point theorem is satisfied, at least one Nash Equilibrium will exist and all the rational players will choose the strategy that belongs to Nash Equilibrium. Otherwise, the player will choose the strategy based on the probability model since there is no Nash Equilibrium. Definition 2 Pareto optimality.
Definition 3 social optimality. If and only if where represents the weight of th player, the strategy is called social optimal SO. PO is used to analyze the efficiency of each player. SO is used to analyze the efficiency of the entire network with the different weight of the players. Moreover, one kind of dynamic game model repeated game is introduced to further improve the efficiency.
Repeated game in game theory is often used to enhance the performance of the Nash Equilibrium by punishing the selfish node. If players interacted by playing a similar stage game numerous times, this game is defined as a repeated game.
As we mentioned earlier, the player set is denoted as. In a repeated game, the historic utility function of a player will influence the current utility function of the player. Normally the effect of the round decay is made by adopting the exponential decay. The discount factor is , where ; when , the history of the player has no impact to the current utility.
When , the history of player has the same impact as the current utility. The utility function of player at round can be expressed as From the equation, it is easy to observe that the utility at round will decrease by a factor of when estimating the utility of round.
This factor can be used to model service which has the different priority. By giving the delay sensitive players small discount factor , and delay insensitive players big discount factor , the efficiency of Nash Equilibrium can be significantly increased by decreasing the loss of throughput.
We assumed all the players in this game are rational players, and every node tries to choose the strategies that can maximize the utility, where the functions and represented throughput gain and energy consumption. Consider the most fundamental game model, where and as shown in Table 2 , which denotes the fact that there are only two strategies that can be selected: forwarding the packet with power and data rate or not forwarding the packet.
The utility function of the player is the payoff function minus the cost function a linear function [ 5 , 20 , 21 ]. It is noteworthy that the interaction between two players needs to be considered. When two players transmit at the same time, we can use 5 to evaluate SINR for each node. This section complements the analysis of the different types of the game formulation mentioned in Section 3.
At the same time, the optimal solution will be compared to find out the most suitable one according to the specific environment. In our simulation, we first showed the defect of mandatory cooperation based on the delay variation, and then we discussed the proposed game theory model where the player number has been set to 2.
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Students of other degrees can participate in this module without capacity testing. Game Theory in Wireless Communication Networks. Navigation To modulepage. Display language German English. Credits: 6 LP.
Cambridge Core - Communications and Signal Processing - Game Theory in Wireless and Communication Networks. Game Theory in Wireless and Communication Networks. Theory, Models, and pp i-vi. Access. PDF; Export citation.
The popularity of smart phones and other mobile devices has brought about major expansion in the realm of wireless communications. With this growth comes the need to improve upon network capacity and overall user experience, and game-based methods can offer further enhancements in this area. Game Theory Framework Applied to Wireless Communication Networks is a pivotal reference source for the latest scholarly research on the application of game-theoretic approaches to enhance wireless networking. Featuring prevailing coverage on a range of topics relating to the advanced game model, mechanism designs, and effective equilibrium concepts, this publication is an essential reference source for researchers, students, technology developers, and engineers. This publication features extensive, research-based chapters across a broad scope of relevant topics, including potential games, coalition formation game, heterogeneous networks, radio resource allocation, coverage optimization, distributed dynamic resource allocation, dynamic spectrum access, physical layer security, and cooperative video transmission.
To add more greens into such kind of complicated and time-varying mobile network, we specifically investigate the throughput and transmission delay performances for real-time and delay sensitive services through a repeated game theoretic solution.Downgreatmulmisp 16.03.2021 at 17:06
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Game theory GT is a mathematical method that describes the phenomenon of conflict and cooperation between intelligent rational decision-makers.