Joint Channel Bandwidth Assignment and Relay Positioning for Predictive Flying Networks

2025-03-18

AI Summary

To address the unreliability of predictive aerial network backhaul links in post-disaster emergency scenarios, this paper investigates joint optimization of channel bandwidth allocation and 3D relay placement under trajectory prior constraints, limited bandwidth, and minimum rate guarantees. We innovatively incorporate trajectory prior knowledge into the joint optimization framework and propose an enhanced simulated annealing algorithm featuring a penalty-function mechanism, achieving near-global optimality while significantly reducing computational complexity. Experimental results demonstrate that the proposed method attains throughput and latency performance close to the optimum while satisfying all minimum rate constraints, with computational overhead reduced by over 90% compared to exhaustive search. This work provides an efficient and practical solution for predictive wireless resource management in dynamic aerial networks.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Optimizes network performance in predictive Flying Networks
• Ensures reliable traffic relay through backhaul links
• Dynamically adjusts relay positioning and bandwidth allocation

Innovation

• Simulated Annealing optimizes predictive Flying Networks.
• Dynamic relay positioning using prior trajectory knowledge.
• Penalty functions reduce computational complexity effectively.