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arxiv: 2604.19010 · v1 · submitted 2026-04-21 · 📡 eess.SP

SSB-Based Sensing-Assisted Robust Beamforming for High-Mobility UAV Communications in LAWN

Aimin Tang , Shuhan Wang , Yin Xu This is my paper

Pith reviewed 2026-05-10 02:53 UTC · model grok-4.3

classification 📡 eess.SP
keywords sensing-assisted beamformingUAV communicationsrobust beamformingsynchronization signal blockhigh-mobilitypredictive trackingsum-rate maximizationLAWN
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The pith

Sensing from periodic synchronization signals enables robust beamforming for high-mobility UAVs by replacing explicit channel feedback with predictive tracking and uncertainty modeling.

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

The paper sets out to demonstrate that reliable beamforming for fast-moving UAVs in low-altitude networks is possible without the overhead and misalignment that come from sending channel state information back to the base station. It replaces feedback with sensing performed on the always-on synchronization signal blocks, using a layered algorithm to extract range, velocity, and angle estimates even from hybrid antenna arrays. An extended Kalman filter bridges the gaps between infrequent signal bursts while adding a correction term to capture the extra uncertainty from sudden maneuvers. These estimates are turned into statistical channel descriptions that feed a robust optimization problem whose goal is to maximize the average sum-rate across multiple users. If the approach works, networks could maintain high data rates and stable links even when UAV trajectories change rapidly and feedback intervals are long.

Core claim

The central claim is that an SSB-based sensing-assisted predictive robust beamforming framework, built on a hierarchical sensing algorithm that combines 2D range-velocity profiling with augmented beamspace MUSIC, an extended Kalman filter for inter-burst tracking, and covariance correction for maneuver uncertainties, allows the communication channel to be represented by predictive correlation matrices rather than instantaneous CSI, enabling a multi-user sum-rate maximization problem under uncertainty that is solved efficiently by successive convex approximation and alternating minimization.

What carries the argument

The predictive correlation matrices derived from sensing-driven state estimation and uncertainty correction, which replace instantaneous CSI in the robust beamforming optimization.

If this is right

  • Explicit CSI feedback is eliminated, lowering overhead and latency in high-mobility UAV scenarios.
  • Average network sum-rate and link stability improve over both feedback-based and non-robust beamforming designs, with the largest gains appearing at high mobility and large SSB intervals.
  • Maneuver-induced prediction errors are explicitly captured through covariance correction inside the tracking filter.
  • The resulting non-convex optimization problem remains tractable through successive convex approximation and alternating minimization.

Where Pith is reading between the lines

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

  • The same sensing-plus-tracking structure could be adapted to other high-mobility settings such as ground vehicles or low-altitude delivery drones.
  • Extending the covariance correction to include additional sensor data like inertial measurements might further tighten the uncertainty model for abrupt turns.
  • Larger SSB intervals made possible by this method would free spectrum resources for actual data transmission.
  • Field trials comparing measured channel statistics against the predicted distributions would directly test whether the modeling step holds in real environments.

Load-bearing premise

The hierarchical sensing algorithm together with the extended Kalman filter and covariance correction must generate statistical distributions of range and angular parameters that correctly describe the actual communication channel.

What would settle it

A simulation or real-world test showing that the proposed method does not achieve higher spectral efficiency and link stability than feedback-based beamforming when UAVs move at high speed and SSB intervals are large would falsify the central performance claim.

Figures

Figures reproduced from arXiv: 2604.19010 by Aimin Tang, Shuhan Wang, Yin Xu.

Figure 1
Figure 1. Figure 1: System architecture and an illustration of SSB-base [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: An illustration of the SSB-assisted communication b [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison between the feedback-based beamforming [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Sensing results for the three targets within the cove [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Communication rate over a nonlinear trajectory. [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Average communication rate under different levels o [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Predictive beam patterns for the three-user case. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
read the original abstract

High-mobility uncrewed aerial vehicle (UAV) communications in low-altitude wireless networks (LAWN) demand reliable beamforming, while conventional feedback-based schemes suffer from excessive overhead and severe misalignment under rapid trajectory variations. To address this challenge, this paper proposes an SSB-based sensing-assisted predictive robust beamforming framework that replaces explicit channel state information (CSI) feedback with sensing-driven state estimation and uncertainty-aware optimization. Leveraging the periodic 'always-on' synchronization signal block (SSB), a hierarchical sensing algorithm tailored for hybrid digital-analog uniform planar arrays is developed, combining 2D range-velocity profiling and augmented beamspace multiple signal classification (MUSIC). By integrating a locally-focused analog receive beamformer, the proposed sensing design can ensure energy accumulates across different radio-frequency (RF) chains while resolving angular ambiguity. An extended Kalman filter (EKF) is further employed to track UAV states between sparse synchronization-signal (SS) bursts, and a covariance correction is introduced to characterize maneuver-induced prediction uncertainties. Based on the derived statistical distributions of range and angular parameters, the communication channel is modeled through predictive correlation matrices rather than instantaneous CSI, leading to a multi-user robust beamforming formulation that maximizes average network sum-rate under uncertainty. The resulting nonconvex problem is efficiently solved via successive convex approximation and alternating minimization. Simulation results demonstrate that the proposed framework significantly enhances spectral efficiency and link stability compared with feedback-based beamforming and non-robust beamforming design, particularly in high-mobility and large-SSB-interval scenarios.

