Jonathan Edwin Swindell

In reconfigurable array transmissions, a phased-array transmitter changes its characteristics, yet must still be able to control its transmission while optimizing its performance. To enable full reconfiguration while transmitting predictably, performing accurate, real-time measurements within the transmit chain is useful. This recently developed in-situ measurement approach, shown in multiple previous contributions, is summarized in this paper. It serves two purposes: (1) informing the real-time optimization algorithm whether changes in transmitter characteristics improve or worsen performance, and (2) updating the array calibration to obtain the desired transmit array pattern. This will enable real-time, “on the fly” optimizations of transmitters to coexist with other wireless devices in an increasingly congested spectral environment. A four-port coupler, with two monitoring outputs, is used to monitor the total voltage and current between a reconfigurable impedance tuner and the antenna in each element of a transmit array chain. Experimental work from the different prior contributions shows the overall trajectory, reliability, and proposed applications of this in-situ measurement technique. Less than 1 mV of error vector magnitude is shown in vector network analyzer methods compared with simulations using the in-situ coupler approach. The integration and calibration of a software-defined radio to perform antenna input current in-situ measurements has been implemented, with an average current error vector magnitude of 258 µA when comparing the software-defined radio measurements with simulations. Simulation results have shown that in-situ measurements can successfully correct input voltage waveforms for accurate directionally modulated transmissions, lessening reliance on fixed transmitter array pre-calibrations.

Electronic design automation software is used to streamline the iterative design process and automate many tedious tasks. Optimizers often modify design parameters to minimize or maximize a user-defined cost function. Simulators are returning to a need to use user-imported code to assist simulation-based design. This paper describes examples of how user code can be integrated into the microwave design process in standard computer-aided design software tools.

Nonlinear amplifier designers benefit from fast, accurate performance assessment and optimization techniques for computer-aided design. We demonstrate the synthesis of missing measurement dataset portions, even when extrapolating data beyond the boundary of the partial dataset’s domain. A novel method is presented to optimize device load impedance in conjunction with a third parameter. We demonstrate that this method can optimize settings for generic devices where an a priori estimate of the data range is unknown. A modified neural network architecture is used for improved data quality, and Bayesian optimization is applied to infer proper neural network normalization parameters for an unknown device. After applying the learned normalization parameters, the extrapolated dataset realizes a predicted optimal reflection coefficient per input power within a vector distance of about 0.1 on the Smith chart, with a mean output power error of less than 0.5 dB. This was accomplished using only 32 measurements—about 0.1% of the full set of 24192 measurements. If an a priori estimate of the normalization parameters is available, similar performance can be achieved in as few as 16 measurements. This is expected to be a useful tool for reducing design time and improving optimization accuracy in the computer-aided design of transistor amplifiers.