Analysis of inter-modulation products and nonlinear distortion in RF OFDM transmitter systems

Postprint (published version

Bandlimited Digital Predistortion of Wideband RF Power Amplifiers

The increase in the demand for high data rates has led to the deployment of wider bandwidths and complex waveforms in wireless communication systems. Multicarrier waveforms such as orthogonal frequency division multiplexing (OFDM) employed in modern systems are very sensitive to the transmitter chain nonidealities due to their high peak-to-average-power-ratio (PAPR) characteristic. They are therefore affected by nonlinear transmitter components particularly the power amplifier (PA). Moreover, to enhance power efficiency, PAs typically operate near saturation region and hence become more nonlinear. Power efficiency is highly desirable especially in battery powered and portable devices as well as in base stations. Hence there is a clear need for efficient linearization algorthms which improve power efficiency while maintaining high spectral efficiency. Digital predistortion (DPD) has been recognized as one of the most effective methods in mitigating PA nonlinear distortions. The method involves the application of inverse PA nonlinear function upstream of the PA such that the overall system output has a linear amplification. The computation of the nonlinearity profile and the inversion of the PA function are particularly difficult and complicated especially when involving wideband radio access waveforms, and therefore memory effects, which are being employed in modern communication systems, such as in Long Term Evolution/Advanced (LTE/LTE-A). In the recent technical literature, different approaches which focus on the linearization of specific frequency bands or sub-bands only have been developed to alleviate this problem, thereby reducing the complexity of DPD. In this thesis, we focus on the development and characterization of a bandlimited DPD solution specifically tailored towards the linearization at and around the main carrier(s) in single carrier deployment or contiguous carrier aggregation of two or more component carriers. In terms of parameter identification, the solution is based on the reduced-complexity closed-loop decorrelation-based parameter learning principle, which is also able to track time-varying changes in the transmitter components adaptively. The proposed bandlimited solution is designed to linearize the inband and out-of-band (OOB) distortions in the immediate vicinity of the main carrier(s) while assuming the distortions more far away in the spectrum are suppressed by transmit or duplex filters. This is implemented using FIR filters to limit the bandwidth expansion during basis functions generation and to restrain the bandwidth of the feedback observation signal, thus reducing the DPD sample rates in both the main path processing and the parameter learning. The performance of the proposed bandlimited DPD solution is evaluated using comprehensive simulations involving memoryless and memory-based PA models, as well as true RF measurements using commercial LTE-A base station and mobile device PAs. The achieved results validate and demonstrate efficient suppression of inband and OOB distortions in real-world application scenarios. Furthermore, the bandlimited DPD consistently outperforms the conventional DPD solutions in the memory-based PA model and practical PA scenarios in suppressing the OOB distortion in the immediate vicinity of the main carrier(s) by approximately 1 - 2 dB. The results provide sufficient grounds for the application of the bandlimited DPD solution in the classical single carrier deployment or in contiguous carrier aggregation of two or more component carriers where conventional DPD solutions would otherwise be highly complex

Development of digital predistorters for broadband power amplifiers in OFDM systems using the simplicial canonical piecewise linear function

