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Spatio-Temporal Wireless-Optical Planning for Multi-UAV Networks

Binglei Wang, Huiru Ao, Fan Yang, Zhenjie Zhou, Zhonghua Peng, Jialong Li

arXiv:2609.39156v1cs.NI

TL;DR

Existing communication-aware path planning can overlook shared optical backhaul bottlenecks even when wireless access is acceptable. STWO jointly considers flight distance, wireless quality, and time-varying optical load, updating occupancy during sequential multi-UAV planning; at N = 200 it lowers peak offered-load ratio and congestion ratio against the reported baselines.

  • Problem

    Existing path-planning methods focus on flight distance or wireless access quality, while traffic can still concentrate on shared optical backhaul links and create transport-side bottlenecks.

    Source: §I · p. 1, §II · p. 2

  • Method

    STWO combines flight distance, wireless link quality, and time-varying optical offered-load ratio, updating optical-link occupancy as UAVs are planned sequentially.

    Source: §I · p. 1

  • Results

    At N = 200, STWO lowers peak offered-load ratio from 280.00% and 256.00% to 122.00% (56.4% and 52.3% reductions), and congestion ratio from 15.92% and 21.22% to 5.82%.

    Source: A. Experimental Setup · p. 3, B. Impact of UAV Density and Traffic… · p. 4

  • Takeaways & Limitations

    The experiments support joint wireless and optical-backhaul awareness as important for reliable multi-UAV transmission in urban low-altitude networks.

    Source: §V · p. 5

Abstract

from arXiv · show

Multi-unmanned aerial vehicle (UAV) networks in urban low-altitude environments couple UAV mobility, wireless access, and optical backhaul resources. Existing path-planning methods optimize flight distance or wireless signal quality, but can still concentrate traffic on shared optical backhaul links. We present Spatio-Temporal Wireless-Optical (STWO) planner, a backhaul-aware path-planning algorithm that jointly considers flight distance, wireless link quality, and time-varying optical-link offered-load ratio. STWO updates backhaul occupancy during sequential multi-UAV planning, allowing later UAVs to avoid congested optical paths while maintaining wireless connectivity. Experiments show that STWO reduces peak optical-link offered-load ratio by up to 56.4\% and congestion ratio by up to 72.6\% under dense UAV deployment, demonstrating the importance of wireless-optical awareness for reliable multi-UAV transmission.

I. INTRODUCTION

Existing communication-aware path planning emphasizes flight distance or wireless quality, but can overlook shared optical backhaul bottlenecks. STWO jointly accounts for flight distance, wireless quality, and time-varying optical load, with reported reductions in peak load and congestion under dense deployment.

I. INTRODUCTION

  • UAV mobility affects flight distance, obstacle avoidance, serving base-station selection, and end-to-end transmission as UAVs upload sensing data during flight.The paper motivates planning beyond the air-to-ground wireless link.Source: §I · p. 1
  • Existing communication-aware planners mainly optimize wireless coverage, reliability, trajectory, or handovers, while often overlooking optical backhaul after traffic enters base stations.Traffic can concentrate on shared AGG–Core links even when wireless access appears reliable.Source: §I · p. 1
  • Under dense deployment, STWO reduces peak optical-link offered-load ratio by up to 56.4% and congestion ratio by up to 72.6% versus Vanilla A* and Signal-Aware A*.The planner jointly considers flight distance, wireless link quality, and time-varying optical-link offered-load ratio.Source: §I · p. 1
  • STWO updates optical-link occupancy after each UAV is planned, so later UAVs can account for existing backhaul load and avoid congested optical paths.Its joint path-search cost combines flight distance, wireless link quality, and time-varying optical offered-load ratio.Source: §I · p. 1

II. COUPLED WIRELESS-OPTICAL MODEL

UAV paths determine serving base stations, which map traffic onto corresponding BS–AGG–Core optical backhaul paths. The resulting coupling means a path with good wireless access can still load shared transport links.

II. COUPLED WIRELESS-OPTICAL MODEL

  • A UAV path affects its serving base stations, and each selected base station maps traffic onto a corresponding BS–AGG–Core optical backhaul path.The paper treats UAV mobility, wireless access, and optical backhaul load as inherently coupled.Source: §II · p. 1, Figure 1 · p. 2
  • Optimizing only signal quality can draw multiple UAVs to strongly covered base stations and concentrate traffic on shared optical links.The figure's example maps BS 6 traffic to already congested AGG Node 3, while BS 5 uses a less-loaded backhaul path.Source: §II · p. 2
  • The planning objective should jointly account for flight distance, wireless connection quality, and time-varying optical backhaul offered-load ratio.It should avoid wireless-blocked areas while preventing multiple UAVs from overloading the same AGG–Core links.Source: §II · p. 2

III. STWO ALGORITHM DESIGN

STWO searches over space-time states using flight distance, wireless link quality, and time-varying optical-backhaul load. Sequential load updates let later UAVs account for occupancy on previously selected backhaul paths while maintaining wireless connectivity.

