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arxiv: 2410.12746 · v2 · pith:XQTQCIMMnew · submitted 2024-10-16 · 📡 eess.SP

DRIP: A Versatile Family of Space-Time ISAC Discrete-time Sequences

Dexin Wang , Ahmad Bazzi , Marwa Chafii This is my paper

Pith reviewed 2026-05-23 19:01 UTC · model grok-4.3

classification 📡 eess.SP
keywords ISACPAPR controlspace-time waveformsbeampattern designmulti-user interferencecoordinate descentradar chirps
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The pith

DRIP waveforms control PAPR while producing multi-target beampatterns and low multi-user interference for ISAC.

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

The paper introduces DRIP as a family of discrete-time space-time sequences for integrated sensing and communications. These sequences are shaped so they meet a chosen peak-to-average power ratio while their beampatterns focus energy on several directions and suppress interference, resembling classic radar chirps. For the communication side the same sequences are optimized to reduce interference among multiple users for different signal constellations. A block cyclic coordinate descent procedure is presented to solve the non-convex design problem and is claimed to reach an optimal waveform. If the construction succeeds, a single waveform family can be used for both sensing and communication tasks without violating transmitter power limits.

Core claim

DRIP waveforms are constructed to satisfy prescribed PAPR constraints, exhibit desired beampatterns for multi-target sensing that resemble radar chirps, and minimize multi-user interference for communication. The design is achieved through a block cyclic coordinate descent algorithm that handles the non-convex optimization and converges to an optimal solution, as validated by simulations showing superior performance and favorable trade-offs.

What carries the argument

DRIP waveform family, generated by dual beam-similarity aware optimization under PAPR control and solved via block cyclic coordinate descent.

If this is right

  • The waveforms can simultaneously target multiple desired directions while suppressing interference for multi-target sensing.
  • They closely resemble radar chirps while satisfying the PAPR specification.
  • Multi-user interference is minimized across a range of constellations for the communication function.
  • The block cyclic coordinate descent procedure converges to an optimal ISAC solution under the stated constraints.
  • Dynamic adjustment of PAPR becomes possible without redesigning the entire waveform family.

Where Pith is reading between the lines

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

  • The same construction could be examined for compatibility with existing OFDM-based communication standards.
  • Hardware experiments would be needed to check whether the simulated PAPR and beampattern properties survive amplifier nonlinearities.
  • The method might be extended to larger antenna arrays or to joint design with receive processing.
  • Trade-off curves between sensing resolution and achievable data rate could be derived from the same optimization framework.

Load-bearing premise

The block cyclic coordinate descent algorithm reliably solves the non-convex optimization and reaches an optimal DRIP waveform.

What would settle it

Apply the algorithm to a concrete PAPR target and multi-target beampattern requirement; if the output sequence fails to meet the exact PAPR level or the stated beampattern null depths within the tolerance shown in the paper, the convergence claim is false.

Figures

Figures reproduced from arXiv: 2410.12746 by Ahmad Bazzi, Dexin Wang, Marwa Chafii.

Figure 1
Figure 1. Figure 1: An ISAC scenario composed of P users for MU-communications and Q multiple targets in the presence of I interferers. antennas, is to support MU communications toward K DL communication single-antenna users, while sensing Q targets2 in the vicinity, in the presence of I interfering targets, which can also be classified as clutter. Moreover, the radar targets of interest are assumed to be positioned at θ1 . .… view at source ↗
Figure 2
Figure 2. Figure 2: Average radar SINR of the first target over iterations, when two [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: The performance of the proposed waveform in terms of average sum [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 8
Figure 8. Figure 8: Also, an increase in the QAM size slightly gives a less [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 5
Figure 5. Figure 5: Average sum rate of the proposed waveform over [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: DRIP constellations over varying ϵ values. The number of Tx antennas is 12, the signal length L = 7, the number of communication users P = 5, and η = 6 dB. The x and y axis represent the in-phase and quadrature components of the constellations. 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 28 30 32 34 36 38 40 42 44 46 48 󰂃 A v e r a g e SIN R (d B) 10◦ 30◦ Target 64-QAM 64-PSK 16-QAM 16-PSK Constellation 1 1.2 1.… view at source ↗
Figure 7
Figure 7. Figure 7: Trade-off within sensing between target SINR and [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Resulting beampatterns for different 2-target configurations. The number of transmit antennas is [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: CCDF of PAPR for two different sets of testing parameters. The parameters [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: MUI distribution with respect to ϵ. The number of Tx antennas is 12, the signal length L = 7, the number of communication users P = 4 , and the modulation used is 16-QAM. 0 1 2 3 4 5 6 7 8 9 −12 −8 −4 0 4 8 12 η (dB) A v e r a g e M UI (d B) 󰂃 = 0.01 󰂃 = 0.45222 󰂃 = 0.89444 󰂃 = 1.1156 󰂃 = 1.3367 󰂃 = 1.5578 󰂃 = 1.7789 󰂃 = 2 8.85 9 −15 −14.5 −14 −13.5 −13 [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Average MUI with respect to η. The number of Tx antennas is 12, the signal length L = 7, the number of communication users P = 4 , and the modulation used is 16-QAM. is obtained by varying a wide range of η values spanning 0 dB to 9 dB, a wide range of g¯q from 10 dB and 20 dB, and different constellations of QPSK, 16-QAM, 64-QAM, 16- PSK, and 64-PSK. It can be seen that the average empirical similarity i… view at source ↗
read the original abstract

