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Joint Spectrum and Airspace Resource Optimization for Low-altitude Wireless Network

Yafei Guo, Ziye Jia, Lei Zhang, Jingxian Liu, Qiuming Zhu, Qihui Wu

arXiv:2608.26830v1eess.SY

TL;DR

Limited spectrum and airspace resources complicate safe, collision-free UAV operation and coordinated corridor planning. The paper discretizes airspace, sequentially allocates spectrum with CVAM, and plans corridors with JSARA; simulations report lower traversal costs than baselines.

  • Problem

    Limited spectrum and airspace resources make efficient UAV task completion difficult while increasing interference and collision risks.

    Source: §I · p. 1

  • Method

    The paper discretizes airspace into risk-labeled grids, uses CVAM for spectrum allocation, and applies JSARA for joint spectrum and airspace planning.

    Source: §I · p. 1, A. Digital Air Corridor Model · p. 2, B. Joint Spectrum and Airspace… · p. 4

  • Results

    The proposed algorithms significantly reduce total traversal costs for multiple air corridors compared with baseline methods.

    Source: §V · p. 6

  • Takeaways & Limitations

    Coupling spectrum and airspace allocation provides a systematic basis for cross-domain optimization of multiple low-altitude air corridors.

    Source: §V · p. 6

Abstract

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Low-altitude wireless networks have emerged as a promising platform for enabling the safe and efficient operation of unmanned aerial vehicles (UAVs). However, due to the limited spectrum and airspace resources, it is challenging to efficiently accomplish UAV flight tasks without collisions. In this paper, we propose a sequential framework with two coupled stages that coordinates spectrum allocation and airspace planning to construct efficient low-altitude air corridors. Specifically, the low-altitude airspace is discretized into a set of digital grids, where obstacles are modeled as impermeable units. Then, we formulate an optimization problem to minimize the total traversal cost of air corridors, which is challenging to solve due to the tight coupling between spectrum allocation and path planning.Therefore, we first design a constrained Vickrey-Clarke-Groves (VCG) ascending auction mechanism to allocate the spectrum resources. Then, we propose a joint spectrum and airspace resource allocation algorithm to minimize the total traversal cost of air corridors. Finally, simulation results show that the proposed algorithms achieve lower total costs than the baseline algorithms.

I. INTRODUCTION

Limited spectrum and airspace resources make collision avoidance, interference reduction, and efficient UAV task completion challenging. The paper proposes a coupled framework that sequentially allocates spectrum and plans air corridors.

  • Large-scale UAV deployment increases collision risks and same-frequency interference under limited spectrum and airspace resources.Source: §I · p. 1
  • Coordinated spectrum and airspace allocation for multiple low-altitude air corridors remains underexplored.Source: §I · p. 1
  • The framework discretizes low-altitude airspace into permeable digital grids and impermeable obstacle grids managed by an airspace management system.Source: §I · p. 1
  • CVAM first allocates spectrum resources, after which JSARA uses those results to minimize total air-corridor traversal cost.Source: §I · p. 1
  • The proposed framework couples spectrum allocation with airspace planning despite the resulting computational complexity.Source: §I · p. 1

A. Digital Air Corridor Model

The digital air corridor model converts continuous low-altitude airspace into connected three-dimensional grids with risk-based obstacle classification. UAV corridors are represented as grid sequences from specified origins to destinations.

B. Spectrum Resource Model

The spectrum resource model partitions available bandwidth into orthogonal blocks and represents heterogeneous UAV valuations and allocations. Jain’s index and a fairness threshold constrain allocation balance.

C. Joint Spectrum and Airspace Allocation

The joint allocation model evaluates air corridors through distance and grid-risk costs, while spectrum allocation changes the risk-cost coefficient. Tunable weights combine these costs for optimization.

2) Distance Cost of Air Corridor:

The distance cost of an air corridor is proportional to the number of traversed grids, excluding the starting grid.

D. Problem Formulation

The formulation minimizes total air-corridor traversal cost while enforcing connectivity, non-overlap, endpoint, spectrum, and feasible-grid constraints.

D. Problem Formulation

III. ALGORITHM DESIGN

The solution framework uses sequential spectrum allocation and joint spectrum–airspace optimization to reduce corridor traversal costs and conflicts.

