A Digital Predistortion Scheme Exploiting Degrees-of-Freedom for Massive MIMO Systems

The primary source of nonlinear distortion in wireless transmitters is the power amplifier (PA). Conventional digital predistortion (DPD) schemes use high-order polynomials to accurately approximate and compensate for the nonlinearity of the PA. This is not practical for scaling to tens or hundreds of PAs in massive multiple-input multiple-output (MIMO) systems. There is more than one candidate precoding matrix in a massive MIMO system because of the excess degrees-of-freedom (DoFs), and each precoding matrix requires a different DPD polynomial order to compensate for the PA nonlinearity. This paper proposes a low-order DPD method achieved by exploiting massive DoFs of next-generation front ends. We propose a novel indirect learning structure which adapts the channel and PA distortion iteratively by cascading adaptive zero forcing precoding and DPD. Our solution uses a 3rd order polynomial to achieve the same performance as the conventional DPD using an 11th order polynomial for a 100x10 massive MIMO configuration. Experimental results show a 70% reduction in computational complexity, enabling ultra-low latency communications.Comment: IEEE International Conference on Communications 201

The digital predistorter goes multi-dimensional: DPD for concurrent multi-band envelope tracking and outphasing power amplifiers

Over at least the last two decades, digital predistortion (DPD) has become the most common and widespread solution to cope with the power amplifier's (PA's) inherent linearity-versus-efficiency tradeoff. When compared with other linearization techniques, such as Cartesian feedback or feedforward, DPD has proven able to adapt to the always-growing demands of technology: wider bandwidths, stringent spectrum masks, and reconfigurability. The principles of predistortion linearization (in its analog or digital forms) are straightforward, and the linearization subsystem precedes the PA (a nonlinear function in a digital signal processor in the case of DPD or nonlinear device in the case of analog predistortion and counteracts the nonlinear characteristic of the PA. Some excellent overviews on DPD can be found in [1]-[4]. Let us now look at the challenges that DPD linearization has faced and will continue to face in the near future with 5G new radio (5G-NR).This work has been supported in part by the Spanish Government and FEDER under MICINN projects TEC2017-83343-C4-1-R and TEC2017-83343-C4-2-R and by the Generalitat de Catalunya under Grant 2017 SGR 813

Digital Predistortion for Multi-Antenna Transmitters Affected by Antenna Crosstalk

In this paper, a digital predistortion (DPD) technique for wideband multi-antenna transmitters is proposed. The proposed DPD compensates for the combined effects of power amplifier (PA) nonlinearity, antenna crosstalk and impedance mismatch. The proposed technique consists of a linear crosstalk and mismatch model block shared by all transmit paths, and a dual-input DPD block in every transmit path. By avoiding the use of multi-input DPD blocks in every transmit path, the complexity of the proposed technique is kept low and scales more favorably with the number of antennas than competing techniques. It is shown that all blocks can be identified from measurements of the PA output signals using least-squares estimation. Measurement results of a four-path transmitter are presented and used to evaluate the proposed DPD technique against existing techniques. The results show that the performance of the proposed DPD technique is similar to those of existing techniques, while the complexity is lower

Active Transmitter Antenna Array Modeling for MIMO Applications

The rapid growth of data traffic in mobile communications has attracted interests to the Multiple-Input-Multiple-Output (MIMO) communication systems at millimeter-wave (mmWave) frequencies.\ua0 MIMO systems exploit active transmitter antenna arrays for higher energy efficiency and providing beamforming flexibility. The close integration of multiple PAs and antennas increases the transmitter analysis complexity. Moreover, due to the small antenna element spacing at mm-wave frequencies, isolators are too bulky and cannot be used. Therefore, including the effects of interactions between the antenna array and PAs is a significant aspect in the analysis of MIMO transmitters. For large active arrays, applying joint circuit and EM simulation tools for the analysis is a complicated and time-consuming task. In these occasions, behavioral models are the key to the fast and accurate evaluation of active transmitter antenna arrays.In this thesis, a technique for modeling the active transmitter antenna array performance is presented. The proposed model considers the effect of PAs nonlinearity as well as the coupling and mismatch in the antenna array. With this model, a comprehensive prediction of radiation pattern and signal distortions in the far-field is feasible. The model is experimentally verified by a mmWave active subarray antenna for a beam steering scenario and by performing over-the-air measurements. The measurement results effectively validate the modeling technique for a wide range of steering angles.\ua0\ua0 Furthermore, a linearity analysis is provided to predict transmitter performance in conjunction with beam-dependent digital predistortion (DPD) linearization. The study reveals the model potential in evaluating different DPD approaches as well as predicting the performance of linearized transmitters. The demonstration shows that the variation of nonlinear distortion versus steering angle depends significantly on the array configuration and beam direction.In summary, the proposed model allows for the prediction of the active transmitter antenna array performance in the early design stages with low computational effort. It can provide design guides for developing large-scale active arrays and can be employed for evaluating the DPD and transmitter linearity performance