Power amplifiers (PAs) are inherently nonlinear devices. Linearity of a PA can be achieved by backing off the PA to its linear region at the expense of power efficiency loss. For signals with high envelope fluctuation such OFDM system, large backoff is required, causing significant loss in power efficiency. Thus, backoff is not a favourable solution. Digital predistorters (PDs) are widely employed for linearizing PAs that are driven to the nonlinear regions. In broadband systems where PAs exhibit memory effects, the PDs are also required to compensate the memory effects. This thesis deals with the development of digital PDs for broadband PAs in OFDM systems using the Simplicial Canonical Piecewise Linear (SCPWL) function. The SCPWL function offers a few advantages over polynomial models. It imposes a saturation after the last breakpoint, making it suitable for modelling nonlinearities of PA and PD. The breakpoints of the function can be freely placed to allow optimum fitting of a given nonlinearity. It is suitable for modeling strong nonlinearities. Analysis of the SCPWL spectra property shows that the function models infinite order of intermodulation distortion, even with small number of breakpoints. The accuracy of the model can be improved by increasing the number of breakpoints. The original real-valued SCPWL function is extended to include memory structure and complex-valued coefficients, resulting in the proposed baseband SCPWL model with memory. The model is adopted in the development of the Hammerstein-SCPWL PD and memory-SCPWL PD. Vector projection methods are developed for static SCPWL PDs identification. Adaptive algorithms employing the indirect and direct learning architectures are developed for identifying the Hammerstein-SCPWL PD and memory-SCPWL PD. By exploiting the properties of the SCPWL function, the algorithms are simplified. A modified Wiener model estimator is employed to circumvent the non-convex cost function problem of block models. This further reduces the complexity of the Hammerstein PD algorithms. The thesis also analyses the effects of measurement noise on indirect learning SCPWL filter. Due to its linear basis function, the SCPWL filter coefficients do not suffer the coefficient bias effects which are observed in polynomial models. The performance of the proposed SCPWL PDs are compared with state-of-the-art polynomial-based PDs by simulations and measurements

WAVEFORM AND TRANSCEIVER OPTIMIZATION FOR MULTI-FUNCTIONAL AIRBORNE RADAR THROUGH ADAPTIVE PROCESSING

Pulse compression techniques have been widely used for target detection and remote sensing. The primary concern for pulse compression is the sidelobe interference. Waveform design is an important method to improve the sidelobe performance. As a multi-functional aircraft platform in aviation safety domain, ADS-B system performs functions involving detection, localization and alerting of external traffic. In this work, a binary phase modulation is introduced to convert the original 1090 MHz ADS-B signal waveform into a radar signal. Both the statistical and deterministic models of new waveform are developed and analyzed. The waveform characterization, optimization and its application are studied in details. An alternative way to achieve low sidelobe levels without trading o range resolution and SNR is the adaptive pulse compression - RMMSE (Reiterative Minimum Mean-Square error). Theoretically, RMMSE is able to suppress the sidelobe level down to the receiver noise floor. However, the application of RMMSE to actual radars and the related implementation issues have not been investigated before. In this work, implementation aspects of RMMSE such as waveform sensitivity, noise immunity and computational complexity are addressed. Results generated by applying RMMSE to both simulated and measured radar data are presented and analyzed. Furthermore, a two-dimensional RMMSE algorithm is derived to mitigate the sidelobe effects from both pulse compression processing and antenna radiation pattern. In addition, to achieve even better control of the sidelobe level, a joint transmit and receive optimization scheme (JTRO) is proposed, which reduces the impacts of HPA nonlinearity and receiver distortion. Experiment results obtained with a Ku-band spaceborne radar transceiver testbed are presented

Design and Linearization of Energy Efficiency Power Amplifier in Nonlinear OFDM Transmitter for LTE-5G Applications. Simulation and measurements of energy efficiency power amplifier in the presence of nonlinear OFDM transmitter system and digital predistortion based on Hammerstein-Wiener method

