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The class J design space is investigated with half wave current excitation for a solid-state RF amplifier capable of delivering hundreds of watts. Unlike conventional class J designs, the present analysis aims to explore a continuous design space in order to operate a commercially available device, within its practical limits of drain voltage. This design analysis together with package effects and the inclusion of non-linear capacitor is verified experimentally by fabricating a high-power (550 W CW) high-efficiency (62.8%) solid-state amplifier operating at 505.8 MHz. This power was obtained by in-phase combining two similar continuous class J stages, each one contributing half of the total power. For high-power lateral diffused metal-oxide semiconductor devices, the class J design space is found to be more realisable than popular modes of operation in view of the large non-linear output capacitance of the device. The measured output power, efficiency, spurious response and large signal output reflection coefficients are satisfactory and as anticipated from the design analysis. Since the final application of this amplifier is for a solid-state transmitter, a study of repeatability in terms of phase and amplitude imbalances was carried out by fabricating and evaluating multiple amplifiers, each one working with the proposed design principle.

A four-element MIMO array antenna for the generation of 5G cellphones to operate at lower sub-6 GHz systems and which is optimized using the current design approach. For acceptable performance related to the optimum design parameters EM simulations are performed instead of traditional optimizations method due to time-consuming process for antenna design. Depending on the nonlinear relationship and complexity for design characteristics an effective method of deep learning (DL) is to determining optimum physical parameters. For designing of the MIMO antenna array this technique proposes resource efficient and time using DL approach. To reduce design space and generation of an effective dataset the technique applied is feature reduction method. To predict the S-parameters developing a novel dual-channel deep neural network. The designed antenna is optimized and achieved the − 10 dB impedance bandwidth of 40% (2.8–4.2 GHz), ECC is 0.018, TARC value is − 22 dB, isolation is − 35 dB, mean effective gain ratio is approaches to unity and CCL is 0.32 bps/Hz.