A New Design of a Wideband Miniature Antenna Array

In this work, we present a new configuration of a new miniature microstrip antenna array having a wide frequency band and with a circular polarization. The bandwidth is about 2GHz for a reflection coefficient under -10dB and centered on the ISM ‘Industrial Scientific Medical’ band at 5.8 GHz. To design such array, we have started the design by validating one antenna element at 10 GHz and after that by using the technique of defected ground, we have validated the antenna array in the frequency band [4 GHz -6 GHz] which will permit to miniature the dimensions. The final fabricated antenna array is mounted on an FR4 substrate, the whole area is 102.48 X 31.39 mm2  with a gain of 5dBi at 4GHz

OPTIMIZATION OF PATH LOSS PREDICTION IN MILLIMETER WAVE COMMUNICATIONS USING POLYNOMIAL REGRESSION MODEL AND GENETIC ALGORITHM

<p><strong>Summary</strong></p><p>Millimeter wave (mmWave) technology have attracted significant interest due to bandwidth availability improvement offering huge amount of spectrum to fifth generation (5G). The shorter wavelength of mmWave signals allows for greater data transmission rates and bandwidth, but it also makes them more susceptible to various forms of attenuation and absorption between the transmitting and the receiving antennas, also referred to as path losses. The path loss model is an important tool in wireless network planning; allowing network planner to optimize the cell towers distribution and meet expected service level requirements. However, each type of path loss propagation model is designed to predict path loss in a particular environment that may be inaccurate in other different environment. Improving the existing models and developing new models are is vital for characterizing the wireless communication channel in both indoor and outdoor environments. This paperpresents an efficient and novel path loss model based on polynomial regression analysis for predicting signal strength in millimeter bands. A genetic algorithm is used to optimize the parameters of the polynomial regression model by minimizing the sum of squared errors of the proposed model of path loss. The performance and accuracy of the polynomial regression model are evaluated and compared to both the measured path loss values and those obtained by lognormal shadowing model. The results show the close fit of the polynomial model to the field measurements with significantly lower root mean square error (RMSE) compared to the distance-shadowing model which proves the validity and accuracy of the proposed model.</p&gt