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Massive multiple-input multiple-output (MIMO) is a key promising technology for 5G wireless networks that allows significant increase in spectral and energy efficiency without increasing the spectrum and/or the number of cell sites. However, the computational time and resources required to rigorously analyze the capacity offered by a base station (BS) equipped with a large number of antenna elements increase with the increase of the number of antenna elements in the array. In this paper, we present a novel approximation approach for modeling large microstrip antenna arrays in BSs of massive MIMO systems. The approach subdivides an M × N array into columns, rows, rectangular, or square subarrays, each consisting of a group of elements. The coupling is rigorously taken into account within each subarray, but it is ignored between subarrays, using just an array factor instead. Results are demonstrated for an 8 × 8 = 64 element patch array. It is shown that the difference in the capacity evaluated using rigorous electromagnetic simulations and the proposed approach is less than 0.79% using the 2 × (8 × 4) approach for both the suburban macrocell model of the Extended Spatial Channel Model (SCME/suburban macro cell (SMa)) outdoor propagation model and three-dimensional independent identically distributed model with a significant reduction of 44% and 50%, respectively, in computational time as compared to the full-wave antenna array modeling approach.

Vehicle Ad-hoc network (VANET) have sensing nodes moving with different speeds. The vehicular cloud is a group of vehicles communicating with each other to share the resources provided from the central cloud. The end-to-end throughput for multi-hop wireless network is analysed in this paper using a set of non-linear equations. The interference due to the hidden nodes and the neighbouring nodes is considered into computing the end-to-end throughput. The contention graph is used to clarify the connection among the nodes into the network to find the capacity of the individual links to facilitate estimating the end-to-end throughput. Effect of the cloud on the end-to-end throughput is also illustrated in this paper. The results show that the throughput with employing cloud is lower than that without using the cloud.

Orthogonal frequency division multiple access (OFDMA) is one of the most popular access schemes adopted in recent wireless systems including, but not limited to, 4G-LTE. Consequently, OFDMA resource allocation raises as one of the key research topics since the capacity and quality of service depend on optimising the usage of the available resources. Resource allocation of OFDMA systems relies on power and subcarrier allocations of each user for different operational scenarios and channel conditions. In this paper, we propose and implement bat pack algorithm (BPA) to find the optimal, or near optimal, power and subcarrier allocations for OFDMA systems. The proposed algorithm considers power and subcarrier allocation constrains, channel and noise distributions, distance between user equipment and base station, user priority weight. Four cases are addressed, simulated, and analysed employing the BPA algorithm under specific operational scenarios to meet the standard specifications of current communication systems.

A new load balancing algorithm is presented based on power management of Access Points (APs) to reduce congestion at hot spots in Wireless Local Area Networks (WLANs) and to assign channels to APs The algorithm first finds the Most Congested Access Point (MCAP), then decreases its transmitted power in discrete steps, and the process continues until the users’ assignment which leads to a high balance index is reached. A new mathematical programming formulation is then applied to assign channels to the APs such that the Signal-to-Interference Ratio (SIR) at the users’ level is maximized. Results show that the algorithm is capable of reducing the overall congestion at hot spots in a WLAN and increases the SIR significantly for cases involving relatively large WLANs. In the process, network throughput is increased.

In this paper, we present a versatile mathematical formulation of a newly developed 3-D locally conformal Finite Difference (FD) thermal algorithm developed specifically for coupled electromagnetic (EM) and heat diffusion simulations utilizing Overlapping Grids (OGFD) in the Cartesian and cylindrical coordinate systems. The motivation for this research arises from an attempt to characterize the dominant thermal transport phenomena typically encountered during the process cycle of a high-power, microwave-assisted material processing system employing a geometrically composite cylindrical multimode heating furnace. The cylindrical FD scheme is only applied to the outer shell of the housing cavity whereas the Cartesian FD scheme is used to advance the temperature elsewhere including top and bottom walls, and most of the inner region of the cavity volume. The temperature dependency of the EM constitutive and thermo-physical parameters of the material being processed is readily accommodated into the OGFD update equations. The time increment, which satisfies the stability constraint of the explicit OGFD time-marching scheme, is derived. In a departure from prior work, the salient features of the proposed algorithm are first, the locally conformal discretization scheme accurately describes the diffusion of heat and second, significant heat-loss mechanisms usually encountered in microwave heating problems at the interfacial boundary temperature nodes have been considered. These include convection and radiation between the surface of the workload and air inside the cavity, heat convection and radiation between the inner cavity walls and interior cavity volume, and free cooling of the outermost cavity walls.

