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Transmission Line Design Considerations

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Related Experiment Videos

Resource-Adaptive Semantic Transmission and Client Scheduling for OFDM-Based V2X Communications.

Jiahao Liu1, Yuanle Chen1, Wei Wu1

  • 1College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

Federated learning in vehicle-to-everything (V2X) communication is improved by adaptive token budgeting and channel-aware client selection, ensuring efficient data transmission and accurate model training for safety applications.

Keywords:
OFDM resource adaptationadaptive transmissionchannel-aware resource allocationclient schedulingintegrated sensing and communicationsemantic communicationvehicle-to-everything (V2X)

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Area of Science:

  • Vehicular communication systems
  • Machine learning for intelligent transportation systems
  • Wireless network resource management

Background:

  • Orthogonal frequency-division multiplexing (OFDM) based vehicle-to-everything (V2X) uplink scheduling faces challenges with variable resource-block allocation impacting semantic encoder output.
  • Conventional encoders produce fixed token counts, leading to data truncation and corrupted federated learning gradients, particularly for non-line-of-sight vehicles.
  • Urban V2X deployments exhibit spatial patterns where vehicles at intersections have poor conditions but crucial data, yet are excluded by throughput-driven client selection.

Purpose of the Study:

  • To develop a unified framework addressing token budget violations and biased client selection in OFDM-based V2X federated learning.
  • To enhance the efficiency and accuracy of federated learning models for V2X safety applications by optimizing data transmission and client representation.

Main Methods:

  • Implemented a Sensing-Guided Adaptive Modulation (SGAM) module for per-slot token budget derivation and Gumbel-TopK pruning with hard capacity clipping.
  • Introduced a Channel-Decoupled Federated Learning (CDFL) module for independent client partitioning by channel quality and data complexity.
  • Utilized facility location optimization for diverse client representative selection and inverse propensity weighting for partition-size imbalance correction.

Main Results:

  • Achieved a Macro-F1 score of 0.710, an 8.7-point improvement over the baseline, on the NuScenes dataset with 20 non-IID vehicular clients.
  • Demonstrated zero budget violations throughout training and a 75% reduction in training variance.
  • Significantly improved worst-class F1 score, more than doubling it compared to FedAvg, indicating better performance for underrepresented clients.

Conclusions:

  • The proposed framework effectively resolves token budget constraints and improves client selection fairness in OFDM-based V2X federated learning.
  • The SGAM and CDFL modules enhance model accuracy, reduce training variance, and ensure critical safety data from challenging environments is utilized.
  • This approach offers a robust solution for real-world V2X federated learning, particularly in complex urban scenarios.