Search

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 paper, we present a class of cascaded nonlinear models for complex-valued system identification, aimed at baseband modeling of nonlinear radio systems. The proposed models consist of serially connected elementary linear and nonlinear blocks, with the nonlinear blocks implemented as uniform spline-interpolated look-up tables (LUT) and the linear blocks as FIR filters. Wiener, Hammerstein, and Wiener-Hammerstein models are built, and simple but efficient gradient based adaptive learning rules are derived for all the models. This approach leads to remarkably simple solutions in terms of computational complexity, making the techniques suitable for real-time implementation. The proposed methods are then applied to full-duplex self-interference cancellation and digital predistortion in various real-life scenarios. First, evaluations with measured data from an in-band full-duplex prototype working at 2.4 GHz ISM band show that the algorithms are capable of obtaining similar cancellation performance as existing state-of-the-art solutions, regardless of the clearly reduced complexity. Second, a mmW active antenna array working at 28 GHz center frequency is digitally predistorted with the proposed solutions. The unwanted emissions and nonlinear distortion are suppressed to similar levels as with other state-of-the art solutions, and the corresponding linearity specifications are fulfilled in all cases, while the processing complexity is again drastically reduced. [ABSTRACT FROM AUTHOR]

This article presents a power amplifier (PA) design, which consists of eight class-D PA units on a single 28-nm CMOS die and a coupled-line power combiner on printed circuit board. The PA utilizes tri-phasing modulation, which combines polar and outphasing components in a way that eliminates linearity-degrading effects of multilevel outphasing while maintaining the back off efficiency.Each PA unit contains a cascoded output stage with a 3.6-V supply voltage, and multilevel operation is enabled by ON/OFF logic circuitry. Our analysis shows that the choice of power-combiner type is vital for reducing PA supply and ground ripple and thus ensuring reliable operation.Accordingly, the power combiner is implemented with extended Marchand baluns, which consist of input transmission lines and coupled-line sections. Unlike the original Marchand balun, our new topology is feasible for implementation under the layout restrictions caused by the multiple-unit PA on a single die.Measurement results show the PA achieving a peak output power of 29.7 dBm with a 34.7% efficiency, and operation with aggregated Long Term Evolution (LTE) signals at 1.7-GHz carrier frequency is verified with bandwidths up to 100 MHz. [ABSTRACT FROM AUTHOR]

We present a prototype RF transmitter with an integrated multilevel class-D power amplifier (PA), implemented in 28-nm CMOS. The transmitter utilizes tri-phasing modulation, which combines three constant-envelope phase-modulated signals with coarse amplitude modulation in the PA. This new architecture achieves the back-off efficiency of multilevel outphasing, without linearity-degrading discontinuities in the RF output waveform. Because all signal processing is performed in the time domain up to the PA, the entire system is implemented with digital circuits and structures, thus also enabling the use of synthesis and place-and-route CAD tools for the RF front end. The effectiveness of the digital tri-phasing concept is supported by extensive measurement results. Improved wideband performance is validated through the transmission of orthogonal frequency-division multiplexing (OFDM) bandwidths up to 100 MHz. Enhanced reconfigurability is demonstrated with non-contiguous carrier aggregation and digital carrier generation between 1.5 and 1.9 GHz without a frequency synthesizer. For a 20-MHz 256-QAM OFDM signal at 3.5% error vector magnitude (EVM), the transmitter achieves 22.6-dBm output power and 14.6% PA efficiency. Thanks to the high linearity enabled by tri-phasing, no digital predistortion is needed for the PA. [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, we provide detailed modeling of the spurious intermodulation distortion products appearing in the own receiver (RX) operating band as a result of coexisting transmitter (TX) and RX nonlinearities with noncontiguous carrier aggregation transmissions. Furthermore, an efficient digital front-end signal processing technique is proposed, which can flexibly mitigate the resulting RX in-band self-interference caused either by individual or simultaneously coexisting TX and RX nonlinearities. The technique is based on accurately estimating the effective leakage channel that models the nonlinearities of the TX and RX chains and the duplexer filters characteristics. In the parameter estimation stage, an observation RX chain is adopted for separately estimating the TX passband leakage response, which facilitates efficient joint estimation and regeneration of the TX- and RX-induced self-interference. In the online digital cancellation, the actual transmit data are used in conjunction with the estimated channel responses to generate a replica of the overall nonlinear self-interference, which is subsequently suppressed by subtracting it from the actual observation. In general, the proposed technique can efficiently estimate and suppress the self-interference at arbitrary spurious sub-bands located at the RX band. The performance evaluations with comprehensive numerical simulations and practical RF measurements indicate highly accurate and efficient operation, with up to 28 dB of measured self-interference suppression. [ABSTRACT FROM PUBLISHER]

This paper presents a novel digital self-interference canceller for an inband multiple-input-multiple-output (MIMO) full-duplex radio. The signal model utilized by the canceller is capable of modeling the in-phase quadrature (IQ) imbalance, the nonlinearity of the transmitter power amplifier, and the crosstalk between the transmitters, thereby being the most comprehensive signal model presented thus far within the full-duplex literature. Furthermore, it is also shown to be valid for various different radio frequency (RF) cancellation solutions. In addition to this, a novel complexity reduction scheme for the digital canceller is also presented. It is based on the widely known principal component analysis, which is used to generate a transformation matrix for controlling the number of parameters in the canceller. Extensive waveform simulations are then carried out, and the obtained results confirm the high performance of the proposed digital canceller under various circuit imperfections. The complexity reduction scheme is also shown to be capable of removing up to 65% of the parameters in the digital canceller, thereby significantly reducing its computational requirements. [ABSTRACT FROM AUTHOR]

In wireless communication systems, high-power transmitters suffer from nonlinearities due to power amplifier (PA) characteristics, I/Q imbalance, and local oscillator (LO) leakage. Digital Predistortion (DPD) is an effective technique to counteract these impairments. To help maximize agility in cognitive radio systems, it is important to investigate dynamically reconfigurable DPD systems that are adaptive to changes in the employed modulation schemes and operational constraints. To help maximize effectiveness, such reconfiguration should be performed based on multidimensional operational criteria. With this motivation, we develop in this paper a novel evolutionary algorithm framework for multiobjective optimization of DPD systems. We demonstrate our framework by applying it to develop an adaptive DPD architecture, called the adaptive, dataflow-based DPD architecture (ADDA), where Pareto-optimized DPD parameters are derived subject to multidimensional constraints to support efficient predistortion across timevarying operational requirements and modulation schemes. Through extensive simulation results, we demonstrate the effectiveness of our proposed multiobjective optimization framework in deriving efficient DPD configurations for runtime adaptation. [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]

In this paper, we present a novel digital predistorter structure for joint mitigation of frequency-dependent power amplifier (PA) and in-phase and quadrature (I/Q) modulator impairments in direct-conversion radio transmitters. The predistorter is based on an extended parallel Hammerstein structure, yielding a predistorter that is fully linear in the parameters. In the parameter estimation stage, the indirect learning architecture is utilized. The proposed technique is the first technique in the literature to consider the joint estimation and mitigation of frequency-dependent PAand I/Q modulator impairments. Extensive simulation and measurement analysis is carried out to verify the operation and efficacy of the proposed predistortion structure. It is shown that the adjacent channel power ratio is increased by more than 20 dB in all experiments when using the proposed method, and that the performance of the reference techniques is clearly exceeded. [ABSTRACT FROM AUTHOR]