Modeling Approaches for Active Antenna Transmitters

The rapid growth of data traffic in mobile communications has attracted interest to Multiple-Input-Multiple-Output (MIMO) communication systems at millimeter-wave (mmWave) frequencies. MIMO systems exploit active antenna arrays transmitter configurations to obtain higher energy efficiency and beamforming flexibility. The analysis of transmitters in MIMO systems becomes complex due to the close integration of several antennas and power amplifiers (PAs) and the problems associated with heat dissipation. Therefore, the transmitter analysis requires efficient joint EM, circuit, and thermal simulations of its building blocks, i.e., the antenna array and PAs. Due to small physical spacing at mmWave, bulky isolators cannot be used to eliminate unwanted interactions between PA and antenna array. Therefore, the mismatch and mutual coupling in the antenna array directly affect PA output load and PA and transmitter performance. On the other hand, PAs are the primary source of nonlinearity, power consumption, and heat dissipation in transmitters. Therefore, it is crucial to include joint thermal and electrical behavior of PAs in analyzing active antenna transmitters. In this thesis, efficient techniques for modeling active antenna transmitters are presented. First, we propose a hardware-oriented transmitter model that considers PA load-dependent nonlinearity and the coupling, mismatch, and radiated field of the antenna array. The proposed model is equally accurate for any mismatch level that can happen at the PA output. This model can predict the transmitter radiation pattern and nonlinear signal distortions in the far-field. The model\u27s functionality is verified using a mmWave active subarray antenna module for a beam steering scenario and by performing the over-the-air measurements. The load-pull modeling idea was also applied to investigate the performance of a mmWave spatial power combiner module in the presence of critical coupling effects on combining performance. The second part of the thesis deals with thermal challenges in active antenna transmitters and PAs as the main source of heat dissipation. An efficient electrothermal modeling approach that considers the thermal behavior of PAs, including self-heating and thermal coupling between the IC hot spots, coupled with the electrical behavior of PA, is proposed. The thermal model has been employed to evaluate a PA DUT\u27s static and dynamic temperature-dependent performance in terms of linearity, gain, and efficiency. In summary, the proposed modeling approaches presented in this thesis provide efficient yet powerful tools for joint analysis of complex active antenna transmitters in MIMO systems, including sub-systems\u27 behavior and their interactions

Modeling and Compensation of Nonlinear Distortion in Multi-Antenna RF Transmitters

Multi-antenna systems are utilized as a way to increase spectral efficiency in wireless communications. In a transmitter, the use of several parallel transmit paths and antennas increases system complexity and cost. Cost-efficient solutions, which employ active antenna arrays and avoid expensive isolators, are therefore preferred. However, such solutions are vulnerable to crosstalk due to mutual coupling between the antennas, and impedance mismatches between amplifiers and antennas. Combined with the nonlinear behavior of the power amplifiers, these effects cause nonlinear distortion, which deteriorates the quality of the transmitted signals and can prevent the transmitter from meeting standard requirements and fulfilling spectrum regulations. Analysis, assessment and, if necessary, compensation of nonlinear distortion are therefore essential for the design of multi-antenna transmitters.In this thesis, a technique for modeling and predicting nonlinear distortion in multi-antenna transmitters is presented. With this technique, the output of every individual transmit path, as well as the radiated far-field of the transmitter can be predicted with low computational effort. The technique connects models of the individually characterized transmitter components. It can be used to investigate and compare the effects of different power amplifier and antenna array designs at early design stages without complicated and expensive measurements.Furthermore, a digital predistortion technique for compensating nonlinear distortion in multi-antenna transmitters is presented. Digital predistortion is commonly used in transmitters to compensate for undesired nonlinear hardware effects. The proposed solution combines a linear function block with dual-input predistorters. The complexity is reduced compared to existing techniques, which require highly complex multivariate predistorter functions. Finally, a technique for identifying multi-antenna transmitter models and predistorters from over-the-air measurements using only a small set of observation receivers is presented. Conventional techniques require a dedicated observation receiver in every transmitter path, or one or more observation receivers that are shared by several paths in a time-interleaved manner. With the proposed technique, each receiver is used to observe several transmitter paths simultaneously. Compared to conventional techniques, hardware cost and complexity can be reduced with this approach. In summary, the signal processing techniques presented in this thesis enable a simplified, low-cost design process of multi-antenna transmitters. The proposed algorithms allow for feasible, low-complexity implementations of both digital and analog hardware even for systems with many antennas, thereby facilitating the development of future generations of wireless communication systems

