Many research efforts are attempted to reduce the SLL of the linear antenna array by introducing the non-uniform element spacing between the antenna array elements with the help of evolutionary algorithms such as Genetic algorithm[1], Differential evolutionary algorithm[5], particle swarm optimisation[6], etc. Unfortunately, these algorithms are limited for little number of antenna elements and these iterative methods suffer some draw backs as, a small reduction in the **side** lobes affects much broadening in the half power beam width. **Side** **lobe** **Level** reduction technique was proposed for optimisation using only Genetic Algorithm in [1]. But with the help of only genetic algorithm the **side** lodes are reduced chaotically. In

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This research work proved the power of using Genetic algorithm in solving complex problems where you have conflicting constrains. The first **side** **lobe** **level** which introduces Electromagnetic Interference (EMI) problems is lowered without disturbing the beam width. RGA finds an optimum compromise between the beamwidth and **side**-**lobe** **level** with N=10, 16, 20 and 24. It can be seen that the current excitations obtained from RGA is lower than that of Dolph Chebyshev for all the number of elements simulated, while at the same time, the first null to null beamwidth is slightly reduced. The excitation levels of Dolph Chebyshev method are tapered in nature from the centre of the array. However, the excitation levels computed using RGA is random in amplitude with no regular taper. But the excitation levels in both the cases are symmetric to the centre of the array

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which corresponds to 2 nd peak **side** **lobe** of the uniform array radiation pattern. Imposing nulls at the interference direction tends to decrease the reduction in **side** **lobe** **level** with respect to main beam there by we have to control the **side** **lobe** **level** also. By adjusting the 4 least significant bits of the attenuator the perturbed patterns for the genetic algorithm are shown in Fig 3-4. Fig 3 corresponds to placing null at the interference location with a null depth of -78.7 dB but the peak **side** **lobe** **level** degraded to -12.74 dB. Fig 4 corresponds to the control of both **side** **lobe** **level** and null control in the antenna array. In this the peak **side** **lobe** **level** is optimized to -19.8dB with a null depth of -73.92 dB with the excitation levels shown in Fig 4. By comparing the perturbed patterns with the uniform array radiation pattern we can observe that there is a slight shift at the interference location i.e., in the GA applied new pattern the interference location is away from the peak of the 3 rd **side** **lobe** **level** with a minimal component and this is negligible.

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Abstract—Distributed beamforming (DBF) is an eﬃcient technique for reliable communications in wireless sensor networks (WSNs). In DBF based networks, the randomly distributed nodes cooperate to form a randomly distributed antenna array (RAA) which has a main beam directed towards the intended receiver. Due to the nodes randomness, the DBF results in poor pattern characteristics such as high **side** **lobe** **level** (SLL) and pattern asymmetry around the main beam sides. In this paper, a fast deterministic algorithm for SLL reduction of open loop distributed antenna arrays is introduced. Unlike the existing state of the art optimization techniques for SLL reduction, the proposed algorithm provides a fast deterministic solution for energy transmission or the weight of each node without changing its location. Consequently, the exhaustive search burden of the optimization based techniques for the optimum weights is avoided. The simulation results reveal that the proposed algorithm has superior performance to the optimization techniques in terms of execution time, synthesized SLL, and half power beamwidth (HPBW).

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reduces to the binomial design. The trade-off between the **side**-**lobe** levels (SLL) and the half-power beam width (HPBW) stimulate the question answered primarily by Dolph of obtaining the narrowest possible beam width for a given **side**-**lobe** **level** or the smallest **side**-**lobe** **level** for a given beam width. This was possible by using the orthogonal functions of Chebyshev in order to design an optimum radiation pattern. However, for large number of elements this procedure becomes quite cumbersome since it requires matching the array factor expression with an appropriate Chebyshev function. To over-come this deficiency, proposed a new formulation for the design of Chebyshev arrays based on solving a system of linear equations. Iterative procedure was used to produce the desired pattern.

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can be made conformal to a surface and do not require feed line network but suffer from low efficiency due to dielectric losses. A number of gain enhancement techniques based on Fabry Perot Cavity (FPC) are reported [5–8]. In these structures, the dielectric layer is placed at approximately 0.5λ above a ground plane, which acts as a Partially Reflecting Surface (PRS). Gain of such an antenna depends on the reflection coefficient of PRS and radiation characteristics of feed element. These of structures are fed using a dipole, waveguide or MSA. High gain antennas using Artificial Magnetic Conductors (AMC) based on FPC model, Frequency Selective Surface (FSS), Electromagnetic Band Gap (EBG) resonator have also been proposed [9–13]. The analysis of antennas using the resonance gain method and Finite-Difference Time-Domain (FDTD) techniques have been studied [14, 15]. In addition, leaky wave analysis has been carried out for explaining high gain phenomenon [16]. Space fed three dimensional, efficient and directive antenna arrays using a single feed patch have been proposed, but these antennas suffer from high **Side** **Lobe** **Level** (SLL) for small array size [17].

