We address the throughput maximization problem for downlink transmission in DF-relay-assisted cognitive radio networks (CRNs) based on simultaneous wireless information and power transfer (SWIPT) capability. In this envisioned network, multiple-input multiple-output (MIMO) relay and secondary user (SU) equipment are designed to handle both radio frequency (RF) signal energy harvesting and SWIPT functional tasks. Additionally, the cognitive base station (CBS) communicates with the SU only via the MIMO relay. Based on the considered network model, several combined constraints of the main problem complicate the solution. Therefore, in this paper, we apply heuristic guidelines within the convex optimization framework to handle this complexity. First, consider the problem of maximizing throughput on both sides of the relay separately. Second, each side progresses to solve the complex problem optimally by adopting strategies for solving sub-problems. Finally, these optimal solutions are synthesized by proposing a heuristic iterative power allocation algorithm that satisfies the combinatorial constraints with short convergence times. The performance of the optimal proposed algorithm (OPA) is evaluated against benchmark algorithms via numerical results on optimality, convergence time, constraints’ compliance, and imperfect channel state information (CSI) on the CBS-PU link.

Since their introduction, cognitive radio networks (CRN), as a new solution to the problem of spectrum scarcity, have received great attention from the research society. An important  eld in database-driven CRN studies is pivoted on their security issues. A critical issue in this context is user's location privacy, which is potentially under serious threat. The query process by secondary users (SU) from the database is one of the points where the problem rises. In this paper, we propose a Privacy-Preserving Query Process (PPQP), accordingly. This method lets SUs deal in the process of spectrum query without sacri cing their location information. Analytical assessment of PPQP's privacy preservation capability shows that it preserves location privacy for SUs against di erent adversaries, with very high probability. Relatively low communicational cost is a signi cant property of our protocol.

In this paper, we consider cognitive radio network in which two cognitive radio sources communicate with two cognitive destinations via a relay node. The decode and forward (DF) relay node employs physical layer network coding (PLNC) to improve the data rate. Based on the availability of the spectrum bands at the source, relay and destination, the network employs three different diversity schemes namely source to relay diversity, relay to destination diversity and combination of earlier two diversity schemes with overall source to destination diversity schemes. The optimal allocation of channel and power with per band and sum power constraints of a node in the network is formulated as convex optimization problem to improve the end to end throughput of the cognitive radio network. Simulation results show that the resultant joint channel and power allocation are superior to the equal power allocation in terms of both end to end throughput and outage probability.

cognitive radio as a key technology is taken into consideration widely to cope with the shortage of spectrum in wireless networks. One of the major challenges to realization of CR networks is security. The most important of these threats is primary user emulation attack, thus malicious user attempts to send a signal same as primary user's signal to deceive secondary users and prevent them from sending signals in the spectrum holes. Meanwhile, causing traffic in CR network, malicious user obtains a frequency band to send their information. In this thesis, a method to identify primary user emulation attack is proposed. According to this method, primary users and malicious users are distinguished by clustering. In this method, the number of active users is recognized in the CR network by clustering. Indeed, by using Dirichlet process mixture model classification based on the Bayesian Nonparametric method, primary users are clustered. In addition, to achieve higher convergence rate, Chinese restaurant process method to initialize and non-uniform sampling is applied to select clusters parameter.

In this paper, we propose a cognitive radio network which consists of a number of secondary users and one primary user. The primary user utilized a successive relay performance. The successive relaying technique creates a full duplex relay performance by two half duplex relays, which improves spectral efficiency. In the presence of secondary users, we use successive relay technique in the primary user to ensure its acceptable performance. Also, the sum rate of secondary users is increased. The challenges of this network are inter-relay interference and inter user interference. The interference alignment method is utilized to eliminate the interferences in the successive relay technique and in the cognitive radio network. Besides, the minimum transmitted power of the primary user is derived to guarantee its quality of service requirement. Two power allocations algorithms are proposed to maximize the sum rate of secondary users and the energy efficiency of the network. In both power allocations algorithms, satisfying the quality of service of the primary user is considered. The closed-form solutions of these algorithms are studied. The fractional programing approach was employed to solve the energy efficiency optimization in two steps.