Nonlinear Power Amplifier Research Articles

Performance Analysis and Power Allocation for Uplink Cell-Free Massive MIMO System With Nonlinear Power Amplifier

Performance Analysis and Power Allocation for Uplink Cell-Free Massive MIMO System With Nonlinear Power Amplifier

Joint estimation of IQ imbalance and PA nonlinearity: An iterative scheme

Power amplifier (PA) nonlinearity and in-phase and quadrature-phase (IQ) imbalance in the transmitter are of critical concern in modern communication systems due to their significant impact on the transmitter performance. In this paper, in order to eliminate their effects without increasing the complexity at the transmitter, we propose an iterative structure for IQ imbalance and PA nonlinearity joint estimation at the receiver side. The strategic design aims to mitigate the influence of one distortion before estimating the other, ultimately improving the accuracy of estimating each distortion. The proposed approach comprises two primary modules: an IQ imbalance estimation module and a PA nonlinearity estimation module. In addition, there are two supporting modules, namely the PA nonlinearity compensation module and the IQ imbalance reconstruction module, positioned before these two primary modules respectively. Furthermore, we have derived the theoretical Cramér–Rao lower bound (CRLB) for the joint distortion model of IQ imbalance and PA nonlinearity. Finally, extensive experiments are conducted to demonstrate the proposed algorithm's capability in estimating hardware distortions and mitigating them.

Evaluation of Joint Technique Iterative Clipping Filtering (ICF) and Neural Network Predistortion on SDR-based MIMO-OFDM System

Multiple-input, multiple-output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is a communications technology that powers numerous modern communication systems, including 5G and WiFi-6. This technology is utilized in current communication systems due to its high performance and extensive channel capacity. MIMO-OFDM does have disadvantages, such as large Peak-to-Average Power Ratio (PAPR) values. If the signal is processed by a nonlinear Power Amplifier (PA) device, a high PAPR value signal can result in both in-band and out-of-band signal distortion. To combat high PAPR values, PAPR reduction strategies such as Iterative Clipping Filtering (ICF) are utilized. From this study, using ICF with iteration 2 and Clipping Ratios (CR) 3 and 4 can improve the system's minimum Bit Error Rate (BER) by about 22.8% and 91.1%, respectively. Choosing the correct CR will improve the system, but using the lower CR will make it worse than a system without ICF. This occurs in systems using ICF with iterations two and CR 2 and at the same SNR conditions as systems without ICF; using ICF with iterations two and CR 2 results in higher BER values. The use of Predistortion Neural Network (PDNN) can overcome this problem. By using PDNN, there is an improvement in the system where the minimum BER value can reach 0.1 × 10-5. The percentage decrease in BER from using PDNN for ICF with iterations two and CR 2, 3, and 4 is 99.88%, 99.86%, and 98.807%, respectively. Thus, the joint techniques of ICF and PDNN can significantly enhance the performance of MIMO-OFDM systems with nonlinear PA. Importantly, the experiment was conducted on an SDR device, ensuring the real-world applicability of the results.

A novel sparse-based approach for joint radio frequency fingerprint and channel estimation

Radio Frequency Fingerprint (RFF) plays a pivotal role in specific emitter identification (SEI) and enhancing the physical layer security of wireless networks. However, past research on RFF extraction has encountered significant challenges, including algorithm instability in the presence of multi-path channels, disparities between actual RFF and proposed models, and the need to compensate for receiver RFF. In this study, we introduce an innovative algorithm designed to simultaneously estimate the transmitter's RFF and the non-line-of-sight (NLOS) channel state in the presence of receivers’ antenna effect. Moreover, we introduce the transmitter antenna feeding current as a modified RFF, accounting for transimtter components such as power amplifier (PA) nonlinearity, in-phase quadrature (IQ) modulator imbalance. Our approach commences with an exploration of wave propagation theory fundamentals, followed by a transformation of the inverse scattering equations into linear equations for joint channel and RFF estimation. Due to the non-convex nature of the derived equation, we employ the sparse lift method to render the problem convex. Leveraging the sparsity of channel coefficients, we devise a compressive sensing linear solution, denoted as the sparse-based approach for joint (SBJ) RFF and channel estimator. Additionally, we propose a calibration method to mitigate the effect of the receiver antennas on the estimated RFF of the transmitter. Through simulation, we demonstrate a 10 dB enhancement in RFF estimation accuracy compared to existing methods. Furthermore, our results indicate that the SBJ algorithm remains robust when faced with the combined effect of transmitter components, whereas previous models exhibit diminished precision as additional component effects are considered.