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In this paper, the differential evolution algorithm has been used for the reduction of **side** **lobe** **level** of a circular antenna array by varying the amplitude excitations. The results show that the algorithm is capable of reducing the **side** **lobe** **level** to about -20 dB.The algorithm can also easily be implemented for arrays with many elements as well. It is easy to implement and provides a better accuracy.

Abstract—In most applications of antenna arrays, **side** **lobe** levels (SLLs) are commonly unwanted. Especially, the ﬁrst **side** **lobe** **level** which determines maximum SLL is the main source of electromagnetic interference (EMI), and hence, it should be lowered. A procedure of ﬁnding the optimum **side** **lobe**- minimizing weights for an arbitrary linear equally spaced array is derived, which holds for any scan direction, beam width, and type of antenna element used. In this science article, the use of convolution procedure and the time scaling property reduces the **side** **lobe** **level** for any type of linear equally spaced array. Results show that by this procedure, the **side** **lobe** **level** is reduced about two times or even more.

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To evaluate the results of GA more objectively, at the same time, the pattern array antennas can be designed based on Chebyshev methods with the same conditions we have observed that From the classical array antennas theories, the lowest **side** **lobe** **level** can be get using Chebyshev method on condition that it is given certain **side** **lobe** bandwidth. But It must satisfy the condition of distance of array elements d ≥2.When d ≤2. Chebyshev method is not best, while GA can still work effectively. Various results for different number of element as shown below.

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Abstract—A low cost and easy fabrication multilayer antenna for wireless applications was presented to cover the industrial, scientiﬁc, and medical ISM band of (5.725–5.875) GHz with a gain of 11.7 dB. The antenna was composed of a feeding patch fabricated on a Rogers RT/Duroid 5880 substrate, and three superstrate layers of Rogers RO3006 were located above the feeding patch at a speciﬁc height for each layer. The superstrate layers were added to enhance the bandwidth and gain of the antenna and reduce its **side**-**lobe** **level** and return loss. The simulated and measured results of the operating frequency, return loss, bandwidth, and gain for the antenna were presented. CST Microwave Studio was used in this design’s simulation.

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In this work, a feeding network with Chebyshev distribution (only one layer) for designing low SLL microstrip antenna arrays will be proposed. The step by step in design process will be presented. A Chebyshev feeding network for a 8×1 linear antenna array with preset SLL of -25 dB has been designed as a demonstration of the procedure. In order to get the output power at each port proportional to Chebyshev weights, unequal T junction power dividers have been used. The obtained results indicate that the amplitude of output signal at each port is proportional to the coefficient of the Chebyshev weights. The phases of signals at each port are also in phase with each other. The array factor of simulated excitation coefficients has been given and compared with that from theory. It is observed that the sidelobe **level** can be reduced to -22 dB.

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According to “fig.11”, Kaiser window gives major **lobe** width 2π×0.0605 rad/sample whereas the major **lobe** width of proposed window is 2π×0.0761 rad/sample. Peak **side** **lobe** **level** of Kaiser window is -6.9704dB and proposed window gives peak **side** **lobe** **level** -17.681dB. Here, it can be said that the Kaiser window has higher power loss in comparison of proposed window. Ripple ratio and fall off ratio of proposed window are -46.768dB and 47.886 whereas Kaiser window gives ripple ratio -37.690dB and **side** **lobe** fall off ratio -11.836dB. On the basis of these parameters value it can be said that the proposed window gives superior results in comparison of Kaiser window.

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Besides the analytical methods, several techniques based on optimization and iterative processes for minimizing SLL have also been reported. In [4] the density ofelements located within a given array length is made proportional to the amplitude distribution ofthe conventional equally spaced array. [5] used statistical thinning ofarrays with quantized element weights to reduce **side** **lobe** **level** considerably in large circular arrays. Another algorithm that has been used recently is the Genetic Algorithm for optimizing the array spacing [6, 7]. An optimization method based on real-coded genetic algorithm (GA) with elitist strategy is represented in [7]. This method is used for thinning a large linear array ofuniformly excited isotropic antennas to yield the maximum relative sidelobe **level** (SLL) equal to or below a ﬁxed **level** and the percentage ofthinning is always kept equal to or above a ﬁxed value.

