Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Joint Channel Bandwidth Assignment and Relay Positioning for Predictive Flying Networks

Ruben Queiros, Megumi Kaneko, Helder Fontes, Rui Campos

TL;DR

This paper addresses reliable connectivity for predictive Flying Networks by jointly optimizing HAP relay placement and channel bandwidth under predefined FEN trajectories and limited backhaul. It introduces SAFnet, a penalty-enhanced Simulated Annealing framework that exploits trajectory knowledge to achieve high performance with lower computational cost than exhaustive search. The approach incorporates constraint-specific penalties and a repair mechanism, yielding significant reductions in relay and backhaul outages and a notable throughput gain over baselines. The work demonstrates practical impact for disaster response and mobile network resilience, offering a scalable, low-complexity method for real-time resource and position optimization in two-tier UAV networks.

Abstract

Flying Networks (FNs) have emerged as a promising solution to provide on-demand wireless connectivity when network coverage is insufficient or the communications infrastructure is compromised, such as in disaster management scenarios. Despite extensive research on Unmanned Aerial Vehicle (UAV) positioning and radio resource allocation, the challenge of ensuring reliable traffic relay through backhaul links in predictive FNs remains unexplored. This work proposes Simulated Annealing for predictive FNs (SAFnet), an innovative algorithm that optimizes network performance under positioning constraints, limited bandwidth and minimum rate requirements. Our algorithm uniquely leverages prior knowledge of the first-tier node trajectories to assign bandwidth and dynamically adjust the position of the second-tier flying relay. Building upon Simulated Annealing, our approach enhances this well-known AI algorithm with penalty functions, achieving performance levels comparable to exhaustive search while significantly reducing computational complexity.

Joint Channel Bandwidth Assignment and Relay Positioning for Predictive Flying Networks

TL;DR

This paper addresses reliable connectivity for predictive Flying Networks by jointly optimizing HAP relay placement and channel bandwidth under predefined FEN trajectories and limited backhaul. It introduces SAFnet, a penalty-enhanced Simulated Annealing framework that exploits trajectory knowledge to achieve high performance with lower computational cost than exhaustive search. The approach incorporates constraint-specific penalties and a repair mechanism, yielding significant reductions in relay and backhaul outages and a notable throughput gain over baselines. The work demonstrates practical impact for disaster response and mobile network resilience, offering a scalable, low-complexity method for real-time resource and position optimization in two-tier UAV networks.

Abstract

Flying Networks (FNs) have emerged as a promising solution to provide on-demand wireless connectivity when network coverage is insufficient or the communications infrastructure is compromised, such as in disaster management scenarios. Despite extensive research on Unmanned Aerial Vehicle (UAV) positioning and radio resource allocation, the challenge of ensuring reliable traffic relay through backhaul links in predictive FNs remains unexplored. This work proposes Simulated Annealing for predictive FNs (SAFnet), an innovative algorithm that optimizes network performance under positioning constraints, limited bandwidth and minimum rate requirements. Our algorithm uniquely leverages prior knowledge of the first-tier node trajectories to assign bandwidth and dynamically adjust the position of the second-tier flying relay. Building upon Simulated Annealing, our approach enhances this well-known AI algorithm with penalty functions, achieving performance levels comparable to exhaustive search while significantly reducing computational complexity.

Paper Structure

This paper contains 12 sections, 4 equations, 3 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Problem Formulation
  4. Benchmark and Proposed Solutions
  5. Conventional Heuristic Method
  6. Conventional Simulated Annealing
  7. Proposed SAFnet Method
  8. Performance Evaluation
  9. Simulation Settings
  10. Time Complexity Analysis
  11. Simulation Results
  12. Conclusions and Future Work

Figures (3)

  • Figure 1: Two-tier predictive Flying Network.
  • Figure 2: HAP positions defined by the benchmark algorithms and Prop. SAFnet.
  • Figure 3: Network performance metrics for larger network scenarios with varying number of FENs and rate requirements.