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Neural network (NN)-based models are perceived as being accurate models for power amplifier (PA) behavioral modeling and digital predistortion (DPD) applications. However, the complexity of NN’s models in terms of the number of coefficients increases with the memory depth, nonlinearity order, signal bandwidth, and the number of carriers. In this article, a novel augmented convolutional neural network (ACNN)-based DPD is proposed to linearize the concurrent multiband PAs. In the ACNN model, the convolution layer with a fully connected layer serves to model the intermodulation distortion (IMD), cross-modulation distortion (CMD), and nonlinearities of the concurrent multiband PA. The pooling layer in the ACNN reduces the model’s complexity. The measurement results show that the proposed DPD requires significantly fewer coefficients and floating-point operations (FLOPs) than the state-of-the-art DPDs. This model’s complexity reduction arises with the number of carriers in the multiband aggregated signal. The measurement results also show that the proposed DPD has a better linearization performance in terms of metrics, such as normalized mean square error (NMSE), adjacent channel power ratio (ACPR), and error vector magnitude (EVM). [ABSTRACT FROM AUTHOR]

The behavior of a power amplifier (PA) is substantially affected by the output mismatch and mutual coupling in modern compact transmitters. To date, different works in the literature address the problem of coupling and mismatch separately, which are limited in terms of performance with a higher level of mismatch and mutual coupling. This paper identifies the reflection at the PA–antenna interface to be an effective parameter to unify the problem of mismatch and mutual coupling. Thus, a reflection-aware PA modeling and linearization method is proposed to compensate the adverse effect of mismatch and/or mutual coupling. The performance of the proposed method is investigated with a class AB and a Doherty PA under wide range of output mismatch and/or mutual coupling conditions with a 20-MHz WCDMA signal. The proposed method shows robust performance with an average adjacent channel power ratio better than 45 dBc. Such robust linearization performance under diverse output mismatch and mutual coupling conditions is highly desirable for modern and future communication systems that are subject to undergo rapid fluctuations under antenna matching and cross-coupling conditions. [ABSTRACT FROM AUTHOR]

In this paper, an augmented dual-band digital predistortion (DPD) technique for reducing the cross-band intermodulation distortion (IMD) using predictive injection technique is proposed to address some of the shortcomings of dual-band DPDs. The technique alleviates the need to observe the cross-band third-order IMD (IMD3) terms in the feedback loop by predicting the distortion terms and generating synthetic signals, which are then injected at the transmitter side. To highlight the issue, the wideband and dual-band DPD architectures and their respective limitations are briefly outlined. The theory behind the proposed concept is developed, and practical measurements performed using a Class AB power amplifier driven by a long-term evolution signal are provided to support the theory. The results obtained show that the proposed method achieves promising performance in mitigating the cross-band IMD3 issue faced by dual-band DPDs. This technique can be extended to mitigate higher order cross-band distortion as well. [ABSTRACT FROM PUBLISHER]

In this paper, an augmented crossover memory polynomial model (A-COMPM) is proposed and developed which can be used for characterizing and linearizing multiple-input multiple-output (MIMO) transmitters in the presence of linear and nonlinear crosstalk. The proposed model significantly improves the performance of the crossover memory polynomial model (CO-MPM) by more accurately incorporating the effect of crosstalk. The proposed model performs comparably to the $2\times 2$ parallel Hammerstein ($2\times 2$ PH) model, while requiring the same number of coefficients as CO-MPM and a lower number of coefficients than the $2\times 2$ PH. The model was tested for forward modeling and digital predistortion (DPD) applications in the presence of both linear and nonlinear crosstalk. Experimental results show that the model outperforms the CO-MPM and $2\times 2$ PH DPDs, at a lower number of coefficients compared to both models. Furthermore, the issue of numerical stability of the DPD extraction and implementation procedures is addressed in the model. [ABSTRACT FROM PUBLISHER]

This paper presents a method for pruning power amplifier (PA) behavioral models and digital predistorters based on compressed sampling theory. Using this method, the number of coefficients required by behavioral models and predistorters can be significantly reduced while achieving comparable performance in terms of both modeling accuracy and suppressing distortions. Hardware measurements obtained for a Doherty PA driven by a five-carrier 100-MHz wide long-term evolution-advanced signal demonstrate the capability of the proposed method to linearize a highly nonlinear PA prototype using a minimal number of coefficients, revealing the attractive properties of the proposed method and its desirable performance. Using the proposed technique, predistorters achieving similar linearization performance while requiring significantly less coefficients than the traditional models were demonstrated. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper addresses the problem of model size estimation in power amplifiers behavioural modeling and predistortion applications using multi-basis functions memory polynomials. A low complexity sequential approach is proposed along with a suitable model. The proposed model and size estimation approach are experimentally assessed for the modeling and predistortion of an LTE envelope-tracking power amplifier. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:726-732, 2016 [ABSTRACT FROM AUTHOR]