Contribution to dimensionality reduction of digital predistorter behavioral models for RF power amplifier linearization

The power efficiency and linearity of radio frequency (RF) power amplifiers (PAs) are critical in wireless communication systems. The main scope of PA designers is to build the RF PAs capable to maintain high efficiency and linearity figures simultaneously. However, these figures are inherently conflicted to each other and system-level solutions based on linearization techniques are required. Digital predistortion (DPD) linearization has become the most widely used solution to mitigate the efficiency versus linearity trade-off. The dimensionality of the DPD model depends on the complexity of the system. It increases significantly in high efficient amplification architectures when considering current wideband and spectrally efficient technologies. Overparametrization may lead to an ill-conditioned least squares (LS) estimation of the DPD coefficients, which is usually solved by employing regularization techniques. However, in order to both reduce the computational complexity and avoid ill-conditioning problems derived from overparametrization, several efforts have been dedicated to investigate dimensionality reduction techniques to reduce the order of the DPD model. This dissertation contributes to the dimensionality reduction of DPD linearizers for RF PAs with emphasis on the identification and adaptation subsystem. In particular, several dynamic model order reduction approaches based on feature extraction techniques are proposed. Thus, the minimum number of relevant DPD coefficients are dynamically selected and estimated in the DPD adaptation subsystem. The number of DPD coefficients is reduced, ensuring a well-conditioned LS estimation while demanding minimum hardware resources. The presented dynamic linearization approaches are evaluated and compared through experimental validation with an envelope tracking PA and a class-J PA The experimental results show similar linearization performance than the conventional LS solution but at lower computational cost.La eficiencia energetica y la linealidad de los amplificadores de potencia (PA) de radiofrecuencia (RF) son fundamentales en los sistemas de comunicacion inalambrica. El principal objetivo a alcanzar en el diserio de amplificadores de radiofrecuencia es lograr simultaneamente elevadas cifras de eficiencia y de linealidad. Sin embargo, estas cifras estan inherentemente en conflicto entre si, y se requieren soluciones a nivel de sistema basadas en tecnicas de linealizacion. La linealizacion mediante predistorsion digital (DPD) se ha convertido en la solucion mas utilizada para mitigar el compromise entre eficiencia y linealidad. La dimension del modelo del predistorsionador DPD depende de la complejidad del sistema, y aumenta significativamente en las arquitecturas de amplificacion de alta eficiencia cuando se consideran los actuales anchos de banda y las tecnologfas espectralmente eficientes. El exceso de parametrizacion puede conducir a una estimacion de los coeficientes DPD, mediante minimos cuadrados (LS), mal condicionada, lo cual generalmente se resuelve empleando tecnicas de regularizacion. Sin embargo, con el fin de reducir la complejidad computacional y evitar dichos problemas de mal acondicionamiento derivados de la sobreparametrizacion, se han dedicado varies esfuerzos para investigar tecnicas de reduccion de dimensionalidad que permitan reducir el orden del modelo del DPD. Esta tesis doctoral contribuye a aportar soluciones para la reduccion de la dimension de los linealizadores DPD para RF PA, centrandose en el subsistema de identificacion y adaptacion. En concrete, se proponen varies enfoques de reduccion de orden del modelo dinamico, basados en tecnicas de extraccion de caracteristicas. El numero minimo de coeficientes DPD relevantes se seleccionan y estiman dinamicamente en el subsistema de adaptacion del DPD, y de este modo la cantidad de coeficientes DPD se reduce, lo cual ademas garantiza una estimacion de LS bien condicionada al tiempo que exige menos recursos de hardware. Las propuestas de linealizacion dinamica presentados en esta tesis se evaluan y comparan mediante validacion experimental con un PA de seguimiento de envolvente y un PA tipo clase J. Los resultados experimentales muestran unos resultados de linealizacion de los PA similares a los obtenidos cuando se em plea la solucion LS convencional, pero con un coste computacional mas reducido.Postprint (published version