III. ALGORITHM DESIGN

  • CVAM first allocates spectrum resources among all available UAV types.Source: §III · p. 3
  • JSARA then targets lower total air-corridor traversal cost and reduced conflict probability between corridors.Source: §III · p. 3
  • The two algorithms address problem P0 through sequential spectrum allocation followed by joint spectrum and airspace resource allocation.Source: §III · p. 3

A. Constrained VCG Ascending Auction Mechanism

The constrained VCG ascending auction mechanism allocates spectrum iteratively using UAV-type valuations, allocation limits, demand conditions, and price updates.

A. Constrained VCG Ascending Auction Mechanism

B. Joint Spectrum and Airspace Resource Allocation Algorithm

JSARA couples spectrum auction with improved A* airspace planning to generate multiple efficient and safe corridors while minimizing traversal cost.

Algorithm design

  • JSARA generates multiple efficient and safe air corridors using an improved A* algorithm that weighs distance and grid risk.Its heuristic uses f(v) = g(v) + h(v), with g(v) combining distance and grid-risk costs.Source: B. Joint Spectrum and Airspace… · p. 4
  • The algorithm discretizes continuous airspace into digital grids, identifies obstacles, auctions spectrum blocks, and computes grid-risk coefficients before path search.Algorithm 2 initializes open and closed lists with each UAV type's starting grid.Source: B. Joint Spectrum and Airspace… · p. 4

Path search

  • At each iteration, JSARA selects the open grid with the smallest estimated cost, moves it to the closed list, and updates its risk index.Neighboring grids are examined while obstacles and closed grids are excluded from expansion.Source: B. Joint Spectrum and Airspace… · pp. 4–5
  • When a destination is reached, JSARA backtracks the selected grids to obtain the complete corridor path and calculates its total cost.After path generation, grids whose risk exceeds the threshold are added to the obstacle set before processing subsequent UAV types.Source: B. Joint Spectrum and Airspace… · pp. 4–5

IV. SIMULATION RESULTS

Simulations evaluate corridor cost, execution time, and auction fairness across expanding airspaces, multiple corridors, and fading models. JSARA generally lowers traversal cost, while improved A* balances solution quality with computational expense and linear fading provides the selected valuation model.

Simulation setup

  • The simulations use a 100 MHz bandwidth divided into 20 spectrum blocks and a 10-meter unit grid across airspaces up to 1,000 meters long.The experiments run in MATLAB R2022b on an Intel Core i7-10750H computer with 16 GB of RAM.Source: §IV · p. 5

Single-corridor performance

  • The improved A* algorithm reduces total cost relative to traditional A* and grid-constrained RRT*, while remaining marginally costlier than grid-constrained Dijkstra.Dijkstra's global optimality comes with significantly higher computational complexity.Source: §IV · p. 5
  • The improved A* algorithm achieves the shortest execution time, whereas grid-constrained Dijkstra's execution time grows exponentially with simulation-space expansion.The other three algorithms show linear execution-time scaling, indicating a quality–expense balance for improved A*.Source: §IV · p. 5

Multiple-corridor performance

  • JSARA consistently records the lowest total traversal cost for two and four air corridors among JSARA, ARAWS, JSGRA, and GRAWS.All algorithms incur increasing costs as the digital airspace environment expands.Source: §IV · pp. 5–6

Fading-model comparison

  • With more than 10 auctioned spectrum blocks, fairness under exponential and linear fading is approximately 20% to 30% higher than under logarithmic fading.Linear fading is more stable than exponential fading, so the linear model is selected for spectrum valuation.Source: §IV · p. 6

V. CONCLUSIONS

The proposed framework couples spectrum and airspace allocation through CVAM and JSARA, and simulations show significantly reduced traversal costs across diverse environments.

  • CVAM optimizes spectrum allocation, while JSARA minimizes the total traversal cost of multiple air corridors.Source: §V · p. 6
  • The framework couples spectrum and airspace resource allocation to provide a systematic basis for cross-domain optimization.Source: §V · p. 6
  • Extensive simulations across diverse environments demonstrate significantly lower total traversal costs than baseline methods.Source: §V · p. 6
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