Source-linked AI summary
Joint Spectrum and Airspace Resource Optimization for Low-altitude Wireless Network
Yafei Guo, Ziye Jia, Lei Zhang, Jingxian Liu, Qiuming Zhu, Qihui Wu
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.
Method
The paper discretizes airspace into risk-labeled grids, uses CVAM for spectrum allocation, and applies JSARA for joint spectrum and airspace planning.
Results
The proposed algorithms significantly reduce total traversal costs for multiple air corridors compared with baseline methods.
Takeaways & Limitations
Coupling spectrum and airspace allocation provides a systematic basis for cross-domain optimization of multiple low-altitude air corridors.
Abstract
from arXiv · showhide
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.
- Coordinated spectrum and airspace allocation for multiple low-altitude air corridors remains underexplored.
- The framework discretizes low-altitude airspace into permeable digital grids and impermeable obstacle grids managed by an airspace management system.
- CVAM first allocates spectrum resources, after which JSARA uses those results to minimize total air-corridor traversal cost.
- The proposed framework couples spectrum allocation with airspace planning despite the resulting computational complexity.
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.
- Continuous airspace is discretized into digital grids using the physical dimensions and unit grid length.
- Each grid stores a spatial index and risk index, with six Cartesian neighbors defining grid connectivity.
- The airspace management system estimates grid risk from local traffic density and white Gaussian noise interference.
- Grids with risk index ρm ≤ ρ0 are permeable, while those with ρm > ρ0 are impermeable obstacles.
- Each UAV task type receives an origin-to-destination corridor represented as an ordered sequence of risk-indexed grid steps.
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.
- Available bandwidth B is partitioned into orthogonal spectrum blocks of bandwidth ψ under the LSA framework.
- Each UAV type assigns an estimated value Vh,n to spectrum block n, reflecting heterogeneous spectrum requirements.
- The allocation records kh, the number of spectrum blocks assigned to UAV type h.
- Jain’s fairness index χ ranges from 1/H to 1, with higher values indicating more balanced auction outcomes.
- The auction imposes χ > χmin to ensure fairness among UAV types.
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.
- Total traversal cost combines each corridor’s distance cost and grid-risk cost.
- The weights ω1 and ω2 tune the relative contributions of path length and grid risk according to operational requirements.
- The grid-risk cost uses γh as the unit cost incurred per unit of grid risk index.
- Spectrum allocation is tightly coupled with corridor selection because allocated blocks determine the grid-risk cost coefficient.
- γh decreases monotonically with allocated blocks kh; when kh exceeds κK, the grid-risk penalty is negligible.
- The single-corridor distance cost is based on the number of traversed grids excluding the starting grid.
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.
- Jr,h = δ(|Lh| −1) defines the distance cost for air corridor Lh.δ is the unit cost of traversing one grid, while |Lh| −1 excludes the starting grid.
- The distance cost coefficient δ represents the unit cost of traversing a single grid.
- The term |Lh| −1 counts the corridor’s grids other than its 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: The optimization problem minimizes total traversal cost while ensuring corridor safety.
- D. Problem Formulation: Constraint (10) requires consecutive corridor grids to remain connected.
- D. Problem Formulation: Constraint (11) prevents different air corridors from overlapping at the same grid step.
- D. Problem Formulation: Constraints (12) and (13) require corridors to start at Si and end at Se.
- D. Problem Formulation: Constraint (14) limits the total spectrum allocation to K blocks, while constraint (15) restricts selected grids to the permitted airspace.
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.
- III. ALGORITHM DESIGN: JSARA then targets lower total air-corridor traversal cost and reduced conflict probability between corridors.
- III. ALGORITHM DESIGN: The two algorithms address problem P0 through sequential spectrum allocation followed by joint spectrum and airspace resource allocation.
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: CVAM is inspired by the ascending implementation of the VCG mechanism and is designed for efficient spectrum allocation.The mechanism is presented in Algorithm 1.
- A. Constrained VCG Ascending Auction Mechanism: UAV types that reach their allocation limits kmax are excluded from the current bidding round, while other types calculate demand normally.
- A. Constrained VCG Ascending Auction Mechanism: The algorithm initializes auction rounds, starting price P, spectrum valuations Vh,n, spectrum requirements Dh, and locked blocks ξh.
- A. Constrained VCG Ascending Auction Mechanism: When aggregate demand is below the remaining supply, a proportional allocation phase is activated while per-type limits remain enforced.
- A. Constrained VCG Ascending Auction Mechanism: A locking rule guides spectrum-block allocation, and any remaining unallocated blocks trigger a price increase and another auction round.
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.
- Algorithm design: 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.
- 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.
- Path search: 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.
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.
- 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.
- Single-corridor performance: 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*.
- 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.
- 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.
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.
- The framework couples spectrum and airspace resource allocation to provide a systematic basis for cross-domain optimization.
- Extensive simulations across diverse environments demonstrate significantly lower total traversal costs than baseline methods.