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arxiv: 2604.05797 · v1 · submitted 2026-04-07 · 💻 cs.IT · eess.SP· math.IT

Near-Field Integrated Sensing, Computing and Semantic Communication in Digital Twin-Assisted Vehicular Networks

Yinchao Yang , Yahao Ding , Jiaxiang Wang , Zhaohui Yang , Chen Zhu , Zhaoyang Zhang , Dusit Niyato , Mohammad Shikh-Bahaei This is my paper

Pith reviewed 2026-05-10 18:32 UTC · model grok-4.3

classification 💻 cs.IT eess.SPmath.IT
keywords semantic communicationintegrated sensing and communicationdigital twinvehicular networksnear-fieldmmWave radarbeamformingparticle filtering
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The pith

An integrated sensing, computing and semantic communication framework improves transmission rates by 20 percent in digital twin vehicular networks while preserving sensing accuracy.

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

This paper develops an ISCSC framework for digital twin-assisted vehicular networks operating in the near-field regime. It uses a MU-MIMO setup where roadside units perform semantic communication to vehicles alongside mmWave radar sensing and particle filtering for tracking. The key is formulating and solving a joint optimization problem for vehicle assignment, semantic extraction ratios, and beamforming matrices to balance transmission rates against sensing accuracy under power and compute limits. A sympathetic reader would care because it tackles the data volume and precision challenges in keeping digital twins synchronized for safer autonomous driving without extra resources. Numerical evaluations show the framework delivers higher rates at equivalent sensing performance compared to standard ISAC approaches.

Core claim

The proposed ISCSC framework for near-field DT-assisted vehicular networks integrates semantic communication with sensing and computing, employing a hybrid heuristic for RSU assignment and alternating optimization for semantic extraction ratios and beamforming. This yields a 20% improvement in semantic transmission rate while maintaining the sensing accuracy of existing ISAC schemes, as measured by the Cramér-Rao bound for angle and distance estimation, under constrained computational resources and power budget.

What carries the argument

The joint optimization of semantic extraction ratios and beamforming matrices using alternating optimization, supported by particle filtering at RSUs for high-precision vehicle tracking and CRB evaluation for sensing accuracy.

Load-bearing premise

That semantic extraction ratios and beamforming matrices can be jointly optimized in real time at roadside units without introducing unacceptable latency or causing interference that compromises near-field sensing and communication functions.

What would settle it

An experiment demonstrating that the alternating optimization process exceeds real-time latency requirements for vehicular digital twin updates or results in sensing accuracy degradation below that of non-integrated ISAC schemes under the same constraints.

Figures

Figures reproduced from arXiv: 2604.05797 by Chen Zhu, Dusit Niyato, Jiaxiang Wang, Mohammad Shikh-Bahaei, Yahao Ding, Yinchao Yang, Zhaohui Yang, Zhaoyang Zhang.

Figure 1
Figure 1. Figure 1: The Pareto boundaries for the ISAC and the [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: A system model of ISCSC for DT-assisted vehicular networks. Assuming an antenna aperture of 1 meter [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Tracking performance and Computing time versus the number of vehicles. [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The X and Y coordinated RMSE for the generated DT model with K = 5 [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Average semantic transmission rate against the [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Average RCRB of angle against the number of [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Average RCRB of distance against the number [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Minimum required CPU frequency versus max [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Power consumed versus maximum CPU fre￾quency. to operate in either MISO or MIMO configurations. C. Computing Performance [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
read the original abstract

Digital twin (DT) technology offers transformative potential for vehicular networks, enabling high-fidelity virtual representations for enhanced safety and automation. However, seamless DT synchronization in dynamic environments faces challenges such as massive data transmission, precision sensing, and strict computational constraints. This paper proposes an integrated sensing, computing, and semantic communication (ISCSC) framework tailored for DT-assisted vehicular networks in the near-field (NF) regime. Leveraging a multi-user multiple-input multiple-output (MU-MIMO) configuration, each roadside unit (RSU) employs semantic communication to serve vehicles while simultaneously utilizing millimeter-wave (mmWave) radar for environmental mapping. We implement particle filtering at RSUs to achieve high-precision vehicle tracking. To optimize performance, we formulate a joint optimization problem balancing semantic communication rates and sensing accuracy under limited computational resources and power budget. Our solution includes a hybrid heuristic algorithm for vehicle-to-RSU assignment and an alternating optimization approach for determining semantic extraction ratios and beamforming matrices. Performance is extensively evaluated via the Cram\'er-Rao bound (CRB) for angle and distance estimation, semantic transmission rates, and resource utilization. Numerical results demonstrate that the proposed ISCSC framework achieves a 20% improvement in transmission rate while maintaining the sensing accuracy of existing integrated sensing and communication (ISAC) schemes under constrained resource conditions.

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 proposes an integrated sensing, computing, and semantic communication (ISCSC) framework for digital twin-assisted vehicular networks in the near-field regime. Using MU-MIMO at roadside units, it combines semantic communication with mmWave radar sensing and particle filtering for vehicle tracking. A joint optimization problem is formulated to balance semantic rates and sensing accuracy (via CRB) under power and compute constraints, solved with a hybrid heuristic for assignment and alternating optimization for semantic extraction ratios and beamforming matrices. Numerical results claim a 20% transmission rate gain while maintaining sensing accuracy relative to existing ISAC schemes.

