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arxiv: 2511.13006 · v2 · submitted 2025-11-17 · 📡 eess.SY · cs.SY

Cooperative ISAC for LAE: Joint Trajectory Planning, Power allocation, and Dynamic Time Division

Fangzhi Li , Zhichu Ren , Cunhua Pan , Hong Ren , Jing Jin , Qixing Wang , Jiangzhou Wang This is my paper

Pith reviewed 2026-05-17 21:24 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords ISACUAV trajectory planningcooperative sensingpower allocationdynamic time divisionradar mutual informationlow-altitude economy
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The pith

Joint optimization of UAV trajectories, power splits, and dynamic sensing-communication time ratios maximizes total data rates while meeting cumulative radar information targets in multi-base-station setups.

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

This paper develops a cooperative integrated sensing and communication system in which ground base stations jointly serve multiple UAVs. A dynamic time-division approach reserves part of each slot for beam scanning to collect radar mutual information before switching to data transmission. The objective is to maximize the aggregate communication rate subject to a mission-level cumulative sensing requirement. The authors jointly tune the UAV flight paths, the allocation of transmit power between sensing and communication, and the sensing-to-communication time split, solving the resulting non-convex problem with an alternating optimization and successive convex approximation procedure that produces a non-decreasing objective sequence converging to a finite value.

Core claim

The proposed joint design, solved via alternating optimization and successive convex approximation, yields significantly higher sum communication rates than benchmarks that use static trajectories, partial resource optimization, or single non-cooperative base-station transmission, while still satisfying the required cumulative radar mutual information; sensitivity analysis further shows how sensing thresholds and UAV count separately shape resource allocation and spatial layout.

What carries the argument

Alternating optimization and successive convex approximation applied to the coupled variables of UAV trajectories, sensing/communication power allocation, and per-slot time-division ratio.

If this is right

  • Cooperative multi-base-station transmission improves both communication rates and sensing efficiency compared with single-base-station operation.
  • Allowing the sensing-communication time ratio to vary per slot produces better rate-sensing trade-offs than any fixed ratio.
  • Higher sensing thresholds force UAVs into more clustered flight formations to maintain coverage.
  • Adding more UAVs requires corresponding adjustments in spatial separation to keep the objective from saturating.

Where Pith is reading between the lines

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

  • The same joint-optimization structure could be adapted to energy-limited UAV missions by adding explicit battery or flight-time constraints.
  • Extending the model to include moving ground users or time-varying interference would test robustness of the dynamic time-division rule.
  • A hardware-in-the-loop testbed with real phased-array radar and 5G waveforms could directly validate whether the mutual-information threshold maps to usable target detection probability.

Load-bearing premise

The radar mutual information model and wireless channel assumptions remain accurate enough that the optimized solution translates to real-world performance, and the iterative procedure reaches a point close to the global optimum despite non-convexity.

What would settle it

A measurement campaign that records realized sum rates and achieved radar mutual information for the computed trajectories and power allocations, then checks whether the gap between simulated and measured values stays within the model's predicted error bounds.

Figures

Figures reproduced from arXiv: 2511.13006 by Cunhua Pan, Fangzhi Li, Hong Ren, Jiangzhou Wang, Jing Jin, Qixing Wang, Zhichu Ren.

Figure 1
Figure 1. Figure 1: System model. The system operates under a time-division duplexing (TDD) protocol and adopts a quasi-static channel model, assuming that the wireless channel remains constant within each coherence frame of duration τ = T /N. Each frame is partitioned into two non-overlapping phases: a sensing phase followed by a communication phase, as shown in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Time slot division. In Phase I (sensing phase), all BSs perform directional beam scanning to sense the surrounding environment, including the positions and angular features of UAVs. The duration of this phase is δ[n] · τ , where δ[n] ∈ (0, 1), ∀n ∈ N . Specifically, BS m employs a prede￾fined two-dimensional beamforming codebook denoted by Bm = {wm q,l}, where wm q,l[n] denotes a beam steering vector towar… view at source ↗
Figure 3
Figure 3. Figure 3: Optimized UAV Trajectories under Different Position Settings and thresholds of MI Constraints. [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The speed of the UAVs in each time slot. [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: The power allocated by the BSs to each UAVs in [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Sum communication rate of the proposed algorithm and benchmark schemes under various transmit powers. by UAV 1 during its high-rate operational phase. This continuous adjustment of power distribution highlights the algorithm’s adaptability to the rapidly changing mo￾bile channel environment. The overall power expenditure profile mirrors the sum communication rate performance shown in [PITH_FULL_IMAGE:figu… view at source ↗
Figure 8
Figure 8. Figure 8: Sum communication rate of the proposed algorithm and benchmark schemes under various MI requirements [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
read the original abstract

To enhance the performance of aerial-ground networks, this paper proposes an integrated sensing and communication (ISAC) framework for multi-UAV systems. In our model, ground base stations (BSs) cooperatively serve multiple unmanned aerial vehicles (UAVs), employing a dynamic time-division strategy where beam scanning for sensing precedes data communication in each time slot. To maximize the sum communication rate while satisfying a mission-level cumulative radar mutual information (MI) requirement, we jointly optimize the UAV trajectories, communication and sensing power allocation, and the time-division ratio. The resulting highly coupled non-convex optimization problem is efficiently solved using an alternating optimization (AO) and successive convex approximation (SCA) framework, which yields a non-decreasing objective sequence and convergence to a finite objective value under the adopted surrogate-based iterative procedure. Extensive simulation results demonstrate that our proposed joint design significantly outperforms benchmark schemes with static trajectories, partially optimized resources, or non-cooperative single-BS transmission. Furthermore, a comprehensive sensitivity analysis reveals the distinct mechanisms by which sensing thresholds and the number of UAVs influence resource allocation and spatial organization, highlighting the critical importance of dynamic, multi-dimensional resource management for effectively navigating the sensing-communication trade-off in low-altitude economies.

