Design of RF Energy Harvesting Antenna using Optimization Techniques

: This paper deals with the design, analysis and Optimization of Radio Frequency (RF) energy harvesting antenna for Wireless Local Area Network (WLAN) sources .The designed antenna is simulated using ANSYS HFSS(High Frequency Structure Simulator) and FR4 epoxy material as a substrate with a dielectric constant of 4.4 with a loss tangent of 0.02. It is a rectangular microstrip patch antenna with H&E slots and consists of a radiating element with 50 ohm microstrip inset feed. Moreover, the combined configuration of proposed antenna with H&E shaped slots in design provides maximum efficiency.More simulation iterations are performed in order to maximize the Gain of ISM band operating at a frequency of 2.45GHz which provides a return loss of -24.77 dB, Voltage Standing Wave Ratio (VSWR) of 1.07 and Gain of 6.58 dB.The optimization of the antenna is carried out by employing Genetic Algorithm (GA). Antenna optimization and gain enhancement by applying genetic algorithm is implemented with the help of MATLAB and ANSYS Optimetrics tool. After applying optimization algorithm the performance of the antenna has been improved with a Gain of 7dB. The design methodology of microstrip patch antenna and optimizations are presented.


I. INTRODUCTION
The energy harvesting is the method through which energy is derived from external sources like: solar power, thermal energy, wind energy and kinetic energy (eg: ambient energy) which are captured and fed to small electronic wireless operating devices like wireless sensor networks etc., [1]. Lot of RF energy gets wasted due to non reception of the device. In order to make use of renewable energy resources (Radio Frequency) to drive low power appliances and make it battery free RF energy harvesting antenna is mainly used. This enhances the usability and reliability of the device [2]. The idea of energy harvesting is not innovative rather it came hundred years ago. The method of energy harvesting is to extract energy from the environment in order to produce electricity is called energy harvesting or energy scavenging [3]. Usually the energy harvesters will not provide sufficient amount of power for feeding electronic devices, the reason for this is mainly because there are no such technologies have developed in order to extract more and more RF energy. But this technology can provide enough amount amount of energy sufficient of operating low power devicesin order to operate them separately [4].However it is observed that a standard microstrip patch antennas should possess very narrow operating bandwidth. Many procedures have been introduced to solve this problem to some extent they are listed as: use a substrate of high dielectric permittivity [5], use of defected ground structures at the ground plane [6], use of metamaterials at the ground plane [7] and addition of slots on the patches [8] and use of patches with H & E Shape instead of other shapes and optimization of patch shapes and gain by introducing one of the optimization algorithms like genetic algorithm for better antenna performance. These challenges are efficient in order to attain broader bandwidth and to achieve more gain and directivity for energy harvesting application [9][10][11][12]. In this paper RF energy is harvested using microstrip patch antenna and the performance is improved by applying genetic algorithm optimization technique [GA].
II. ANTENNA DESIGN Microstrip patch antennas are more popular because of low cost and ease of fabrication. Microstrip patch antenna consists of dielectric material, radiating element and the ground plane. The rectangular shape is widely considered to realize the microstrip antenna. The simulation tool used for antenna design is High Frequency Structure Simulator version 2017(HFSS 17.2). The simple microstrip patch antenna is designed using the substrate FR4_epoxy because of its low cost and ease of fabrication. It has the dielectric constant value of 4.4 and the loss tangent is 0.02 and the resonating frequency is 2.45GHz. The detailed parameters are given in the Table 1. The formulae to determine the patch dimensions are as follows: Using the above equations (1) and (2) we have calculated the width and length of the patch.
The proposed microstrip patch antenna is designed with an input impedance of 50Ω.The parametric values are calculated from the given specific equations and the values are mentioned in the given Table 1 For an antenna to radiate, it should attain a return loss of more than -10dB. In order to further improve the return loss for better performance two slots are introduced on the patch. Thus in this proposed design H&E shaped slots are introduced in the antenna. Here two patches are introduced (i.e. Patch 1 and Patch 2). Patch 1 is fed by microstrip inset feed and due to mutual conductance the feed is automatically fed to Patch 2 through Patch 1.This enhances the better performance of the antenna with improved return loss and vswr values. In order to improve the gain and directivity, defected ground structures (DGS) i.e., metamaterials are introduced on the ground in antenna design. This improves the gain, directivity and overall efficiency of the antenna. The design specifications are mentioned in the below Table 1.