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 / 2 minor

Summary. The manuscript proposes an SSB-based sensing-assisted predictive robust beamforming framework for high-mobility UAV communications in LAWN. It replaces explicit CSI feedback with sensing-driven estimation by developing a hierarchical sensing algorithm (2D range-velocity profiling combined with augmented beamspace MUSIC on hybrid digital-analog UPAs), integrating a locally-focused analog receive beamformer, applying EKF tracking between sparse SS bursts with a covariance correction to capture maneuver-induced uncertainties, deriving statistical distributions of range and angular parameters to form predictive correlation matrices, and formulating a multi-user robust beamforming problem that maximizes average network sum-rate under uncertainty. The nonconvex problem is solved via successive convex approximation and alternating minimization. Simulations report gains in spectral efficiency and link stability versus feedback-based and non-robust baselines, particularly for high-mobility and large-SSB-interval cases.

Significance. If the predictive correlation matrices faithfully capture the channel statistics, the framework offers a practical way to reduce CSI feedback overhead while improving reliability for UAVs in dynamic low-altitude environments. The approach builds on established tools (MUSIC, EKF, SCA) and supplies simulation evidence of performance advantages in the targeted regimes; these elements constitute a coherent contribution to sensing-assisted beamforming.

major comments (1)
  1. [Abstract] Abstract (final paragraph): the central claim that the hierarchical sensing algorithm plus EKF tracking with covariance correction yields statistical distributions of range/angular parameters that accurately produce the predictive correlation matrices for the robust beamforming optimization is load-bearing. No derivation steps, explicit assumptions on the distributions (e.g., Gaussianity), or validation metrics against ground-truth channel statistics are provided, leaving open whether maneuver-induced uncertainties are correctly characterized in high-mobility/large-SSB-interval regimes; this directly affects whether the reported sum-rate and stability gains versus baselines can be expected to hold.
minor comments (2)
  1. [Simulations] Simulation results section: error bars, standard deviations, or confidence intervals on the spectral-efficiency and stability curves are not mentioned; their inclusion would strengthen the quantitative comparison to the feedback-based and non-robust baselines.
  2. [Abstract] Abstract and method description: the specific assumptions underlying the 2D range-velocity profiling, augmented beamspace MUSIC, and covariance correction (e.g., array response model, noise statistics, or post-hoc parameter choices) should be stated explicitly to allow reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive review, positive assessment of the framework's coherence, and recommendation for major revision. We address the single major comment below and will incorporate clarifications and additional validation to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract (final paragraph): the central claim that the hierarchical sensing algorithm plus EKF tracking with covariance correction yields statistical distributions of range/angular parameters that accurately produce the predictive correlation matrices for the robust beamforming optimization is load-bearing. No derivation steps, explicit assumptions on the distributions (e.g., Gaussianity), or validation metrics against ground-truth channel statistics are provided, leaving open whether maneuver-induced uncertainties are correctly characterized in high-mobility/large-SSB-interval regimes; this directly affects whether the reported sum-rate and stability gains versus baselines can be expected to hold.

    Authors: We appreciate the referee's identification of this load-bearing aspect. The manuscript derives the statistical distributions of range and angular parameters in Section III-C via the EKF prediction step with the introduced covariance correction for maneuver uncertainties; the predictive correlation matrices are then constructed in Section IV-A under the assumption of Gaussianity for the state estimation errors. However, we agree that the abstract is too concise and that explicit validation metrics (e.g., comparison of predicted versus empirical channel statistics) are not sufficiently highlighted. In the revised version we will (i) add a brief statement of the Gaussianity assumption and key derivation steps to the abstract, (ii) include a new paragraph in Section III summarizing the distribution derivations, and (iii) add Monte-Carlo validation results in Section V that quantify the fidelity of the predicted correlation matrices against ground-truth realizations, particularly for high-mobility and large-SSB-interval cases. These additions will directly support the reported performance gains. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation chain is self-contained

full rationale

The paper constructs a sensing-to-beamforming pipeline by first applying a hierarchical sensing algorithm (2D range-velocity profiling plus augmented beamspace MUSIC) to SSB signals, then using EKF tracking with an added covariance correction term to produce statistical distributions of range and angular parameters. These distributions are inserted into predictive correlation matrices that feed a standard robust sum-rate maximization solved by successive convex approximation and alternating minimization. Each step applies well-known techniques (MUSIC, EKF, SCA) whose outputs are not algebraically forced to equal their inputs; the final performance claims rest on simulation comparisons against explicit feedback-based and non-robust baselines rather than on any self-referential redefinition or fitted-parameter renaming. No load-bearing self-citation or uniqueness theorem is invoked in the provided derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Insufficient detail in abstract to enumerate specific free parameters or axioms; the approach relies on standard signal processing components (MUSIC, EKF) whose assumptions are not explicitly audited here.

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discussion (0)

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