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In modern communication systems advanced techniques such as digital predistortion (DPD) are required to satisfy stringent demands on transmitter linearity and efficiency. DPD, however, increases the hardware and computational complexity of transmitters. In this article we show that the computational complexity of DPD adaptation can be drastically reduced if a low number of samples is properly selected for DPD adaptation. For this purpose we propose methods of sample selection for DPD adaptation. Among the proposed methods, the highest computational complexity reduction is achieved by a method based on the histogram of signal magnitudes, optimised with respect to characteristics of the radio frequency power amplifier and of the transmitted signal. Simulations indicate that this proposed method can reduce the computational complexity of DPD adaptation by a factor of up to 400 while the linearisation performance of conventional methods is preserved. Besides simulations for three models of distinct power amplifiers, measurements on a real power amplifier further verify the linearisation capabilities of the proposed methods.

This paper presents a performance comparison between the single-carrier frequency-domain equalization (SCFDE) system and an experimental, linear minimum-mean-square-error (LMMSE) based single-carrier system operating in the time domain (SC-TDE). For the absence of cyclic-prefix (CP) and frame processing, the SC-TDE presents significant increase in spectral efficiency while marginally increasing the bit-error-rate (BER). Base-band models of both systems are derived together with analytical formulas of mean-square-error (MSE) and average BER which are used for system comparison in the frequency-selective Rayleigh channel.

Over the past five years, there has been a focus on creating deep learning architectures for radio modulation recognition. Many of these architectures are either convolutional neural networks, recurrent neural networks, or the combination of those and contain over a million trainable parameters. This paper focuses on a reduction of the parameters in the networks while maintaining comparable accuracy. We introduce two architectures, a convolutional neural network (CNN) and a convolutional long-short-term deep neural network (CLDNN). The results presented in this paper are comparable to or even outperform architectures with up to 20 times more parameters. Significant parameter reduction presented in this paper is more suitable for later model deployment on an embedded platform with limited memory size and computational power. We further discuss other needed steps before implementing the architectures on an embedded platform other than the model size reduction at the end of the paper.

Millimeter waves will play an important role in communication systems in the near future. On the one hand, the bandwidths available at millimeter-wave frequencies allow for elevated data rates, but on the other hand, the wide bandwidth accentuates the effects of wireless front-end impairments on transmitted waveforms and makes their compensation more difficult. Research into front-end impairment compensation in millimeter-wave frequency bands is currently being carried out, mainly using expensive laboratory setups consisting of universal signal generators, spectral analyzers and high-speed oscilloscopes. This paper presents a detailed description of an in-house built MATLAB-controlled 60 GHz measurement test-bed developed using relatively inexpensive hardware components that are available on the market and equipped with digital compensation for the most critical front-end impairments, including the digital predistortion of the power amplifier. It also demonstrates the potential of digital predistortion linearization on two distinct 60 GHz power amplifiers: one integrated in a direct-conversion transceiver and an external one with 24 dBm output power.

A GaN based dual-input digital Doherty power amplifier is demonstrated for small SATCOM terminals for use in lightweight UAV applications. A novel algorithm is presented that helps operate the Doherty power amplifier at optimum conditions in terms of efficiency and/or linearity. Digital pre-distortion linearization is deployed together with the digital Doherty to achieve low spectral regrowth in narrowband satellite uplink.

Interference between users in adjacent channels negatively affects throughput of mobile networks. In this paper we aim at cancellation of interference caused by a nonlinear power amplifier in a generalized orthogonal frequency division system. We propose an interference cancellation method to subtract these out-of-band emissions from the received signal. In contrast to state-of-the-art methods, our proposed method employs over-the-air estimation of power amplifier model parameters together with a particular frequency domain filtering method that allows to generate the required training data. The proposed interference cancellation method is also verified by an experiment on a software defined radio test bench.

This paper deals with a pilot power allocation in the orthogonal time-frequency space (OTFS) modulation system. In our simulations, the pilot is used for a channel estimation. Subsequently, the channel effects are equalized via a deconvolution. It is shown that proper solution of the pilot power allocation problem has significant impact on the bit-error rates. We also show that the increase of the pilot power causes notable increase of the peak-to-average (PAPR) power ratio. In consequence, high PAPR is one of the limiting factors of the pilot power increase. Considering a non-linear power amplifier, high PAPR is demonstrated to limit the OTFS bit-error rate performance.

Linearization of power amplifiers by means of digital predistortion has widely been researched for more than last two decades, with applications to microwave bands. With increasing demands for spectrum, the use of millimeter frequencies is expected in the future. In order to implement digital predistortion at millimeter-wave frequencies, well calibrated transmitter and feedback receiver is necessary. This paper describes the most important signal processing algorithms and their practical implementation in in-house built MATLAB-controlled setup aimed for research and evaluation for digital predistortion algorithms in 60 GHz band.

