Power amplifiers in modern wireless systems must meet increasingly stringent spectral constraints while still operating with high power efficiency. A new peak-search algorithm is demonstrated to find the Pareto optimum of power-added efficiency (PAE) while producing an adjacent channel power ratio (ACPR) value under a specified maximum. A peak-search is first performed for the maximum PAE, and then a small-step steepest descent walk in the ACPR is taken from the PAE maximum toward the ACPR minimum until ACPR requirements are met. This approach reasonably approximates the Pareto optimum for the device. Simulation and measurement data are presented for searches from multiple starting points, and good correspondence is obtained for the Pareto optimum reflection coefficient, as well as the optimum PAE and ACPR values. This measurement-based algorithm is expected to find useful application in real-time adaptive radio and radar transmitters, as well as in bench-top measurements for amplifier design.

Amplifier design, both in traditional approaches and real-time circuit optimization, greatly benefits from fast and thorough extraction of information from measurement data. Using only a few performance samples at varying impedances, deep learning image completion techniques can be utilized to extrapolate an entire set of Smith chart load-pull contours. In addition to speeding nonlinear device characterizations, this extrapolation can be performed in an iterative fashion for use as a circuit optimization algorithm with a very low number of measurements. The techniques of this work have been tested in the measurement of a nonlinear, large-signal amplifier. The load impedance can be estimated with a typical error of < 0.1 linear units using as few as seven impedances and yields even better accuracy with larger sample sizes.

Block loss and propagation error due to cell loss or missing packet information during the transmission over lossy networks can cause severe degradation of block and predictive-based video coding. Herein, new fast spatial and temporal methods are presented for block loss recovery. In the spatial algorithm, missing block recovery and edge extention are performed by pixel replacement based on range constraints imposed by surrounding neighborhood edge information and structure. In the temporal algorithm, an adaptive temporal correlation method is proposed for motion vector (MV) recovery. Parameters for the temporal correlation measurement are adaptively changed in accordance to surrounding edge information of a missing macroblock (MB). The temporal technique utilizes pixels in the reference frame as well as surrounding pixels of the lost block. Spatial motion compensation is applied after MV recovery when the reference frame does not have sufficient information for lost MB restoration. Simulations demonstrate that the proposed algorithms recover image information reliably using both spatial and temporal restoration. We compare the proposed algorithm with other procedures with consistently favorable results.

In the increasingly congested wireless spectrum, phased-array transmitters can be used to provide spatial diversity as an additional measure of coexistence. Reconfigurable matching networks placed between the power amplifier device and the antenna can allow the amplifiers in the transmitter elements to maximize output power while maintaining the desired beam fidelity. A challenge with the use of reconfigurable matching networks in array transmitters is that they alter the system calibration. This article provides measurement validation of an in situ measurement approach to assess antenna input current using a dual-directional coupler placed in an array element. Measurement validation was performed using a two-port vector network analyzer (VNA) with a load—pull tuner emulating changing antenna impedance. This approach is expected to ease the process of maintaining the pattern and is expected to ease issues stemming from changing array calibration with reconfigurable array element circuits.