This research work has made an effort to understand a novel line of radio frequency power amplifiers (RFPAs) that address initiatives for efficiency enhancement and linearity compensation to harmonize the fifth generation (5G) campaign. The objective is to enhance the performance of an orthogonal frequency division multiplexing-long term evolution (OFDM-LTE) transmitter by reducing the nonlinear distortion of the RFPA. The first part of this work explores the design and implementation of 15.5 W class AB RF power amplifier, adopting a balanced technique to stimulate efficiency enhancement and redeeming exhibition of excessive power in the transmitter. Consequently, this work goes beyond improving efficiency over a linear RF power amplifier design; in which a comprehensive investigation on the fundamental and harmonic components of class F RF power amplifier using a load-pull approach to realise an optimum load impedance and the matching network is presented. The frequency bandwidth for both amplifiers was allocated to operate in the 2.620-2.690 GHz of mobile LTE applications. The second part explores the development of the behavioural model for the class AB power amplifier. A particular novel, Hammerstein-Wiener based model is proposed to describe the dynamic nonlinear behaviour of the power amplifier. The RF power amplifier nonlinear distortion is approximated using a new linear parameter approximation approach. The first and second-order Hammerstein-Wiener using the Normalised Least Mean Square Error (NLMSE) algorithm is used with the aim of easing the complexity of filtering process during linear memory cancellation. Moreover, an enhanced adaptive Wiener model is proposed to explore the nonlinear memory effect in the system. The proposed approach is able to balance between convergence speed and high-level accuracy when compared with behavioural modelling algorithms that are more complex in computation. Finally, the adaptive predistorter technique is implemented and verified in the OFDM transceiver test-bed. The results were compared against the computed one from MATLAB simulation for OFDM and 5G modulation transmitters. The results have confirmed the reliability of the model and the effectiveness of the proposed predistorter.Fundacão para a Ciência e a Tecnologia, Portugal, under European Union’s Horizon 2020 research and innovation programme ... grant agreement H2020-MSCA-ITN- 2016 SECRET-722424 I also acknowledge the role of the National Space Research and Development Agency (NASRDA) Sokoto State Government Petroleum Technology Trust Fund (PTDF

A fast engineering approach to high efficiency power amplifier linearization for avionics applications