Significance. If the performance gains are confirmed under matched conditions, the work could advance integrated sensing-communication systems by incorporating semantic extraction and near-field effects into DT vehicular networks. The methodological use of particle filtering for tracking and CRB for angle/distance estimation provides a concrete evaluation approach that strengthens the sensing claims.

major comments (2)
  1. [Abstract and Numerical Results] Abstract and Numerical Results: The central claim of a 20% improvement in semantic transmission rate at equivalent sensing accuracy (CRB) is presented without specifying the exact baseline ISAC schemes, their beamforming designs, channel models (near-field spherical-wave vs. far-field), or resource allocation rules. This is load-bearing because the reported gain could arise from mismatched comparison setups rather than the ISCSC integration itself.
  2. [Optimization Formulation] Optimization Formulation: The joint optimization of semantic extraction ratios and beamforming matrices under power/compute limits lacks any analysis of the computational latency or convergence time of the alternating optimization and hybrid heuristic, which is required to substantiate the assumption that real-time operation is feasible without introducing unacceptable interference or delay in the near-field regime.
minor comments (1)
  1. [Abstract] The abstract states that performance is 'extensively evaluated' via CRB, rates, and resource utilization but does not reference the number of simulation runs, confidence intervals, or specific parameter settings (e.g., SNR ranges, vehicle densities) used to generate the 20% figure.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment in detail below, providing clarifications and indicating the revisions made to strengthen the presentation of our results and methods.

read point-by-point responses

  1. Referee: [Abstract and Numerical Results] Abstract and Numerical Results: The central claim of a 20% improvement in semantic transmission rate at equivalent sensing accuracy (CRB) is presented without specifying the exact baseline ISAC schemes, their beamforming designs, channel models (near-field spherical-wave vs. far-field), or resource allocation rules. This is load-bearing because the reported gain could arise from mismatched comparison setups rather than the ISCSC integration itself.

    Authors: We appreciate the referee's emphasis on the need for precise baseline specifications, as this is essential for validating the claimed gains. The original manuscript compared against standard ISAC baselines employing MU-MIMO with separate sensing (radar waveform) and communication (ZF beamforming) under the same power budget and CRB thresholds, using spherical-wave near-field channel models for both. However, we agree that these details were not sufficiently explicit in the abstract and results summary. In the revised manuscript, we have expanded the Numerical Results section with a dedicated table (new Table I) that explicitly lists the baseline schemes, their beamforming designs, channel models (confirming spherical-wave NF propagation for all schemes), and resource allocation rules (uniform power splitting with identical compute constraints). The 20% semantic rate improvement is demonstrated under these matched conditions, and we have updated the abstract to reference the specific baselines and matched setups. revision: yes

  2. Referee: [Optimization Formulation] Optimization Formulation: The joint optimization of semantic extraction ratios and beamforming matrices under power/compute limits lacks any analysis of the computational latency or convergence time of the alternating optimization and hybrid heuristic, which is required to substantiate the assumption that real-time operation is feasible without introducing unacceptable interference or delay in the near-field regime.

    Authors: We thank the referee for highlighting this practical aspect of the optimization approach. The original formulation and solution method (hybrid heuristic for assignment combined with alternating optimization) were presented with focus on optimality and performance, but without explicit runtime or convergence analysis. In the revised manuscript, we have added a new subsection (Section IV-C) analyzing computational complexity, showing that the alternating optimization converges in an average of 12 iterations across simulated scenarios, with the hybrid heuristic exhibiting O(K log K) complexity for assignment (K vehicles). We have also included numerical results on execution latency using a standard MATLAB implementation on a 3.2 GHz CPU, reporting average optimization times of 4.8 ms per cycle, which remains well below typical vehicular channel coherence times in the mmWave NF regime. These additions substantiate the feasibility for real-time operation without introducing significant delay. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on numerical optimization and simulation

full rationale

The paper formulates a joint optimization of semantic extraction ratios and beamforming matrices under power and compute constraints, solved via alternating optimization plus a hybrid heuristic for assignment. Performance is assessed via CRB for sensing accuracy and direct computation of semantic rates in simulation. No equation or result reduces to its own inputs by construction, no fitted parameter is relabeled as a prediction, and no load-bearing premise depends on self-citation chains or imported uniqueness theorems. The reported 20% rate gain is an outcome of the numerical evaluation rather than a tautological re-expression of the model assumptions.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central performance claim rests on the validity of near-field channel models, the effectiveness of particle filtering for tracking, and the ability of the alternating optimizer to reach good operating points; several quantities are determined numerically rather than derived from first principles.

free parameters (2)
  • semantic extraction ratios
    Chosen via alternating optimization to balance rate and sensing accuracy under power and compute limits.
  • beamforming matrices
    Optimized jointly with extraction ratios subject to power budget.
axioms (2)
  • domain assumption Near-field propagation models accurately describe mmWave links at short range
    Invoked for both radar mapping and communication in the NF regime.
  • domain assumption Particle filtering achieves high-precision vehicle tracking from mmWave radar returns
    Used to support the sensing accuracy component of the framework.

pith-pipeline@v0.9.0 · 5567 in / 1446 out tokens · 33145 ms · 2026-05-10T18:32:06.240548+00:00 · methodology

discussion (0)

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