Distortion-aware beamforming design for multi-beam satellite communications with nonlinear power amplifiers

Distortion-aware beamforming design for multi-beam satellite communications with nonlinear power amplifiers

Power Amplifier Modeling Framework for Front-End-Aware Next-Generation Wireless Networks

Next-generation wireless systems require increased spectral and energy efficiency in terminals. This can only be achieved if the nonlinear characteristics of the radio front-end, primarily the power amplifier (PA), are known and considered while designing algorithms or optimizing radio links. PA datasheets contain limited information, and wideband PA measurements typically require advanced equipment and software. This paper proposes a measurement and signal processing framework that enables the acquisition of nonlinear PA characteristics using generic complex sample transmitters and receivers, e.g., software-defined radio (SDR). This paper also proposes a multistage signal calibration and synchronization procedure that allows obtaining nonlinear PA characteristics that are non-distorted by other phenomena. The correctness and usefulness of the proposed framework are demonstrated by measuring three PAs, each under varying supply voltage and carrier frequency. At the same time, the high variability of the obtained nonlinear characteristic justifies the need for PA measurements to obtain awareness of nonlinear characteristics.

Nonlinear-tolerant two-dimensional distribution matcher scheme for probabilistic shaping.

This Letter proposes a nonlinear-tolerant two-dimensional distribution matcher (2D-DM) scheme. It removes the corner points of probabilistically shaped quadrature amplitude modulation (QAM) to obtain better nonlinear tolerance. Because the remaining number of points is not a power of 2, we propose to divide constellation points into different layers and symbols. Then, the proposed 2D-DM performs matching using one-dimensional shapers, which generates the in-phase and quadrature components of QAM together. In fact, it realizes two-dimensional shapers from one-dimensional shapers. Simulation results show that two-dimensional shapers generated by the proposed 2D-DM have higher tolerance to power amplifier nonlinearity and fiber nonlinearity compared to one-dimensional shapers.

Memory diversity combination algorithm for nonlinear power amplifier in wireless communication systems

The nonlinearity of power amplifier (PA) can lead to performance degradation, so alleviating this adverse effect has been regarded as an essential issue to secure normal transmission for wireless communication systems. For the general nonlinear post-distortion schemes, such as memory polynomial (MP) and power amplifier nonlinearity cancellation (PANC), the memory effect is usually treated similarly as the nonlinearity, which are both removed from the received distorted signal. However, the valid information concealed in the memory could be exploited to supply diversity for detection, so to release this potential, a memory diversity combination (MDC) scheme is proposed to enhance the overall property. In MDC, the memory components are firstly reconstructed using the solution obtained by MP or PANC, which are then combined together, thereby obtaining the diversity. Simulation results show that, MP- and PANC-MDCs can make significant performance improvements over MP and PANC, which is accomplished by merging the memory diversity of PA.

Performance enhancement and PAPR reduction for MIMO based QAM-FBMC systems.

Filter Bank Multi-Carrier (FBMC) is attracting significant interest as a multi-carrier modulation (MCM) approach for future communication systems. It offers numerous advantages in contrast to Orthogonal Frequency Division Multiplexing (OFDM). Nonetheless, similar to many other MCM techniques, FBMC encounters a significant challenge with a high Peak-to-Average Power Ratio (PAPR). Additionally, incorporating Multiple-Input and Multiple-Output (MIMO) into FBMC presents heightened difficulties due to the presence of complex interference and increased computational complexity. In this paper, we first study the performance analysis of MIMO based Quadrature Amplitude Modulation (QAM)-FBMC systems considering the system complexity and interference. To enhance coverage effectively using beamforming with multiple antennas, it is essential to reduce PAPR to minimize the input backoff (IBO) required by nonlinear power amplifiers. Therefore, we propose new PAPR reduction method for MIMO based QAM-FBMC systems leveraging the null space within the MIMO channel using clipping and filtering (CF) technique. The PAPR reduction signals generated in this process are then mapped to the null space of the overall MIMO channel for each frequency block. Through computer simulations using a nonlinear power amplifier model, we illustrate that the proposed method substantially enhances both PAPR and throughput of MIMO based FBMC systems compared to conventional methods.

