Estimation of resonant frequency and bandwidth of compact unilateral coplanar waveguide-fed flag shaped monopole antennas using artificial neural network

Neural network based estimation of resonant frequency and bandwidth of compact unilateral coplanar waveguide (CPW)-fed flag shaped printed monopole antennas is presented. The proposed antennas are similar to CPW-fed antenna; however, by replacing unilateral CPW feed instead of CPW feed, compactness of about 48.615 percent is achieved. These are designed on an inexpensive FR4-epoxy substrate with dielectric constant of 4.4 and thickness of 1.6 mm. Resonant frequencies and bandwidths of the flag shaped antennas with different dimensions are computed using method of moment electromagnetic solver IE3D 15.10 and they have been given as training and test data for the proposed multilayered perceptron feed forward neural network with Levenberg–Marquardt training algorithm to estimate the resonant frequency and bandwidth of the proposed antennas. The estimated values of resonant frequency and bandwidth with average percentage of error are 1.275 and 0.325, respectively. For verification, the proposed antenna is fabricated and measured with resonant frequency 5.78 GHz and bandwidth of 1.0 GHz for HiperLAN/2 and IEEE802.11a applications. It has impedance bandwidth from 5.0 to 6.0 GHz for return loss lower than −10 dB and good omnidirectional radiation performance over entire frequency range, with a compact size of 11.07 × 27.5 × 1.6 mm3. Various features such as compactness, simple geometry, and low cost, uniplanar structure make the antennas suitable for modern wireless communication systems. This approach replaces the use of very complicated analysis. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:337–342, 2015