Potential Game Model Research Articles

A game theoretic framework for petroleum refinery strategic production planning

A game theoretic framework for strategic refinery production planning is presented in which strategic planning problems are formulated as non‐cooperative potential games whose solutions represent Nash equilibria. The potential game model takes the form of a nonconvex nonlinear program (NLP) and we examine an additional scenario extending this to a nonconvex mixed integer nonlinear program (MINLP). Tactical planning decisions are linked to strategic decision processes through a potential game structure derived from a Cournot oligopoly‐type game in which multiple crude oil refineries supply several markets. Two scenarios are presented which illustrate the utility of the game theoretic framework in the analysis of production planning problems in competitive scenarios. Solutions to these problems are interpreted as mutual best responses yielding maximum profit in the competitive planning game. The resulting production planning decisions are rational in a game theoretic sense and are robust to deviations in competitor strategies. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2751–2763, 2017

Evolutionarily stable opportunistic spectrum access in cognitive radio networks

In order to fully utilise limited spectrum resources of multiple channels and multiple radios in cognitive radio networks, the authors propose a potential game model for opportunistic spectrum access based on both accurate and inaccurate spectrum state estimation with considering the interference constraints of licensed users. Three algorithms are proposed to achieve equilibrium of the proposed game. First, assuming spectrum sensing results are accurate, a joint strategy fictitious play-based channel selection algorithm with incomplete information is presented, and it can achieve a pure Nash equilibrium (NE) of the proposed game. Second, in order to make the outcomes of game robust, an evolutionary spectrum access mechanism with complete information is introduced by using evolutionary game theory based on inaccurate spectrum state estimation so that evolutionary stable strategy (ESS) can be achieved. Finally, with incomplete network information, a distributed learning algorithm is proposed to achieve a mixed NE, which is proved to be an ESS. Simulation results show that these algorithms can significantly improve spectrum allocation efficiency while reducing mutual collision.

Multipath network coding and multicasting for content sharing in wireless P2P networks: A potential game approach

Motivated by the popularity of the peer to peer (P2P) content sharing over internet and the development of wireless device to device (D2D) communications, this paper considers the problem of delivering K different contents to D destinations from N sources in wireless networks. Traffic in opposite directions over two wireless hops can utilize the advantage of network coding (NC) in order to decrease the number of transmissions used. We call such coded hops as “NC-links”. On the other hand, the multicast (MC) links can transmit data to several nodes at the same time, also yielding the improvement of the transmissions efficiency. However, there exists a certain level of ambiguity regarding how to coordinate NC and MC to improve the file sharing performance in wireless P2P content distribution systems. Considering both NC and MC gains, we investigate the content traffic splitting using a potential game model. Subsequently, we identify the equilibrium solution to this game and develop a two-level distributed control algorithm that allows each destination to select the source and split the traffic so as to adjust the content traffic based on the potential function in a distributed way. Through theoretical analysis and simulation results, we show that the proposed scheme is stable and effective.

Interference Mitigation Based on Intelligent Location Selection in a Canonical Communication Network

AbstractIn this letter, the interference mitigation in a canonical communication network is discussed from the perspective of intelligent location selection. A potential game model is constructed and a location-selection algorithm is designed combining no-regret procedure. With the proposed algorithm, all nodes can update their strategies with limited information exchange. Specifically, our proposed algorithm can converge to a set of correlated equilibria which are the globally or locally optimal solution to the problem of interference minimization. Moreover, our proposed algorithm can achieve distributed implementation without a central node. Simulation results demonstrate that the total interference can be mitigated efficiently with our proposed algorithm. And the proposed algorithm can converge fast.

A QoS-Based Topology Control Algorithm for Underwater Wireless Sensor Networks

The topology control techniques of underwater wireless sensor networks and terrestrial wireless sensor networks are significantly different because of the particularity of underwater environments and acoustic communication. In this paper, an underwater wireless sensor network model was constructed, and six universal topology control objectives were concluded. The QoS topology control problem was mapped into an ordinal potential game model, and a distributed strategy adjustment algorithm for nodes was designed accordingly. The strategy vector resulting from the algorithm converges to the Nash equilibrium; minor complexity and preferable approximate ratios can be represented by the algorithm as well. The performance of the algorithm was analyzed through simulation experiments which indicate a well-constructed topology. Every objective was upgraded when model parameters were set suitable.

Interference avoidance in networks with distributed receivers

Direct extensions of distributed greedy interference avoidance (IA) techniques developed for centralized networks to networks with multiple distributed receivers (as in ad hoc networks) are not guaranteed to converge. Motivated by this fact, we develop a waveform adaptation (WA) algorithm framework for IA based on potential game theory. The potential game model ensures the convergence of the designed algorithms in distributed networks and leads to desirable network solutions. Properties of the game model are then exploited to design distributed implementations of the algorithm that involve limited feedback in the network. Finally, variations of IA algorithms including IA with respect to legacy systems and IA with combined transmit-power and WA adaptations are investigated.

Multihoming of Users to Access Points in WLANs: A Population Game Perspective

We consider non-cooperative mobiles, each faced with the problem of which subset of WLANs access points (APs) to connect and multihome to, and how to split its traffic among them. Considering the many users regime, we obtain a potential game model and study its equilibrium. We obtain pricing for which the total throughput is maximized at equilibrium and study the convergence to equilibrium under various evolutionary dynamics. We also study the case where the Internet service provider (ISP) could charge prices greater than that of the cost price mechanism and show that even in this case multihoming is desirable.