In order to describe the Radio Frequency (RF) power amplifier with memory effect more quickly and accurately, a new X-parameter power amplifier modeling method was proposed based on the traditional X-parameter model combined with the memory effect and load-pulling of the power amplifier. Firstly,the load reflection coefficient was introduced into the new scheme. Secondly,the two-memory path model was used to extract the nonlinear function to represent the memory effect instead of the kernel function. Three variables including amplitude, load reflection coefficient and frequency were regarded as output signals to build the new Feed-Forward (FF) structure. In the end, the step signal was used instead of the original two-tone signal to simplify the model extraction method and improve the feasibility of model extraction. By using the proposed new X-parameter modeling program to model power amplifier, the simulation results of the power amplifier CGH40045F data showed that compared with the traditional X-parameter model, FF structure X-parameter model and FeedBack (FB) structure X-parameters model, the relative error was reduced; compared with the FF model and the FB model, the simulation time was reduced by 4.08 s and 1.64 s, respectively. The results prove that the model modeled by the proposed mehtod can characterize the amplifier with non-linear memory effect more quickly and effectively

In view of the shortcomings of the initial value limitation of traditional algorithms and slow convergence speed of intelligent algorithms in harmonic balance analysis, an adaptive bee colony algorithm based on local search strategy of Broyden-Fleteher-Goldfarl-Shanno (BFGS) algorithm was proposed. Based on the basic bee colony algorithm, nonlinear dynamic adjustment factor was introduced to replace the random variables in the formula, thus improving the adaptability of searching. Meanwhile, BFGS algorithm was applied to the later period of bee colony algorithm to speed up the local search capability. Simulation results show that compared with the standard bee colony algorithm, the number of iterations of the improved algorithm was reduced by 51.9%, and the proposed algorithm has better convergence performance compared with the traditional BFGS algorithm and some other improved intelligent algorithms.

To quickly design a two-sided dipole antenna, which is used to Wireless Local Area Network (WLAN), with small size, dual-band and broadband characteristics, the dipole patch were printed on opposite sides of the dielectric substrate, and balun feed mode was used to achieve better broadband matching. Two arms of the dipole were slotted to achieve the small and dual-band characteristics, and to meet the dual-band requirements of WLAN of 2.45GHz and 5.49GHz. The size of the whole antenna is 28mm×44mm×1.6mm. And the Neural Network (NN) with electromagnetic simulation software named High Frequency Structure Simulator (HFSS) was combined to optimize the key size of the antenna and speed up the design process. The simulation results show that, when the S11 is less than -10dB, the bandwidth of the antenna in low frequency and high frequency can reach 470MHz (2.29—2.76GHz) and 3650MHz (4.96—8.61GHz) respectively; When the S11 is less than -14dB, the bandwidth of the antenna can reach 210MHz (2.36—2.57GHz) and 770MHz (5.13—5.9GHz) respectively. The pattern has good omni-direction, and the measurement and simulation results are in good consistency. The antenna can meet the requirements of WLAN.