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

An efficient CS-CPWL Based Predistorter

We study the performance of Hammerstein predistorters (PD) to model and compensate nonlinear effects produced by a high power amplifier with memory. A novel Hammerstein model is introduced that includes, as the basic static nonlinearity, the complex simplicial canonical piecewise linear (CS-CPWL) description. Previous results by the authors have shown that the use of this kind of static nonlinearity leads to an efficient representation of basic nonlinear models. Furthermore, different tradeoffs between modeling capability and performance are considered.Fil: Bruno, Marcelo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Cousseau, Juan Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Werner, Stefan. Helsinki University Of Technology. Departament Of Signal Processing And Acoutics; FinlandiaFil: Figueroa, Jose Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Cheong, Mei Yen. Helsinki University Of Technology. Departament Of Signal Processing And Acoutics; FinlandiaFil: Wichman, R.. Helsinki University Of Technology. Departament Of Signal Processing And Acoutics; Finlandi

Prototype Implementation of Two Efficient Low-Complexity Digital Predistortion Algorithms

Predistortion (PD) lineariser for microwave power amplifiers (PAs) is an important topic of research. With larger and larger bandwidth as it appears today in modern WiMax standards as well as in multichannel base stations for 3GPP standards, the relatively simple nonlinear effect of a PA becomes a complex memory-including function, severely distorting the output signal. In this contribution, two digital PD algorithms are investigated for the linearisation of microwave PAs in mobile communications. The first one is an efficient and low-complexity algorithm based on a memoryless model, called the simplicial canonical piecewise linear (SCPWL) function that describes the static nonlinear characteristic of the PA. The second algorithm is more general, approximating the pre-inverse filter of a nonlinear PA iteratively using a Volterra model. The first simpler algorithm is suitable for compensation of amplitude compression and amplitude-to-phase conversion, for example, in mobile units with relatively small bandwidths. The second algorithm can be used to linearise PAs operating with larger bandwidths, thus exhibiting memory effects, for example, in multichannel base stations. A measurement testbed which includes a transmitter-receiver chain with a microwave PA is built for testing and prototyping of the proposed PD algorithms. In the testing phase, the PD algorithms are implemented using MATLAB (floating-point representation) and tested in record-and-playback mode. The iterative PD algorithm is then implemented on a Field Programmable Gate Array (FPGA) using fixed-point representation. The FPGA implementation allows the pre-inverse filter to be tested in a real-time mode. Measurement results show excellent linearisation capabilities of both the proposed algorithms in terms of adjacent channel power suppression. It is also shown that the fixed-point FPGA implementation of the iterative algorithm performs as well as the floating-point implementation

Adaptive Piecewise Linear Predistorters for Nonlinear Power Amplifiers With Memory

We propose novel direct and indirect learning predistorters (PDs) that employ a new baseband simplicial canonical piecewise linear (SCPWL) function. The performance of the proposed PDs is easily controlled by varying the number of segments of the SCPWL function. When comparing to polynomial-based PDs, our SCPWL-based PDs are more robust for modeling strong nonlinearities and are less sensitive to input noise. In particular, we show that noise appearing in the feedback path of an indirect learning SCPWL-PD has negligible effect on the performance while the polynomial counterpart suffers from a noise-induced coefficient bias. We consider adaptive implementations of both Hammerstein-based and memory-based SCPWL PDs; the former featuring less parameters to be identified while the latter renders more straightforward parameter identification. When deriving the PD algorithms, we avoid a separate PA identification step which allows for a true real-time, or sample-by-sample, implementation without an alternating PA and PD identification procedure. However, to arrive at efficient sample-by-sample algorithms for Hammerstein PDs we need to bypass the problem of the associated nonconvex cost function. This is done by employing a modified, linear-in-the-parameter, Wiener model whose parameters can be explicitly or implicitly used for both indirect and direct learning. Extensive simulations confirm that the proposed SCPWL PDs outperform their polynomial counterparts, especially when noise is present in the feedback path of the indirect learning structure. The same is also verified by circuit level simulations on the Freescale MRF6S23100H class-AB PA in an 802.16d WiMAX system.Fil: Cheong, Mei Yen. Alto University. School of Electrical Engineering; FinlandiaFil: Werner, Stefan. Alto University. School of Electrical Engineering; FinlandiaFil: Bruno, Marcelo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Figueroa, Jose Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Cousseau, Juan Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Wichman, Risto Ilari. Alto University. School of Electrical Engineering; Finlandi

Deficiency of the intestinal enzyme acyl CoA:monoacylglycerol acyltransferase-2 protects mice from metabolic disorders induced by high-fat feeding.

Animals are remarkably efficient in absorbing dietary fat and assimilating this energy-dense nutrient into the white adipose tissue (WAT) for storage. Although this metabolic efficiency may confer an advantage in times of calorie deprivation, it contributes to obesity and associated metabolic disorders when dietary fat is abundant. Here we show that the intestinal lipid synthesis enzyme acyl CoA:monoacylglycerol acyltransferase-2 (MGAT2) has a crucial role in the assimilation of dietary fat and the accretion of body fat in mice. Mice lacking MGAT2 have a normal phenotype on a low-fat diet. However, on a high-fat diet, MGAT2-deficient mice are protected against developing obesity, glucose intolerance, hypercholesterolemia and fatty livers. Caloric intake is normal in MGAT2-deficient mice, and dietary fat is absorbed fully. However, entry of dietary fat into the circulation occurs at a reduced rate. This altered kinetics of fat absorption apparently results in more partitioning of dietary fat toward energy dissipation rather than toward storage in the WAT. Thus, our studies identify MGAT2 as a key determinant of energy metabolism in response to dietary fat and suggest that the inhibition of this enzyme may prove to be a useful strategy for treating obesity and other metabolic diseases associated with excessive fat intake