Search

The power amplifier (PA) is the most critical subsystem in terms of linearity and power efficiency. Digital predistortion (DPD) is commonly used to mitigate nonlinearities while the PA operates at levels close to saturation, where the device presents its highest power efficiency. Since the DPD is generally based on Volterra series models, its number of coefficients is high, producing ill- conditioned and over-fitted estimations. Recently, a plethora of techniques have been independently proposed for reducing their dimensionality. This paper is devoted to presenting a fair benchmark of the most relevant order reduction techniques present in the literature categorized by the following: (i) greedy pursuits, including Orthogonal Matching Pursuit (OMP), Doubly Orthogonal Matching Pursuit (DOMP), Subspace Pursuit (SP) and Random Forest (RF); (ii) regularization techniques, including ridge regression and least absolute shrinkage and selection operator (LASSO); (iii) heuristic local search methods, including hill climbing (HC) and dynamic model sizing (DMS); and (iv) global probabilistic optimization algorithms, including simulated annealing (SA), genetic algorithms (GA) and adaptive Lipschitz optimization (adaLIPO). The comparison is carried out with modeling and linearization performance and in terms of runtime. The results show that greedy pursuits, particularly the DOMP, provide the best trade-off between execution time and linearization robustness against dimensionality reduction. Ministerio de Ciencia, Innovación y Universidades TEC2017-83343-C4-2-R Unión Europea TEC2017-83343-C4-2-R Ministerio de Ciencia e Innovación PID2020-113832RB-C21 Generalitat de Catalunya 2017-SGR-813 Unión Europea 2017-SGR-813 Generalitat de Catalunya 2021-FI-B-137 Unión Europea 2021-FI-B-137

The power amplifier (PA) is the most critical subsystem in terms of linearity and power efficiency. Digital predistortion (DPD) is commonly used to mitigate nonlinearities while the PA operates at levels close to saturation, where the device presents its highest power efficiency. Since the DPD is generally based on Volterra series models, its number of coefficients is high, producing ill-conditioned and over-fitted estimations. Recently, a plethora of techniques have been independently proposed for reducing their dimensionality. This paper is devoted to presenting a fair benchmark of the most relevant order reduction techniques present in the literature categorized by the following: (i) greedy pursuits, including Orthogonal Matching Pursuit (OMP), Doubly Orthogonal Matching Pursuit (DOMP), Subspace Pursuit (SP) and Random Forest (RF), (ii) regularization techniques, including ridge regression and least absolute shrinkage and selection operator (LASSO), (iii) heuristic local search methods, including hill climbing (HC) and dynamic model sizing (DMS), and (iv) global probabilistic optimization algorithms, including simulated annealing (SA), genetic algorithms (GA) and adaptive Lipschitz optimization (adaLIPO). The comparison is carried out with modeling and linearization performance and in terms of runtime. The results show that greedy pursuits, particularly the DOMP, provide the best trade-off between execution time and linearization robustness against dimensionality reduction.

We demonstrate experimentally, for the first time, a distributed digital predistortion model for radio-over-fiber (RoF) downlink suitable for broadband 5G signals. The model reduces the analog-to-digital converter sampling frequency up to 1/3 of the required sampling in the example of the conventional memory polynomial DPD model, hence reducing demodulator costs and hardware complexity. This model also provides better intercarrier interference by reducing the adjacent channel leakage ratio (ACLR). This leads to reduced crosstalk, which impacts the multi-core and multimode RoF links. Long-term evolution advanced signals of a 256-quadratic-amplitude modulation of different bandwidths are used to validate the model. We achieved an average reduction of 2.5 dB in ACLR for signal bandwidths in the range 100–300 MHz while maintaining minimum error vector magnitude (EVM). The RoF downlink can deliver a peak bit rate of 2.016 Gb/s with a spectral efficiency of 6.72 b/Hz and EVM less than 4%.

Over the past half century, the improvements in spacecraft technology have been primarily in the areas of microelectronics for on-board processing, high frequency electronic devices, and integrated circuits for communications and navigation, solar cells and batteries for on-board power generation and storage among many others. Despite the fact that energy-storage technologies have advanced dramatically over the past years, the power consumption of on-board communications, sensors and digital signal processing systems is of paramount importance in battery or solar powered systems such as small satellites, HAPs or UAVs (drones). There is multiple applications that involves the use of these systems, e.g., Earth observation applications, surveillance, broadcast communications, scientific research, etc. In wireless communications, the power amplifier is a critical subsystem in the transmitter chain. Not only because it is one of the most power hungry devices that accounts for most of the direct current power consumption, but also because it is the main source of nonlinear distortion in the transmitter. Amplitude and phase modulated communications signals presenting high peak-to-average power ratio have a negative impact in the transmitter's power efficiency, because the PA has to be operated at high power back-off levels to avoid introducing nonlinear distortion. Digital predistortion (DPD) linearization is the most common and spread solution to cope with power amplifiers (PA) inherent linearity versus efficiency trade-off. When considering wide bandwidth signals, such as Doherty PAs, envelope tracking PAs or outphasing transmitters, the number of parameters required in the DPD model to compensate for both nonlinearities and memory effects can be very high. This has a negative impact in the DPD ceofficients extraction, because increases the computational complexity and drives to over-fitting and uncertainty. However, by applying dimensionality reduction techniques we can both avoid the numerical ill-conditioning of the estimation and reduce the number of coefficients of the DPD function, which ultimately impacts the baseband processing computational complexity and power consumption. In this Project, several dimensionality reduction techniques will be described and compared in terms of model order reduction capabilities and evaluation performance. In particular, some of the machine learning techniques for dimensionality reduction will be studied.

This paper evaluates the effect of the phase shift between the two control signals in a dual-input load modulated balanced amplifier (LMBA). While the drain efficiency of the LMBA is not significantly affected by the phase shift between the control signals, the LMBA linearity instead can be significantly degraded if the phase shift parameter is not properly chosen. Therefore, we can predict the optimal phase shift between both LMBA control signals to enhance linearity levels when considering a 20 MHz bandwidth LTE transmission over a center frequency of operation ranging from 1.8 GHZ to 2.5 GHz. Experimental results showing the LBMA linearity and power efficiency after digital predistortion (DPD) linearization are presented. Finally, additional key parameters contributing to improve the LBMA power efficiency are discussed. This work was supported in part by the Spanish Government and FEDER under Grant TEC2017-83343-C4-2-R and in part by the Generalitatde Catalunya under Grant 2017 SGR 813

"© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works." This paper presents a new variant of the orthogonal matching pursuit (OMP) algorithm for reducing the computational complexity of the digital predistortion (DPD) behavioral model in the forward path. The proposed spectral weighting OMP (SW-OMP) algorithm focuses on selecting the most relevant basis functions to compensate for the out-of-band residual distortion which may eventually be masked by the dominant in-band residual error. This basis selection is carried out in an off-line process that does not affect the computational complexity of the real-time closed-loop DPD but, on the contrary, reduces its complexity while enhancing the robustness. Experimental results show that by selecting the DPD coefficients with the SW-OMP, the inherent ACLR and NMSE degradation suffered when reducing the number of coefficients is mitigated under strong nonlinear operation, when compared to using the basis functions selected by the classical OMP algorithm.