Niclas Björsell

Performance Evaluation of Peak-to-Average Power Ratio Reduction and Digital Pre-Distortion for OFDM Based Systems

In this paper, we evaluate the effect of applying peak-to-average power ratio (PAPR) reduction and digital pre-distortion (DPD) on two types of radio frequency power amplifiers when an orthogonal frequency division multiplexing (OFDM) signal is used. The power amplifiers under test are a standard class-AB amplifier and a Doherty amplifier. The PAPR reduction methods are based on a state-of-the art convex optimization formulation and on the standard clipping and filtering technique. The DPD method consists of modeling the behavior of the power amplifier using a parallel Hammerstein model, and then extracting the inverse parameters based on the indirect learning architecture. To achieve better DPD performance, extracting the DPD parameters based on multiple-step iterations is investigated. The cases where PAPR reduction and DPD are applied separately and combined are studied and investigated. Power amplifier figures of merit are evaluated. Good performance is shown when combining both pre-processing techniques up to a certain operating point where DPD performance deteriorates due to generation of strong peaks in the signal. In addition, a difference in the power amplifier behavior is reported and analyzed.

Measuring Volterra kernels of analog to digital converters using a stepped three-tone scan

The Volterra theory can be used to mathematically model nonlinear dynamic components such as analog-to-digital converters (ADCs). This paper describes how frequency-domain Volterra kernels of an ADC are determined from measurements. The elements of the Volterra theory are given, and practical issues are considered, such as methods for signal conditioning and finding the appropriate test signals scenario and suitable sampling frequency. The results show that, for the used pipeline ADC, the frequency dependence is significantly stronger for second-order difference products than for sum products and the linear frequency dependence was not as pronounced as that of the second-order Volterra kernel. It is suggested that the Volterra kernels have the symmetry properties of a specific box model, namely, the parallel Hammerstein system.

Achievable ADC performance by postcorrection utilizing dynamic modeling of the integral nonlinearity

There is a need for a universal dynamic model of analog-to-digital converters (ADCs) aimed for postcorrection. However, it is complicated to fully describe the properties of an ADC by a single model. An alternative is to split up the ADC model in different components, where each component has unique properties. In this paper, a model based on three components is used, and a performance analysis for each component is presented. Each component can be postcorrected individually and by the method that best suits the application. The purpose of postcorrection of an ADC is to improve the performance. Hence, for each component, expressions for the potential improvement have been developed. The measures of performance are total harmonic distortion (THD) and signal to noise and distortion (SINAD), and to some extent spurious-free dynamic range (SFDR).

Postcorrection of Pipelined Analog–Digital Converters Based on Input-Dependent Integral Nonlinearity Modeling

The integral nonlinearity (INL) is used for the postcorrection of analog-digital converters (ADCs). An input-frequency-dependent INL model is developed for the postcorrection. The model consists of a static term that is dependent on the ADC output code and a dynamic term that has an additional dependence on the input frequency. The concept of ADC digital output postcorrection by INL is first introduced. The INL model is subtracted from the digital output for postcorrection. The static compensation part is made by adjacent sets of gains and offsets, where each set corrects a range of output codes. The dynamic information, i.e., the frequency dependence of the INL dynamic component is used to construct a set of filter blocks that performs ADC compensation in the time domain. The compensation scheme is applied to the measured data of two ADCs of the same type (Analog Devices AD9430). Performance improvements in terms of spurious-free dynamic range, signal-to-noise and distortion ratio, intermodulation distortion, and noise are obtained. The dynamic compensation part, due to its frequency dependence, can be generalized; hence, a postcorrection block model can be used for compensating multiple ADCs of the same type.

Blind Spectrum Sensing for Cognitive Radios Using Discriminant Analysis: A Novel Approach

In this paper, we present a new spectrum sensing technique for cognitive radios based on discriminant analysis called spectrum discriminator. The presented technique uses the knowledge of the noise uncertainty and a probabilistic validation to overcome the limitations of the discriminant analysis. A comparative study between the proposed technique and the maximum-minimum eigenvalue detection has been performed based on two performance metrics: the probability of false alarm and the probability of detection. The spectrum discriminator has been further developed to a peel-off technique where all primary users can be detected. The performance of the spectrum discriminator and the peel-off technique has been tested on simulations and experimentally verified. The comparative study is based on simulations as well as measurements.

