چكيده به لاتين
In cognitive radio networks which dynamically allocate frequency spectrum to the users, the secondary users (SUs) get permission to use the frequency sources whenever the interference power at the primary user (PUs) does not exceed predefined threshold. Using spatial filtering, SUs are able to control interference at the PUs.
In this thesis, a new method of beamforming and power allocation for cooperative cognitive radio networks consisting arbitrary number of secondary transmitter-receiver pairs, is proposed, in which the SUs will be allowed to use the frequency spectrum at the cost of participating in the submission of PU’s data. An algorithm is presented to find the optimum transmit powers of the secondary users, secondary and cooperative beamforming vectors, as well as the cooperation factor of the SUs. Simulation results confirm that the cooperation of SUs results in increasing the data rata of the PU.
Considering error in the estimation of the channel state information (CSI), A new robust beamforming method maximizing the minimum achievable rate of the SUs is proposed for cognitive radios comprising arbitrary number of SUs. Using Bernstein inequalities and theorems in linear algebra, the non-convex original problem is replaced by a quasi-convex optimization problem. inequalities are introduce to replace infinite non-convex outage constraints. Considering the quasi-convexity of the new problem, an algorithm is developed to obtain optimal beamforming vectors. Proof of the convergence of this algorithm is given. Simulation results show that we reach more achievable rates for the SUs in comparison with the previous related works.
Considering the importance of using new sources as the energy, the application of simultaneous wireless information and power transformation (SWIPT) in the cognitive radio networks has been investigated in this thesis. A new robust beamforming method for MISO downlink cognitive radio networks providing SWIPT is proposed. The proposed method is developed for networks comprising arbitrary number of users. The objective function is maximizing the minimum achievable rate of the users. Outage constraints on the minimum SINR of the users, interference at the PUs, RF received power at the energy harvesting users, and the amount of information leakage at the energy harvesting receivers are imposed to guarantee performance of the network. Due to the large number of constraints, the new quasi-convex optimization problem is written in such a way that the computational costs to solve this problem are as low as possible. In another part of this thesis, a new beamforming method for cooperative MIMO cognitive radio networks with SWIPT is proposed. In this method, the link between primary transmitter and receiver is too weak to transmit data. SUs which provide their energies from RF signals, relay the primary user’s data. Using mathematical techniques, a quasi-convex optimization problem is introduced to replace the original non-convex optimization problem and an iterative algorithm is developed to solve the optimization problem. Simulation results confirm that in both the methods, all constraints are satisfied. Advantage of the proposed methods are shown by performing the simulations.
