Linearization as a Solution for Power Amplifier Imperfections: A Review of Methods

Abstract

Development of 5G networks requires a substantial increase to both spectral and power efficiency of transmitters. It is known that these two parameters are subjected to a mutual trade-off. To increase the linearity without losing power efficiency, linearization techniques are applied to power amplifiers. This paper aims to compare most popular linearization techniques to date and evaluate their applicability to upcoming 5G networks. The history of each respective linearization technique is followed by the main principle of operation, revealing advantages and disadvantages supported by concluding the latest research results. Three main groups of linearization methods currently known are feedforward, feedback, and predistortion, each with its own tradeoffs. Although digital predistortion seems to be the go-to method currently, other techniques with less research attention are still non-obsolete. А generalized discussion and a direct comparison of techniques analyzed are presented at the end of this paper. The article offers a systematic view on PA linearization problems which should be useful to researchers of this field. It is concluded that there are still a lot of problems that need to be addressed in every linearization technique in order to achieve 5G specifications.

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

“…The prime focus of each contribution is to reduce the PAPR level while evading inherent signal distortion or compromising spectral efficiency (SE). However, such insistence demand additional behavioral baseband optimization for the nonlinearity prediction at the • The linearization and PAPR-reduction techniques are partially discussed in terms of the performance-complexity profile, including their advantages and limitations [42], 2021…”

A Crest Factor Reduction Technique for LTE Signals with Target Relaxation in Power Amplifier Linearization

“…Both types can be modeled as a black box based on behavior modeling with memory [ 1 ]. To improve the performance in linearity and power efficiency in data waveforms, crest factor reduction (CFR) in conjunction with digital predistortion (DPD) as linearization technique are used [ 2 ]. In [ 3 ], a novel multi-objective predistortion linearization approach based on a learning procedure constrained algorithm with prescribed linearity levels is able to maximize the RF output power.…”

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Upgrading Behavioral Models for the Design of Digital Predistorters

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