Genetically Evolved Phase-Aggregation Technique for Linear Arrays Control

Abstract

Abstract-This paper presents a modified Genetic Algorithm (GA) technique in which the large phase perturbations are calculated by aggregating small phase increments. The proposed aggregation GA technique overcomes the major drawback of the large solution space required by the classical GA techniques.The proposed method adopts small ranges for increments of the parameters and the optimality is reached via aggregation of the best increments of phases. Consequently, the GA searches in a smaller solution space and finds the solution with reduced number of iterations. Simulation results show the achieved improvement of the proposed technique over the classical GA. The suppressed sectors using phase-only control are accomplished with and without element failures. Problems like imposing symmetrical nulls around the mainbeam and compensation for the failure of center element have been achieved.

Analytical array polynomial method for linear antenna arrays with phase-only control

“…In order to achieve an array with all the elements being active, Eqs. (6), (13), and (14) require that the number of elements, N, must be expressed as where n is an integer number. Also, as expected, the current amplitudes of the active elements are equal to one.…”

“…The linear optimization algorithms can be used if small phases are assumed [7,8] or with the concept of dual phase configuration [9]. However, solving the problem of unconstrained values of the phases requires nonlinear optimization methods [10][11][12][13][14].…”

Smart Antenna Array Patterns Synthesis: Null Steering and Multi-User Beamforming by Phase Control

“…However, several challenges remain in the development of these adaptive systems. The techniques of placing nulls in the antenna patterns to suppress interference and the maximizing their gain in the direction of desired signal has received considerable attention in the past and still of great interest [4][5][6][7][8][9][10][11][12][13][14]. These techniques are very important in communication system, sonar, and radar applications for maximizing signal-to-interference ratio (or signal to interference and noise ratio) [11].…”