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arxiv: 2607.01565 · v1 · pith:SBGMZRHBnew · submitted 2026-07-02 · 📡 eess.SP

Waveform Design for Underwater Simultaneous Acoustic Information and Power Transfer

Jinheng Kang , Yizhe Zhao , Kun Yang , Jun Liu This is my paper

Pith reviewed 2026-07-03 00:21 UTC · model grok-4.3

classification 📡 eess.SP
keywords underwater acoustic communicationsimultaneous information and power transferwaveform optimizationenergy harvestingmulticarrier signalstransducer frequency responserectifier nonlinearity
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The pith

Joint optimization of multicarrier waveforms for underwater SAIPT improves acoustic energy transfer efficiency when transducer frequency response and rectifier nonlinearity are included.

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

The paper examines waveform design in a multicarrier underwater system for simultaneous acoustic information and power transfer. It optimizes the waveform first for power transfer using successive convex approximation under power constraints. Then it jointly optimizes power splitting and waveforms via alternating optimization for both power and rate constraints. A sympathetic reader would care because this could enable self-sustainable underwater sensor networks without frequent battery replacements. The results indicate that including real hardware characteristics like frequency dependence and nonlinearity, along with high-PAPR multicarrier signals, significantly boosts energy transfer efficiency.

Core claim

By incorporating the frequency-dependent characteristics of acoustic transducers and the nonlinear behavior of rectifier circuits into the waveform optimization for multicarrier SAIPT systems, the proposed SCA and AO methods achieve higher acoustic energy transfer efficiency compared to designs that ignore these effects.

What carries the argument

Successive convex approximation (SCA) for waveform vector optimization under power constraints, extended to alternating optimization (AO) for joint power splitting and waveform design in SAIPT.

Load-bearing premise

The frequency-dependent transducer model and nonlinear rectifier model accurately represent real underwater hardware behavior.

What would settle it

An experiment measuring the DC output power from a real rectifier driven by the optimized multicarrier acoustic signal versus a non-optimized one in an underwater channel.

Figures

Figures reproduced from arXiv: 2607.01565 by Jinheng Kang, Jun Liu, Kun Yang, Yizhe Zhao.

Figure 1
Figure 1. Figure 1: SAIPT system model. where hˆ n denotes the estimated channel coefficient at fre￾quency fn obtained from the large-scale path loss model, i.e., hˆ n = 1 10PL(fn,d)/20 . (4) ∆hn ∼ N(0, σ2 e |hˆ n| 2 ) represents the channel estimation error accounting for residual uncertainty arising from weak multi￾path components and temporal variations, and σ 2 e denotes the magnitude of the relative channel estimation er… view at source ↗
Figure 2
Figure 2. Figure 2: Measured and fitted gain of the BII-7511 transducer. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: DC output versus subcarrier spacing ∆f at N = 20, d = 2m, Rth = 10Kbps and σ 2 e = 0.05 in SAIPT. The label “AO” refers to the proposed alternating optimization algorithm, whereas “GA” denotes the genetic algorithm employed as a benchmark scheme. The GA is implemented with a population size of 200 and terminated after 105 generations or when convergence is reached. 0  0 0 0 0 & #! $&## #$ 0 0… view at source ↗
Figure 5
Figure 5. Figure 5: DC output versus number of subcarriers N at Pavg = 2 W, d = 2 m, ∆f = 300 Hz, Rth = 2000 bps, and σ 2 e = 0.01 in SAIPT. “Nonlinear Rect.” denotes the proposed nonlinear rectifier model, while “Linear Rect.” refers to the linear rectifier model adopted from [26]. “Joint Opt.” represents the proposed joint waveform optimization scheme, whereas “Uniform Power” corresponds to equal power allocation for both i… view at source ↗
Figure 7
Figure 7. Figure 7: Waveform shaping results of SAIPT with N = 20, Pavg = 2 W, d = 2 m, ∆f = 200 Hz, R = 22 Kbps, and σ 2 e = 0.02: (a) Amplitude and phase of the information transfer vector wˆ I ; (b) Amplitude and phase of the power transfer vector wˆ P. 000 00 00 00 00 00     " σ     [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: DC output versus estimation error variance [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
read the original abstract