III. STWO ALGORITHM DESIGN

  • STWO combines flight distance, wireless quality, and optical offered-load ratio in a space-time A* path search.Its evaluation function adds accumulated path cost and a heuristic distance estimate.Source: §III · pp. 2–3
  • For each candidate base station, STWO maps traffic to its BS–AGG–Core path and weighs wireless quality against optical offered-load ratio.The path is the set of optical links carrying traffic when a UAV accesses that base station.Source: §III · pp. 2–3
  • The optical-link offered-load ratio normalizes offered traffic by link capacity; values above 1 indicate demand oversubscription, not physical utilization above 100%.STWO includes this time-varying ratio in path search.Source: §III · p. 2
  • STWO models received signal strength using a calibrated reference-power offset, distance, and blockage loss, then penalizes deficits below a target signal level.The model provides calibrated dBm-scale values for comparing wireless quality across paths, rather than site-specific field measurements.Source: §III · p. 2
  • After each UAV path is planned, STWO updates time-indexed optical-link loads so subsequent UAVs perceive existing backhaul occupancy.This sequential mechanism guides later paths away from high-load optical paths while maintaining wireless connectivity.Source: §III · p. 3
  • The transition cost weights flight distance, wireless penalty, and optical-load penalty; the latter increases quadratically above a congestion-warning threshold.The threshold is θ, and k controls the penalty for exceeding it.Source: §III · pp. 2–3

IV. EXPERIMENTAL RESULTS AND ANALYSIS

The evaluation varies UAV density, traffic rate, and backhaul capacity, comparing STWO with distance-based and signal-aware baselines. It examines congestion reduction alongside wireless connectivity and path length.

IV. EXPERIMENTAL RESULTS AND ANALYSIS

  • The evaluation tests whether STWO reduces optical-link congestion while preserving wireless connectivity and moderate path length.The comparison includes distance-based and signal-aware baselines.Source: §IV · p. 3
  • The evaluation varies UAV density, traffic rates, and backhaul capacities.Source: §IV · p. 3
  • Table I is identified as summarizing the key simulation parameters.Source: Table I · p. 3

A. Experimental Setup

The simulations use a grid-based urban scenario with fixed default UAV traffic and backhaul capacities. STWO is compared with distance-based and signal-aware planners using optical, wireless, and path-length metrics.

A. Experimental Setup

  • The 1000 m × 1000 m area uses a 100 × 100 grid, 10 m resolution, 100 m flight altitude, three buildings, five base stations, and three aggregation nodes.All UAVs share destination (900, 900); each base station maps to one AGG–Core optical-backhaul path.Source: A. Experimental Setup · p. 3
  • Unless varied, the setup uses 50 Gbps BS–AGG capacity, 100 Gbps AGG–Core capacity, 120 UAVs, and 1000 Mbps per UAV, with zero background load.Source: A. Experimental Setup · p. 3
  • STWO is compared with Vanilla A∗ and Signal-Aware A∗, with Sequential Optical-only A∗ as an ablation, using PeakLoad, congestion, hotspot, average RSRP, and average path length.PeakLoad is the maximum optical-link demand-to-capacity ratio; Cong counts samples above 1.0 and Hotspot above 0.8.Source: A. Experimental Setup · p. 3

B. Impact of UAV Density and Traffic Rate

As UAV density and traffic rate rise, STWO keeps optical-load and congestion measures lower than the baselines. Its results include reduced peak offered-load ratios at N = 200 and at 1.5 Gbps.

B. Impact of UAV Density and Traffic Rate

C. Impact of Backhaul Capacity

Across the tested AGG–Core capacity range, STWO achieves the lowest peak AGG–Core offered-load ratio. At 50 Gbps, it also reports lower congestion than both baselines.

C. Impact of Backhaul Capacity

D. Hotspot Ratio and Ablation Study

STWO reduces both severe optical-link congestion and near-capacity hotspot operation, while balancing wireless quality against backhaul load. Its hotspot ratio remains lower than both baselines across settings, and its ablation results show the trade-offs among the objectives.

D. Hotspot Ratio and Ablation Study

  • STWO consistently lowers hotspot ratio than Vanilla A∗ and Signal-Aware A∗ across settings, including at N = 200 and as traffic rates increase.Hotspot ratio counts optical-link offered-load ratios above the 0.8 warning threshold.Source: D. Hotspot Ratio and Ablation Study · p. 4
  • Signal-Aware A∗ achieves the best AvgRSRP but higher PeakLoad, while Sequential Optical-only A∗ reduces optical load at the cost of degraded AvgRSRP.These ablation results expose the trade-off between wireless quality and optical backhaul load.Source: D. Hotspot Ratio and Ablation Study · p. 4
  • STWO matches Sequential Optical-only A∗ in PeakLoad and improves AvgRSRP from −78.92 dBm to −77.26 dBm, with only a slight congestion-ratio increase.The reported comparison supports STWO's balance of wireless connectivity and optical backhaul load under the joint objective.Source: D. Hotspot Ratio and Ablation Study · p. 4
  • At largest scale, Hotspot (> 0.8) falls from 20.26% and 23.83% to 16.78%, and true congestion ratio falls from 15.92% and 21.22% to 5.82%.The passage does not identify the baselines corresponding to the first two values.Source: D. Hotspot Ratio and Ablation Study · p. 4, Table II · p. 5

V. CONCLUSION

The conclusion presents STWO as a multi-UAV planner that jointly considers flight distance, wireless signal quality, and time-varying optical-link utilization. Experiments show lower peak offered-load and congestion ratios while maintaining competitive wireless quality and path length; future work will address handovers and backhaul orchestration.

V. CONCLUSION

  • STWO coordinates multi-UAV path planning with access and backhaul conditions by jointly considering flight distance, wireless signal quality, and time-varying optical-link utilization.Source: §V · p. 5
  • Experiments show that STWO reduces peak optical-link offered-load and congestion ratios while maintaining competitive wireless quality and path length.Source: §V · p. 5
  • Future work will extend STWO to handover-aware trajectory optimization and backhaul resource orchestration.Source: §V · p. 5
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