Korany R. Mahmoud

Optimal design of vertical silicon nanowires solar cell using hybrid optimization algorithm

Abstract. An approach to enhance the ultimate efficiency of the silicon nanowires (Si NWs) solar cell is proposed based on a hybrid population-based algorithm. The suggested technique integrates the ability of exploration in a gravitational search algorithm (GSA) with the exploitation capability of particle swarm optimization (PSO) to synthesize both algorithms’ strengths. The hybrid GSA-PSO algorithm in MATLAB® code is linked to finite-difference time-domain solution technique based on Lumerical-software to simulate and optimize the Si NWs’ geometrical parameters. The suggested GSA-PSO algorithm has advantages in terms of better convergence and final fitness values than that of the PSO algorithm. Further, the Si NWs lattice with optimized diameters and heights shows a high ultimate efficiency of 42.5% with an improvement of 42.8% over the Si NWs lattice with the same diameters and heights. This enhancement is attributed to the different generated optical modes combined with multiple scattering and reduced reflection due to the different heights and different diameters, respectively.

Optimised diffusion spots' locations for simultaneous improvement in SNR and delay spread

In this paper, two recent global optimisation techniques: particle swarm optimisation and central force optimisation are considered for optimising the location of the diffusion spots, which are used as transmitters in an indoor optical wireless communications system. The diffusion spots are uniformly distributed along a circle on the ceiling of the communications environment. The location of the diffusion spots is optimised for maximising the signal-to-noise ratio and minimising the root-mean-square delay spread at the receivers in the presence of noise sources. A performance comparison between the central located position of the spots and the optimised positions for three different scenarios is considered to validate and show the strengths of the applied optimisation techniques in solving this sort of problems. The used techniques can provide the communication system with the ability to adapt to changes in the surrounding environment, which will benefit future switching towards real-time intelligent systems.

Super directive Yagi–Uda nanoantennas with an ellipsoid reflector for optimal radiation emission

In this paper, the optimal designs of silicon Yagi–Uda nanoantennas (NAs) with an ellipsoid reflector have been proposed and analyzed using the 3D finite-difference time-domain method. The combination of nanospheres, nanowires, and ellipsoid reflectors has been employed to enhance the antenna directivity. The nanoantenna geometrical parameters are optimized using the particle swarm optimization algorithm. The optimized spherical NA with an ellipsoid reflector shows high directivity of 19.89, which is higher than the conventional counterpart by 65.75%. This enhancement is attributed to the different supported modes by the ellipsoid reflector, which increases the forward radiation and suppresses the backward one. Further, the optimized nanowire design with an ellipsoid reflector has achieved a directivity of 23.4. In addition, the radiation efficiency has been increased to 80.2% and 75.9% for optimized spherical and nanowire antennas, respectively. This enhancement is attributed to the efficient coupling between array elements as well as reduced sidelobe and back-lobe levels.

Efficient WMMSE Beamforming for 5G mmWave Cellular Networks Exploiting the Effect of Antenna Array Geometries

Millimetre wave (mmWave) transmission is considered as one of the potential technologies in fifth generation (5G) cellular networks that could allocate more bandwidth to support the rapid growth of mobile data services. However, mmWave signals experience large path loss which can overcome by employing directional beamforming with high-dimensional antenna arrays. In this study, a downlink multiuser multiple-input multiple-output beamforming optimisation problem is considered for maximising the system utility subject to per-user quality-of-service constraint as well as per-antenna and base station transmit power constraints. The difficulty of this problem lies in its non-convex nature. To address this challenge, a weighted minimum mean square error (WMMSE)-based solution is proposed to reformulate the non-convex problem into equivalent convex one. Then, the block coordinate descent method is used to determine beamforming vectors. Moreover, the different antenna array geometries either two-dimensional (2D) such as planar array and circular array (CiA), or 3D such as conformal array and conical array (CoA) are designed at the base station and their effect on the system performance is exploited. Extensive simulations show that the performance of the proposed WMMSE-based solution with CiA and CoA geometries outperforms the comparable algorithms in terms of system throughput, system power consumption and access rate.