In this paper, we present numerical results obtained from a robust, locally conformal 3-D Orthogonal Grid Finite Difference (OGFD) thermal algorithm introduced in Part I of our current investigation [Al-Rizzo et al., 2006] integrated with an Orthogonal Grid Finite-Difference Time Domain (OGFDTD) scheme [Al-Rizzo et al., 2000], which accurately models the volumetric electromagnetic (EM) power deposition pattern. A unified meshing scheme, which utilizes identical overlapping grids in Cartesian and cylindrical coordinates, is employed within the load zone in the OGFDTD and OGFD models. Local temperature profiles excited by the absorbed microwave energy were measured at seven locations within the sample as a function of heating time. In order to benchmark, or validate our model, an alternative analysis of the coupled EM and thermal simulations was performed using state-of-the-art, Finite Element Method-based Ansoft’s High Frequency Structure Simulator (HFSS) and the coupled thermal/stress analysis tool ePHYSICS (http://www.ansoft.com). Additionally, we compare our numerical simulations against measured dynamic temperature profiles induced within a mineral ore sample maintained for exposure period of 28.5 minutes inside a cylindrical multimode heating furnace energized at 915 MHz with a microwave source power of 12.5 kW and accompanied with significant temperature elevation. A combination of convective and radiation thermal boundary conditions are considered at the interfaces between the cavity walls, air, and sample. There is a general agreement between simulated and measured spatial and temporal temperature profiles, which validates the proposed model. Results indicate that inevitable fluctuations in the frequency spectrum and output power of the magnetron, non-uniformity of sample packing, and heat released by uncontrolled exothermic chemical reactions have a significant effect on the comparisons between measured and computed temperature patterns.

A versatile electromagnetic (EM) computational algorithm, based on the Finite-Difference Time-Domain (FDTD) technique, is developed to analyze longitudinally oriented, square-ended, single slot fixtures and slot-pair configurations cut in the broad wall of a WR-975 guide operating at a frequency of 915 MHz. The finite conductivity of the wave guide walls is accountedfor by employing a time-domain Surface-Impedance Boundary Conditions (SIBC) formulation. The proposed FDTD algorithm has been validated against measurements performed on a probe-excited slot cut along the center line of the broad wall of a WR-284 guide and available experimental data for energy coupled from a longitudinal slot pair in the broad wail of a WR-340 guide. Numerical results are presented to exploit the influence of the constitutive parameters of the processed material as well as protective insulating window slabs mounted on the exterior surface of the slots. Particular attention is given to the resonant length, scattering parameters, and the electric field distribution within lossy objects placed in the near-field region over a range of slot offsets and workloads with extensive results being reportedfor the first time. It is shown that the FDTD technique can accurately predict the coupling and power absorption characteristics in loads located in the near field zone of the slotted wave guide structures and, therefore, should prove to be a powerful design tool applicable to a wide class of slotted waveguide applicators that may be difficult to analyze using other available techniques.

Internet of Things (IoT) is dramatically growing in support of the recent revolutionary cloud‐based survivability applications. It has to meet the performance expectations for these applications in real‐time while optimizing the available cloud resources. In this paper, we propose a cooperative resource scheduling in energy‐constrained applications for a reliable and fault‐tolerant performance. We present a task scheduling algorithm based on robust canonical particle swarm optimization (CPSO) and fully informed particle swarm optimization (FIPS) algorithm to solve the problem of resource allocation in both homogeneous and heterogeneous cloud‐based IoT applications. Our objective is to satisfy the quality of service in terms of throughput and delay by performing optimal task scheduling while considering the different experienced data traffic categories. Our results show that throughput and delay can be significantly improved while using the FIPS approach in comparison to the CPSO optimization algorithm. In this paper, we propose a cooperative resource scheduling in energy‐constrained applications for a reliable and fault‐tolerant Cloud‐based IoT platforms. We present a task scheduling algorithm based on a robust canonical particle swarm optimization (CPSO) and fully informed particle swarm optimization (FIPS) algorithm in both homogeneous and heterogeneous systems. Our results show that throughput and delay can be significantly improved while using the FIPS approach in comparison to CPSO.