RESOURCE ALLOCATION IN COGNITIVE RADIO AD HOC NETWORKS: A POTENTIAL GAME PERSPECTIVE

Abstract

In wireless cognitive radio networks, natural antagonism arises among unlicensed users when nodes opportunistically compete for unused frequency bands and the operations are seriously hampered by acute scarcity of resources. The transmitted power, which is inherently pertinent to the signal-to-interference-plus-noise ratio, cog- nition methodology, and lack of central management, must be preserved for longer network lifetime. In the midst of this struggle to acquire desired frequency band, where the performance of the entire network is dependent upon the behavior and etiquette exhibited by individual nodes, it is pivotal to introduce an e ective cooper- ation mechanism in order to improve the vital network parameters. In this work, we employ the concepts of game theory to develop an e cient and sustainable coopera- tion mechanism for e cient cognition and improved spectrum utilization. Instead of focusing merely on the interference a user observes, cooperation is ensured by taking into consideration the amount of interference a user creates for other network users. With the introduction of unlicensed users in licensed bands, the operations and interests of licensed users need to be protected, hence the spectrum owners are given an advantage and control over the multiple access policy. We address the problems in spectrum access and channel selection equilibrium in a leader-follower setup. In contrast to the game formulations that lack e cient power and pricing schemes, we present a cooperative Stackelberg potential game for cognitive players. A dynamic cost function is articulated to induce awareness in players to mitigate the e ects of sel sh choices in spectrum access while at the same time steer the distributive network towards achieving Nash equilibrium. The proposed scheme is mutually bene cial for i ii all players and focuses on improving the network performance and power e ciency. We design the network potential function such that the nodes have performance based incentives to cooperate and achieve a Nash equilibrium solution for e cient channel acquisition and capacity. Simulation results show fast convergence in channel selection strategies and increase in capacity for the entire network. In order to avoid anarchy in this uncontrolled and sometimes hostile environment, it is important to inhibit the nodes in making potentially risky decisions that may eventually jeopardize the stability and performance of the entire network. We present a game theoretic approach to combat the e ects of uncontrolled and sel sh behav- ior exhibited by cognitive network nodes. A sustainable solution is proposed that employs nonlinear learning in conjunction with potential function to alleviate the im- plications of disruptive behavior that is usually demonstrated in the access of scarce spectrum resources. The regret information in decision making is exploited along with history statistics to minimize information exchange and achieve swift convergence of strategies. Moreover, incorporating learning allows the cognitive players to select the channels in a simultaneous fashion instead of waiting for their turns to change their channel choices. This considerably reduces the delay in achieving network stability.