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In this article, we study closed-loop digital predistortion (DPD) systems and associated learning algorithms. Specifically, we propose various low-complexity approaches to estimate and manipulate the inverse of the input data covariance matrix (CM) and combine them with the so-called self-orthogonalized (SO) learning rule. The inherent simplicity of the SO algorithm, combined with the proposed solutions, allows for remarkably reduced complexity in the DPD system while maintaining similar linearization performance compared to other state-of-the-art methods. This is demonstrated with thorough over-the-air (OTA) mmW measurement results at 28 GHz, incorporating a state-of-the-art 64-element active antenna array, and very wide channel bandwidths up to 800 MHz. In addition, complexity analyses are carried out, which together with the measured linearization performance demonstrates favorable performance–complexity tradeoffs in linearizing mmW active array transmitters through the proposed solutions. The techniques can find application in systems where the power amplifier (PA) nonlinearities are time-varying and thus frequent or even constant updating of the DPD is required, good examples being mmW adaptive antenna arrays as well as terminal transmitters in 5G and beyond networks. [ABSTRACT FROM AUTHOR]

Piecewise behavioral models are commonly adopted for modeling and linearization of RF power amplifiers (PAs) that exhibit strong amplitude-dependent nonlinear distortion characteristics, as global polynomial approximations tend to underperform in such scenarios. In this article, we consider a new piecewise model for PAs based on the mixture of experts (ME) approach, which builds on a probabilistic model that allows the different submodels to cooperate—as opposed to operating in an independent fashion that is commonly the case in existing reference methods. We first introduce the ME framework theory while also extend it such that it can be applied to model complex baseband signals and nonlinearities. Then, we show how the ME model allows overcoming some of the intrinsic shortcomings that existing piecewise behavioral models commonly exhibit, which translates into improved modeling accuracy and improved linearization performance. Furthermore, the extension of the ME approach to a tree-structured regression model, referred to as the hierarchical ME model, is also introduced and shown to provide further performance improvements over the basic ME approach. The proposed solutions are validated with extensive RF measurements, covering both PA direct modeling and digital predistortion (DPD)-based linearization, on a gallium nitride (GaN) load-modulated balanced PA, on a GaN Doherty PA, and on a class AB GaN high electron mobility transistor PA, while being compared against several state-of-the-art piecewise methods. The results demonstrate that the ME framework-based models outperform the state of the art. [ABSTRACT FROM AUTHOR]

In this article, we study digital predistortion (DPD)-based linearization with a specific focus on millimeter-wave (mmW) active antenna arrays. Due to the very large-channel bandwidths and beam-dependence of nonlinear distortion in such systems, we present a closed-loop DPD learning architecture, lookup table (LUT)-based memory DPD models, and low-complexity sign-based estimation algorithms such that even continuous DPD learning could be technically feasible. To this end, three different learning algorithms—sign, signed regressor, and sign–sign—are formulated for the LUT-based DPD models such that the potential rank deficiencies, experienced in earlier methods, are avoided while facilitating greatly reduced learning complexity. The injection-based LUT DPD structure is also shown to allow for low numbers and reduced dynamic range of the involved LUT entries. Extensive RF measurements utilizing a state-of-the-art mmW active antenna array system at 28 GHz are carried out and reported to validate the methods, incorporating very wide channel bandwidths of 400 and 800 MHz while pushing the array close to saturation. In addition, the processing and learning complexities of the considered techniques are analyzed, which, together with the measured linearization performance figures, allows to assess the complexity–performance tradeoffs of the proposed solutions. Overall, the results show that efficient mmW array linearization can be obtained through the proposed methods at very low complexity. [ABSTRACT FROM AUTHOR]

