Adaptive resource slicing improves aerial network reliability and speed
Adaptive and Resilient Dual-Layer Resource Slicing for Hovering Aerial Backhaul Networks
Multiagent SystemsNetworking and Internet Architecture
Summary
Managing network resources for different types of 5G and 6G services is tricky, especially when using flying devices that move and change. The authors created a smart system called RAPO-TD3 that adapts automatically to changing network demands, making sure urgent data like emergency messages get through quickly. This system keeps network delays very low even when traffic spikes, and it works fast enough to meet strict timing rules. Their tests show it performs better than other common solutions.
What this means in practice
- •For wireless network operators: Maintain low-latency and reliable 5G/6G backhaul links using adaptive resource allocation for aerial relay platforms during sudden demand surges.
- •For drone network engineers: Design UAV-based backhaul systems that dynamically adjust bandwidth allocation to ensure critical communication services meet stringent latency requirements.
Authors
Chuan-Chi Lai, Jen-Hsiang Li
Abstract
This paper investigates adaptive and resilient dual-layer resource slicing in hovering aerial agent (HAA)-assisted backhaul networks for heterogeneous 5G/6G services, including enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLC), and massive machine-type communications (mMTC). To address the complex coupling of this dual-layer architecture in non-stationary environments, we propose the resilient adaptive priority orchestration enhanced twin delayed deep deterministic policy gradient (RAPO-TD3) framework. We introduce a novel double soft-max projection mechanism to map the continuous action space into physically feasible bandwidth distributions, ensuring strict constraint adherence. Additionally, a resilient adaptive priority orchestration (RAPO) mechanism is embedded to safeguard mission-critical URLLC latency. Crucially, we establish a rigorous mathematical foundation proving that our framework ensures Lipschitz continuity and satisfies the Robbins-Monro conditions for stable asymptotic convergence. Extensive simulations under non-stationary traffic demonstrate that our RAPO-TD3 framework achieves superior performance relative to PPO, DDPG, and traditional solvers. Notably, via the RAPO mechanism, our approach maintains URLLC satisfaction levels closely approaching theoretical optima even during 500% demand surges. Furthermore, scalability evaluations indicate that sub-millisecond execution latencies strictly satisfy the 1 ms URLLC budget, demonstrating the performance efficacy of our proposed framework.