جستجوی مقالات مرتبط با کلیدواژه « convex optimization » در نشریات گروه « برق »

Cooperative Networks are novel technologies for improving wireless system performance; therefore, they are a good candidate for future cellular networks. In these networks, nodes cooperate with their data and information to each other to increase the network's performance. Cooperative Networks have different architectures. Hundreds of wireless systems are located in a small area in some architecture. These architectures are named dense cooperative networks. Resource allocation can improve the performance of dense cooperative networks. Optimal resource allocation is a strategy for improving system performance and guaranteeing quality of service; hence, in this paper, we attempt to calculate optimal transmit power for nodes to achieve maximum allowable capacity by applying the convex optimization method. Convex optimization is a powerful tool for resource allocation and signal processing in wireless networks. In addition, due to the Complexity of the optimal solution, we introduce a novel suboptimal power allocation algorithm. Simulation results indicate that our proposed method can improve the performance of cellular networks.

Presently, due to emergence of new generation of wireless telecommunication networks, some appropriate capacity and coverage have been provided for end-users by new hybrid terrestrial-satellite networks, consisting of two or more satellites in different orbits and terrestrial equipment. Today, due to the lack of spectral resources, a method, such as cognitive radio is used to allow for coexistence of spectrum between different nodes. Therefore, in this paper, spectral coexistence method between two satellites was applied over a common region based on cognition link to manage energy efficiency. Also, for mitigating interferences between satellites in downlink channel, the Stackelberg game was exploited. According to simulation results, the proposed algorithm for a primary satellite system with a main node had more energy efficiency compared to the other algorithms, such as sequential convex approximation (SCA)-based precoding, multi-beam interference mitigation (MBIM), and zero-forcing (ZF)-based precoding.

We study the problem of physical layer security in massive multiple-input multiple-output (MaMIMO) cognitive radio networks (CRN). In particular, we investigate the design of a smart jamming attack on the uplink transmission of a CRN in the presence of a single antenna jammer. The jammer is aware of the transmission protocol as well as the pilot set used for channel training in the MaMIMO systems. It attacks both the training and data transmission phases but with different powers. To have the most destructive attack, the jammer optimally divides its power between two phases to minimize the maximum energy efficiency of the secondary system. The resulting power optimization is a non-convex problem and to solve it, we propose a method to transform it into a convex optimization problem. Numerical results illustrate the effectiveness of the proposed smart jamming attack in decreasing the energy efficiency of the secondary MaMIMO system.

One of the interesting topics in the field of social networks engineering is opinion change dynamics in a discussion group and how to use real experimental data in order to identify an interaction pattern among individuals. In this paper, we propose a method that utilizes experimental data in order to identify the influence network between individuals in social networks. The employed method is based on convex optimization and can identify interaction patterns precisely. This technique considers individuals’ opinions in multiple dimensions. Moreover, the opinion dynamics models that have been introduced in the literature are investigated. Then, the three models which are the most comprehensive and vastly accepted in the literature, are considered. These three models are then proven to satisfy the convexity condition, which means they can be used for the introduced method of identification. Four real experiments have been conducted in this research that their results verify the application of our method. The outcomes of these experiments are presented in this paper.