Simultaneous acoustic information and power transfer (SAIPT) plays a crucial role in enabling self-sustainable and maintenance-free Internet of Underwater Things (IoUT) networks. This paper studies a multicarrier underwater SAIPT system that jointly considers the frequency-dependent characteristics of acoustic transducers and the nonlinear behavior of rectifier circuits. The waveform vector is firstly optimized using the successive convex approximation (SCA) method under constraints on average and peak transmit power for acoustic power transfer (APT). Then, in the SAIPT scenario, both the power splitting factor and waveform vectors are jointly optimized through an alternating optimization (AO) framework based on SCA, subject to transmit power and achievable rate constraints. Simulation results demonstrate that incorporating the transducer's frequency response, rectifier nonlinearity, and the high peak-to-average power ratio (PAPR) of multicarrier waveforms leads to a significant improvement in acoustic energy transfer efficiency. The results also show that the energy harvesting DC output can be further enhanced by properly choosing system parameters, such as the number of subcarriers and subcarrier spacing.

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 / 1 minor

Summary. The manuscript studies multicarrier waveform design for underwater simultaneous acoustic information and power transfer (SAIPT). It first optimizes the waveform vector via successive convex approximation (SCA) under average and peak transmit power constraints for acoustic power transfer (APT). It then jointly optimizes the power splitting factor and waveform vectors via an alternating optimization (AO) framework based on SCA, subject to transmit power and rate constraints. The abstract claims that simulation results show significant improvement in acoustic energy transfer efficiency when the transducer frequency response, rectifier nonlinearity, and high PAPR of multicarrier waveforms are incorporated, with further gains possible by tuning the number of subcarriers and subcarrier spacing.

Significance. If the claimed simulation improvements hold under validated models, the work could support more efficient self-sustainable IoUT networks by enabling better acoustic energy harvesting while maintaining information transfer. The use of standard SCA and AO methods on realistic frequency-dependent and nonlinear models is a reasonable direction, but the absence of any quantitative results, baselines, or hardware validation in the provided text prevents determining whether the approach yields practically meaningful gains over existing designs.

major comments (2)
  1. [Abstract] Abstract: The central claim that 'simulation results demonstrate... a significant improvement in acoustic energy transfer efficiency' is unsupported by any quantitative data, specific efficiency deltas, baseline comparisons, error bars, or verification that the SCA/AO solutions achieve the stated gains. This evidence gap directly undermines assessment of the headline result.
  2. [Abstract] Abstract: No equations, explicit objective functions, or constraint formulations are supplied for the SCA optimization (APT case) or the AO framework (SAIPT case), nor is any convergence analysis or sensitivity study to the transducer/rectifier model parameters provided. These elements are load-bearing for the efficiency claims.
minor comments (1)
  1. [Abstract] The abstract would be strengthened by including at least one concrete numerical result (e.g., efficiency improvement percentage or harvested DC power value) to substantiate the 'significant improvement' statement.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment below, agreeing where the abstract can be strengthened and proposing targeted revisions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that 'simulation results demonstrate... a significant improvement in acoustic energy transfer efficiency' is unsupported by any quantitative data, specific efficiency deltas, baseline comparisons, error bars, or verification that the SCA/AO solutions achieve the stated gains. This evidence gap directly undermines assessment of the headline result.

    Authors: We agree that the abstract would benefit from quantitative support to substantiate the efficiency claims. We will revise the abstract to include specific simulation outcomes, such as the observed percentage gains in DC output power relative to baselines that omit transducer frequency response or rectifier nonlinearity, along with the subcarrier configurations used. revision: yes

  2. Referee: [Abstract] Abstract: No equations, explicit objective functions, or constraint formulations are supplied for the SCA optimization (APT case) or the AO framework (SAIPT case), nor is any convergence analysis or sensitivity study to the transducer/rectifier model parameters provided. These elements are load-bearing for the efficiency claims.

    Authors: Abstracts are conventionally high-level summaries and do not contain equations or detailed formulations, which appear in Sections III and IV of the manuscript. To improve clarity, we will revise the abstract to explicitly name the SCA and AO methods, the power and rate constraints, and note that convergence behavior and parameter sensitivity are analyzed in the full text. revision: partial

Circularity Check

0 steps flagged

No circularity; standard optimization applied to external models

full rationale

The abstract describes waveform optimization via successive convex approximation (SCA) under power constraints, followed by alternating optimization (AO) jointly with power splitting under rate constraints. Simulation results are reported as direct outputs of these standard numerical methods applied to given parametric models of transducer frequency response and rectifier nonlinearity. No equations, self-citations, fitted parameters, or uniqueness theorems appear in the text that would reduce any claimed result to its inputs by construction. The derivation chain is therefore self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; no information available to populate the ledger.

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