This paper investigates digital predistortion (DPD) linearization of hybrid beamforming large-scale antenna transmitters. We propose a novel DPD processing and learning technique for an antenna subarray, which utilizes a combined signal of the individual power amplifier (PA) outputs in conjunction with a decorrelation-based learning rule. In effect, the proposed approach results in minimizing the nonlinear distortions in the direction of the intended receiver. This feature is highly desirable, since emissions in other directions are naturally weak due to beamforming. The proposed parameter learning technique requires only a single observation receiver, and therefore supports simple hardware implementation. It is also shown to clearly outperform the current state-of-the-art technique that utilizes only a single PA for learning. Analysis of the feedback network amplitude and phase imbalances reveals that the technique is robust even to high levels of such imbalances. Finally, we also show that the array system out-of-band emissions are well-behaving in all spatial directions, and essentially below those of the corresponding single-antenna transmitter, due to the combined effects of the DPD and beamforming. [ABSTRACT FROM AUTHOR]

In this paper, a novel decorrelation-based concurrent digital predistortion (DPD) solution is proposed for dual-band transmitters (TXs) employing a single wideband power amplifier (PA), and utilizing only a single feedback receiver path. The proposed decorrelation-based parameter learning solution is both flexible and simple, and operates in a closed-loop manner, opposed to the widely applied indirect learning architecture. The proposed decorrelation-based learning and DPD processing can also be effectively applied to more ordinary single carrier/band transmissions, as well as generalized to more than two transmit bands. Through a comprehensive analysis covering both the DPD parameter learning and the main path processing, it is shown that the complexity of the proposed concurrent DPD is substantially lower compared with the other state-of-the-art concurrent DPD methods. Extensive set of simulation and RF measurement results are also presented, using base-station PAs as well as a commercial LTE-Advanced mobile PA, to evaluate and validate the effectiveness of the proposed DPD solution in various real world scenarios, incorporating both single-band and dual-band TX cases. The simulation and RF measurement results demonstrate excellent linearization performance of the proposed concurrent DPD, even outperforming current state-of-the-art methods, despite the significantly lower complexity. [ABSTRACT FROM AUTHOR]

Spectrally non-contiguous transmissions pose serious transceiver design challenges due to the nonlinear PA. When two or more non-contiguous carriers with close proximity are amplified by the same PA, spurious emissions inside or in the vicinity of the transmitter RF band are created. These spurious emissions may violate emission limits or otherwise compromise network coverage and reliability. Lowering the transmit power is a straightforward remedy, but it will reduce throughput, coverage, and power efficiency of the device. To improve linearity without sacrificing performance, several DPD techniques have recently been proposed to target the spurious emissions explicitly. These techniques are designed to minimize the computational and hardware complexity of DPD, thus making them better suited for mobile terminals and other lowcost devices. In this article, these recent advances in DPD for non-contiguous transmission scenarios are discussed, with a focus on mitigating the spurious emissions in the concrete example case of non-contiguous dual-carrier transmission. The techniques are compared to more traditional DPD approaches in terms of their computational and hardware complexities, as well as linearization performance. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel digital predistortion (DPD) solution based on a direct learning approach, which is capable of reducing the unwanted emissions resulting from the power amplifier (PA) at any prespecified frequency located in the transmitter's out-of-band or spurious domain. The proposed scheme is based on evaluating the power spectral density (PSD) of the PA output signal and optimizing the DPD coefficients iteratively in order to minimize the output PSD around the prespecified frequency. To highlight the feasibility of the proposed implementation, the predistortion processing is kept as simple as possible, deploying quasi-memoryless polynomial models. Efficient mitigation of unwanted emissions around the target frequency is demonstrated via simulations and actual RF measurements, in both single- and dual-carrier waveform scenarios, using memoryless and memory-based PAs. The proposed DPD solution could be potentially employed in applications such as mobile devices utilizing noncontiguous multicarrier transmission, where the intermodulation spurs may overlap with the device's own receiver band, or could be potentially violating the spurious emission limits. Another target application is cognitive radio, where the PA may produce unwanted emissions that are interfering with primary-user transmissions. To the best of the authors' knowledge, there does not exist a similar technique in the open literature, and thus, the purpose of this paper is to encourage scientific discussions and technological innovations toward the creation of relatively low-complexity frequency-optimized predistortion techniques employed against selected unwanted emissions produced by the transmitter. [ABSTRACT FROM AUTHOR]