Review on Linear Array Antenna with Minimum Side Lobe Level Using Genetic Algorithm Review on Linear Array Antenna with Minimum Side Lobe Level Using Genetic Algorithm Review on Linear Array Antenna with Minimum Side Lobe

Antenna array is formed by assembly of radiating elements in an electrical or geometrical configuration. In most cases the elements are identical. In this paper proposed a very simple and powerful method for the synthesis of linear array antenna and GA. This method reduced the desired level of side lobe level (SLL) as well as to steer the main beam at different different angle. A new method for adaptive beam forming for a linear antenna arrays using genetic algorithm (GA) are also proposed.

assembly of radiating elements in an electrical or geometrical configuration. In most cases the elements are identical. In this paper proposed a very simple and powerful method for the synthesis of linear array antenna and GA. This ed level of side lobe level (SLL) as well as to steer the main beam at differentdifferent angle. A new method for adaptive beam forming for a linear antenna arrays using genetic Genetic Algorithm (GA), Linear Arrays, In design of antenna arrays, one of the most important parameter is sidelobe level (SLL). High side-lobes are undesirable as they result in Electro Magnetic Interference (EMI) which degrades the overall system performance [1], [2]. In wireless communication, one of the most recent inventions to overcome the problem of increasing demand for capacity is to 4]. Smart antennas have also adaptability to introduce new services, increased range, faster bit rate, multi use interference, space division multiplexing (SDMA), more security, reduction of errors due to multipath fading etc. Usage of the antenna arrays can improve the capacity and the spectral efficiency of a wireless communication system [3,4]. For example, the fifth generation (5G) communications adopt the millimetre wave (mmwave) and beam forming technologies based on antenna arrays, to improve the spectral efficiency and

A. Side-Lobes
No antenna is able to radiate all the energy in one preferred direction. Some is inevitably radiated in other directions. The peaks are referred to as sidelobe, commonly specified in dB down from the main lobe, In figure 1 shows sidelobe and main lobe.

B. Nulls
In an antenna radiation pattern, a which the effective radiated power is at a minimum. A null often has a narrow directivity angle compared to that of the main beam. Thus, the null is useful for several purposes, such as suppression of interfering signals in a given direction. No antenna is able to radiate all the energy in one erred direction. Some is inevitably radiated in other directions. The peaks are referred to as sidelobe, commonly specified in dB down from the main lobe, In figure 1 shows sidelobe and main lobe.
In an antenna radiation pattern, a null is a zone in which the effective radiated power is at a minimum. A null often has a narrow directivity angle compared to that of the main beam. Thus, the null is useful for several purposes, such as suppression of interfering International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456 @ IJTSRD | Available Online @ www.ijtsrd.com

C. Polarization
Polarization is defined as the orientation of the electric field of an electromagnetic wave. Polarization is in general described by an ellipse. Two special cases of elliptical polarization are linear polarization and circular polarization. The initial polarization of a radio wave is determined by the antenna. polarization the electric field vector stays in the same plane all the time. Vertically polarized radiation is somewhat less affected by reflections over the transmission path. Omni directional antennas always have vertical polarization. With polarization, such reflections cause variations in received signal strength. Horizontal ant likely to pick up man-made interference, which ordinarily is vertically polarized. In circular polarization the electric field vector appears to be rotating with circular motion about the direction of propagation, making one full turn for ea This rotation may be right hand or left hand. Choice  Polarization is defined as the orientation of the wave. Polarization is in general described by an ellipse. Two special polarization are linear polarization and circular polarization. The initial polarization of a radio wave is determined by the antenna. With linear polarization the electric field vector stays in the same olarized radiation is somewhat less affected by reflections over the transmission path. Omni directional antennas always have vertical polarization. With horizontal polarization, such reflections cause variations in Horizontal antennas are less made interference, which vertically polarized. In circular polarization the electric field vector appears to be rotating with circular motion about the direction of propagation, making one full turn for each RF cycle. This rotation may be right hand or left hand. Choice of polarization is one of the design choices available to the RF system designer. From the comparison, it can be observed that the fitness function (MF) along with the IPSO method gives better radiation pattern than that obtained from uniformly excited ( Im = 1) linear array with spacing of λ/ 2 between elements, although the number of elements are the same. The SLL reduced to -13.14 dB and -13.21 dB SLL IPSO with optimal non-uniform current excitation and inter element spacing provide maximum SLL reductions to -42.7 dB, -43.5 dB respectively. The programming has been written in Matlab language using MATLAB 7.5 version on core (TM) 2 duo processor, 3.00 GHz with 2 GB RAM. interferences. Our all results and graphs are simulated using MATLAB software. This problem is solved with the RLS algorithm by replacing the gradient step size _ with a gain matrix. It was noticed that increasing the number of elements of the antenna array ensures better performance. Also conclude that the optimum spacing beam between the elements is half wave length.

III.
SMART ANTENNA CONSTRUCTION Omni-directional or sectored antennas used in current wireless communication systems, can be considered as an inefficient use of power as most of it has been radiated in other directions than toward the user. Signals that miss the intended user will cause interference to other users in the same or adjoining cells [1]. The concept of smart antennas is to employ base station antenna patterns that are not fixed in any direction but adapt to the current radio conditions. In other words, the antenna is to direct a single beam to each user. Smart antennas direct their main lobe, with increased gain, in the direction of the user, and they direct nulls in directions away from the main lobe [2][3].  Figure 3, consist of an array of antenna elements and a smart processing of antenna signals. We will concentrate on the adaptive arrays that make use of the Direction of Arrival (DOA) information from the desired user to steer the main beam towards the desired user. The signals received by each antenna element are weighted and combined to create a beam in the direction of the mobile by utilizing signal processing signal processing algorithms [4]. These algorithms determine the uplink weight vectors for performing beam-forming on the received signals as well as the downlink weight vectors for performing beam forming on the transmitted signals [3].

IV. GENETIC ALGORITHM
Genetic Algorithm is an evolutionary algorithm developed on the principles of genetics [5]. It is a nature inspired algorithm [6]. A genetic algorithm is a search technique used in computing to find exact or approximate solutions to optimization and search problems.

Fig. 4: flowchart of Genetic Algorithm
Genetic algorithms are a particular class of evolutionary algorithms (also known as evolutionary computation) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination). Genetic algorithms are search algorithms based on mechanics of natural selection and natural genetics. In every generation, a new set of artificial creatures or strings is created using bits and pieces of the fittest of the old. The one of the most important parameters in array designing is side lobe level (SLL) and first null beam width (FNBW). In array antenna, the desired value of parameter can be achieved by number of ways such as by having variation in the geometry configuration of antenna, variation in current amplitude or phase feed to the antenna elements. A flowchart of Genetic Algorithm is shown in fig.4. The steps required for implementing the algorithm are as follows: 1. Define the fitness function, select parameters to be optimized by GA, 2. Generate initial population, 3. Calculate fitness, 4. Selection, 5. Crossover, 6. Mutation, 7. Check for stopping criteria, stop if it is satisfied, 8. Go to step 3.

V. SIMULATION BLOCK DIAGRAM
The simulation block diagram for optimization of linear array antenna using GA for reduction in Side Lobs Levels, as shown in figure 5 .

V. CONCLUSION
Genetic Algorithms are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics. As such they represent an intelligent exploitation of a random search used to solve optimization problems. Antenna pattern synthesis is an important topic in the smart antenna. This is the process of choosing various antenna parameter to obtain the given radiation pattern of antenna array like beam width, specific position of null, side lobe level etc. smart antenna which have great interest in many scientific fields such as telecommunication, medicine, military and astronomy thank to their precision.