(d) Gain
The observed Gain for given H&E shaped microstrip patch antenna before optimization is 5dB which is given in the below Figure 6.

(f) Current Distribution
The observed current distribution of simulated H&E shaped microstrip patch antenna before optimization is shown in the Figure 8.  Initially Genetic Algorithm is performed by creation of initial population. Each individuals in the populations are coded as string of bits they are represented as chromosomes. They are created randomly. The fitness of each individual is determined by the cost function or objective function. A good chromosome is always determined by the best value of the objective function. The objective function is used to calculate the fitness of these individuals.By mating these individuals new generation will be formed. The individuals who have higher fitness values are chosen for reproduction. Crossover and mutations are generally used for global search of the objective function or the cost function.The best individuals will be obtained in the next generation without any change. This process will be repeated continuously untill end of the evolution is reached.In this paper,Genetic Algorithm is used for has been used to enhance the performance of microstrip patch antenna (MPA) by obtaining high gain and directivity and reduced size at the given resonating frequency by optimizing the substrate dimensions and patch dimensions. The antenna design is simulated in Ansys HFSS 2017 software and the genetic algorithm optimization is done through MATLAB software. The resultant results from the MATLAB simulation is taken and applied in the HFSS environment to perform further simulations.

A.GENETIC ALGORITHMIMPLEMENTATION
The Optimization steps for obtaining the best results using GA in MATLAB are summarized as below: Step 1:Study the parameters that need to be optimized using GA from HFSS antenna design.
Step 2:Design the variable behavior for mapping.
Step 3: Formulate the constraints in Matlab.
Step 4:Model the objective function equation and Genetic algorithm program for the design parameters.
Step 5:Setup the bound values ( i.e, Upper and lower limits ) for the desired parameters.
Step 6: Run optimization process and Analyse the results.
Step 7: Select the best optimal antenna design values www.ijert.org Step 8:Apply thebest values which are obtained from the MATLAB in HFSS antenna design.
Step 9:Run and Analyse the results.

B. RESULTANT MICROSTRIP PATCH ANTENNA (AFTER OPTIMIZATION)
The resultant microstrip H & E shaped patch antenna after optimization using genetic algorithm for energy harvesting is shown in the Figure 10 and 11.

(c) Gain
The observed Gain for given H&E shaped microstrip patch antenna after optimization is 6.58 dB which is given in the below Figure 14.   patch antenna for energy harvesting in one of the well known simulators known as Ansys High Frequency Structure Simulator (HFSS) and the results will be further improved by using one of the optimization techniques i.e., Genetic Algorithm (GA) in well known software called MATLAB 2019a and then the optimized results will be fed into HFSS inorder to obtain the high gain and antenna efficiency. The simulation results shows that the antenna will radiate at 2.45GHz frequency which will be included under ISM band operating range. The aim of this work is to improve gain of the antenna inorder to improve the antenna performance for RF energy harvesting capability.This proposed design exhibits high gain of 6.5 dB with a return loss of -24.77 dB and vswr of 1.12 and directivity of 7 dB. The efficiency of the proposed design is reached upto 95% and thus the it can provide far field radiation and makes the antenna size as much more compact for easy handling capability. Hence the antenna can be used as the front end section of the energy harvesting system. Therefore the antenna can be suitable for RF energy harvesting applications.Finally it is concluded that the proposed energy harvesting antenna can be used for providing power on demand for short range sensing applications.