Transmitter antenna arrays are typically coupled to several RF chains, which imposes stringent requirements on the linearization of each power amplifier (PA) present in the system. For this and other reasons discussed in this work, Over-the-air (OTA) linearization methods are considered to linearize transmitter antenna arrays in 5G scenarios. However, several factors need to be considered when applying OTA linearization methods. In this paper, an extended critical review of validated OTA linearization methods is presented. The main goal is to point out and discuss the most prominent methods, in order to determine which one is the most suitable for a specific application. In particular, analysis for each method is performed and, subsequently, their benefits and the disadvantages are systematically discussed. This is sought to fill-in a gap in the scientific literarure and, thus, to help radio-frequency engineers in the implementation of OTA digital pre-distortion (DPD) techniques for multiple input multiple output (MIMO) systems.

A Survey on Over-The-Air Linearization Methods for MIMO Systems

This article studies advanced digital self-interference (SI) cancellation algorithms for wireless full-duplex (FD) transceivers. While the majority of works in this area consider linear SI models and corresponding cancellation algorithms, we take also both the transmitter (TX) and receiver (RX) nonlinearities into account. Specifically, we develop a Hammerstein-Wiener type of digital SI cancellation system where the two involved nonlinearities are efficiently implemented through spline-interpolated look-up tables (LUTs). Also, adaptive parameter estimation solutions are pursued and discussed, and comprehensive cancellation performance examples and processing complexity comparisons are provided. The obtained results show that taking both the TX and RX nonlinearities into account in the digital cancellation stage can be beneficial, particularly when the TX power amplifier is operating close to saturation and when the RF isolation and RF cancellation prior to the RX low-noise amplifier (LNA) is limited, such that also the LNA is distorting the SI waveform.

Adaptive Cancellation of Nonlinear Self-Interference in Wireless Full-Duplex: Cascaded Spline-Interpolated Methods

The multi-band and spectrally flexible transmission requires a wideband transmitter system to cover a wider frequency band of interest. A wideband power amplifier (PA) is required to achieve the desired signal strength for covering long-distance communication. Due to PA’s nonlinear characteristics, spectral growth and harmonics occur, which may cause interference with the other band of interest. This paper presents a linearization technique, denoted as baseband fusion technique (BFT), for the wideband filter-less transmission system. The proposed method simultaneously reduces the spectral growth around the fundamental frequency and cancels out harmonics. The presented technique uses the signal cancellation approach in baseband and avoids modeling as required in digital predistortion. This method may eliminate the need for bulky filters to suppress harmonics. The proposed method has much lower complexity in digital signal processor resources than the Volterra-based models and their derivatives. We have experimentally tested with long-term-evolution (LTE), four-tone, and 256-QAM signal to validate the proposed method. This method can also be useful for cognitive radios where PA’s unwanted emission interferes with the other user transmission.

Baseband Fusion Technique for Filter-Less Wideband Transmitters

In this article, we study closed-loop digital predistortion (DPD) systems and associated learning algorithms. Specifically, we propose various low-complexity approaches to estimate and manipulate the inverse of the input data covariance matrix (CM) and combine them with the so-called self-orthogonalized (SO) learning rule. The inherent simplicity of the SO algorithm, combined with the proposed solutions, allows for remarkably reduced complexity in the DPD system while maintaining similar linearization performance compared to other state-of-the-art methods. This is demonstrated with thorough over-the-air (OTA) mmW measurement results at 28 GHz, incorporating a state-of-the-art 64-element active antenna array, and very wide channel bandwidths up to 800 MHz. In addition, complexity analyses are carried out, which together with the measured linearization performance demonstrates favorable performance–complexity tradeoffs in linearizing mmW active array transmitters through the proposed solutions. The techniques can find application in systems where the power amplifier (PA) nonlinearities are time-varying and thus frequent or even constant updating of the DPD is required, good examples being mmW adaptive antenna arrays as well as terminal transmitters in 5G and beyond networks. 