Peak-Power Controlling Technique for Enhancing Digital Pre-Distortion of RF Power Amplifiers

In this paper, we present a method to limit the generation of signal peak power at the output of a digital pre-distorter that is applied to a RF power amplifier (PA) operating in strong compression. The method can be considered as a joint crest-factor reduction and digital pre-distortion (DPD). A challenging characteristic of DPD when applied to a PA in strong compression is the generation of relatively high peaks due to the DPD expansion behavior. Such high peaks generation, which may be physically unrealistic, can easily damage the amplification system. Such a phenomenon, referred in this study as DPD-avalanche, is more noticed when the signal exciting the PA is compressed due to crest-factor reduction. The suggested method for controlling such DPD-avalanche is based on shaping the input signal to the DPD in such a way to keep the pre-distorted signal peak power below or near the maximum allowed peak power of the PA. The suggested method is tested experimentally on a Class-AB and a Doherty PA when excited with a wideband orthogonal frequency-division multiplexing (OFDM) signal. Scenarios for an OFDM signal with and without crest-factor reduction are evaluated. Measurement results when using the proposed DPD-avalanche controller show smooth deterioration of the in-band and out-of-band linearity compared to steep deterioration when no controller is used. In addition, the suggested controller offers a higher operating power range of the DPD while fulfilling out-of-band distortion requirements and preserving low in-band error.

Model based dynamic characterization of analog-digital-converters at radio frequency

A dynamic characterization of analog-digital converter integral nonlinearity (INL) is considered. When using a plurality of test frequencies in the measurement set-up, the dynamic errors of the converter are characterized. The INL is modeled by low and high code components - LCF and HCF, respectively. The LCF and HCF are parameterized and a least squares method is derived for the estimation of the parameter values from obtained measurements. A closed form solution to the estimation problem is derived and its performance is illustrated by a numerical example. The proposed method is believed to be fruitful in wide-band characterization of analog-digital converters at radio frequency, and thus of importance for the evaluation of modern and future wireless communication systems.

Energy and Eigenvalue Based Combined Fully Blind Self Adapted Spectrum Sensing Algorithm

In this paper, a comparison between energy and maximum-minimum eigenvalue (MME) detectors is performed. The comparison has been made concerning the sensing complexity and the sensing accuracy in terms of the receiver operating characteristic (ROC) curves. The impact of the signal bandwidth compared with the observation bandwidth is studied for each detector. For the energy detector, the probability of detection increases monotonically with the increase in the signal bandwidth. For the MME detector, an optimal value of the ratio between the signal bandwidth and the observation bandwidth is found to be 0.5 when reasonable values of the system dimensionality are used. Based on the comparison findings, a combined two-stage detector is proposed. The combined detector performance is evaluated based on simulations and measurements. The combined detector achieves better sensing accuracy than the two individual detectors with complexity that lies in between the two individual complexities. The combined detector is fully blind and self-adapted as the MME detector estimates the noise and feeds it back to the energy detector. The performance of the noise estimation process is evaluated in terms of the normalized mean square error (NMSE).

A test-bed designed to utilize Zhu's general sampling theorem to characterize power amplifiers

Characterizing power amplifiers require test set-ups with performance superior to the power amplifiers. A commonly used method is to use an IQ-demodulator. However, problem arises due to imperfections in the demodulator such as IQ-imbalance; an alternative method is to use a direct down converter to intermediate frequency. The drawback then is the limited bandwidth. However, the required bandwidth of the ADC does not need to be exceptional. According to Zhus general sampling theorem is it enough to sample the output signal at the Nyquist rate of the input. However, even though the required sampling rate is reduced the demands on the analog bandwidth remains. Unfortunately, commercially available instruments such as vector signal analyzers can not be used for this purpose since their analog bandwidth is too small. In this paper a test-bed is designed to utilize the Zhus general sampling theorem. The RF front-end has frequency range of 500 MHz - 2.7 GHz and a bandwidth of 1 GHz. All performance data are verified with measurements.

Spectrum sensing through spectrum discriminator and maximum minimum eigenvalue detector: A comparative study

In this paper we present a new spectrum sensing technique for cognitive radios based on discriminant analysis called spectrum discriminator and compare it with the maximum minimum eigenvalue detector. The common feature between those two techniques is that neither prior knowledge about the system noise level nor the primary user signal, that might occupy the band under sensing, is required. Instead the system noise level will be derived from the received signal. The main difference between both techniques is that the spectrum discriminator is a non-parametric technique while the maximum minimum eigenvalue detector is a parametric technique. The comparative study between both has been done based on two performance metrics: the probability of false alarm and the probability of detection. For the spectrum discriminator an accuracy factor called noise uncertainty is defined as the level over which the noise energy may vary. Simulations are performed for different values of noise uncertainty for the spectrum discriminator and different values for the number of received samples and smoothing factor for the maximum minimum eigenvalue detector.