Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Pareto-Optimal Sampling and Resource Allocation for Timely Communication in Shared-Spectrum Low-Altitude Networks

Bowen Li, Jiping Luo, Themistoklis Charalambous, Nikolaos Pappas

TL;DR

The paper tackles the problem of delivering timely aerial data from low-altitude UAVs in shared-spectrum settings, framing the trade-off between aerial energy consumption and terrestrial spectrum occupation through a Pareto frontier between $E$ and $\theta$ under strict timeliness. It introduces a predictive, status-aware, two-layer optimization that separates sampling control (outer problem) from per-interval resource allocation (inner problem), and solves it with a graph-based method that recovers the complete frontier with low complexity. A key result is the monotonic relationship between $E^*(\theta)$ and $\theta$, which enables a shortest-path formulation to obtain sampling instants and a per-interval convex optimization to allocate power and spectrum efficiently. Numerical results show substantial gains, including up to a six-fold reduction in resource blocks or about 6 dB energy savings, while satisfying aerial timeliness, highlighting practical impact for safe and efficient spectrum sharing between aerial and terrestrial networks.

Abstract

Guaranteeing stringent data freshness for low-altitude unmanned aerial vehicles (UAVs) in shared spectrum forces a critical trade-off between two operational costs: the UAV's own energy consumption and the occupation of terrestrial channel resources. The core challenge is to satisfy the aerial data freshness while finding a Pareto-optimal balance between these costs. Leveraging predictive channel models and predictive UAV trajectories, we formulate a bi-objective Pareto optimization problem over a long-term planning horizon to jointly optimize the sampling timing for aerial traffic and the power and spectrum allocation for fair coexistence. However, the problem's non-convex, mixed-integer nature renders classical methods incapable of fully characterizing the complete Pareto frontier. Notably, we show monotonicity properties of the frontier, building on which we transform the bi-objective problem into several single-objective problems. We then propose a new graph-based algorithm and prove that it can find the complete set of Pareto optima with low complexity, linear in the horizon and near-quadratic in the resource block (RB) budget. Numerical comparisons show that our approach meets the stringent timeliness requirement and achieves a six-fold reduction in RB utilization or a 6 dB energy saving compared to benchmarks.

Pareto-Optimal Sampling and Resource Allocation for Timely Communication in Shared-Spectrum Low-Altitude Networks

TL;DR

The paper tackles the problem of delivering timely aerial data from low-altitude UAVs in shared-spectrum settings, framing the trade-off between aerial energy consumption and terrestrial spectrum occupation through a Pareto frontier between and under strict timeliness. It introduces a predictive, status-aware, two-layer optimization that separates sampling control (outer problem) from per-interval resource allocation (inner problem), and solves it with a graph-based method that recovers the complete frontier with low complexity. A key result is the monotonic relationship between and , which enables a shortest-path formulation to obtain sampling instants and a per-interval convex optimization to allocate power and spectrum efficiently. Numerical results show substantial gains, including up to a six-fold reduction in resource blocks or about 6 dB energy savings, while satisfying aerial timeliness, highlighting practical impact for safe and efficient spectrum sharing between aerial and terrestrial networks.

Abstract

Guaranteeing stringent data freshness for low-altitude unmanned aerial vehicles (UAVs) in shared spectrum forces a critical trade-off between two operational costs: the UAV's own energy consumption and the occupation of terrestrial channel resources. The core challenge is to satisfy the aerial data freshness while finding a Pareto-optimal balance between these costs. Leveraging predictive channel models and predictive UAV trajectories, we formulate a bi-objective Pareto optimization problem over a long-term planning horizon to jointly optimize the sampling timing for aerial traffic and the power and spectrum allocation for fair coexistence. However, the problem's non-convex, mixed-integer nature renders classical methods incapable of fully characterizing the complete Pareto frontier. Notably, we show monotonicity properties of the frontier, building on which we transform the bi-objective problem into several single-objective problems. We then propose a new graph-based algorithm and prove that it can find the complete set of Pareto optima with low complexity, linear in the horizon and near-quadratic in the resource block (RB) budget. Numerical comparisons show that our approach meets the stringent timeliness requirement and achieves a six-fold reduction in RB utilization or a 6 dB energy saving compared to benchmarks.

Paper Structure

This paper contains 28 sections, 6 theorems, 38 equations, 7 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Predictive Channel Model
  4. Transmission Model
  5. Metrics for Coexistence
  6. Timeliness requirement for aerial traffic
  7. Fairness requirement for coexistence
  8. Energy efficiency for aerial traffic
  9. Age-Aware Sampling Controller
  10. Problem Formulation
  11. Pareto Analysis and Problem Decomposition
  12. Pareto Analysis
  13. Problem Decomposition
  14. Graph-Based Algorithm for Optimal Control
  15. Graph-Based Outer Solution
  16. ...and 13 more sections

Key Result

Proposition 2

(Pareto frontier.) The set is the Pareto frontier of $\mathscr{P}1$, where $\underline{\theta}\triangleq\theta^{*}$, and $\{\theta^{*},E^{*}\}$ is the utopian point of the frontier (see Figure fig:Pareto_illu).

Figures (7)

  • Figure 1: Illustration of periodic versus status-aware sampling policies. (a) A periodic policy enforces equal spacing and causes the update to fall within a poor-channel interval. (b) A status-aware policy adapts the sampling time to bypass the poor interval and satisfy the freshness constraint. Note that although status-aware sampling may involve more sampling and transmission instants, it exploits better channel intervals and therefore uses fewer and less energy.
  • Figure 2: telemetry system model. The symbols along the trajectory illustrate the 's positions at different time instants. The reports its state information or sensing data to a fusion center through .
  • Figure 3: Illustration of Pareto optimality for a two-variable, two-objective optimization problem. (a) The nonconvex feasible set. (b) Bi-objective $(E,\theta)$ space, where the feasible space and the objective space are non-convex and non-continuous, thereby classical heuristic algorithms are challenging to find the complete Pareto frontier. (c) Bi-objective $(f_2,f_1)$ space, where the Pareto frontier is non-concave, thereby, the weighted-sum method cannot guarantee the discovery of all Pareto optima.
  • Figure 4: Illustration of the timing-control graph, where each vertex represents a possible sampling instant, each directed edge denotes a transmission during the interval between the two sampling instants, and the edge weight indicates the optimal energy consumption for the transmission induced by the edge.
  • Figure 5: The simulation layout with $N=5$ and one patrol .
  • ...and 2 more figures

Theorems & Definitions (7)

  • Definition 1
  • Proposition 2
  • Corollary 3
  • Proposition 4
  • Proposition 5
  • Lemma 6
  • Lemma 7