In this paper, the systematic design of a multiple antenna system for 5G smartphone operating at 3.5 GHz for multiple-input multiple-output (MIMO) operation in smartphones is proposed. The smartphone is preferred to be lightweight, thin, and attractive, and as a result metal casings have become popular. Using conventional antennas, such as a patch antenna, Inverted-F antennas, or monopole, in proximity to metal casing leads to decreasing its total efficiency and bandwidth. Therefore, a slot antenna embedded in the metal casing can be helpful, with good performance regarding bandwidth and total efficiency. The proposed multiple antenna system adopted the unit open-end slot antenna fed by Inverted-L microstrip with tuning stub. The measured S-parameters results agree fairly with the numerical results. It attains 200 MHz bandwidth at 3.5 GHz with ports isolation of (≤−13 dB) for any two antennas of the system. The influence of the customer’s hand for the proposed multiple antenna system is also considered, and the MIMO channel capacity is computed. The maximum achievable MIMO channel capacity based on the measured result is 31.25 bps/Hz and is about 2.7 times of 2 × 2 MIMO operation.

Electromagnetic design problems are generally formulated as nonlinear programming problems with multimodal objective functions and continuous variables. These can be solved by either a deterministic or a stochastic optimization algorithm. Recently, many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), have been proposed and applied to electromagnetic design problems with promising results. However, there is no universal algorithm which can be used to solve engineering design problems. In this paper, a stochastic smart quantum particle swarm optimization (SQPSO) algorithm is introduced. In the proposed SQPSO, to tackle the premature convergence problem in order to improve the global search ability, a smart particle and a memory archive are adopted instead of mutation operations. Moreover, to enhance the exploration searching ability, a new set of random numbers and control parameters are introduced. Experimental results validate that the adopted control policy in this work can achieve a good balance between exploration and exploitation. Finally, the SQPSO has been tested on well-known optimization benchmark functions and implemented on the electromagnetic TEAM workshop problem 22. The simulation result shows an outstanding capability of the proposed algorithm in speeding convergence compared to other algorithms.

Particle Swarm Optimization (PSO) is a member of the swarm intelligence-based on a metaheuristic approach which is inspired by the natural deeds of bird flocking and fish schooling. In comparison to other traditional methods, the model of PSO is widely recognized as a simple algorithm and easy to implement. However, the traditional PSO’s have two primary issues: premature convergence and loss of diversity. These problems arise at the latter stages of the evolution process when dealing with high-dimensional, complex and electromagnetic inverse problems. To address these types of issues in the PSO approach, we proposed an Improved PSO (IPSO) which employs a dynamic control parameter as well as an adaptive mutation mechanism. The main proposal of the novel adaptive mutation operator is to prevent the diversity loss of the optimization process while the dynamic factor comprises the balance between exploration and exploitation in the search domain. The experimental outcomes achieved by solving complicated and extremely high-dimensional optimization problems were also validated on superconducting magnetic energy storage devices (SMES). According to numerical and experimental analysis, the IPSO delivers a better optimal solution than the other solutions described, particularly in the early computational evaluation of the generation.

Purpose: The purpose of this paper is to explore the potential of standard quantum-based particle swarm optimization (QPSO) methods for solving electromagnetic inverse problems. Design/methodology/approach: A modified QPSO algorithm is designed. Findings: The modified QPSO algorithm is an efficient and robust global optimizer for optimizing electromagnetic inverse problems. More specially, the experimental results as reported on different case studies demonstrate that the proposed method can find better final optimal solution at an early stage of the iterating process (uses less iterations) as compared to other tested optimal algorithms. Originality/value: The modifications include the design of a new position updating formula, the introduction of a new mutation strategy and a dynamic control parameter to intensify the convergence speed of the algorithm.