Abstract Introducing orthogonal frequency division multiplexing (OFDM) into cognitive radio (CR) can potentially increase the spectrum efficiency, but it also leads to further challenges for the resource allocation of CR networks. In OFDM‐based cognitive relay networks, two of the most significant research issues are subcarrier selection and power allocation. In this paper, a non‐cooperative game model is proposed to maximize the system throughput by jointly optimizing subcarrier selection and power allocation. First, taking the direct and relay links into consideration, an equivalent channel gain is presented to simplify the cooperative relay model into a non‐relay model. Then, a variational inequality method is utilized to prove the existence and uniqueness of the Nash equilibrium solution of the proposed non‐cooperative game. Moreover, to compute the solution of the game, a suboptimal algorithm based on the Lagrange function and distributed iterative water‐filling algorithm is proposed. The proposed algorithm can jointly optimize the process of subcarrier selection and power allocation. Finally, simulation results are shown to demonstrate the effectiveness of the proposed joint subcarrier selection and power allocation scheme. Copyright © 2015 John Wiley & Sons, Ltd.

It is a complex task to allocate the secondary users' power in OFDM-based cognitive radio networks which have many sub-carriers, especially when the channel uncertainty and coupled interference exist in cognitive networks. To address the issue, a worst-case robust distributed power allocation scheme is proposed for OFDM-based cognitive radio networks in this paper. Firstly, a robust power allocation model is constructed using worst-case approach with the uncertain channel state information. A non-cooperation game framework is introduced to optimize secondary users' rate from their own profit individually with the primary user interference constraint The variational inequality is employed to analyze the existence and uniqueness of the Nash equilibrium. The proposed scheme could optimize the power allocation under uncertain channel information and maximize secondary users' rate. And the primary user interference temperature could also be satisfied even the channel in the worst case. The simulation is presented to valid its applicability and robust in a variety of scenes.

We have developed a genetic algorithm (GA) approach to the problem of task scheduling for multiprocessor systems. Our approach requires minimal problem specific information and no problem specific operators or repair mechanisms. Key features of our system include a flexible, adaptive problem representation and an incremental fitness function. Comparison with traditional scheduling methods indicates that the GA is competitive in terms of solution quality if it has sufficient resources to perform its search. Studies in a nonstationary environment show the GA is able to automatically adapt to changing targets.