Distortion-Aware Power Allocation for Multi-Stream Distributed Massive MIMO System With Nonlinear Power Amplifier

Distortion-Aware Power Allocation for Multi-Stream Distributed Massive MIMO System With Nonlinear Power Amplifier

Signal Detection in MIMO Systems With Hardware Imperfections: Message Passing on Neural Networks

We investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical application. In this work, we investigate how to achieve efficient Bayesian signal detection in MIMO systems with hardware imperfections. Characterizing combined hardware imperfections often leads to complicated signal models, making Bayesian signal detection challenging. To address this issue, we first train an NN to ‘model’ the MIMO system with hardware imperfections and then perform Bayesian inference based on the trained NN. Modelling the MIMO system with NN enables the design of NN architectures based on the signal flow of the MIMO system, minimizing the number of NN layers and parameters, which is crucial to achieving efficient training with limited pilot signals. We then represent the trained NN with a factor graph, and design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm. The implementation of a turbo receiver with the proposed Bayesian detector is also investigated. Extensive simulation results demonstrate that the proposed technique delivers remarkably better performance than state-of-the-art methods.

Downlink Performance of MU-MIMO-FBMC in Power Amplifier Nonlinearity

Downlink Performance of MU-MIMO-FBMC in Power Amplifier Nonlinearity

Capacity Analysis and Throughput Maximization of NOMA with Non-linear Power Amplifier Distortion

Capacity Analysis and Throughput Maximization of NOMA with Non-linear Power Amplifier Distortion

Power Amplifier Optimization for M2M Node using DPD and Hybrid DFT-s-OFDM with CFR

Power Amplifiers (PAs) play a vital role in mobile communication. However, their inherent nonlinearity can lead to issues such as unwanted radiation, interference with neighboring channels, and distortion within the desired frequency range. To address these problems, PAs are typically operated at a lower power level than their saturation point, sacrificing power efficiency to improve Error Vector Magnitude (EVM) performance. Consequently, more than 70-80% of the DC power is wasted as heat. This inefficiency necessitates the exploration of techniques to enhance PA efficiency while maintaining acceptable linearity and performance. Digital Pre-Distortion (DPD) is a useful technique to linearize PAs. DPD allows affordable nonlinear PAs to function with minimal distortion in their nonlinear operating regions. This results in amplified signals with increased power output and improved power efficiency. Using nonlinear functions, the output signal of the PA can be linearized, mitigate distortions, and be effectively optimized. When using various operating channels and varying time, temperature, and PA nonlinearity, the DPD must adjust. In order to increase the power efficiency of 5G systems, DPD-enabled systems are inculcating with adaptive DFT-s-OFDM employed 5G physical layer along with the Crest Factor Reduction (CFR) algorithm.

Outage Probability Analysis and Joint Optimization for UAV-Aided FSO/RF Systems With Nonlinear Power Amplifiers

Outage Probability Analysis and Joint Optimization for UAV-Aided FSO/RF Systems With Nonlinear Power Amplifiers

Error Vector Magnitude as a Performance Standard for Antennas in the Millimeter-Wave Era: Part 2: Modeling, simulation, and measurement methods

The error vector magnitude (EVM) is a comprehensive measure of the amplitude and phase distortion of digitally modulated signals and a potential tool for characterizing the performance of active beamforming arrays, especially at millimeter-wave frequencies, because of the high level of integration and limited spacing between the radiating elements. A review of the EVM measurement process and fundamental principles for time-domain demodulation with a vector signal analyzer (VSA) and spectral correlation with a vector network analyzer (VNA) was presented in [1]. This article demonstrates how researchers have modeled and measured impairments from antenna and phased array signal transmission to characterize their influence on the EVM of single and multicarrier digital signals. It presents examples of simulation models and measurements of the distortion from ultrawideband (UWB) passive antenna transmission, power amplifier (PA) nonlinearities, and phased array beam squint and beam scan intersymbol interference (ISI). A brief survey of over-the-air (OTA) test ranges for EVM analysis is also provided. This overview of state-of-the-art EVM simulation and measurement methods is intended to help interested readers understand and enhance the EVM performance analysis methods applied to antennas and phased arrays in this new era of millimeter-wave communications.

