Resource management of cognitive radio networks with optimization methods.

Abstract

The deployment of cognitive radio networks provides a promising future for wireless communications with higher spectrum efficiency. A cognitive radio transceiver senses its environment and dynamically adapts its transmission methods and the channel usage so that the induced interference to the licensed user is regulated. The dissertation focuses on solving three kinds of problems of the cognitive radio networks: (1) Multiple-input and multiple-output (MIMO) transmission of cognitive radio networks; (2) Joint reduction of the out-of-band power (OBP) and the peak-to-average power ratio (PAPR) for non-contiguous orthogonal frequency-division multiplexing (NC-OFDM) systems; (3) Common control channels assignment of cognitive radio networks. MIMO cognitive radio networks adjust the transmit signal covariance matrices. Each secondary transmitter competes with others to increase its own information rate while generating limited interferences to the primary receivers. Secondary user transmission is modeled as a cooperative game. In order to reduce the computational complexity, a simplified case is discussed. The strategy of each secondary user is the power allocation along the eigenmode directions. When considering orthogonal access to the spectrum by secondary users, MIMO cognitive radio networks are further simplified, and a parallel sub-channel transmission method is proposed. NC-OFDM is a promising technique for cognitive radio systems. There are two main drawbacks: the high OBP and the high PAPR. An algorithm is proposed to jointly reduce the OBP and the PAPR based on the method of alternating projections onto convex sets. In order to reduce the computational complexity further, two more algorithms are proposed. In cognitive radio networks, common channels are needed. Common channels assignment is modeled as a strategic game. Corresponding utility function is designed so that it becomes a potential game. A best response algorithm is proposed to solve the common channels assignment. Moreover, simulated annealing is applied in order for the algorithm to escape a local optimum.