The following paper introduces Dual beam-similarity awaRe Integrated sensing and communications (ISAC) with controlled Peak-to-average power ratio (DRIP) waveforms. DRIP is a novel family of space-time ISAC waveforms designed for dynamic peak-to-average power ratio (PAPR) adjustment. The proposed DRIP waveforms are designed to conform to specified PAPR levels while exhibiting beampattern properties, effectively targeting multiple desired directions and suppressing interference for multi-target sensing applications, while closely resembling radar chirps. For communication purposes, the proposed DRIP waveforms aim to minimize multi-user interference across various constellations. Addressing the non-convexity of the optimization framework required for generating DRIP waveforms, we introduce a block cyclic coordinate descent algorithm. This iterative approach ensures convergence to an optimal ISAC waveform solution. Simulation results validate the DRIP waveforms' superior performance, versatility, and favorable ISAC trade-offs, highlighting their potential in advanced multi-target sensing and communication 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

1 major / 0 minor

Summary. The paper introduces DRIP, a family of space-time ISAC discrete-time sequences designed for dynamic PAPR adjustment. The waveforms target multiple directions with beampattern matching and interference suppression for multi-target sensing (resembling radar chirps) while minimizing multi-user interference for communications across constellations. A block cyclic coordinate descent algorithm is proposed to solve the underlying non-convex optimization framework, with the claim that it ensures convergence to an optimal ISAC waveform solution. Simulation results are presented to validate superior performance, versatility, and favorable ISAC trade-offs.

Significance. If the central claims hold, DRIP provides a versatile waveform family for ISAC applications with explicit PAPR control, multi-target beampattern flexibility, and communication interference minimization. The block cyclic coordinate descent approach and simulation-based validation of performance trade-offs represent constructive contributions to ISAC waveform design.

major comments (1)
  1. [Abstract] Abstract: The assertion that the block cyclic coordinate descent algorithm 'ensures convergence to an optimal ISAC waveform solution' for the non-convex framework (PAPR control, beampattern matching, multi-user interference minimization) is not supported by standard convergence results. Block coordinate descent on non-convex problems converges at best to stationary points in general; global optimality requires additional structure (e.g., convexity after relaxation or problem-specific bounds) that is not indicated. This claim is load-bearing for the paper's algorithmic contribution and must be revised or substantiated with a proof.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful reading and constructive feedback on the convergence claim in our abstract. We address the single major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The assertion that the block cyclic coordinate descent algorithm 'ensures convergence to an optimal ISAC waveform solution' for the non-convex framework (PAPR control, beampattern matching, multi-user interference minimization) is not supported by standard convergence results. Block coordinate descent on non-convex problems converges at best to stationary points in general; global optimality requires additional structure (e.g., convexity after relaxation or problem-specific bounds) that is not indicated. This claim is load-bearing for the paper's algorithmic contribution and must be revised or substantiated with a proof.

    Authors: We agree that the original wording overstates the theoretical guarantee. Standard results for block coordinate descent on non-convex problems establish convergence to stationary points (under mild conditions such as continuous differentiability of the objective and compactness of the feasible set), but not necessarily to a global optimum. The manuscript does not provide a proof of global optimality, nor does it invoke problem-specific structure that would guarantee it. We will revise the abstract (and any corresponding statements in the main text) to state that the algorithm converges to a stationary point of the non-convex problem. The practical performance of the obtained waveforms will continue to be supported by the simulation results. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper formulates a non-convex optimization problem for DRIP waveforms with explicit constraints on PAPR, beampattern matching, and multi-user interference, then proposes a block cyclic coordinate descent algorithm to solve it. No load-bearing step reduces by construction to its own inputs: the optimality claim is a statement about the algorithm's convergence behavior on the defined problem, not a self-definition or a fitted parameter renamed as a prediction. No self-citation chains or ansatzes imported from prior author work are invoked to justify the core result. The derivation is self-contained as a constructive proposal of sequences and solver, with simulations serving as external validation rather than circular input.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Based solely on abstract; no explicit free parameters, axioms, or invented entities beyond the named DRIP family and the optimization algorithm are detailed.

axioms (1)
  • domain assumption Non-convex waveform optimization problem is solvable to optimality via block cyclic coordinate descent
    Invoked in abstract to justify the algorithm choice
invented entities (1)
  • DRIP waveform family no independent evidence
    purpose: Space-time ISAC sequences with controlled PAPR, beampattern, and interference properties
    New family introduced to meet the stated ISAC requirements

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