Inverse Covariance Matrix Estimation for Low-Complexity Closed-Loop DPD Systems: Methods and Performance

The next-generation (5G/6G) wireless communication aims to leapfrog the currently occupied sub-6 GHz spectrum to the wideband millimeter-wave (MMW) spectrum. However, MMW spectrums with high-order modulation schemes drive the power amplifier (PA) at significant back-off, causing severe nonlinear distortions, thus deteriorating the transceiver&#x2019;s (TRXs) modulation process. Typically, the TRX efficacy is quantified with standardized linearization matrices, which take advantage of different predistortion (PD) schemes to handle deep compression of the PA. In this regard, TRX baseband signals are mostly linearized in the digital domain, where digitally controlled linearization needs higher sampling rates due to increasing MMW bandwidths to compensate for intermodulation (IMD) products, resulting in increased system cost and power consumption. Alternately, the digitally controlled analog-based linearization, i.e., the hybridization of digital predistortion (DPD) and analog predistortion (APD), is highly productive and cost-effective for fulfilling the linearized energy-efficient design vision of MMW networks. Therefore, this paper puts an extensive spotlight on the progress in PD-based linearization for 5G and beyond communications. It first provides background information on the advancements of PD schemes through recent surveys, then classifies the general roadmap of PD waveform processing across the TRX system models as preliminary. After this, we present three prominent PD architectures and their design approaches with intrinsic performance metrics. Finally, we explore four case studies encompassing PD operation under certain nonlinear constraints of different communication schemes. We examine the suitability of PD-based linearization solutions, both existing and proposed till the first quarter of 2022, and identify the potential prospects in this domain. 

Predistortion-Based Linearization for 5G and Beyond Millimeter-Wave Transceiver Systems: A Comprehensive Survey

In this article, we study digital predistortion (DPD)-based linearization with a specific focus on millimeter-wave (mmW) active antenna arrays. Due to the very large-channel bandwidths and beam-dependence of nonlinear distortion in such systems, we present a closed-loop DPD learning architecture, lookup table (LUT)-based memory DPD models, and low-complexity sign-based estimation algorithms such that even continuous DPD learning could be technically feasible. To this end, three different learning algorithms—sign, signed regressor, and sign–sign—are formulated for the LUT-based DPD models such that the potential rank deficiencies, experienced in earlier methods, are avoided while facilitating greatly reduced learning complexity. The injection-based LUT DPD structure is also shown to allow for low numbers and reduced dynamic range of the involved LUT entries. Extensive RF measurements utilizing a state-of-the-art mmW active antenna array system at 28 GHz are carried out and reported to validate the methods, incorporating very wide channel bandwidths of 400 and 800 MHz while pushing the array close to saturation. In addition, the processing and learning complexities of the considered techniques are analyzed, which, together with the measured linearization performance figures, allows to assess the complexity–performance tradeoffs of the proposed solutions. Overall, the results show that efficient mmW array linearization can be obtained through the proposed methods at very low complexity.

Closed-Loop Sign Algorithms for Low-Complexity Digital Predistortion: Methods and Performance

This paper studies closed-loop digital predistortion systems, with special focus on linearization of mmW active antenna arrays. Considering the beam-dependent nonlinear dis - tortion and very high DPD processing rates, a modified self-orthogonalized (SO) learning solution is proposed, which is capable of reducing the computational complexity compared to other similar solutions, while at the same time obtaining a comparable linearization performance. The modified SO consits of a novel method for efficiently calculating the inverse of the input data covariance matrix. Thorough RF measurement results at 28 GHz band featuring a state-of-the-art 64 element active array and channel bandwidths up to 800 MHz, are reported A complexity analysis is also carried out which, together with the obtained results, allow to asses the performance-complexity trade-offs. Altogether, the results show that the proposed methods can facilitate efficient mmW active antenna array linearization.

Yan Guo

Papers

Closed-Loop Sign Algorithms for Low-Complexity Digital Predistortion: Methods and Performance

Efficient Inverse Covariance Matrix Estimation for Low-Complexity Closed-Loop DPD Systems