Stable-Sample-Level-Based Digital Predistortion for 2-D Millimeter-Wave Phased Array Steering

Digital predistortion (DPD) of a phased array requires that multiple transmit paths must be measured by a feedback (FB) receiver (Rx), This paper proposes a simplified FB architecture to be used for phased array DPD. A single FB line collects the waveform samples from the parallel paths to the FB Rx. The gain and phase of the common FB line is obtained by comparing the FB outputs to over-the-air (OTA) measurements. The overall DPD training is done by collecting the PA outputs with the common FB line, post-equalizing them to model the main lobe waveform and combining them to create the object used for array DPD. The DPD performance was verified by OTA measurements with 5GNR waveform and 28 GHz 8-path phased array transmitter. The DPD trained through the local FB line with the proposed calibration method achieved < -45dB ACPR which was close to OTA DPD performance.

Unlocking the potential of millimeter-wave (mmWave) phased array systems demands robust nonlinear transmitter modeling and digital pre-distortion (DPD) techniques. In this article, we present a novel behavioral modeling approach and the corresponding linearization solution for beamforming antenna arrays comprising multiple and mutually interacting nonlinear power amplifier (PA) units. Our non-recursive transmitter model simplifies numerical evaluations across diverse phased array/multiple-input multiple-output (MIMO) configurations under crosstalk-induced load modulation. We introduce a novel, nonlinear forward model parameter identification algorithm tailored for crosstalk-prone array systems and applicable in arbitrary MIMO transmitter configurations, enabling precise modeling and characterization using over-the-air (OTA) observations. Furthermore, we propose an offline direct learning architecture based DPD method, harnessing the estimated nonlinear array forward model and specific beam-sweeping procedure, for linearizing phased arrays under severe load modulation. Numerical assessments across various scenarios demonstrate superior performance, while physical validation on a measurement test bench reinforces our methodology’s real-world applicability. Overall, this work paves the way for advanced nonlinear array transmitter optimization and linearization, vital for next-generation wireless communication networks.

This paper proposes a near-field dual-polarized feedback (NFDPF) technique for tracking real-time status and improving linearity of 5G RF-beamforming millimeter-wave (mm-wave) phased array. The feedback signal captured by NFDPF can be used for array self-calibration and digital predistortion (DPD). A 2×2 dual-polarized 24.25-27.5-GHz phased array prototype with integrated NFDPF is designed for validation of the scheme. Using NFDPF, four RF chains of dualpolarizations are calibrated which is similar to far-field (FF) calibration. Furthermore, the DPD is applied to validate the performance of feedback structure. Experimental results show that the adjacent channel power ratio (ACPR) and normalized mean squared error (NMSE) can be improved from -40dBc and 28dB to -51dBc and -40dB respectively, which is close to the performance of FF DPD.

Two novel models with lower complexity are proposed for linearization of 2×2 MIMO transmitters with nonlinear crosstalk. Crossover modified memory polynomial (COMMP) model is introduced as an extension of the 2-D modified memory polynomial (2-D MMP) model and COMMP-CPWL model is derived from the COMMP model but without high-order terms which are replaced by the canonical piecewise linear function. Genetic Algorithm (GA) is used to achieve optimal digital predistortion (DPD) performance and least-square (LS) algorithm is adopted to extract the models coefficients. Experimental results show that both models can achieve nearly the same DPD performance and model accuracy as the 2×2 Parallel Hammerstein (PH) model while requiring fewer model coefficients.

This paper proposes a digital predistortion (DPD) technique to improve linearization performance when the power amplifier (PA) is driven near the saturation region. The PA is a non-linear device in general, and the nonlinear distortion becomes severer as the output power increases. However, the PA’s power efficiency increases as the PA output power increases. The nonlinearity results in spectral regrowth, which leads to adjacent channel interference, and degrades the transmit signal quality. According to our simulation, the linearization performance of DPD is degraded abruptly when the PA operates in its saturation region. To relieve this problem, we propose an improved DPD technique. The proposed technique performs on/off control of the adaptive algorithm based on the magnitude of the transmitted signal. Specifically, the adaptation normally works for small and medium signals while it stops for large signals. Therefore, harmful coefficient updates by saturated signals can be avoided. A computer simulation shows that the proposed method can improve the linearization performance compared with the conventional DPD method in highly driven PAs.

A digital predistortion (DPD) technique based on an iterative adaptation structure is proposed for linearizing power amplifiers (PAs). To obtain proper DPD parameters, a feedback path that converts the PA’s output to a baseband signal is required, and memory is also needed to store the baseband feedback signals. DPD parameters are usually found by an adaptive algorithm by using the transmitted signals and the corresponding feedback signals. However, for the adaptive algorithm to converge to a reliable solution, long feedback samples are required, which increases hardware complexity and cost. Considering that the convergence time of the adaptive algorithm highly depends on the initial condition, we propose a DPD technique that requires relatively shorter feedback samples. Specifically, the proposed DPD iteratively utilizes the short feedback samples in memory while keeping and using the DPD parameters found at the former iteration as the initial condition at the next iteration. Computer simulation shows that the proposed technique performs better than the conventional technique, as the former requires much shorter feedback memory than the latter.

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.

