Due to the high-power consumption and complexity of fully digital baseband precoding, its implementation in massive millimeter-wave multiple-input multiple-output (MIMO) systems is not cost-efficient and practical; for this reason, Hybrid precoding has attracted a lot of attention in recent years.  Most Hybrid precoding techniques concentrate on the fully-connected structure, although they require lots of phase shifters, which is high energy-consuming. On the contrary, the partially-connected structure has low power consumption, nevertheless, suffers from a severe decrease in spectral efficiency (SE). To enhance SE, this paper proposed a dynamic Hybrid precoding structure where a switch network is able to provide dynamic connections from phase shifters to radio frequency (RF) chains. To determine the digital precoder and the states of switch, a novel alternating minimization algorithm is proposed, which leverages closed-form solutions at each iteration to efficiently converge to an optimal solution. Furthermore, the phase shifter matrix is optimized through an iterative solution. The simulation results show that in terms of SE, the proposed algorithm with a dynamic structure achieves higher performance than the partial structure. Also, since the proposed structure reduces the number of phase shifters, it can guarantee better energy efficiency (EE) than the fully connected structure.

Millimeter wave (mmWave) communication, which utilizes massive multiple input multiple output (MIMO) techniques, is one of the key enabling technologies for high capacity 5G cellular networks.However, the hardware complexity and the high power consumption in massive-MIMO array hinder its integration.Hybrid precoding technique, which combines large-dimensional analog preprocessing with low-dimensional digital processing can be used to reduce both hardware costs and power consumption in massive MIMO systems, as a potential method. In this paper, we introduce a Hybrid precoding structure design in both narrowband and wideband massive MIMO inspired by the effective alternating minimization (AltMin) algorithm with the Least Squares Amendment (LSA). To be specific, in the proposed design, the analog Radio Frequency (RF) precoder structure employs the Discrete Fourier Transform (DFT) processing which in turn affects the performance of the system. In addition, the proposed design relies on Space-Time Block Coding (STBC) to attain diversity and further enhances the system reliability. We evaluate bit error rate (BER) performance of proposed massive MIMO system using various Hybrid precoding techniques. Numerical simulations (Monte Carlo) are performed to check the precision of proposed BER analytical expression. Our simulation results demonstrate significant performance gains of the proposed STBC-based Hybrid precoding with DFT processing over existing Hybrid precoding algorithms.

Particle swarm optimization (PSO) is one of the practical metaheuristic algorithms which is applied for numerical global optimization. It bene ts from the nature inspired swarm intelligence, but it su ers from a local optima problem. Recently, another nature inspired metaheuristic called Symbiotic Organisms Search (SOS) is proposed, which doesn't have any parameters to set at start. In this paper, the PSO and SOS algorithms are combined to produce a new Hybrid metaheuristic algorithm for the global optimization problem, called PSOS. In this algorithm, a minimum number of the parameters are applied which prevent the trapping in local solutions and increase the success rate, and also the SOS interaction phases are modi ed. The proposed algorithm consists of the PSO and the SOS phases. The PSO phase gets the experiences for each appropriate solution and checks the neighbors for a better solution, and the SOS phase bene ts from the gained experiences and performs symbiotic interaction update phases. Extensive experimental results showed that the PSOS outperforms both the PSO and SOS algorithms in terms of the convergence and success rates.

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.

Bitcoin and digital currencies have emerged as a new market for investment. Therefore, the prediction of their future trend and prices is highly significant. In this research, the factors influencing the price of bitcoin were identified and extracted based on previous researches. The identified factors include the US dollar index, CPI index, S and P 500, Dow Jones, and gold price. Considering the performance of metaheuristic algorithms in predicting bitcoin price, this research utilized genetic algorithm and particle swarm optimization algorithm, and proposed a Hybrid algorithm to improve their performance.According to our results, among the investigated factors, the US dollar index has the greatest impact on bitcoin price, followed by inflation rate and the CPI index. Additionally, the proposed Hybrid algorithm outperforms the particle swarm optimization and genetic algorithms, with a prediction error of 7.3%. It should be noted that the type and magnitude of the impact of the investigated factors may change over time. For example, a factor that previously had a direct impact may become reversed or neutralized over time.

Numerous algorithms have recently been invented with varying strengths and weaknesses, none of which is the best for all cases. Herein, a Hybrid optimization method known as a PSOHHO optimization algorithm is presented. There are two methods for combining algorithms: parallel and sequential. We adopted the parallel method and optimized the algorithm's performance. We cover the weaknesses of one algorithm with the strengths of another algorithm using a new method of combination. In this method, using several formulas, the top populations are exchanged between the two algorithms, and a new population is created. With this ability, the strengths of an algorithm can be used to compensate for the weaknesses of the other algorithm. In this method, no changes are made to the algorithms. The main goal is to use existing algorithms. This method aims to attain the optimal solution in the shortest time possible. Two algorithms of particle swarm optimization (PSO) and Harris Hawks optimization (HHO) were used to present this method and five truss samples were considered to confirm the performance of this method. Based on the results, this method has rapid convergence speed and acceptable results compared to the other methods. It also yields better results than its basic algorithms.

In this paper, a new and an effective combination of two metaheuristic algorithms, namely Firefly algorithm and the Differential evolution, has been proposed. This Hybridization called as HFADE, consists of two phases of Differential Evolution (DE) and Firefly algorithm (FA). Firefly algorithm is the nature-inspired algorithm which has its roots in the light intensity attraction process of firefly in the nature. Differential evolution is an Evolutionary algorithm that uses the evolutionary operators like selection, recombination and mutation. FA and DE together are effective and powerful algorithms but FA algorithm depends on random directions for search which led into retardation in finding the best solution and DE needs more iteration to find proper solution. As a result, this proposed method has been designed to cover each algorithm deficiencies so as to make them more suitable for optimization in real world domain. To obtain the required results, the experiment on a set of benchmark functions was performed and findings showed that HFADE is a more preferable and effective method in solving the high-dimensional functions.