Acceleration of Gradient-Based Algorithms for Array Antenna Synthesis With Far-Field or Near-Field Constraints

Abstract

This work presents a technique for the acceleration of gradient-based algorithms that employ finite differences in the calculation of the gradient for the optimization of array antennas. It is based on differential contributions, which takes advantage of the fact that when an array is optimized, each element is analyzed independently from the rest. Thus, the computation of the gradient of the cost function, which is typically the most time consuming operation of the algorithm, can be accelerated. A time cost study is presented and the technique is implemented, as an example, in the generalized Intersection Approach algorithm for array optimization in near and far fields. Several syntheses are performed to assess the improvement of this technique. In the far field, it is compared for periodic and aperiodic arrays using different approaches for the computation of the gradient, including the analytic derivative. A reflectarray is also optimized in the near field with the goal of improving its quiet zone. The technique of differential contributions shows important reductions in the time per iteration in all three syntheses, specially in that of aperiodic arrays and near field optimization, where the time saved in the evaluation of the gradient is greater than 99%.

Demonstration of a Reflectarray With Near-Field Amplitude and Phase Constraints as Compact Antenna Test Range Probe for 5G New Radio Devices

“…Due to the LMA is a gradient-based algorithm, the differential contributions technique (DFC) [26] can be applied. This notably speeds up the computation of the functional gradient reaching computing times similar to analytical derivatives, whenever are available [27].…”

An Overview on Synthesis Techniques for Near-Field Focused Antennas

“…The main drawback of this technique is the high computational cost required by the LMA to evaluate the gradient, as far as it performs multiple evaluations of the NF at the focusing region from the known field at the aperture. This drawback can be overcome by applying the differential technique presented in [36] where the new E NF,LMA evaluations are calculated in terms of the differential variations produced at each iteration without requiring the whole evaluation of the field in the NF region. This gradient evaluation in terms of differential contributions (DFC) is faster than using FFT for the FF problems.…”

Design of miniaturized tri‐band antenna based on differential evolution algorithm

“…8,9 Evolutionary methods, such as the evolution algorithm, have found widespread application in fields ranging from engineering, economics to artificial intelligence, [10][11][12] furthermore, Increasing computational power will strengthen the role of numerical approaches in solving complex problems in the near future. Differential evolution algorithm is a combination between evolutionary optimization variant and genetic algorithm, recently applied in antenna problems, 13,14 its main idea is to use individual differences to generate temporary individuals within populations; than the population evolution restructure randomly. The algorithm is very suitable for solving a variety of numerical optimization problems because its better global convergence and robustness, which quickly makes the algorithm a good candidate, used in current optimization problems.…”