Radio Map Assisted Routing and Predictive Resource Allocation over Dynamic Low Altitude Networks

TL;DR

This paper introduces a novel dynamic space-time graph model and optimization framework for routing and resource allocation in UAV-based low altitude networks, significantly improving performance over traditional methods.

Contribution

It develops a cross-layer optimization approach with explicit bounds and monotonicity properties, enabling globally optimal solutions for joint routing and resource allocation in dynamic UAV networks.

Findings

Achieves over 30 dB performance gain compared to classical methods.

Extends to multi-commodity transmission with 100X improvements in dense scenarios.

Enables additional 20 dB gain through data segmenting.

Abstract

Dynamic low altitude networks offer significant potential for efficient and reliable data transport via unmanned aerial vehicles (UAVs) relays which usually operate with predetermined trajectories. However, it is challenging to optimize the data routing and resource allocation due to the time-varying topology and the need to control interference with terrestrial systems. Traditional schemes rely on time-expanded graphs with uniform and fine time subdivisions, making them impractical for interference-aware applications. This paper develops a dynamic space-time graph model with a cross-layer optimization framework that converts a joint routing and predictive resource allocation problem into a joint bottleneck path planning and resource allocation problem. We develop explicit deterministic bounds to handle the channel uncertainty and prove a monotonicity property in the problem structure that enables us to efficiently reach the globally optimal solution to the predictive resource allocation subproblem. Then, this approach is extended to multi-commodity transmission tasks through time-frequency allocation, and a bisection search algorithm is developed to find the optimum solution by leveraging the monotonicity of the feasible set family. Simulations verify that the single-commodity algorithm approaches global optimality with more than 30 dB performance gain over the classical graph-based methods for delay-sensitive and large data transportation. At the same time, the multi-commodity method achieves 100X improvements in dense service scenarios and enables an additional 20 dB performance gain by data segmenting.