Exploring Evolutionary Multi-Objective Techniques in Self-Organizing Networks

Future networks are promising to solve current issues and provide new features. Self-organizing network (SON) paradigm is one of the anticipated solutions. It involves the use of cognition concept and optimization techniques to enhance the network performance. In this paper, we propose the use of two multi-objective optimization techniques, namely, the multi-objective particle swarm optimization (MOPSO) and the multi-objective central force optimization (MOCFO) in future SON to manage system resources efficiently. Therefore, the used system design and implementation are provided. In addition, the evaluation results of the proposed two methods are compared with those obtained using the non-dominated sorting genetic algorithm (NSGA-II). Extensive simulations carried out using MATLAB package showed that MOPSO is comparable to NSGA-II and outperforms MOCFO in the network throughput. In addition, considering the needed computation time for algorithm convergence, MOPSO is faster than NSGA-II and MOCFO by 5.8 times and 9.9 times on average, respectively. Moreover, this paper provides a study on algorithm convergence rate, solution diversity, and station load.

Analysis of anisotropic metasurfaces using generalized sheet transition condition

Abstract This paper presents an analysis of anisotropic metasurfaces using generalized sheet transition condition (GSTC). Unlike previously studied GSTC, the present analysis takes into consideration the coupling between transverse field components. Based on the shape of the metasurface element, coupling between transverse fields can be obtained. This coupling between transverse field components introduces polarization rotation in reflected and transmitted fields. This property can be used to control the wave polarization. Using anisotropic metasurfaces as superstrate for a printed antenna can control the resulting radiation pattern as well as the polarization of the radiated field.

Resource optimizer for Cognitive Network using multi-objective particle swarm system

Recently, Cognitive network has drawn the attention as a promising technology to enhance communication system performance by efficiently utilizing system resources. It provides prompt response to dynamic changes. In this paper, a modified multi-objective particle swarm optimization (M-MOPSO) is proposed in Cognitive IP Multimedia Subsystem (CogIMS) to improve the global network performance. The implementation and evaluation results of the system design using the algorithm is provided and compared with those obtained using Non-Dominated Sorting Genetic Algorithm (NSGA-II). Extensive simulations are carried out by using MATLAB software showed that M-MOPSO is comparable to NSGA-II in the network throughput. However, on average, M-MOPSO is faster than NSGA-II by 6.25 times considering the needed computation time for algorithm convergence.

Generalized Sheet Transition Condition (GSTC) for anisotropic metasurfaces

This paper presents Generalized Sheet Transition Condition (GSTC) for analysis of anisotropic metasurfaces. The present GSTC takes into consideration the coupling between transverse field components on metasurfaces. Based on the shape and dimensions for the element of the metasurface, coupling between transverse fields can be obtained. This coupling between transverse field components can be used to control the polarization of transmitted and reflected fields. Using anisotropic metasurfaces as superstrate for a printed antenna would also enable controlling the polarization of the radiated fields. An example of converting linearly polarized bow-tie antenna to circularly polarized antenna by using anisotropic metasurface is presented as verification for the present analysis.

Metasurface-loaded dielectric rod leaky wave antenna

Abstract This paper introduces an analytical solution for a dielectric rod leaky wave antenna loaded by a metasurface. The advantage of adding metasurface on the dielectric rod is that it introduces an additional degree of freedom in the design of this antenna by controlling the outer boundary condition. Modified Generalized Sheet Transition Condition on a cylindrical geometry is used to model the metasurface on the dielectric rod. The analysis is based on developing the characteristic equation of the proposed structure. This characteristic equation is used to obtain the complex propagation constant of this structure and the corresponding field distribution inside and outside the rod including the resulting radiated fields. Parameters of the metasurface are extracted from the reflection and transmission properties of its elements in infinite periodic structure. An example of a dielectric rod loaded by a metasurface is presented analytically based on the introduced analysis and also simulated numerically for comparison. The effect of different polarizabilities of the loading metasurface on the leaky wave properties of the dielectric rod is discussed.