This paper presents the theory and design methodology of broadband RF-input continuous mode load modulated balanced power amplifier (CM-LMBA) by introducing the continuous mode output matching networks in the LMBA architecture. It is illustrated that the continuous mode impedance condition can be achieved by properly adjusting the phase difference between the different PA branches in the proposed CM-LMBA during the entire load modulation process. An RF-input CM-LMBA with 1.45-2.45 GHz bandwidth using commercial GaN transistors is designed and implemented to validate the proposed architecture. The fabricated CM-LMBA attains a measured 11.2-13.4 dB gain and around 40 Watts saturated power. Power added efficiency (PAE) of 46.4-56.5% and 43.2-50.3% is achieved at 6 dB and 8 dB output power back-off throughout the designed band. When driven by a 100 MHz OFDM signal with 8 dB peak to average power ratio (PAPR), the proposed CM-LMBA achieves better than -46 dBc adjacent channel leakage ratio (ACLR) and higher than 45% average PAE after digital pre-distortion at 1.8 GHz and 2.1 GHz.

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In this article, we present a novel digital predistortion (DPD) architecture for multiple-input-multiple-output (MIMO) transmitters using a real-time single-channel over-theair (OTA) data acquisition loop. The proposed feedback data acquisition strategy captures OTA signals from a fixed location and indirectly identifies the nonlinear behavior of all power amplifiers (PAs) in the array, as well as their combined signals in the far-field direction. The DPD can, therefore, be effectively constructed without direct measurement at PA output or at user end. The proposed linearization solution can run in realtime and, thus, does not interfere with data transmission in the MIMO transmitters. It can also achieve robust performance when mutual coupling occurs between antenna elements. Simulation and experimental results demonstrate that the proposed scheme can accurately estimate both PA outputs and far-field main beam data. Excellent linearization performance can be achieved with low complexity hardware implementation and reduced computational complexity.

In this paper, we present a novel technique to build digital predistorters (DPD) that can linearize broadband power amplifiers (PA) using reduced sampling rates. In contrast to conventional DPDs where oversampling is necessary to avoid aliasing effect, the proposed method cancels the aliasing distortion using a sliced multi-stage cancellation scheme. A large reduction of sampling rate can be achieved in digital implementation of DPD, significantly reducing power consumption and implementation cost. Experimental results show that a DPD with a sampling rate of merely 1.5 times, instead of 5 times, signal bandwidth, can still produce satisfactory performance within the linearization bandwidth but consume only one third of power, compared with that using the conventional approaches. The proposed technique provides a promising solution for next generation 5G systems where large signal bandwidths are required.

In this article, we present a novel recurrent neural network (RNN)-based behavioral model to linearize radio frequency (RF) power amplifiers (PAs) under wideband excitations. Based on the lightweight Just Another NETwork (JANET) unit, we propose a new neural network structure that is especially suitable for modeling the complex behavior of the RF PAs. A novel signal preprocessing technique is developed to model the complex interaction between amplitude information and phase information in the digital predistortion (DPD) model. By integrating the preprocessing stage into the RNN model, the complete phase-gated JANET (PG-JANET) model can provide enhanced linearization performance with lower complexity compared to the existing models.