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 manuscript proposes a cooperative ISAC framework for multi-UAV systems in low-altitude economy networks. Ground BSs cooperatively serve UAVs using dynamic time-division (sensing via beam scanning followed by communication) in each slot. The objective is to maximize the sum communication rate subject to a cumulative radar mutual-information requirement by jointly optimizing UAV trajectories, communication/sensing power allocation, and the time-division ratio. The resulting non-convex problem is solved via an alternating optimization (AO) plus successive convex approximation (SCA) procedure that produces a non-decreasing objective sequence converging to a finite value. Extensive simulations show the joint design outperforming benchmarks with static trajectories, partially optimized resources, and non-cooperative single-BS transmission; a sensitivity analysis on sensing thresholds and UAV count is also presented.

Significance. If the reported simulation gains hold under varied initializations and the MI model remains representative, the work demonstrates the practical benefit of joint multi-dimensional resource optimization for sensing-communication trade-offs in aerial networks. The explicit statement of a non-decreasing objective sequence under the surrogate procedure and the parameter-sensitivity study are strengths that support applicability to LAE scenarios.

major comments (2)
  1. [Section IV (AO-SCA Algorithm)] Section IV (AO-SCA Algorithm): The procedure is shown to generate a non-decreasing objective that converges to a finite value, yet no analysis of initialization sensitivity or comparison against global bounds is supplied. Because the central claim of significant outperformance rests entirely on the quality of the attained local solutions, an empirical study (multiple random feasible starts or small-instance global comparison) is required to confirm that the reported rate-MI gains are attributable to the joint formulation rather than solver artifacts.
  2. [Section V (Numerical Results)] Section V (Numerical Results), benchmark definitions: The partial-optimization and static-trajectory baselines are compared, but the precise manner in which each benchmark fixes or relaxes subsets of the variables (trajectory, power, time ratio) is not fully specified. Without these details the magnitude of the reported gains cannot be reproduced or assessed for robustness across different constraint tightness levels.
minor comments (2)
  1. [Abstract and Section I] Abstract and Section I: The acronym 'LAE' appears before its expansion; spelling out 'low-altitude economy' on first use would improve accessibility.
  2. [Section III] Notation in Section III: The time-division ratio variable is introduced alongside power variables; a short table or explicit reminder of its range [0,1] per slot would reduce reader confusion in the subsequent optimization.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which have helped us identify areas where additional analysis and clarity can strengthen the manuscript. We address each major comment below and commit to revisions that incorporate the suggested improvements.

read point-by-point responses

  1. Referee: [Section IV (AO-SCA Algorithm)] Section IV (AO-SCA Algorithm): The procedure is shown to generate a non-decreasing objective that converges to a finite value, yet no analysis of initialization sensitivity or comparison against global bounds is supplied. Because the central claim of significant outperformance rests entirely on the quality of the attained local solutions, an empirical study (multiple random feasible starts or small-instance global comparison) is required to confirm that the reported rate-MI gains are attributable to the joint formulation rather than solver artifacts.

    Authors: We appreciate the referee's suggestion to verify the robustness of the AO-SCA solutions with respect to initialization. In the revised manuscript, we will add an empirical study with multiple random feasible starts. The results of this study will show that the reported outperformance is maintained across different initializations, supporting the validity of our local solutions for the joint design. While exact global optimization is intractable for the problem sizes considered, this additional analysis will help alleviate concerns about solver artifacts. revision: yes

  2. Referee: [Section V (Numerical Results)] Section V (Numerical Results), benchmark definitions: The partial-optimization and static-trajectory baselines are compared, but the precise manner in which each benchmark fixes or relaxes subsets of the variables (trajectory, power, time ratio) is not fully specified. Without these details the magnitude of the reported gains cannot be reproduced or assessed for robustness across different constraint tightness levels.

    Authors: We agree that the benchmark schemes require more precise definitions to ensure reproducibility. Accordingly, we will revise Section V to clearly specify how each baseline is constructed. For the static-trajectory benchmark, the UAV trajectories are fixed to straight-line paths, with optimization performed only over power allocation and time-division ratios. For partial-optimization benchmarks, we will detail the specific subsets of variables that are optimized versus fixed. This will allow readers to assess the gains under various constraint settings. revision: yes

Circularity Check

0 steps flagged

Derivation is self-contained; no circular reductions identified.

full rationale

The paper formulates an optimization problem directly from standard rate and radar mutual-information expressions, then applies AO-SCA to obtain a non-decreasing sequence that converges to a finite value. No step reduces a claimed prediction or result to a fitted parameter, self-citation, or ansatz that is itself defined by the target quantity. The simulation comparisons to static and single-BS baselines are external to the derivation and do not rely on quantities defined by the optimizer itself. The central claim therefore rests on independent modeling assumptions and numerical evaluation rather than on any self-referential loop.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard wireless channel and radar signal models plus the effectiveness of the AO-SCA solver; no new physical entities or ad-hoc fitted constants are introduced beyond the optimization variables themselves.

axioms (1)
  • domain assumption Standard wireless channel models and radar mutual information expressions accurately represent the physical layer.
    Used to formulate the objective and cumulative MI constraint.

pith-pipeline@v0.9.0 · 5538 in / 1194 out tokens · 44775 ms · 2026-05-17T21:24:34.005332+00:00 · methodology

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Reference graph

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