QoS-Constrained Profit Maximization for Primary Users in Competitive Cognitive Radio Networks

Abstract

In Cognitive Radio Networks (CRNs) licensed users called primary users (PUs) tend to sell unused spectrum to secondary users (SUs). At first each PU notify vacant channels after setting unit price spectrum band based on available market information of opponent and QoS penalty, then SUs use cognitive radio to get access to purchased bands. However, in crowded wireless networks such as 5G and beyond, PUs are behaving in a selfish and private manner this may lead to information scarcity. Many recent works employ the cooperative spectrum management. However, in this manuscript a new decentralized scheme is suggested to momentarily estimate the unit-price using bounded rationality approach, this method reduce the exchanged information and the complexity of the network where we considered a non-cooperative Cournot dynamic game to model egoist PUs behavior. We used bounded rationality learning algorithm to attain Nash Equilibrium (NE) which is the solution of this game to ensures that the PUs increase their profit and lease unexploited spectrum to SUs with a competitive price. Numerical simulations showed that when a PU adapt appreciate system settings such as bounded rationality learning rate, packet loss rate and spectral efficiency, the chaotic behaviors are delayed and the maintainability of NE stability is achieved.