X-Band UAV-enabled Integrated Sensing and Communications for Vehicular Networks

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, long

Summary

This paper proposes and investigates an optimal time allocation strategy for Uncrewed Aerial Vehicle (UAV)-enabled Integrated Sensing and Communication (ISaC) systems operating in the X-band (specifically 10.05–10.5 GHz) for vehicular networks. The research develops an optimization framework to dynamically split total signal duration between sensing and communication modes, addressing the trade-off between sensing accuracy and communication performance. It considers realistic UAV-to-ground channel conditions, including both single-shadowing (SS) and double-shadowing (DS) models. Simulation results demonstrate that the proposed adaptive time allocation outperforms equal allocation, particularly in the 0–35 dBm power range, by ensuring minimum communication rates and sufficient sensing reliability. Key findings show that optimal communication time increases with distance (50m to 2000m) and required data rates (1500 to 2500 bits/frame), while sensing time decreases.

Key takeaway

For AI Scientists or Robotics Engineers designing UAV-enabled vehicular networks, you should implement dynamic time allocation for Integrated Sensing and Communication (ISaC) systems. This approach significantly improves power efficiency and ensures reliable performance compared to static allocation, especially in the 0–35 dBm range. Adapt your system's sensing and communication time split based on real-time factors like UAV-to-ground distance and required data rates to optimize resource utilization.

Key insights

Optimal time allocation in UAV-enabled ISaC systems dynamically balances sensing and communication under varying channel conditions.

Principles

• Channel conditions and target distance dictate sensing-communication time split.
• Optimal time allocation enhances power efficiency over equal distribution.
• Higher communication power eventually saturates throughput gains.

Method

A convex optimization problem maximizes sum capacity by allocating time (θ) between sensing and communication, subject to minimum communication rate and radar SNR constraints. The optimal solution is found at the upper boundary of the feasible interval.

In practice

• Implement adaptive time allocation based on UAV-to-ground distance.
• Adjust time split according to required communication data rates.
• Prioritize optimal allocation in low-to-mid power ranges for efficiency.

Topics

• UAV-enabled ISaC
• Vehicular Networks
• X-band Communications
• Time Allocation Optimization
• Channel Modeling
• Power Efficiency

Best for: AI Scientist, Research Scientist, Robotics Engineer

Related on AIssential

• X-Band UAV-enabled Integrated Sensing and Communications for Vehicular Networks — Optimal time allocation in X-band UAV-enabled ISaC systems balances sensing accuracy and communication performance for vehicular networks.
• A Camera-Cooperative ISAC Framework for Multimodal Non-Cooperative UAVs Sensing — Multimodal sensing, combining camera and ISAC, significantly optimizes resource allocation for non-cooperative UAV detection and tracking.
• Digital Twin-Assisted Adaptive Multi-Agent DRL for Intelligent Spectrum and Resource Management in Open-RAN UAV-Enabled 6G Networks — A digital twin-assisted adaptive multi-agent DRL framework optimizes spectrum and resource management in dynamic Open-RAN UAV-enabled 6G networks.
• When Simultaneous Localization and Mapping Meets Wireless Communications: A Survey — SLAM and wireless communications mutually enhance autonomous systems' localization, mapping, and control capabilities.
• Over-the-Air Computation Uses Radio Interference to Crunch Data — Over-the-air computation merges wireless communication and computation by exploiting signal superposition for direct in-air data processing.
• CesiumAstro acquires Vidrovr to embed AI in communications systems — Embedding AI directly into space telecommunications payloads enables adaptive RF optimization and autonomous, real-time decision-making at the edge.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.