This PhD thesis provides a fast engineering approach to the design of digital predistortion (DPD) linearizers from several perspectives: i) enhancing the off-line training performance of open-loop DPD, ii) providing robustness and reducing the computational complexity of the parameters identification subsystem and, iii) importing machine learning techniques to favor the automatic tuning of power amplifiers (PAs) and DPD linearizers with several free-parameters to maximize power efficiency while meeting the linearity specifications. One of the essential parts of unmanned aerial vehicles (UAV) is the avionics, being the radio control one of the earliest avionics present in the UAV. Unlike the control signal, for transferring user data (such as images, video, etc.) real-time from the drone to the ground station, large transmission rates are required. The PA is a key element in the transmitter chain to guarantee the data transmission (video, photo, etc.) over a long range from the ground station. The more linear output power, the better the coverage or alternatively, with the same coverage, better SNR allows the use of high-order modulation schemes and thus higher transmission rates are achieved. In the context of UAV wireless communications, the power consumption, size and weight of the payload is of significant importance. Therefore, the PA design has to take into account the compromise among bandwidth, output power, linearity and power efficiency (very critical in battery-supplied devices). The PA can be designed to maximize its power efficiency or its linearity, but not both. Therefore, a way to deal with this inherent trade-off is to design high efficient amplification topologies and let the PA linearizers take care of the linearity requirements. Among the linearizers, DPD linearization is the preferred solution to both academia and industry, for its high flexibility and linearization performance. In order to save as many computational and power resources as possible, the implementation of an open-loop DPD results a very attractive solution for UAV applications. This thesis contributes to the PA linearization, especially on off-line training for open-loop DPD, by presenting two different methods for reducing the design and operating costs of an open-loop DPD, based on the analysis of the DPD function. The first method focuses on the input domain analysis, proposing mesh-selecting (MeS) methods to accurately select the proper samples for a computationally efficient DPD parameter estimation. Focusing in the MeS method with better performance, the memory I-Q MeS method is combined with feature extraction dimensionality reduction technique to allow a computational complexity reduction in the identification subsystem by a factor of 65, in comparison to using the classical QR-LS solver and consecutive samples selection. In addition, the memory I-Q MeS method has been proved to be of crucial interest when training artificial neural networks (ANN) for DPD purposes, by significantly reducing the ANN training time. The second method involves the use of machine learning techniques in the DPD design procedure to enlarge the capacity of the DPD algorithm when considering a high number of free parameters to tune. On the one hand, the adaLIPO global optimization algorithm is used to find the best parameter configuration of a generalized memory polynomial behavioral model for DPD. On the other hand, a methodology to conduct a global optimization search is proposed to find the optimum values of a set of key circuit and system level parameters, that properly combined with DPD linearization and crest factor reduction techniques, can exploit at best dual-input PAs in terms of maximizing power efficiency along wide bandwidths while being compliant with the linearity specifications. The advantages of these proposed techniques have been validated through experimental tests and the obtained results are analyzed and discussed along this thesis.Aquesta tesi doctoral proporciona unes pautes per al disseny de linealitzadors basats en predistorsió digital (DPD) des de diverses perspectives: i) millorar el rendiment del DPD en llaç obert, ii) proporcionar robustesa i reduir la complexitat computacional del subsistema d'identificació de paràmetres i, iii) incorporació de tècniques d'aprenentatge automàtic per afavorir l'auto-ajustament d'amplificadors de potència (PAs) i linealitzadors DPD amb diversos graus de llibertat per poder maximitzar l’eficiència energètica i al mateix temps acomplir amb les especificacions de linealitat. Una de les parts essencials dels vehicles aeris no tripulats (UAV) _es l’aviònica, sent el radiocontrol un dels primers sistemes presents als UAV. Per transferir dades d'usuari (com ara imatges, vídeo, etc.) en temps real des del dron a l’estació terrestre, es requereixen taxes de transmissió grans. El PA _es un element clau de la cadena del transmissor per poder garantir la transmissió de dades a grans distàncies de l’estació terrestre. A major potència de sortida, més cobertura o, alternativament, amb la mateixa cobertura, millor relació senyal-soroll (SNR) la qual cosa permet l’ús d'esquemes de modulació d'ordres superiors i, per tant, aconseguir velocitats de transmissió més altes. En el context de les comunicacions sense fils en UAVs, el consum de potència, la mida i el pes de la càrrega útil són de vital importància. Per tant, el disseny del PA ha de tenir en compte el compromís entre ample de banda, potència de sortida, linealitat i eficiència energètica (molt crític en dispositius alimentats amb bateries). El PA es pot dissenyar per maximitzar la seva eficiència energètica o la seva linealitat, però no totes dues. Per tant, per afrontar aquest compromís s'utilitzen topologies amplificadores d'alta eficiència i es deixa que el linealitzador s'encarregui de garantir els nivells necessaris de linealitat. Entre els linealitzadors, la linealització DPD és la solució preferida tant per al món acadèmic com per a la indústria, per la seva alta flexibilitat i rendiment. Per tal d'estalviar tant recursos computacionals com consum de potència, la implementació d'un DPD en lla_c obert resulta una solució molt atractiva per a les aplicacions UAV. Aquesta tesi contribueix a la linealització del PA, especialment a l'entrenament fora de línia de linealitzadors DPD en llaç obert, presentant dos mètodes diferents per reduir el cost computacional i augmentar la fiabilitat dels DPDs en llaç obert. El primer mètode se centra en l’anàlisi de l’estadística del senyal d'entrada, proposant mètodes de selecció de malla (MeS) per seleccionar les mostres més significatives per a una estimació computacionalment eficient dels paràmetres del DPD. El mètode proposat IQ MeS amb memòria es pot combinar amb tècniques de reducció del model del DPD i d'aquesta manera poder aconseguir una reducció de la complexitat computacional en el subsistema d’identificació per un factor de 65, en comparació amb l’ús de l'algoritme clàssic QR-LS i selecció de mostres d'entrenament consecutives. El segon mètode consisteix en l’ús de tècniques d'aprenentatge automàtic pel disseny del DPD quan es considera un gran nombre de graus de llibertat (paràmetres) per sintonitzar. D'una banda, l'algorisme d’optimització global adaLIPO s'utilitza per trobar la millor configuració de paràmetres d'un model polinomial amb memòria generalitzat per a DPD. D'altra banda, es proposa una estratègia per l’optimització global d'un conjunt de paràmetres clau per al disseny a nivell de circuit i sistema, que combinats amb linealització DPD i les tècniques de reducció del factor de cresta, poden maximitzar l’eficiència de PAs d'entrada dual de gran ample de banda, alhora que compleixen les especificacions de linealitat. Els avantatges d'aquestes tècniques proposades s'han validat mitjançant proves experimentals i els resultats obtinguts s'analitzen i es discuteixen al llarg d'aquesta tesi