Deep neural network based downlink power domain multi-user NOMA-OFDM signal detection

Non-orthogonal multiple access-based orthogonal frequency division multiplexing (NOMA-OFDM) is a promising waveform-based multiple access technology for future wireless networks for multiple-user symbol transmission (MUST) in the same time-frequency resource block. However, it differs in the power domain, enhancing its spectrum efficiency. This is essential to meet the high data rate required for ever-increasing connected devices and the Internet of Things (IoT). However, NOMA-OFDM systems suffer from impairments such as imperfect successive interference cancellation (SIC) caused by channel impairments like channel fading, carrier frequency offset, and non-linearity caused by non-linear power amplifiers. This paper identifies and addresses the key impairments mentioned in the NOMA-OFDM system and proposes DNN-based estimation in offline training and detection in online testing for downlink power domain multi-user NOMA-OFDM symbols. The reported 2 dB SNR gain compared to least square-SIC/minimum mean square error SIC-based methods is a significant finding and demonstrates the robustness of the proposed DNN-aided approach against various channel impairments.

A Low-Complexity Digital Predistorter for Large-Scale MIMO Systems With Crosstalk

Massive multiple-input multiple-output (MIMO) technology is a key enabler for future wireless communication systems. On the other hand, crosstalk is inevitable in the highly integrated large-scale antenna array. The nonlinearity of power amplifiers (PAs) coupled with crosstalk is much more complicated when the number of antennas is large. Digital predistortion (DPD) can compensate for the PA nonlinearity without sacrificing efficiency and is widely applied in both base stations and mobile devices. Extending existing solutions to the large-scale MIMO system usually suffers from performance degradation or greatly increased complexity. In this paper, we consider the nonlinearity with crosstalk for the large-scale MIMO scenario. A much simplified DPD model is proposed by ignoring the high-order crosstalk terms generated from the complete nonlinear model. Compared to existing solutions, the proposed DPD can compensate for both the nonlinear and linear crosstalk of the system. It achieves satisfactory linearization performance with much reduced computational complexity. The complexity of the proposed DPD is linearly related to the number of transmitter branches. Thus, it is scalable in the large-scale MIMO scenario. Moreover, the proposed DPD can be extended to the partially-connected hybrid beamforming system with crosstalk. Simulation results validate the effectiveness of the proposed DPD scheme.

Improper Gaussian Signaling for IRS Assisted Multiuser SWIPT Systems With Hardware Impairments

In this paper, we focus on intelligent reflecting surface (IRS) assisted multiuser simultaneous wireless information and power transfer (SWIPT) systems with hardware impairments (HWIs), which result from the aggregate effect of in-phase and quadrature-phase imbalance (IQI), power amplifier non-linearity, quantization distortion, phase noise, etc. Improper Gaussian signaling (IGS) is employed to combat the signal distortion incurred by HWIs and the interference from other users. We aim to maximize the minimum achievable information rate among all users by jointly optimizing the active beamforming vectors, passive reflecting coefficients, and power splitting coefficients, subject to the minimum harvested energy requirements of these users and the total power budget at the transmitter. Due to the intricate coupling between the reflecting coefficients and beamforming vectors, we propose a two-level iterative optimization algorithm to solve this non-convex problem. Based on the above, we also study the weighted rate-energy region maximization problem. Numerical results show that the use of IGS significantly outperforms traditional proper Gaussian signaling (PGS) in the considered systems and also affirm the effectiveness of the proposed optimization algorithm.

Semi-Supervised MIMO Detection Using Cycle-Consistent Generative Adversarial Network

In this paper, a new semi-supervised deep multiple-input multiple-output (MIMO) detection approach using a cycle-consistent generative adversarial network (CycleGAN) is proposed for communication systems without any prior knowledge of underlying channel distributions. Specifically, we propose the CycleGAN detector by constructing a bidirectional loop of two modified least squares generative adversarial networks (LS-GAN). The forward LS-GAN learns to model the transmission process, while the backward LS-GAN learns to detect the received signals. By optimizing the cycle-consistency of the transmitted and received signals through this loop, the proposed method is trained online and semi-supervisedly using both the pilots and the received payload data. As such, the demand on labelled training dataset is considerably controlled, and thus the overhead is effectively reduced. Numerical results show that the proposed CycleGAN detector achieves better performance in terms of both bit error-rate (BER) and achievable rate than existing semi-blind deep learning (DL) detection methods as well as conventional linear detectors, especially when considering signal distortion due to the nonlinearity of power amplifiers (PA) at the transmitter.