Per Niklas Landin

On the impact of hardware impairments on massive MIMO

Massive multi-user (MU) multiple-input multiple-output (MIMO) systems are one possible key technology for next generation wireless communication systems. Claims have been made that massive MU-MIMO will increase both the radiated energy efficiency as well as the sum-rate capacity by orders of magnitude, because of the high transmit directivity. However, due to the very large number of transceivers needed at each base-station (BS), a successful implementation of massive MU-MIMO will be contingent on of the availability of very cheap, compact and power-efficient radio and digital-processing hardware. This may in turn impair the quality of the modulated radio frequency (RF) signal due to an increased amount of power-amplifier distortion, phase-noise, and quantization noise. In this paper, we examine the effects of hardware impairments on a massive MU-MIMO single-cell system by means of theory and simulation. The simulations are performed using simplified, well-established statistical hardware impairment models as well as more sophisticated and realistic models based upon measurements and electromagnetic antenna array simulations.

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.

Comparison of evaluation criteria for power amplifier behavioral modeling

In this paper different evaluation criteria for power amplifier behavioral modeling are studied and evaluated using measuremed data. The figure-of-merits are calculated from complex-envelope data of a sampled power amplifier intended for 3G. Both time- and frequency domain methods are included in the study. It is found that a model evaluation criterion should have ability to capture both the linear and nonlinear distortion as well as the memory effects in the power amplifier. The normalized mean square error (NMSE) and the weighted error-to-signal power ratio (WE-SPR) are found to be the strongest candidates for capturing the in-band and the out-of-band errors, respectively. Both are also independent of power amplifier technology and stimuli input.

Behavioral Modeling and Linearization of Crosstalk and Memory Effects in RF MIMO Transmitters

This paper proposes three novel models for behavioral modeling and digital pre-distortion (DPD) of nonlinear 2 × 2 multiple-input multiple-output (MIMO) transmitters in the presence of crosstalk. The proposed models are extensions of the single-input single-output generalized memory polynomial model. Three types of crosstalk effects were studied and characterized as linear, nonlinear, and nonlinear & linear crosstalk. A comparative study was performed with previously published models for the linearization of crosstalk in a nonlinear 2 × 2 MIMO transmitter. The experiments indicate that, depending on the type of crosstalk, the selection of the correct model in the transmitter is necessary for behavioral modeling and sufficient DPD performance. The effects of coherent and partially noncoherent signal generation on the performance of DPD were also studied. For crosstalk levels of -30 dB, the difference in the normalized mean square error and adjacent channel power ratio was found to be 3-4 dB between coherent and partially noncoherent signal generation.

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.

Noise impact on the identification of digital predistorter parameters in the indirect learning architecture

The indirect learning architecture (ILA) is the most used methodology for the identification of Digital Predistorter (DPD) functions for nonlinear systems, particularly for high power amplifiers. The ILA principle works in black box modeling relying on the inversion of input and output signals of the nonlinear system, such that the inverse is estimated. This paper presents the impact of disturbances, such as noise in the DPD identification. Experiments were performed with a state-of-art Doherty power amplifier intended for base station operation in current telecommunication wireless networks. As expected, a degradation in the performance of the DPD (measured in normalized mean square error (NMSE)) is found in our experiments. However, adjacent channel power ratio (ACPR) can be a misleading figure of merit showing improvement in the performance for wrongly estimated DPD functions.

Wideband characterization of power amplifiers using undersampling

In this paper a radio frequency power amplifier is measured and characterized by the use of undersampling based on the generalized Zhu-Frank sampling theorem. A test system has been designed allowing the bandwidth of the stimuli signal to be 100 MHz in the characterization process. That would not be possible with any vector signal analyzer on the market. One of the more challenging problem within the proposed concept is the model validation process. Here, two different techniques for model validation are proposed, the multitone and the spectrum scan validation methods.

Linearization of dual-input Doherty power amplifiers

This paper studies the linearity of dual-input Doherty power amplifiers. We propose a linearization scheme that uses a combination of an efficiency-optimized static splitter and a vector-switched digital predistorter. The performance of the proposed linearization scheme is evaluated on a dual-input Doherty power amplifier operating at 2.0 GHz with 42 dBm peak output power. Experimental results show that the proposed linearization scheme achieves a normalized mean square error of -43.7 dB and an adjacent channel power ratio of -55.8 dBc with a power added efficiency of 42.4%.

Peak-Power Controlled Digital Predistorters for RF Power Amplifiers

This paper investigates the issue of “predistorter blow-up,” i.e., uncontrolled peak expansion caused by the predistorter. To control the peak expansion, an extension of the multistep indirect learning architecture (MS-ILA) is proposed by adding a constraint that describes the allowed peak power of the predistortion signal. The resulting optimization problem is shown to be convex and an optimization method is formulated to solve it. Measurements on a class-AB power amplifier (PA) using orthogonal frequency-division multiplex signals show that the peak control works as intended and prevents the MS-ILA from generating high peaks when the PA is operated in compression. The restriction on the peak power also prevents the performance degradation occurring due to the “blow-up” problem. This makes the proposed controlled MS-ILA a safer option than the standard MS-ILA. In addition to controlling the peak input power to the PA, using the proposed method it was possible to increase the output power by 1.3 dB while fulfilling requirements of less than 40-dB adjacent channel leakage power ratio, compared to the standard five-step MS-ILA. Reduced peak power also reduces the requirements on linearity in signal generation, resolution in computations, and analog-to-digital and digital-to-analog conversion.

Two novel memory polynomial models for modeling of RF power amplifiers

Two novel memory polynomial models are derived based on physical knowledge of a general power amplifier (PA). The derivations are given in detail to facilitate derivations of other model structures. The model error in terms of normalized mean square error (NMSE) and adjacent channel error power ratio (ACEPR) of the novel model structures are compared to that of established models based on the number of parameters using data measured on two different amplifiers, one high-power base-station PA and one low-power general purpose amplifier. The novel models show both lower NMSE and ACEPR for any chosen number of parameters compared to the established models. The low model errors make the novel models suitable candidates for both modeling and digital predistortion.