High-symbol-rate coherentoptical transceivers suffer more from the critical responses of transceiver components at high frequency, especially when applying a higher order modulation format. We recently proposed a neural network (NN)-based digital pre-distortion (DPD) technique trained to mitigate the transceiver response of a 128 GBaud optical coherent transmission system. In this paper, we further detail this work and assess the NN-based DPD by training it using either a direct learning architecture (DLA) or an indirect learning architecture (ILA), and compare performance against a Volterra series-based ILA DPD and a linear DPD. Furthermore, we deliberately increase the transmitter nonlinearity and compare the performance of the three DPDs schemes. The proposed NN-based DPD trained using DLA performs the best among the three contenders. In comparison to a linear DPD, it provides more than 1 dB signal-to-noise ratio (SNR) gains at the output of a conventional coherent receiver DSP for uniform 64-quadrature amplitude modulation (QAM) and PCS-256-QAM signals. Finally, the NN-based DPD enables achieving a record 1.61 Tb/s net rate transmission on a single channel after 80 km of standard single mode fiber (SSMF).

Angular coordinate estimating algorithms in condition of thermal noise and space correlated interferences are considered. There are signal and interference sources in the radiation pattern's main lobe. During an analysis of the thermal noise's space correlation mutual influence is taken into account. The comparative analysis' results of radio source direction's observational error using maximum likelihood estimation and Peebles method are shown.

Power amplifier (PA) nonlinearities and the digital predistortion (DPD) required for linearization are modeled at baseband using the memoryless polynomial, memory polynomial, and pruned Volterra series. The sampling requirements for the nonlinear basis waveforms created from a band-limited signal are determined as a function of the polynomial order. A Gaussian approximation of the channel filtering is used to make the mathematical analysis tractable. For the memory polynomial and pruned Volterra series cases, the spacing between memory taps, referred to as the delay offset, is selected to trade off model accuracy and the condition number of the coefficient estimation. Examples are provided showing how to select the appropriate delay offset for a memory-based amplifier model and its effect on the DPD performance. A pruned Volterra series is proposed, which demonstrates improved DPD performance compared with the memory polynomial.

The inherent nonlinear behavior exhibited by power amplifiers (PAs) in saturation mode is a major impediment in wireless systems to achieve higher power efficiency, and higher spectral efficiency. Although PA behavioral modeling and digital predistortion (DPD) techniques are widely utilized at transmitter to characterize and linearize such distorted nonlinear output of PAs, the nonlinear distortions with memory effects have become more severe due to simultaneous strong crosstalk between multiple PA branches in multiple-input multiple-output (MIMO) arrays. Furthermore, current multi-input behavioral models suffer from a sharp increase in number of coefficients as the number of transmitter path increases in MIMO. In overcoming these challenges, we propose a decomposed cross-correlation based single-input-single-output (CC-SISO) behavioral modeling architecture. The proposed solution utilizes a low-cost, novel cross-correlation based method to estimate and cancel the simultaneous nonlinear and reverse crosstalk from multiple PA branches. Once the crosstalk is mitigated, the MIMO DPD can be implemented with single-input DPD blocks which significantly reduces the complexity. Furthermore, CC-SISO DPD eliminates the requirement for signal feedback paths before and after the PA, and thus reduces overall hardware implementation complexity. Through simulations, we demonstrate that the proposed CC-SISO architecture can reduce the overall complexity of state-of-art multi-input DPD models for MIMO systems.

In this paper, a novel load-mismatch tracking digital predistortion (DPD) technique is proposed for linearizing mobile-terminal power amplifiers (PAs) under varying load-mismatch conditions. A time-domain poly-harmonic distortion (TD-PHD) model is discussed first for the behavioral modeling and linearization of PA under mismatch, followed by a practical model construction method. Then, the proposed DPD technique is derived based on the superposition principle, which enables automatic and fast adjustments of its coefficients to adapt to the PA's nonlinear characteristics under different load-mismatch conditions without re-identification of the DPD model. Experimental validations on a mobile-terminal PA module at 4.6 GHz with a 100 MHz 5G NR signal show that the proposed DPD technique realizes excellent linearization performance among a wide load-impedance varying range with low complexity.

This paper reports a novel digital predistortion (DPD) scheme for concurrent tri-band power amplifier (PA). The proposed three-dimensional tri-band DPD is based on the analysis of crosstalk between the fundamental frequencies, their harmonics and intermodulation products due to the nonlinearity exhibited by the tri-band PA. The DPD performance is validated using a broadband PA driven concurrently with LTE, WIMAX, and WCDMA signals centered at 2.14 GHz, 2.425 GHz, and 2.655 GHz, respectively.

This paper analyses the benefits of combining crest factor reduction (CFR) technique and digital predistortion (DPD). DPD and CFR have been mainly investigated separately, and this is the contribution to the state of the art given here. The combination is attractive in modern wireless transmission systems due to the high peak-to-average-power-ratio (PAPR) of the signals employed. In these cases, the compression of the signal peaks performed by the amplifier in strong saturation region causes uncontrolled in-band and out-of-band distortion that degrades ACLR and EVM of the output signal. The joint architecture can take advantage from the improved linear output power given by DPD together with a controlled reduction of the peaks performed by CFR. The PA can in this way deliver a higher linear output power having at the same time a predictable linearity of the output RF signal. The concept has been implemented and tested on a commercial GaAs PA, obtaining an increased linear output power of 1.9 dB compared to the standalone PA when a WCDMA signal with PAPR of 3.47 dB is employed.

This paper deals with the design of a pattern reconfigurable antenna for implant devices. Radiation pattern reconfiguration means the modification of the spherical distribution of the radiation pattern of the antenna. Beam steering is an application of the pattern reconfiguration. When the direction of the main lobe of radiation pattern is changed it is called beam steering. A monopole antenna is chosen for design for its compact size. This antenna operates at the Med Radio band (401–406 MHz). The antenna to be designed for the implant devices has the dimensions of 28mm*11.5mm*0.6mm (193.2mm3). The designed antenna is fabricated on FR4 substrate (er=4.4). Pin Diodes are used to control the radiation pattern of the antenna. Advanced Design System (ADS) is adopted for antenna design and simulation.