In this paper, we present the quantitative study of the effects of the selected most important transceiver imperfections on the performance of digital baseband pre-distortion linearizers. Starting from the power amplifier model identification based on the measured data captured using the low-cost software defined radio platform (USRP), we evaluate the performance in term of out-of-band signal emissions at the power amplifier output for the case of typical representative of signals envisaged for fifth generation (5G) of mobile communication systems — filtered OFDM. The results are presented for the values of imperfections (IQ imbalances, DC offset) swept within the range of values corresponding to simple, commercially available wireless front-ends. We highlight the fact that the front-end imperfections can significantly affect the influence of pre-distorters.

This paper presents a report on mutual information based on measured indoor millimeter-wave (mmWave) channels with multiple antennas at input and output (MIMO). We show that for fixed indoor communication systems, an optimal antenna element spacing can be found such that the measured mutual information almost reaches the capacity of a perfectly orthogonal (theoretical) MIMO channel (with the same number of receiver (RX) and transmitter (TX) antennas). Secondly, we present, that even though the measured channel transfer functions (CTFs) exhibit large fluctuations (i.e., temporal dispersion), the mutual information is mainly determined by the mean singular value of the line-of-sight (LOS) components. Due to their strong variations over frequency mmWave channels are tedious when describing them with classical methods in the frequency domain. An approximation by numerous flat subbands leads to an error in mutual information (MI) by 2bit/s/Hz (for 80% probability). In comparison, our proposed method in the time domain, however, offers a notably smaller error (1bit/s/Hz for 80% probability).

As the data throughput is still increased in the wireless communication systems, it is required to efficiently utilise the radio frequency spectrum which usually requires linear transmitters. Consequently methods as a digital predistortion (DPD) are developed to linearise nonlinear power amplifiers. To extract precise parameters for the DPD it is essential to finely synchronise measured feedback signal with the known transmitted signal. In this paper we propose an analytical method for the fractional sample period time synchronisation suitable for DPD signals. Finally benefits of the proposed method are presented on results of its usage for the DPD linearisation using a measurement test-bed.

The wireless devices classification based on the transmitter imperfections is one of the promising ways to provide additional security to future wireless communication networks. Although several aspects, both theoretical and practical, have been studied recently, there are no studies investigating the degradation of such authentication due to the environmental changes, such as the device temperature. We present the initial results of measurements of RF-classification method based on the Gaussian mixture model classifier under the influence of temperature variations. With the use of the widely used USRP devices, we also try to estimate which of the RF impairments is sensitive to such changes.

Generalized Memory Polynomial (GMP) models are widely used for the linearization of power amplifiers. They offer a good tradeoff between linearization performance and implementation complexity. Their structure is defined by 8 integer parameters representing different non-linearity orders and memory lengths. These 8 degrees of freedom allow achieving very good linearization performance with a small number of coefficients. But the optimal sizing (determination of the 8 parameters) of such models could require huge computation, for instance, if these 8 parameters are bounded between 1 and 10, there are 108 models to test using an exhaustive search, which is very computationally heavy and time consuming. Therefore optimization algorithms are needed to search for a GMP model structure which provides a good tradeoff between modeling accuracy and complexity. In this paper, we compare two heuristic optimization algorithms, hill-climbing and integer genetic algorithms, in terms of convergence speed, and optimality of the obtained solution regarding the defined criterion. They are evaluated using data measurements from an LDMOS Doherty Power Amplifier dedicated to base stations. The results show that both algorithms allow decreasing very significantly the searching time while giving optimal or close to optimal solutions. Compared with hill-climbing, the genetic approach leads to a more difficult control and interpretation of the path followed by the search algorithm since it is based on random operations (crossovers and mutations).

Novel multicarrier waveforms, such as, Filter Bank Multi Carrier promise lower out-of-band emissions compared to the currently used Orthogonal Frequency Division Multiplex technique. Unfortunately, the power amplifier nonlinearity in the transmitter can significantly degrade the FBMC spectrum wiping-out the differences between these two approaches. In order to compensate for nonlinearities and memory effects introduced by power amplifiers, the digital predistortion is a widely used technique, but also the predistortion itself can significantly be influenced by the transceiver imperfections. This paper thus laid great emphasis on the evaluation of transceiver imperfections and uncertainties on the overall performance of digital predistortion driven with FBMC signals.

In this paper we would like to point out one open issue related with filtered bank multi-carrier signals (FBMC). The experiment was trying to calculate filter coefficients for FBMC signals where non-linear power amplifiers were employed. As one of the merits that have been used were error vector magnitude (EVM) and adjacent channel power (ACP). We have demonstrated that proper calculation of coefficients could improve the merits significantly.

In this paper, electrothermal field phenomena in electronic components are considered. This coupling is tackled by multiphysical field simulations using the Finite Integration Technique (FIT). In particular, the design of bonding wires with respect to thermal degradation is investigated. Instead of resolving the wires by the computational grid, lumped element representations are introduced as point-to-point connections in the spatially distributed model. Fabrication tolerances lead to uncertainties of the wires' parameters and influence the operation and reliability of the final product. Based on geometric measurements, the resulting variability of the wire temperatures is determined using the stochastic electrothermal field-circuit model., Comment: submitted to DATE, available at http://ieeexplore.ieee.org/document/7459510/