Title: Radio Map Assisted Routing and Predictive Resource Allocation over Dynamic Low Altitude Networks Show Chinese title

Title: 基于无线电信号图的路由和动态低空网络中的预测资源分配

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. Show Chinese abstract

Abstract: 动态低空网络通过通常具有预定轨迹的无人机中继提供了高效可靠的数据传输的重要潜力。 然而，由于时变拓扑结构以及需要控制与地面系统的干扰，优化数据路由和资源分配具有挑战性。 传统方案依赖于时间扩展图，采用均匀且精细的时间划分，使其在干扰感知应用中不切实际。 本文开发了一个动态时空图模型和一个跨层优化框架，将联合路由和预测资源分配问题转化为联合瓶颈路径规划和资源分配问题。 我们制定了显式的确定性边界来处理信道不确定性，并证明了问题结构中的单调性性质，使我们能够有效地达到预测资源分配子问题的全局最优解。 然后，通过时间-频率分配，该方法被扩展到多商品传输任务，并开发了一种二分查找算法，通过利用可行集族的单调性找到最优解。 仿真验证了单商品算法在延迟敏感和大数据传输方面比经典基于图的方法性能提高了30 dB以上。 同时，多商品方法在密集服务场景中实现了100倍的改进，并通过数据分段实现了额外的20 dB性能提升。