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arxiv: 1907.07959 · v1 · pith:4HJ25HDInew · submitted 2019-07-18 · 📡 eess.SP

Linearizing Active Antenna Arrays: Method and Measurements

Pith reviewed 2026-05-24 19:45 UTC · model grok-4.3

classification 📡 eess.SP
keywords digital predistortionactive antenna arrayspower amplifier linearizationcombined feedback5G New Radiomillimeter waveover-the-air measurements
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The pith

A single combined feedback signal enables digital predistortion linearization of active antenna arrays containing many different power amplifiers.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a digital predistortion method for active antenna arrays in which multiple power amplifiers can differ in their nonlinear behavior. Instead of separate feedback paths for each amplifier, the method relies on one combined feedback signal that captures the net nonlinear distortion visible at a distant receiver. This combined signal is then used to derive the predistortion coefficients applied to the transmitted waveform. The approach is tested through over-the-air measurements on a 64-element array operating at 28 GHz with a 200 MHz 5G New Radio signal. The measurements show that the resulting linearization meets practical requirements while remaining computationally light for large arrays.

Core claim

The framework uses a combined feedback signal that characterizes the observable nonlinear distortion at the receiving end to linearize an entire active antenna array whose power amplifiers are mutually different; over-the-air measurements on a 64-element 28 GHz transmitter carrying a 200 MHz 5G NR waveform confirm that the method produces effective linearization with low implementation cost.

What carries the argument

The combined feedback signal, formed by aggregating the radiated signals so that it represents the net nonlinear distortion seen by a receiver.

If this is right

  • The number of required feedback receivers stays constant even as the array grows to hundreds of elements.
  • Predistortion coefficients can be updated from a single observation point rather than from per-amplifier sensors.
  • The same framework applies directly to millimeter-wave 5G base-station transmitters carrying wideband NR waveforms.
  • Linearization performance is judged by the distortion that actually reaches the far-field receiver rather than by individual amplifier metrics.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the combined signal remains representative, the method could scale to arrays whose amplifiers age or drift differently over time without requiring recalibration of every unit.
  • The approach implicitly treats beamformed radiation as the quantity that must be linearized, which may matter more for regulatory compliance than per-element linearity.
  • The same combined-signal idea could be tested on arrays that also perform digital beamforming, to check whether the predistortion interacts with the beam weights.

Load-bearing premise

One combined feedback signal is enough to model and cancel the separate nonlinear distortions produced by each of the many different power amplifiers inside the array.

What would settle it

An over-the-air test in which the combined feedback signal is used for predistortion yet the received spectrum still shows out-of-band emissions above the 5G NR mask, or in which individual amplifier distortions remain visible when measured separately.

read the original abstract

In this paper, we provide a novel framework for efficient digital predistortion (DPD) based linearization of active antenna arrays with multiple and mutually different nonlinear power amplifiers. The proposed method builds on the use of a combined feedback signal essentially characterizing the observable nonlinear distortion at the receiving end. The proposed method is validated with extensive over-the-air RF measurements on a 64-element active antenna array transmitter operating at 28 GHz carrier frequency and transmitting a 200 MHz wide 5G New Radio (NR) waveform. The obtained results demonstrate the excellent linearization capabilities of the proposed solution, which allows for a very efficient implementation in practical systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes a novel digital predistortion (DPD) framework for linearizing active antenna arrays with multiple mutually different nonlinear power amplifiers. It relies on a single combined feedback signal that captures the observable nonlinear distortion at the receiving end, derives an effective single DPD model from this summed observation, and validates the approach via over-the-air measurements on a 64-element 28 GHz array transmitting a 200 MHz 5G NR waveform, reporting good ACLR and EVM performance.

Significance. If the combined-feedback approach is shown to handle per-PA differences, the method would enable scalable linearization of massive MIMO arrays without per-element feedback hardware, which is practically significant. The extensive OTA measurements on real 64-element hardware at 28 GHz with a wideband 5G waveform constitute a clear empirical strength.

major comments (2)
  1. [§3] §3: The derivation of a single effective DPD model from the summed combined feedback assumes that the averaged observable distortion can be inverted to correct distinct per-PA nonlinearities. When individual PAs exhibit different AM-AM/AM-PM curves, the linear combination at the receiver produces an effective model whose invertibility per element is not guaranteed; the section should state the conditions under which the single model suffices or provide a supporting analysis/simulation.
  2. [§5] §5: The 64-element OTA results demonstrate improved ACLR/EVM, but the experiments do not report per-PA characterization of the nonlinearities or include a controlled comparison against individual per-element feedback. This leaves the central claim that the method handles mutually different PAs untested in the regime highlighted by the abstract.
minor comments (2)
  1. [§2] The system diagram in §2 would benefit from explicit annotation of how the combined feedback is formed from the array elements.
  2. [Abstract] Quantitative ACLR/EVM improvement values are stated in the body but could be summarized numerically in the abstract for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and the positive assessment of the paper's significance and empirical contributions. We address the major comments point by point below.

read point-by-point responses
  1. Referee: [§3] §3: The derivation of a single effective DPD model from the summed combined feedback assumes that the averaged observable distortion can be inverted to correct distinct per-PA nonlinearities. When individual PAs exhibit different AM-AM/AM-PM curves, the linear combination at the receiver produces an effective model whose invertibility per element is not guaranteed; the section should state the conditions under which the single model suffices or provide a supporting analysis/simulation.

    Authors: The proposed framework in §3 derives an effective single DPD model based on the combined feedback signal that captures the observable nonlinear distortion. This effective model is then inverted for predistortion. Although the per-PA differences result in an averaged distortion, the approach is justified by the fact that only the observable combined signal is available in practical systems. The measurements confirm its effectiveness. In response to this comment, we will revise §3 to include a discussion of the conditions under which the single model is applicable, emphasizing that it targets the effective nonlinearity observed at the receiver. revision: yes

  2. Referee: [§5] §5: The 64-element OTA results demonstrate improved ACLR/EVM, but the experiments do not report per-PA characterization of the nonlinearities or include a controlled comparison against individual per-element feedback. This leaves the central claim that the method handles mutually different PAs untested in the regime highlighted by the abstract.

    Authors: The OTA measurements in §5 were conducted on a commercial 64-element active antenna array at 28 GHz with a 200 MHz 5G NR waveform. The array hardware features multiple PAs that are mutually different, as stated in the abstract and introduction. The combined-feedback method successfully linearizes the overall transmission without requiring per-element feedback or characterization. A controlled comparison to per-element DPD would necessitate a fundamentally different experimental setup with individual feedback paths, which is outside the scope of this work focused on scalable combined feedback. The central claim is thus supported by the real-world validation on hardware with inherent PA variations. revision: no

Circularity Check

0 steps flagged

No circularity: method relies on external measurements, not self-referential derivation

full rationale

The provided abstract and description contain no equations, derivations, or self-citations that reduce a claimed result to its own inputs by construction. The framework is presented as novel and validated via 64-element OTA measurements at 28 GHz; the central sufficiency claim for combined feedback is tested empirically rather than derived from fitted parameters or prior self-citations within the paper. This is the common honest non-finding for measurement-driven papers without load-bearing analytic loops.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no equations or detailed method description, so free parameters, axioms, and invented entities cannot be identified.

pith-pipeline@v0.9.0 · 5644 in / 1016 out tokens · 19848 ms · 2026-05-24T19:45:49.094877+00:00 · methodology

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