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Haupt [1] used GA in process of thinning a linear array of 200 elements, and resulting sidelobe levels were lower than −18 dB in all cases shown. Weile and Michielssen have employed a Pareto Genetic Algorithm (PGA) for the thinning of linear arrays [2]. Johnson and Rahmat-Samii [3] used GA to thin a 40 elements linear array and achieved sidelobe levels of somewhat lower than −20 dB. Mahanti et al. [4] used Real-coded Genetic Algorithm (RGA) to thin a large linear array of uniformly excited isotropic elements to yield **side** **lobe** **level** (SLL) equal to or below a fixed **level**, while keeping the percentage of thinning equal to or above a fixed value. Hamici and Ismail have used Immunity Genetic Algorithm (IGA) based on stochastic crossover evolution to solve the synthesis problem of thinned arrays and have obtained good results [5]. Fern´andez-Delgado et al. [6] have proposed a simple and fast method which accelerates the calculation of the far- field pattern and consequently the evaluation of the fitness function in the global optimization methods used in array thinning. They have reduced the search time of algorithm by 90%. Zhang et al. have employed Orthogonal Genetic Algorithm (OGA) for thinning of planar arrays [8]. Jin and Rahmat-Samii [9] have used Binary Particle Swarm Optimization (BPSO) for the thinning of linear arrays. Quevedo-Teruel and Rajo-Iglesia have applied ACO for the thinning of linear and planar arrays for different scenarios [13]. Razavi and Forooraghi have employed pattern search algorithm for synthesis of linear arrays [14]. Wang et al. have utilised modified iterative Fourier technique for thinning of linear arrays [15].

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This paper introduces the use of a improved binary invasive weed optimization algorithm for thinning periodic linear and planar array to obtain the lowest possible peak **side** **lobe** **level**. An adaptive dispersion mechanism has been adopted to balance between the local search ability and global exploration. A comparison with published results for similar thinned array designs proved that the proposed algorithm achieved the lowest peak **side** **lobe** for all considered cases.

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Here, we use FPA algorithm because it provides better results than other algorithms in many antenna design problems [19–21]. FPA provides better results in synthesis of non-uniformly spaced antenna array than other algorithms to obtain low **side** **lobe** **level** with placement of deep nulls [19, 20] and to achieve low **side** **lobe** **level** under both no beam scanning and beam scanning conditions [21].

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Linear Frequency Modulated pulse compression technique results in higher peak **side** **lobe** **level** in the matched filter output which leads to target masking. Subsequently, piece wise linear frequency modulated pulse compression technique is analyzed in both two and three stages. Using this technique, it is concluded that peak **side** **lobe** levels has been reduced by almost 2dB compared to the LFM without compromising the resolving capability of radar. In order to further reduce the peak sidelobe levels, Non linear Frequency Modulated Pulse Compression techniques are analyzed. One of its type is Tangent based NLFM. This technique results in lower peak sidelobe levels compared to the LFM and PLFM despite main **lobe** broadening. To mitigate this, Hybrid NLFM is analyzed in this paper. This technique reduced the peak sidelobe **level** to the greater extent without compromising the range resolving capability of radar.

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algorithm. The cost function should be designed so that it can lead the search. One way of achieving this is to avoid cost functions where many states return the same value. In antenna array problems there are many factors that can be used to evaluate ﬁtness such as directive gain, SLL, beam width etc. For the current problem, we are interested in designing the geometry of a linear array with minimum **side** **lobe** **level** and null control in speciﬁc directions. Thus, the following ﬁtness function has been used to evaluate the ﬁtness [16].

Abstract—A simple end-ﬁre high-gain antenna with **side**-**lobe** **level** suppression is proposed for 60 GHz technology in this paper. The antenna has a tapered slot radiation patch which is obtained by subtracting a quarter of ellipse from a bigger quarter. Two reﬂecting circle units (RCU) are located at both sides of radiant patch to suppress **side**-**lobe** **level** of radiation patterns. The antenna has a simple two-layered planar structure with on via and is fabricated on a Rogers 4350 substrate with a compact size of 15 mm × 15 mm. Simulated and measured results match well, and both show its good characteristics of impedance matching, stable radiation patterns and steady peak gains above 10 dBi across 57–64 GHz, which makes it ﬁt 60 GHz wireless communication systems.

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Thinning an array means turning off some elements in a uniformly spaced or periodic array to generate a pattern with low **side** **lobe** **level**. In our method, we kept the antennas positions fixed, and all the elements can have only two states either “on” or “off” (Similar to Logic “1” and “0” in digital domain). One can easily interpret that an antenna will be considered to be in “on” state iff it contributes to the total array pattern. While an antenna will be considered “off” iff either the element is passively terminated to a matched load or open circuited. If an antenna element does not contribute to the resultant array pattern, they will be considered “off”. As for non- uniform spacing of the element one has to check an infinite number of possibilities before final placement of the elements, thinning an array [19–21] to produce low **side** lobes is much simpler than the more general problem of non-uniform spacing the elements.

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